Stock Market Volatility Around Market Shock


“Stock Market Volatility around market shocks & event analysis during 2005-2009”


The Project titled Stock Market volatility around market shock & event analysis during 2005-09 is an effort to throw light on Performance Analysis. I have completed this project based on research, under the guidance of name of faculty, my faculty guide. I owe enormous intellectual debt to her as she augmented my knowledge in the field of volatility around market shocks and helped me learn about the topic and gave me valuable insight into the subject matter. My increased spectrum of knowledge in this field is the result of her constant supervision and direction that has helped me to absorb relevant and high quality information. I would like to express my profound gratitude towards COLLEGE NAME for giving me the opportunity to undertake the above research. Last but not the least, I feel indebted to all those persons and organizations which have helped me directly or indirectly in successful completion of this study.

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I Ghayasuddin a student of MBA of College Name respectively hereby declare that the Project Report on “Stock Market volatility around market shock & event analysis during 2005-09” is the outcome of my own work and the same has not been submitted to any other University/Institute for the award of any degree or any Professional diploma.


1. To find out the stock market volatility. 2. To analyze the volatility measure 3. To understand the stock market and its importance 4. To find out the reasons behind the downfall.


A common problem plaguing the low and slow growth of small developing economies is the swallow financial sector. Financial markets play an important role in the process of economic growth and development by facilitating savings and channeling funds from savers to investors. While there have been numerous attempts to develop the financial sector, small island economies are also facing the problem of high volatility in numerous fronts including volatility of its financial sector. Volatility may impair the smooth functioning of the financial system and adversely affect economic performance. Similarly, stock market volatility also has a number of negative implications. One of the ways in which it affects the economy is through its effect on consumer spending (Campbell, 1996; Starr-McCluer, 1998; Ludvigson and Steindel 1999 and Poterba 2000). The impact of stock market volatility on consumer spending is related via the wealth effect. Increased wealth will drive up consumer spending. However, a fall in stock market will weaken consumer confidence and thus drive down consumer spending. Stock market volatility may also affect business investment (Zuliu, 1995) and economic growth directly (Levine and Zervos, 1996 and Arestis et al 2001). A rise in stock market Volatility can be interpreted as a rise in risk of equity investment and thus a shift of funds to less risky assets. This move could lead to a rise in cost of funds to firms and thus new firms might bear this effect as investors will turn to purchase of stock in larger, well known firms. While there is a general consensus on what constitutes stock market volatility and, to a lesser extent, on how to measure it, there is far less agreement on the causes of changes in stock market volatility. Some economists see the causes of volatility in the arrival of new, unanticipated information that alters expected returns on a stock (Engle and Ng, 1993). Thus, changes in market volatility would merely reflect changes in the local or global economic environment. Others claim that volatility is caused mainly by changes in trading volume, practices or patterns, which in turn are driven by factors such as modifications in macroeconomic policies, shifts in investor tolerance of risk and increased uncertainty. The degree of stock market volatility can help forecasters predict the path of an economy’s growth and the structure of volatility can imply that”investors now need to hold more stocks in their portfolio to achieve diversification”(Krainer, J, 2002:1). This case is more serious for small developing economies like Fiji who is attempting to deepen its financial sector by developing its stock market. Unlike mature stock markets of advanced economies, the stock markets of less developed economies like Fiji began to develop rapidly only in the last two decades and are sensitive to factors such as changes in the levels of economic activities, changes in the political and international economic environment and also related to the changes in the macro economic variables. Therefore, in this paper, we examine if Fiji’s Stock market is volatile and if so, then what is the role of interest rate being one of the most important macroeconomic variables on the volatility of stock returns. This article benefits from developments in the measurement of volatility through econometric techniques. Here, the regime-switching- ARCH model introduced by Engle (1982) and its extension, the GARCH model, (Bollerslev, 1986) is used to estimate the conditional variance of Fiji’s daily stock return from January 2001 to December 2005. This method allows for an objective determination of the presence of volatility. The results of estimates of stock return volatility is then related to changes in the interest rates. The second section of the paper provides an overview of Fiji’s stock market. The third section of the paper provides an exposition of the methodology used in this study. The fourth section provides a summary of the results and its discussion. The last section provides a summary and conclusion.


India has struggled financially since independence, experiencing slow economic growth and economic setbacks due to climatic extremes or political disturbances. The country has been gradually transforming its economic base from agrarian to industrial and commercial. Under British rule in the 19th century, India’s cottage industries and thriving trade were virtually destroyed to make way for European manufactured goods, paid for by exports of agricultural products such as cotton, opium, and tea. Beginning in the late 19th century a modern industrial sector and an extensive infrastructure of railways and irrigation works were slowly built with British and Indian capital. Nevertheless, India’s economy stagnated during the last 30 or so years of British rule. At independence in 1947 India was desperately poor, with an aging textile industry as its only major industrial sector. Economic policy after independence emphasized central planning, with the government setting goals for and closely regulating private industry. Self-sufficiency was promoted in order to foster domestic industry and reduce dependence on foreign trade. These efforts produced steady economic growth in the 1950s, but less positive results in the two succeeding decades. By the early 1970s India had achieved its goal of self-sufficiency in food production, although this food was not equally available to all Indians due to skewed distribution and occasional shortfalls in the harvest. In the late 1970s the government began to reduce state control of the economy, making slow progress toward this goal. By 1991, however, the government still regulated or ran many industries, including mining and quarrying, banking and insurance, transportation and communications, and manufacturing and construction. Economic growth improved during this period, at least partially as a result of development projects funded by foreign loans. India’s low average growth rate up to 1980 was derisively referred to as the Hindu rate of growth, because of the contrasting high growth rates in other Asian countries, especially the East Asian Tigers. The economic reforms that surged economic growth in India after 1980 can be attributed to two stages of reforms. The pro-business reform of 1980 initiated by Indira Gandhi and carried on by Rajiv Gandhi, eased restrictions on capacity expansion for incumbents, removed price controls and reduced corporate taxes. The economic liberalisation of 1991, initiated by then Indian prime minister P. V. Narasimha Rao and his finance minister Manmohan Singh in response to a macroeconomic crisis did away with the Licence Raj (investment, industrial and import licensing) and ended public sector monopoly in many sectors, thereby allowing automatic approval of foreign direct investment in many sectors. Since then, the overall direction of liberalisation has remained the same, irrespective of the ruling party at the centre, although no party has yet tried to take on powerful lobbies like the trade unions and farmers, or contentious issues like labour reforms and cutting down agricultural subsidies. Liberalization in India paved the way for lots of foreign companies to come and setup heir base in India and for investors across the globe to invest money in Indian stock Market. Buoyant Indian Economy really raised eyebrows of many and investment in India keeps on surging high year after year touching new height. Since liberalization the foreign investors are on a spree of investment in India both in the form of FDI and FII. Stock Exchange being the only route for FIIs to come into India has been has been spearheading the task of giving investors a bright picture of the economy leading to brining more and more investment into the state. Hence, the vital role of Stock Exchange and the association of Stock Exchange with Foreign Investment can not be undermined. In the later part of the study, we will look into the details of how the Stock Exchange is associated with FIIs and vice versa.A


A stock exchange or bourse is a corporation or mutual organization which provides the facilities for stock brokers to trade company stocks and other securities. Stock exchanges also provide facilities for the issue and redemption of securities, as well as other financial instruments and capital events including the payment of income and dividends. In other words, Stock Exchanges are an organised marketplace, either corporation or mutual organisation, where members of the organisation gather to trade company stocks and other securities. The members may act either as agents for their customers, or as principals for their own accounts. Stock exchanges also facilitates for the issue and redemption of securities and other financial instruments including the payment of income and dividends. The record keeping is central but trade is linked to such physical place because modern markets are computerised. The trade on an exchange is only by members and stock broker do have a seat on the exchange. The securities traded on a stock exchange include shares issued by companies, unit trusts and other pooled investment products as well as bonds. To be able to trade a security on a certain stock exchange, it has to be listed there. Usually there is a central location at least for recordkeeping, but trade is less and less linked to such a physical place, as modern markets are electronic networks, which gives them advantages of speed and cost of transactions. Trade on an exchange is by members only; a stock broker is said to have a seat on the exchange. A stock exchange is often the most important component of a stock market. There is usually no compulsion to issue stock via the stock exchange itself, nor must stock be subsequently traded on the exchange. Such trading is said to be off exchange or over-the-counter. This is the usual way that bonds are traded. The initial offering of stocks and bonds to investors is by definition done in the primary market and subsequent trading is done in the secondary market. Increasingly all stock exchanges are part of a global market for securities. 200 years ago in front of Trinity church in East Manhattan in U.S oldest stock exchange called New York stock exchange emerged, when there were no paper money changing hands and there was not even the idea of stock, people trade silver for papers saying they owned shares in cargo .The trade flourished. During American Revolution, the colonial government needed money to fund its wartime operations. By selling bonds they did this. Bonds are pieces of paper a person buys for a set price, knowing that after a certain period of time; they can exchange their bonds for a profit. Along with bonds, the first of the nation’s bank started to sell parts or shares of their own company to people in order to raise money. Thus they sell the part of the company to whoever wanted to buy it. This led to the emergence of the modern day stock market. The concept of stock markets came to India in 1875, when Bombay Stock Exchange (BSE) was established as ‘The Native Share and Stockbrokers Association’, a voluntary non-profit making association. BSE is the oldest in Asia. Presently India has about 10,000 listed companies, the largest number of listed companies in the world. Stock exchanges in India can be categorized as: 1) Voluntary Associations such as Bombay, Indore and Ahmedabad, 2) Public limited companies such as Calcutta and Delhi, and 3) Guarantee companies such as Hyderabad, Madras and Bangalore. Besides BSE, India’s other major stock exchange is National Stock Exchange (NSE) that was promoted by leading financial institutions and was established in April 1993. Today, these global stock exchanges have become premier institutions and are highly efficient, computerized organizations that have fostered the growth of an open, global securities market. Today India boasts 23 regional Stock Exchanges along with BSE and NSE.


The research has been done by selecting the companies which are the representative of a particular sector on the basis of overall market capitalization, stocks having the highest liquidity and turnover both on the NSE and BSE. A caution was thus taken and by thorough approach the best companies were selected so as to portray a genuine picture of the sector. With the help of SPSS Package and using the quantitative techniques, the statistical analysis has been done. The following analysis has been done for all the 8 companies: 1. Fundamental analysis. 3. Future growth and earnings analysis. 4. Statistical analysis. 5. Technical analysis.


The Stock Exchange provides companies with the facility to raise capital for expansion through selling shares to the investing public.

Mobilising Savings for Investment

When people draw their savings and invest in shares, it leads to a more rational allocation of resources because funds, which could have been consumed, or kept in idle deposits with banks, are mobilised and redirected to promote commerce and industry.

Redistribution of Wealth

By giving a wide spectrum of people a chance to buy shares and therefore become part-owners of profitable enterprises, the stock market helps to reduce large income inequalities because many people get a chance to share in the profits of business that were set up by other people.

Improving Corporate Governance

By having a wide and varied scope of owners, companies generally tend to improve on their management standards and efficiency in order to satisfy the demands of these shareholders. It is evident that generally, public companies tend to have better management records than private companies.

Creates Investment Opportunities for Small Investors

As opposed to other businesses that require huge capital outlay, investing in shares is open to both the large and small investors because a person buys the number of shares they can afford. Therefore the Stock Exchange provides an extra source of income to small savers.

Government Raises Capital for Development Projects

The Government and even local authorities like municipalities may decide to borrow money in order to finance huge infrastructure projects such as sewerage and water treatment works or housing estates by selling another category of shares known as Bonds. These bonds can be raised through the Stock Exchange whereby members of the public buy them. When the Government or Municipal Council gets this alternative source of funds, it no longer has the need to overtax the people in order to finance development.

Barometer of the Economy

At the Stock Exchange, share prices rise and fall depending, largely, on market forces. Share prices tend to rise or remain stable when companies and the economy in general show signs of stability. Therefore the movement of share prices can be an indicator of the general trend in the economy. With countries moving away from socialistic approach and towards globalization of their economies, the role and importance of Stock Exchanges has gone up considerably. Today Stock ExchangesA  depict the financial position of the economy of a country.


In closed economies only the Govt. has the sole responsibility and discretion of investment in various projects in the country. No private parties were allowed to invest in any venture. However, countries where mixed economy exist are liberal to the extent of giving permission to some private parties for investment in some selected sectors. However, countries which adopted globalization made their policies liberal enough to give private players permission to invest and run in any sector of their wish. Globalization has made the world boundary less where free flow of labour, capital exists among member countries. Interdependence among countries has given the drive a real momentum. Seeing the robust growth that some of the Asian countries registered really stunned the other nations which had closed economy. These nations which adopted globalization being the first runners were termed as Asian Tigers. Many followed the suit. Few countries followed the path of economic reforms with an anticipation of the prospective growth while the others due to some economic compulsions. A few countries like India were in real soup with acute financial crisis and were not in a position of running the socialistic approach anymore. A balance of payments crisis at the time opened the way for an International Monetary Fund (IMF) program that led to the adoption of a major reform package. It went ahead with globalization and reform process in a step by step approach. Countries realizing that only domestic investments and resources can not be relied upon for rapid growth in industrialization and economy, red carpet treatment was given to foreign investors. Opening up of economies unseals the doors to the investors from other countries to invest in each others countries. These investments come in two forms, i.e, FDI (Foreign Direct Investment) and FII (Foreign Institutional Investment. FII (Foreign Institutional Investor) is an investor or investment fundthatis from or registered in a country outside of the one in which it is currentlyinvesting. Institutional investorsinclude hedge funds, insurance companies, pension funds and mutual funds. They invest in various companies through Stock Exchange. The term is used most commonly in India to refer to outside companies investing in the financial markets of India. International institutional investors must register with the Securities and Exchange Board of India to participate in the market. One of the major market regulations pertaining to FIIs involves placing limits on FII ownership in Indian companies. Sub-account includes those foreign corporates, foreign individuals, and institutions, funds or portfolios established or incorporated outside India on whose behalf investments are proposed to be made in India by a FII. Where as FDI (Foreign Direct Investment) is a component of a country’s national financial accounts. Foreign direct investment is investment of foreign assets into domestic structures, equipment, and organizations. It does not include foreign investment into the stock markets. Foreign direct investment is thought to be more useful to a country than investments in the equity of its companies because equity investments are potentially “hot money” which can leave at the first sign of trouble, whereas FDI is durable and generally useful whether things go well or badly. Foreign Investors always prefer FII route than FDI route since, the route of investing in stocks is easy and more liquid with less risk involved. Investors can take away their money as and when they need by making short term bucks. If we see from govt’s perspective, FII means incoming of a lot of foreign exchange into the country which boosts the Forex reserve. Where as Govt. is inclined to get more FDI than FII as FDI helps setting up manufacturing or service industry thereby bringing foreign exchange, employing people, business by ancillary industries and tax to govt treasury. Countries across the globe are formulating policies to attract more FDI and FII. Countries like India have modified its investment policies to make it conducive for foreign investment.


Following entities / funds are eligible to get registered as FII:

  1. Pension Funds
  2. Mutual Funds
  3. Insurance Companies
  4. Investment Trusts
  5. Banks
  6. University Funds
  7. Endowments
  8. Foundations
  9. Charitable Trusts / Charitable Societies

Further, following entities proposing to invest on behalf of broad based funds, are also eligible to be registered as FIIs:

  1. Asset Management Companies
  2. Institutional Portfolio Managers
  3. Trustees
  4. Power of Attorney Holders

The parameters on which SEBI decides FII applicants’ eligibility.

  1. Applicant’s track record, professional competence, financial soundness, experience, general reputation of fairness and integrity. (The applicant should have been in existence for at least one year)
  2. whether the applicant is registered with and regulated by an appropriate Foreign Regulatory Authority in the same capacity in which the application is filed with SEBI
  3. Whether the applicant is a fit & proper person.

As the FIIs take the route of investing in Stocks etc through stock exchange, they have to be abide by the SEBI guidelines. SEBI generally takes seven working days in granting FII registration. However, in cases where the information furnished by the applicants is incomplete, seven days shall be counted from the days when all necessary information sought, reaches SEBI. In cases where the applicant is bank and subsidiary of a bank, SEBI seeks comments from the Reserve Bank of India (RBI). In such cases, 7 working days would be counted from the day no objection is received from RBI.

Which financial Instruments are available for FII investment

  1. Securities in primary and secondary markets including shares, debentures and warrants of companies, unlisted, listed or to be listed on a recognized stock exchange in India;
  2. Units of mutual funds;
  3. Dated Government Securities;
  4. Derivatives traded on a recognized stock exchange;
  5. Commercial papers.


Economic growth and GDP:

The country’s GDP at current market prices is projected at Rs. 46, 93,602 crore in 2007-08 by the Central Statistical Organization (CSO). Thus, in the current fiscal year, the size of the Indian economy at market exchange rate will cross US$ 1 trillion. At the nominal exchange rate (average of April-December 2007) GDP is projected to be US$ 1.16 trillion in 2007-08. Per capita income at nominal exchange rate is estimated at US$ 1,021. According to the World Bank system of classification of countries as low income, middle income and high income, India is still in the category of low income countries. The (per capita) GDP at purchasing power parity is conceptually a better indicator of the relative size of the economy than the (per capita)GDP at market exchange rates. There are, however, practical difficulties in deriving GDP at PPP, and we now have two different estimates of the PPP conversion factor for 2005. India’s GDP at PPP is estimated at US$ 5.16 trillion or US$ 3.19 trillion depending on whether the old or new conversion factor is used. In the former case, India is the third largest economy in the world after the United States and China, while in the latter it is the fifth largest (behind Japan and Germany).A GDP at factor cost at constant 1999-2000 prices is projected by the CSO to grow at 8.5 per cent in 2008-09. This represents a deceleration from the unexpectedly high growth of 9.4 per cent, 9.6 per cent and 8.7 per cent respectively, in the previous three years. With the economy modernizing, globalizing and growing rapidly, some degree of cyclical fluctuation is to be expected.

Per capita income and consumption:

Economic growth, and in particular the growth in per capita income, is a broad quantitative indicator of the progress made in improving public welfare. Per capita consumptionis another quantitative indicator that is useful for judging welfare improvement.The pace of economic improvement has moved up considerably during the last five years(including 2007-08). Since 2003, there has been a sharp acceleration in the growth of per capita income, almost doubling to an average of 7.2 per cent per annum (2003-04 to 2007-08).This means that average income would now double in a decade, well within one generation, instead of after a generation (two decades). The growth rate of per capita income in 2007-08 is projected to be 7.2 per cent, the same as the average of the five years to the current year. Per capita private final consumption expenditure has increased in line with per capita income. The growth rate has almost doubled to 5.1 per cent per year from 2003-04 to 2007-08, with the current year’s growth expected to be 5.3 per cent, marginally higher than the five year average. The average growth of consumption is slower than the average growth of income, primarily because of rising saving rates, though rising tax collection rates can also widen the gap (during some periods). Year to year changes in consumption also suggest that the rise in consumption is a more gradual and steady process, as any sharp changes in income tend to get adjusted in the saving rate. Per capita income and consumption (in 1999-2000 prices): Year Income Consumption 2007-08 Rs. Growth (%) Rs. Growth (%) 29,786 7.2 17,145 5.3 Income is taken as GDP at market prices. Consumption is PFCE. Per capita is obtained by dividing these by population.


However, market efficiency -championed in the efficient market hypothesis (EMH) formulated by Eugene Fama in 1970, suggests that at any given time, prices fully reflect all available information on a particular stock and/or market. Thus, according to the EMH, no investor has an advantage in predicting a return on a stock pricebecause no one has access to information not already available to everyone else. (To read more on behavioral finance.

The Effect of Efficiency: Non-Predictability

The nature of information does not have to be limited to financial news and research alone; indeed, information about political, economic and social events, combined with how investors perceive such information, whether true or rumored, will be reflected in the stock price. According to EMH,as prices respond only to information available in the market, and, because all market participants are privy to the same information, no one will have the ability to out-profit anyone else. In efficient markets, prices become not predictable but random, so no investment pattern can be discerned. A planned approach to investment, therefore, cannot be successful. This “random walk” of prices, commonlyspoken aboutin the EMH school of thought, results in the failure of any investment strategy that aims to beat the market consistently. In fact, the EMH suggests that given the transaction costs involved in portfolio management, it would be more profitable for an investor to put his or her money into an index fund.

Anomalies: The Challenge to Efficiency

In the real world of investment, however, there are obvious arguments against the EMH. There are investors who have beaten the market – Warren Buffett, whose investment strategy focuses onundervalued stocks, made millions and set an example for numerous followers. There are portfolio managerswho have better track records than others, and there are investment houses with more renowned research analysis than others. So how can performance be random when people are clearly profiting from and beating the market? Counter arguments to the EMH state that consistent patterns are present. Here are some examples of some of the predictable anomalies thrown in the face of the EMH:the January effectis a patternthat shows higher returns tend to be earned in the first month of the year; “blue Monday on Wall Street” isasaying that discourages buying on Friday afternoon and Monday morning because of the weekend effect, the tendency for prices to be higher on the day before and after the weekend than during the rest of the week. Studies in behavioral finance, which look into the effects of investor psychology on stock prices, also reveal that there are some predictable patterns in the stock market. Investors tend to buy undervalued stocks and sell overvalued stocks and, in a market of many participants, the result can be anything but efficient. Paul Krugman, MIT economics professor, suggests that because of the mass mentality of the trendy, short-term shareholder, investors pull in and out of the latest and hottest stocks. This results in stock prices being distorted and the market being inefficient. Soprices no longer reflect all available information in the market. Prices areinstead beingmanipulated by profit seekers.

The EMH Response

The EMH does not dismiss the possibility of anomalies in the market that result in the generation of superior profits. In fact, market efficiency does not require prices to be equal tofair value all of the time. Prices may be over- or undervalued only in random occurrences, so they eventually revert back to their mean values. As such, because the deviations from a stock’s fair price are in themselves random, investment strategies that result in beating the market cannot be consistent phenomena. Furthermore, the hypothesisargues that an investor who outperforms the market does so not out of skill but out of luck. EMH followers say this is due to the laws of probability: at any given time in a market with a large number of investors, some will outperform while other will remain average.

How Doesa Market Become Efficient?

In order for a market to become efficient, investors must perceive that a market is inefficient and possible to beat. Ironically, investment strategies intended to take advantage of inefficiencies are actually the fuel that keeps a market efficient. A market has to be large and liquid. Information has to be widely available in terms of accessibility and cost and released to investors at more or less the same time. Transaction costs have to be cheaper than the expected profits of an investment strategy. Investorsmust also have enough funds to take advantage of inefficiency until, according to the EMH, it disappears again. Most importantly, an investor has to believe that she or he can outperform the market.

Degrees of Efficiency

Accepting the EMH in its purest form may be difficult; however, there are three identified classifications of theEMH, which are aimed at reflecting the degree to which it can be applied to markets. 1. Strong efficiency – This is the strongest version, which states that all information in a market, whether public or private, is accounted for in a stock price. Not even insider information could give an investor an advantage. 2. Semi-strong efficiency – This form ofEMH implies that all public information is calculated into a stock’s current share price. Neither fundamental nor technical analysis can be used to achieve superior gains. 3. Weak efficiency – This type of EMH claims that all past prices of a stock are reflected in today’s stock price. Therefore, technical analysis cannot be used to predict and beat a market.


We’ve had almost four years of relative calm in the financial markets. Corporate earnings have rebounded from the depths of the 2007 — 2008 stock market collapse. There have been no terror attacks on U.S. soil. Interest rates have remained artificially low. But now, even as foreign economies are gaining in strength, the U.S. economy’s second-breath — as I call it — is ending. Coming next — a series of shocks in the Dow Jones Industrials that could catch investors with their pants down. Dissonance in the Dow Does Not Bode Well for 2007 Take a look at the Dow Jones Industrials index (bottom of the chart) vs. the Dow Jones Transportation index. Notice that the Dow marched to record highs at the end of 2006, while the Dow Transports turned lower. This is called a “Dow Theory non-confirmation,” and it’s especially bearish for most U.S. industrial stocks. Historically, some 80% of Dow Theory non-confirmations have ended in disasters, with the Dow indices losing as much as 40% of their value. The same is possible this time around. What could set this off? One possibility is a crash in the dollar, which is already starting. A plunge in the dollar could force overseas investors to yank their money out of the U.S., setting off a row of financial crises:

  • Banking, already starting to hurt from falling property values and rising mortgage delinquencies and defaults, will get killed.
  • Currency markets could enter a period of extreme volatility, setting off disasters in the highly leveraged hedge fund industry.
  • Big U.S. companies already in deep financial doo-doo (think Ford, GM, Delta) could go bust.

I’m seeing subtle but important confirmations that these forecasts are going to be right. For example … Investors Are the Most Complacent They’ve Been in 12 Years! Crises rarely hit when investors expect them. Instead, they show up when investors are complacent. And that’s exactly what we have right now. Look at my chart of the Volatility Index, or VIX. This index effectively measures investor complacency. When the line in this chart is declining toward the bottom, it means investors have no fear. They think everything is hunky-dory, so they buy stocks with almost reckless abandon. Right now, the VIX Index is at 12-year lows. It’s lower than it was before the 1997-1998 financial crisis … lower than it was before the Long Term Capital Management collapse in 1998 (which almost took down the entire U.S. banking system) … lower than it was at the peak of the greatest stock bull market in history. This is extreme complacency — the kind that crises love to feed on. It’s how the stock market peels money away from unwary investors. And it’s how smart, savvy investors pile up wads of profits. I expect the Dow to tank, and transport stocks to get killed. At the same time, I expect key sectors — especially those driven by the boom in Asia and the rise in natural resources — to soar. Remember, the U.S. is no longer primarily a manufacturing country. And many of our service industries have been outsourced, too. For instance, tech support and IT services have gone to India. What this means: When the dollar falls, we do not gain a competitive edge. Instead, everything we’ve outsourced gets more expensive. Hardly anyone is talking about this. But the weaker dollar is not going to rescue American industry.

Here are the other consequences I see:

  • Inflation will accelerate higher … much higher.
  • China and India will gain greater influence over the global economy.
  • Gold — now trading at nearly $645 an ounce — will head to new record highs, well above $740 an ounce.
  • Oil will hit $100 per barrel. In the malaise of the poor economic environment that will emerge in 2007, the crises with Iraq, Iran, North Korea, and terrorism will all sadly get worse.


Function and purpose

The stock market is one of the most important sources for companies to raise money. This allows businesses to be publicly traded, or raise additional capital for expansion by selling shares of ownership of the company in a public market. The liquidity that an exchange provides affords investors the ability to quickly and easily sell securities. This is an attractive feature of investing in stocks, compared to other less liquid investments such as real estate. History has shown that the price of shares and other assets is an important part of the dynamics of economic activity, and can influence or be an indicator of social mood. An economy where the stock market is on the rise is considered to be an up coming economy. In fact, the stock market is often considered the primary indicator of a country’s economic strength and development. Rising share prices, for instance, tend to be associated with increased business investment and vice versa. Share prices also affect the wealth of households and their consumption. Therefore, central banks tend to keep an eye on the control and behavior of the stock market and, in general, on the smooth operation of financial system functions. Financial stability is the raison d’Aªtre of central banks. Exchanges also act as the clearinghouse for each transaction, meaning that they collect and deliver the shares, and guarantee payment to the seller of a security. This eliminates the risk to an individual buyer or seller that the counterparty could default on the transaction. The smooth functioning of all these activities facilitates economic growth in that lower costs and enterprise risks promote the production of goods and services as well as employment. In this way the financial system contributes to increased prosperity.

Relation of the stock market to the modern financial system

The financial system in most western countries has undergone a remarkable transformation. One feature of this development is disintermediation. A portion of the funds involved in saving and financing flows directly to the financial markets instead of being routed via the traditional bank lending and deposit operations. The general public’s heightened interest in investing in the stock market, either directly or through mutual funds, has been an important component of this process. Statistics show that in recent decades shares have made up an increasingly large proportion of households’ financial assets in many countries. In the 1970s, in Sweden, deposit accounts and other very liquid assets with little risk made up almost 60 percent of households’ financial wealth, compared to less than 20 percent in the 2000s. The major part of this adjustment in financial portfolios has gone directly to shares but a good deal now takes the form of various kinds of institutional investment for groups of individuals, e.g., pension funds, mutual funds, hedge funds, insurance investment of premiums, etc. The trend towards forms of saving with a higher risk has been accentuated by new rules for most funds and insurance, permitting a higher proportion of shares to bonds. Similar tendencies are to be found in other industrialized countries. In all developed economic systems, such as the European Union, the United States, Japan and other developed nations, the trend has been the same: saving has moved away from traditional (government insured) bank deposits to more risky securities of one sort or another.

The stock market, individual investors, and financial risk

Riskier long-term saving requires that an individual possess the ability to manage the associated increased risks. Stock prices fluctuate widely, in marked contrast to the stability of (government insured) bank deposits or bonds. This is something that could affect not only the individual investor or household, but also the economy on a large scale. The following deals with some of the risks of the financial sector in general and the stock market in particular. This is certainly more important now that so many newcomers have entered the stock market, or have acquired other ‘risky’ investments (such as ‘investment’ property, i.e., real estate and collectables). With each passing year, the noise level in the stock market rises. Television commentators, financial writers, analysts, and market strategists are all overtaking each other to get investors’ attention. At the same time, individual investors, immersed in chat rooms and message boards, are exchanging questionable and often misleading tips. Yet, despite all this available information, investors find it increasingly difficult to profit. Stock prices skyrocket with little reason, then plummet just as quickly, and people who have turned to investing for their children’s education and their own retirement become frightened. Sometimes there appears to be no rhyme or reason to the market, only folly. This is a quote from the preface to a published biography about the long-term value-oriented stock investor Warren Buffett. Buffett began his career with $100, and $105,000 from seven limited partners consisting of Buffett’s family and friends. Over the years he has built himself a multi-billion-dollar fortune. The quote illustrates some of what has been happening in the stock market during the end of the 20th century and the beginning of the 21st century.

The behavior of the stock market

From experience we know that investors may ‘temporarily’ move financial prices away from their long term aggregate price ‘trends’. (Positive or up trends are referred to as bull markets; negative or down trends are referred to as bear markets.) Over-reactions may occur—so that excessive optimism (euphoria) may drive prices unduly high or excessive pessimism may drive prices unduly low. New theoretical and empirical arguments have since been put forward against the notion that financial markets are ‘generally’ efficient (i.e., in the sense that stock prices in the aggregate tend to follow a Gaussian distribution). According to the efficient market hypothesis (EMH), only changes in fundamental factors, such as the outlook for margins, profits or dividends, ought to affect share prices beyond the short term, where random ‘noise’ in the system may prevail. (But this largely theoretic academic viewpoint—known as ‘hard’ EMH—also predicts that little or no trading should take place, contrary to fact, since prices are already at or near equilibrium, having priced in all public knowledge.) The ‘hard’ efficient-market hypothesis is sorely tested by such events as the stock market crash in 1987, when the Dow Jones index plummeted 22.6 percent—the largest-ever one-day fall in the United States. This event demonstrated that share prices can fall dramatically even though, to this day, it is impossible to fix a generally agreed upon definite cause: a thorough search failed to detect any ‘reasonable’ development that might have accounted for the crash. (But note that such events are predicted to occur strictly by chance , although very rarely.) It seems also to be the case more generally that many price movements (beyond that which are predicted to occur ‘randomly’) are not occasioned by new information; a study of the fifty largest one-day share price movements in the United States in the post-war period seems to confirm this. However, a ‘soft’ EMH has emerged which does not require that prices remain at or near equilibrium, but only that market participants not be able to systematically profit from any momentary market ‘inefficiencies’. Moreover, while EMH predicts that all price movement (in the absence of change in fundamental information) is random (i.e., non-trending), many studies have shown a marked tendency for the stock market to trend over time periods of weeks or longer. Various explanations for such large and apparently non-random price movements have been promulgated. For instance, some research has shown that changes in estimated risk, and the use of certain strategies, such as stop-loss limits and Value at Risk limits, theoretically could cause financial markets to overreact. But the best explanation seems to be that the distribution of stock market prices is non-Gaussian (in which case EMH, in any of its current forms, would not be strictly applicable). Other research has shown that psychological factors may result in exaggerated (statistically anomalous) stock price movements (contrary to EMH which assumes such behaviors ‘cancel out’). Psychological research has demonstrated that people are predisposed to ‘seeing’ patterns, and often will perceive a pattern in what is, in fact, just noise. (Something like seeing familiar shapes in clouds or ink blots.) In the present context this means that a succession of good news items about a company may lead investors to overreact positively (unjustifiably driving the price up). A period of good returns also boosts the investor’s self-confidence, reducing his (psychological) risk threshold. Another phenomenon—also from psychology—that works against an objective assessment is group thinking. As social animals, it is not easy to stick to an opinion that differs markedly from that of a majority of the group. An example with which one may be familiar is the reluctance to enter a restaurant that is empty; people generally prefer to have their opinion validated by those of others in the group. In one paper the authors draw an analogy with gambling. In normal times the market behaves like a game of roulette; the probabilities are known and largely independent of the investment decisions of the different players. In times of market stress, however, the game becomes more like poker (herding behavior takes over). The players now must give heavy weight to the psychology of other investors and how they are likely to react psychologically. The stock market, as any other business, is quite unforgiving of amateurs. Inexperienced investors rarely get the assistance and support they need. In the period running up to the 1987 crash, less than 1 percent of the analyst’s recommendations had been to sell (and even during the 2000 – 2002 bear market, the average did not rise above 5%). In the run up to 2000, the media amplified the general euphoria, with reports of rapidly rising share prices and the notion that large sums of money could be quickly earned in the so-called new economy stock market. (And later amplified the gloom which descended during the 2000 – 2002 bear market, so that by summer of 2002, predictions of a DOW average below 5000 were quite common.)

Irrational behavior

Sometimes the market seems to react irrationally to economic or financial news, even if that news is likely to have no real effect on the technical value of securities itself. But this may be more apparent than real, since often such news has been anticipated, and a counterreaction may occur if the news is better (or worse) than expected. Therefore, the stock market may be swayed in either direction by press releases, rumors, euphoria and mass panic; but generally only briefly, as more experienced investors (especially the hedge funds) quickly rally to take advantage of even the slightest, momentary hysteria. Over the short-term, stocks and other securities can be battered or buoyed by any number of fast market-changing events, making the stock market behavior difficult to predict. Emotions can drive prices up and down, people are generally not as rational as they think, and the reasons for buying and selling are generally obscure. Behaviorists argue that investors often behave ‘irrationally’ when making investment decisions thereby incorrectly pricing securities, which causes market inefficiencies, which, in turn, are opportunities to make money. However, the whole notion of EMH is that these non-rational reactions to information cancel out, leaving the prices of stocks rationally determined. The Dow Jones Industrial Average biggest gain in one day was 936.42 points or 11 percent, this occurred on October 13, 2008.


A stock market crash is often defined as a sharp dip in share prices of equities listed on the stock exchanges. In parallel with various economic factors, a reason for stock market crashes is also due to panic. Often, stock market crashes end speculative economic bubbles. There have been famous stock market crashes that have ended in the loss of billions of dollars and wealth destruction on a massive scale. An increasing number of people are involved in the stock market, especially since the social security and retirement plans are being increasingly privatized and linked to stocks and bonds and other elements of the market. There have been a number of famous stock market crashes like the Wall Street Crash of 1929, the stock market crash of 1973-4, the Black Monday of 1987, the Dot-com bubble of 2000. One of the most famous stock market crashes started October 24, 1929 on Black Thursday. The Dow Jones Industrial lost 50% during this stock market crash. It was the beginning of the Great Depression. Another famous crash took place on October 19, 1987 – Black Monday. On Black Monday itself, the Dow Jones fell by 22.6% after completing a 5 year continuous rise in share prices. This event not only shook the USA, but quickly spread across the world. Thus, by the end of October, stock exchanges in Australia lost 41.8%, in Canada lost 22.5%, in Hong Kong lost 45.8%, and in Great Britain lost 26.4%. The names “Black Monday” and “Black Tuesday” are also used for October 28-29, 1929, which followed Terrible Thursday–the starting day of the stock market crash in 1929. The crash in 1987 raised some puzzles–main news and events did not predict the catastrophe and visible reasons for the collapse were not identified. This event raised questions about many important assumptions of modern economics, namely, the theory of rational human conduct, the theory of market equilibrium and the hypothesis of market efficiency. For some time after the crash, trading in stock exchanges worldwide was halted, since the exchange computers did not perform well owing to enormous quantity of trades being received at one time. This halt in trading allowed the Federal Reserve system and central banks of other countries to take measures to control the spreading of worldwide financial crisis. In the United States the SEC introduced several new measures of control into the stock market in an attempt to prevent a re-occurrence of the events of Black Monday. Computer systems were upgraded in the stock exchanges to handle larger trading volumes in a more accurate and controlled manner. The SEC modified the margin requirements in an attempt to lower the volatility of common stocks, stock options and the futures market. The New York Stock Exchange and the Chicago Mercantile Exchange introduced the concept of a circuit breaker. The circuit breaker halts trading if the Dow declines a prescribed number of points for a prescribed amount of time. New York Stock Exchange (NYSE) circuit breakers[14] % drop time of drop close trading for 10% drop before 2PM one hour halt 10% drop 2PM – 2:30PM half-hour halt 10% drop after 2:30PM market stays open 20% drop before 1PM halt for two hours 20% drop 1PM – 2PM halt for one hour 20% drop after 2PM close for the day 30% drop any time during day close for the day

Stock market index

The movements of the prices in a market or section of a market are captured in price indices called stock market indices, of which there are many, e.g., the S&P, the FTSE and the Euronext indices. Such indices are usually market capitalization weighted, with the weights reflecting the contribution of the stock to the index. The constituents of the index are reviewed frequently to include/exclude stocks in order to reflect the changing business environment.

Derivative instruments

Financial innovation has brought many new financial instruments whose pay-offs or values depend on the prices of stocks. Some examples are exchange-traded funds (ETFs), stock index and stock options, equity swaps, single-stock futures, and stock index futures. These last two may be traded on futures exchanges (which are distinct from stock exchanges—their history traces back to commodities futures exchanges), or traded over-the-counter. As all of these products are only derived from stocks, they are sometimes considered to be traded in a (hypothetical) derivatives market, rather than the (hypothetical) stock market.

Leveraged strategies

Stock that a trader does not actually own may be traded using short selling; margin buying may be used to purchase stock with borrowed funds; or, derivatives may be used to control large blocks of stocks for a much smaller amount of money than would be required by outright purchase or sale.

Short selling

In short selling, the trader borrows stock (usually from his brokerage which holds its clients’ shares or its own shares on account to lend to short sellers) then sells it on the market, hoping for the price to fall. The trader eventually buys back the stock, making money if the price fell in the meantime or losing money if it rose. Exiting a short position by buying back the stock is called “covering a short position.” This strategy may also be used by unscrupulous traders to artificially lower the price of a stock. Hence most markets either prevent short selling or place restrictions on when and how a short sale can occur. The practice of naked shorting is illegal in most (but not all) stock markets.

Margin buying

In margin buying, the trader borrows money (at interest) to buy a stock and hopes for it to rise. Most industrialized countries have regulations that require that if the borrowing is based on collateral from other stocks the trader owns outright, it can be a maximum of a certain percentage of those other stocks’ value. In the United States, the margin requirements have been 50% for many years (that is, if you want to make a $1000 investment, you need to put up $500, and there is often a maintenance margin below the $500). A margin call is made if the total value of the investor’s account cannot support the loss of the trade. (Upon a decline in the value of the margined securities additional funds may be required to maintain the account’s equity, and with or without notice the margined security or any others within the account may be sold by the brokerage to protect its loan position. The investor is responsible for any shortfall following such forced sales.) Regulation of margin requirements (by the Federal Reserve) was implemented after the Crash of 1929. Before that, speculators typically only needed to put up as little as 10 percent (or even less) of the total investment represented by the stocks purchased. Other rules may include the prohibition of free-riding: putting in an order to buy stocks without paying initially (there is normally a three-day grace period for delivery of the stock), but then selling them (before the three-days are up) and using part of the proceeds to make the original payment (assuming that the value of the stocks has not declined in the interim).

Investment strategies

One of the many things people always want to know about the stock market is, “How do I make money investing?” There are many different approaches; two basic methods are classified as either fundamental analysis or technical analysis. Fundamental analysis refers to analyzing companies by their financial statements found in SEC Filings, business trends, general economic conditions, etc. Technical analysis studies price actions in markets through the use of charts and quantitative techniques to attempt to forecast price trends regardless of the company’s financial prospects. One example of a technical strategy is the Trend following method, used by John W. Henry and Ed Seykota, which uses price patterns, utilizes strict money management and is also rooted in risk control and diversification. Additionally, many choose to invest via the index method. In this method, one holds a weighted or unweighted portfolio consisting of the entire stock market or some segment of the stock market (such as the S&P 500 or Wilshire 5000). The principal aim of this strategy is to maximize diversification, minimize taxes from too frequent trading, and ride the general trend of the stock market (which, in the U.S., has averaged nearly 10%/year, compounded annually, since World War II).


According to much national or state legislation, a large array of fiscal obligations are taxed for capital gains. Taxes are charged by the state over the transactions, dividends and capital gains on the stock market, in particular in the stock exchanges. However, these fiscal obligations may vary from jurisdiction to jurisdiction because, among other reasons, it could be assumed that taxation is already incorporated into the stock price through the different taxes companies pay to the state, or that tax free stock market operations are useful to boost economic growth.


  • Jan 21, 2008: The BSE Sensex fallen by 1408 points its highest loss ever in a day at the end of the session. The Sensex recovered to close at 17,605.40 after it tumbled to the day’s low of 16,963.96, on high volatility as investors panicked following weak global cues amid fears of the US & global recession.
  • Jan 22, 2008: The Sensex saw its biggest intra-day fall on Tuesday when it hit a low of 15,332, down 2,273 points. However, it recovered losses and closed at a loss of 875 points at 16,730. The Nifty closed at 4,899 at a loss of 310 points. Trading was suspended for one hour at the Bombay Stock Exchange after the benchmark Sensex crashed to a low of 15,576.30 within minutes of opening, crossing the circuit limit of 10 per cent.
  • March 3 2008 : the Sensex closed down by 900.84 points at 16677.88 against the previous day closing of 17578 due to Finance minister’s proposal to increase short term capital gains tax to 15% and further adding to it was global pressure
  • May 18, 2006: The Sensex registered a fall of 826 points (6.76 per cent) to close at 11,391, following heavy selling by FIIs, retail investors and a weakness in global markets. The Nifty crashed by 496.50 points (8.70%) points to close at 5,208.80 points.
  • December 17, 2007: A heavy bout of selling in the late noon deals saw the index plunge to a low of 19,177 – down 856 points from the day’s open. The Sensex finally ended with a huge loss of 769 points (3.8%) at 19,261. The NSE Nifty ended at 5,777, down 271 points.
  • October 18, 2007: Profit-taking in noon trades saw the index pare gains and slip into negative zone. The intensity of selling increased towards the closing bell, and the index tumbled all the way to a low of 17,771 – down 1,428 points from the day’s high. The Sensex finally ended with a hefty loss of 717 points (3.8%) at 17,998. The Nifty lost 208 points to close at 5,351.
  • January 18, 2008: Unabated selling in the last one hour of trade saw the index tumble to a low of 18,930 – down 786 points from the day’s high. The Sensex finally ended with a hefty loss of 687 points (3.5%) at 19,014. The index thus shed 8.7% (1,813 points) during the week. The NSE Nifty plunged 3.5% (208 points) to 5,705.
  • November 21, 2007: Mirroring weakness in other Asian markets, the Sensex saw relentless selling. The index tumbled to a low of 18,515 – down 766 points from the previous close. The Sensex finally ended with a loss of 678 points at 18,603. The Nifty lost 220 points to close at 5,561.
  • August 16, 2007: The Sensex, after languishing over 500 points lower for most of the trading sesion, slipped again towards the close to a low of 14,345. The index finally ended with a hefty loss of 643 points at 14,358.
  • April 02, 2007: The Sensex opened with a huge negative gap of 260 points at 12,812 following the Reserve Bank of India [Get Quote] decision to hike the cash reserve ratio and repo rate. Unabated selling, mainly in auto and banking stocks, saw the index drift to lower levels as the day progressed. The index tumbled to a low of 12,426 before finally settling with a hefty loss of 617 points (4.7%) at 12,455.
  • August 01, 2007: The Sensex opened with a negative gap of 207 points at 15,344 amid weak trends in the global market and slipped deeper into the red. Unabated selling across-the-board saw the index tumble to a low of 14,911. The Sensex finally ended with a hefty loss of 615 points at 14,936. The NSE Nifty ended at 4,346, down 183 points. This is the third biggest loss in absolute terms for the index.

Updates (updated on 15th October 2008) Highest daily falls

  • January 21, 2008 — 1,408.35 points
  • March 17, 2008 — 951.03 points
  • January 22, 2008 — 857 points
  • February 11, 2008 — 833.98 points
  • May 18, 2006 — 826 points
  • October 10,2008 — 800.10 points
  • March 13, 2008 — 770.63 points
  • December 17, 2007 — 769.48 points
  • March 31, 2007 — 726.85 points
  • October 06, 2008 — 724.62 points
  • October 17, 2007 — 717.43 points
  • September 15, 2008 — 710.00 points
  • January 18, 2007 — 687.82 points
  • November 21, 2007 — 678.18 points
  • August 16, 2007 — 642.70 points
  • June 27, 2008 — 620.00 points

Lower Circuit in January 2008

In the third week of January 2008, the Sensex experienced huge falls along with other markets around the world. On 21 January 2008, the Sensex saw its highest ever loss of 1,408 points at the end of the session. The Sensex recovered to close at 17,605.40 after it tumbled to the day’s low of 16,963.96, on high volatility as investors panicked following weak global cues amid fears of a recession in the US and other devloped economies. The next day, the BSE Sensex index went into a free fall. The index hit the lower circuit breaker in barely a minute after the markets opened at 10 AM. Trading was suspended for an hour. On reopening at 10.55 AM IST, the market saw its biggest intra-day fall when it hit a low of 15,332, down 2,273 points. However, after reassurance from the Finance Minister of India, the market bounced back to close at 16,730 with a loss of 875 points. Over the course of two days, the BSE Sensex in India dropped from 19,013 on Monday morning to 16,730 by Tuesday evening or a two day fall of 13.9%


Financial market volatility is back again. This time too, it is not scandals but real market risks that have led to market turbulence. The stock price rollercoaster has again shown that things cannot be taken for granted for too long. While the latest episode will dampen interest in equity investment among retail investors for a while, a market recovery will erase memories once more. The point, therefore, is to look for a way to minimise the cost of such volatility. Growth-and-slowdown cycles are not unexpected. What would be of interest is the likely direction that policy might take in that context. Does market turbulence have policy implications? India’s financial markets, even in the absence of capital account liberalisation, are not immune to global market conditions. The frenetic global development of new financial products and tech applications has meant that nothing is immune to world shocks, especially from developed markets. Much of the capital flow into developing countries comes from the developed world. For us in India, while accelerated economic growth is a necessary condition for solving many of our problems, any shock is a cause for anxiety. Whether it is the widening of interest rate spreads between Indian and international markets, oil price shock or global tightening of grain markets, the repercussions are inescapable. However, are these shocks now more bearable than earlier? Clearly, at the aggregate level, India’s capacity to bear risks has risen. Our markets are bigger and more diverse than ever before. The sizable forex and foodgrain reserves, coupled with a stronger fiscal position, offer a good cushion. An important point often made in this context is the external shock-absorber provided by the relatively large size of the domestic economy. Internal sources of demand are expected to keep overall demand growth intact even if external markets soften. Measured in terms of trade in goods and services, we are not as integrated with world markets as China is. A large part of the capital inflows into India has led to the creation of production capacity to meet domestic demand. And capacity creation has been accelerating, as shown by the increasing ratio of capital formation to GDP. However, a significant portion of this investment is in housing and construction, a sector sensitive to volatility in global capital flows. It is capital formation by way of plant & machinery that has been in response to growing domestic demand. It is, however, unrealistic to expect domestic demand to entirely offset a drop in demand from international markets in case of a slowdown in the West. The key determinant of India’s ability to sustain its growth momentum would be how long the slump in international demand is likely to last. Would this be an opportunity to put in place mechanisms to improve long-term productivity in various sectors? Business incentives operate in such a way that input accumulation, capital formation and production have an obvious element of dependency on the general sense of exuberance in international markets and perception of unlimited demand. It would be interesting to examine what is likely to happen in a more cautious climate of demand. Would this be used to encourage innovation and productivity improvements? Would this be used to establish foundations for longer-term growth through investments in human capital? There will be obvious pressures for increased public expenditure. It is premature to pass judgment on the likely impact of the huge stock market swings experienced last week not only here but also in other markets around the globe. Still, it is an opportunity to assess the implications of programmes that define directions of the economy through incentives. These are incentives set not only by government policies, but also by markets themselves. In the public policy arena, two areas which have received much attention in the context of accelerating economic growth are infrastructure and agriculture. In the context of a slower global growth, initiatives for expansion of infrastructure are likely to be sustained. However, there are strong complementarities in the development of rural infrastructure and agriculture. It is reasonable to expect attention to be focused on the rejuvenation of the rural economy, where it is not so much international but domestic economy linkages that are critical for such a revival.


Not only was price volatility at an extreme this year but so was share turnover. Take a look at the following chart of the NYSE Composite Index for 2008. The red dots indicate the number of times the upside/downside or downside/upside trading volume ratio was beyond the boundary that has historically characterized “overbought” or “oversold.” Needless to say 2008 was a record year in terms of volume turnover. We hear much discussion over volatility but few know how to define it. “Volatility is an external manifestation of underlying internal economic instability.” That’s how my colleague Bud Kress defines the dominant market environment we’ve all been subjected to these past three months. That’s certainly one way of putting it, although there’s also a more practical way of explaining it. Indeed, Mr. Kress’ cycles probably provide an even better answer as to why super volatility has beleaguered the financial market in recent months. Extreme volatility is itself a form of change. It’s a state of flux wherein nothing remains constant and little or no progress is made. This year’s motif in the political realm, in fact, has been that of change. “Change now!” has been the rallying cry for millions seeking salvation from this year’s financial turmoil. Change has definitely characterized the economic realm, which in turn is the progenitor of political sentiment. The year 2008 has been one of great change in the monetary and economic realms with the result that millions have demanded change in the political realm. But as someone has said, if people are kept in a constant state of change they never arrive at anything. Thus we see that hyper volatility is the very antithesis of progress. What was responsible for the extreme volatility of 2008? Many pundits pinned the blame on speculators, short sellers, hedge funds, and a host of other culprits. Most of them fell short of the mark in discovering the underlying cause for the volatility. The answer, as usual, is to be found in forces beyond our immediate sight. It was the combination of the opposing Kress cycles with the confidence crisis that forced much of this year’s volatility onto the market. The confidence crisis was in turn a creation of the Fed’s tight money policy since 2004. The market was able to shrug off this tightness to a large extent from 2004 until 2007. But when the Kress 2-year cycle peaked in October 2007 it began the turmoil that would be seen in 2008 when the Kress 6-year cycle bottomed simultaneous with the 12-year cycle peak. Adding to this volatility was the fact that the Kress composite interim cycle is bottoming in December. Thus we had three major cyclical occurrences in the same year, a rare event to be sure. The volatility in recent months has indeed been extremely rough but with the bottoming of the Kress interim weekly cycle this month, we should finally witness a declining in market volatility and along with it the advent of a kinder, gentler trading environment. This seismic volatility has been mainly a function of the cyclical cross-currents since September as discussed. In September-October the 6-year cycle was bottoming while the 12-year cycle was peaking, producing a ramp up in the Volatility Index (VIX) to all-time highs. Then, after the 6-year cycle bottomed in October, the duel in November-December has been between the newly formed 6-year up cycle versus the major composite weekly cycle bottom scheduled for around Dec. 18-21. Dueling cycles always produce major increases in volatility. Now that the 12-year cycle peak has done its damage, however, the 6-year up cycle is starting to assert itself. It’s probable that this important major cycle will overpower the composite weekly cycle scheduled to bottom in a few days and produce a higher low at the upcoming cycle bottom. The bear market price low could therefore be (and is probably) already in for the SPX and the recent improvement in the NYSE hi-lo differential is a strong case in point. Once the Kress interim cycle bottoms later this month the market will be free of all intermediate term cyclical constraints and should be able to commence a new cyclical bull market heading into 2009. Moreover, the important 10-year cycle is peaking in 2009 and should also support a recovery in the first half of the new year. This time, unlike 2008, the three nearest major Kress cycles (including the 6-year and 10-year cycles) will be in harmony on the upside instead of at odds. And the key 6-year cycle bias in 2009 will be up instead of down. Technical traders will have confirmation that the bear market’s back has been broken once the VIX breaks the 60-day moving average uptrend (see chart below). We should also see improved trading conditions entering 2009, with fewer whipsaws and sudden reversals that have characterized the past few months since the volatility explosion. The past year was a challenging one fraught with peril in the face of frozen credit and cyclical cross-currents. Yet for every crisis there is a corresponding opportunity. This year’s crisis has brought equity values to opportunity levels as we enter what promises to be a kinder market environment, thanks in part to the Kress cycle configuration going forward. The fact that the Fed has finally (if belatedly) reversed its tight money stance is also a key factor in the months ahead.


are growing up in times of turmoil. Over the last one decade, the markets have seen an increasing correlation with world markets. Over the past five years, the correlation between Nifty returns and the S&P 500 returns has risen from around 0.25 to 0.38. Thus, when world markets move, the valuation of Indian stocks is affected. Contrary to popular belief, the dominant channel of influence is not FII flows. FII transactions are tiny when compared with the huge size of the Indian spot and derivatives market. The real story lies in the globalisation that started in the early nineties, India’s new degree of openness to world trade and the impact of this globalisation on the character of the Indian firms themselves. The influence shows up in three routes. First, some large firms in India are almost entirely engaged in exports. For instance, a firm like Infosys produces in India and sells to the world. Revenues of Infosys fluctuate in sync with fluctuations in the world economy. Through such export-oriented firms, global volatility affect the customers of Infosys and hence matter for the future profit growth of Infosys. Thus, volatility in the world economy matters to the rational speculator looking at future dividends from Infosys. When world markets fluctuate, the Infosys share price will fluctuate too. This holds similarly true for all firms which do import and export are affected by world markets directly. One of the reasons that this route has not been highlighted could be that these relationships tend to be greatly understated when we look at imports or exports shown in the annual report of companies. This is because many listed firms do import or export through intermediaries that are trading companies. The second route of influence is the equalisation of product prices through import parity. A local steel company may buy steel from a local producer. But it is affected by world fluctuations of steel prices because the local price of steel is set by the world markets. Tata Steel may primarily sell into the local market, but the world price of steel controls its profit rate. As this phenomenon of price equalisation spreads across more products, more and more companies will be affected by the ups and downs of world prices and the world economy. Finally, many large Indian companies are becoming multinationals. Their profits thus rely on future growth of the world economy. Their share prices will naturally be affected by the ups and downs of the world economy. These three factors are at work reshaping the character of share prices of Indian firms, particularly of the top 100 companies that make up Nifty and Nifty Junior. It is reasonable that their prices should fluctuate in response to major changes in the world economy and world asset prices. This helps explain the observed increased correlation between the Indian and the US markets. Further, the increase in correlation looks set to continue, given the rate at which Indian firms are becoming part of the global economy. This includes firms smaller than the top 100 in the Nifty and Junior set as well. This bodes ill for the hope that Indian asset prices will not be affected by global market volatility in the future. There are only two ways to have the Indian stock market not react to world market volatility: (a) to raise entry barriers for the rest of the world into the Indian economy, or (b) to have inefficient markets, with barriers to trade for different participants; such a market would not react to news and thus have low volatility. Globalisation is a two-sided coin: it is a source of tremendous prosperity, and simultaneously changes the risk exposure of firms. Indian firms and investors need to engage more firmly with the world economy by understanding and accessing the world financial markets in order to fully respond to the new realities of India as an open economy. In keeping with India’s status as a rising global power, Indian economy and consequently the Indian capital markets can no longer be insulated from global headwinds. Cooling property prices, risks of US slowdown, declining commodity prices and the cooling of Chinese economy led to heightened volatility in both the emerging markets and the developed markets. The resultant meltdown also impacted India with the Bombay Stock Exchange sensex declining more than 12%. Firstly, interest rates are about 200 basis points higher than they were in May 2006. And unlike May 2006, India has a serious inflation problem. This portends the risk of slower growth. This time rising interest rates have resulted in rise in bank term deposit rates which have reopened an alternative and attractive investment opportunity for retail and high net worth (HNI) investors. Secondly, foreign institutional investors (FIIs) who have been the bulwark of the rise in the Indian markets have started reassessing their portfolio risks and are unwinding their emerging market positions. Also, sub-prime worries in the United States, cooling off of the US housing market, unwinding of yen carry trade positions have accentuated the unwinding of these positions. FII flows in cash equities during January-February 2007 have been lower than past two years with net inflows in January-February ’07 to the tune of US $1,288.2 million as against inflows during January-February 2006 and January-February 2005 to the tune of US $2,396.3 million and US $2,011.2 million respectively. Thirdly, unlike April 2006, India finished February 2007 as the worst performing emerging market in the world — a rare event for India, and this could be a precursor to things to come. On the positive side, the foundations of Indian stock and corporate performance — fundamentals and the growth story are still intact. However, the confidence in these fundamentals has received a mild jolt but the relative low leverage, growth outlook, execution track record which the fourth quarter of 2006-07 (Q4F07) results will herald a turnaround along with a rebound in corporate activity. Also, in spite of hardening of interest rates, which are targeted to curb inflation, liquidity situation at present is much better than it was in May 2006 and credit for corporate capacity expansion is relatively easy to come by. On the policy front, the Budget for 2007-08 further reiterated the government’s focus on growth albeit with controlled inflation and also re- emphasised its thrust on education, agriculture and infrastructure. However, the increase in the corporate dividend distribution tax (from 12.5% to 15%) and increase in excise levied on cement had a negative impact on market sentiment. I firmly believe that the current state of indecision in the markets calls for backing leaders and amongst leaders — large caps, proven track record, growth and stable margins. Factors like long-term viability of business apart from good corporate governance, high visibility and scalability of business reduces the risk element in large caps which makes them attractive propositions in an over heated market. Besides, it has also been observed that when the markets rebound, large caps outperform mid-caps. An analysis of the sensex constituent stock performance highlights the investors’ mindset in picking a few thought leaders. Since 14 June 2006 (sensex’s recent lowest point), only 11 stocks have outperformed the sensex return of 44%. To conclude, in any investment it’s the quality of management and their delivery capabilities that you back and the numbers automatically follow.


First, some preliminaries. Let Xt stand for the price of an investment at time t, with time measured in trading days from the initial purchase. If the investment is sold at sale day t>0, then the return of the investment is Rt = Xt / X0. Investment returns are difficult to compare unless they have been adjusted to take into account the length of time over which they are held. In this essay we will use three distinct ways of performing this adjustment, which if not carefully distinguished and understood can lead to great confusion. Here are the three:

  1. Daily Growth Rate: DGR = (Rt)1/t. This is the growth rate per day which results in a return of Rt after being compounded daily for t days.
  2. Compound Annual Growth Rate: CAGR = (Rt)252/t = DGR252. This is the annualized growth rate, assuming 252 trading days per year.
  3. Continuously Compounded Growth Rate: CCGR = ln( CAGR ). This is the instantaneous rate of growth, with time measured in years.

CAGR is the customary measure of return when comparing the growth rates of investments. When two potential investment are compared using CAGR, we are implicitly assuming that each CAGR can serve as a prediction of the investment rate of return. Volatility, in contrast, is a measure of the uncertainty of the investment rate of return. In principle, every investment return can be broken down into two parts: one part is the return we expected when we made the purchase, and the other is the difference between observed and expected return. The observed return will be different from what we expected whenever there is volatility due to the random influences. To be rigorous about volatility we need to specify the probabilistic structure of the process that causes fluctuations in investment prices. One way to do this is to assume that prices vary continuously in time. A single investment followed through time has a price graph that resembles Figure 1 below, a continuous trajectory that is so irregular that its first derivative almost never exists. Stated in other words, it is constantly changing direction in response to tiny random fluctuations. The histogram of final prices is shown in color in the right-hand margin of the graph; the colors run from yellow for the bin with the highest count, through red and magenta, to blue for bins counts of zero. Despite the fact that the first derivative never exists, it is still possible to state how these tiny random fluctuations cause the price of the investment to change. For example, if the random fluctuations resemble Brownian motion, the microscopic motions of a small particle floating in a liquid, then the random increments will be normally distributed with an infinitesimal variance. This model was first advanced in 1900 by a young French doctoral student of economics named Louis Bachelier. His idea was so radical (and his reputation so small) that it was ignored completely until Albert Einstein independently rediscovered it a few years later. Expressed in modern notation and brought up to date, here is an improved version of Louis Bachelier’s model. Let dXt refer to the change in price Xt over a very small but finite interval dt. As noted above, this change can be broken conceptually into two parts, one predicted and the other unexpected. If the predicted rate of change over dt is ?, then the size of the predicted change will be ?Xtdt. Let us now suppose that the variance of the unexpected part of the price change is proportional to Xtdt. In other words, the increments of Xt are mutually independent and normally distributed: dXt | (Xt = x) ~ N[ ?xdt, (sx)2dt ]. Pure Brownian motion is a similar but simpler process, with mean zero and variance dt. This simpler idea is closer to what Louis Bachelier actually proposed for the movement of stock prices. For any finite dt, the increments of Brownian motion are distributed normally: dBt ~ N[ 0, dt ]. In the limit as dt goes to zero, the relationship between the investment price process and Brownian motion can be neatly and conveniently expressed as a stochastic differential equation (SDE): dXt = ?Xt dt + sXt dBtA (Geometric Brownian Motion) Note how the increment in price in this formula has been expressed as the sum of two parts. The first term is the expected change, and the second term is the random change. The mean of the random change is zero, and its standard deviation is the infinitesimal quantity sXtdt. The parameters ? and s in this model are the fundamental measures of growth and volatility for the investment price process. If there is no volatility whatsoever, i.e. s = 0, then the above model simplifies to the ordinary differential equation dX/dt = ?X, which has the solution Xt = e?t X0. From this it is clear that the parameter ? in the SDE is the continuously compounded growth rate (CCGR), also known as the instantaneous growth rate. If the unit of time is the year, then we can calculate the CCGR from CAGR (the compound annualize growth rate) as CCGR = ln( CAGR ). If the unit of time is the day, and we assume 252 trading days in each year, then the relationship is CCGR = (ln CAGR)/252.

Growth and Volatility in Log Space

The volatility displayed in FigureA  is highly asymmetrical. The pale red envelope encloses the region in which the price trajectory is most likely to move; this region clearly expands much more rapidly in the direction of higher prices than it does towards lower. This region is known as the “2-sigma” envelope. If we transform all prices by taking their logarithms and then replot the graph, then three nice things happen:

  • the predicted trajectory becomes linear,
  • the 2-sigma envelope becomes quadratic about the predicted trajectory, and
  • the probability distribution becomes the Gaussian (normal) distribution.

This is shown in Figure 2. Notice that the 2-sigma envelope now looks like a quadratic curve laid on its side and skewed linearly upwards so that its central axis is the linear predicted log price trajectory for the investment. In terms of the Geometric Brownian Motion model for investment volatility, we need to apply a change of variable formula to the model, using the transformation Yt = ln Xt. Changing variables in an SDE is not quite as easy as it is with ordinary differential equations, because any nonlinear transformation alters not only the variable but also the shape and moments of the probability distribution. ItA´’s change of variable formula, when applied to the logarithmic transformation of price, yields this result: dYt = (? – A½ s2) dt + s dBt. Notice that this SDE implies that Yt-Y0 ~ N[ (? – A½ s2)t, s2t ]. In other words, the log return at time t, i.e. ln( Rt ), is distributed normally with mean (? – A½ s2)t and variance s2t. This is clearly visible in Figure 2: the central red line is the graph of Y0 + (? – A½ s2)t, the histogram on the right is the normal distribution at time t=252, and the standard deviation of Yt increases like t1/2s. Because log returns are normally distributed, we know immediately that the ordinary return Rt has the log-normal distribution. This implies that the median of Rt is the exponential of the mean of ln( Rt ), and therefore the central pale red line in Figure 1 traces out the trajectory of the median return for the investment. In particular, Median[ Rt ] = R0 exp[ (? – A½ s2)t ]. So the effect of increasing volatility is to lower the median rate of return that a population of independent investors will receive. When comparing investment vehicles with differing volatilities, it would be preferable to compare median annualized growth rates, not raw CAGRs.

Measures of Volatility

Just as with measures of growth, there are several different ways of measuring volatility which, if not carefully distinguished and understood, can lead to great confusion. Suppose we start with a large number of daily observations of the price of the investment, with time measured in years so that dt = 1/252. To help with the notation, let D[ X ] stand for the standard deviation of a random variable X, i.e. D[ X ] = ( Var[ X ] )1/2. Then the daily volatility is D[ dYt ] = D[ s dBt ] = s(dt)1/2 = s/(2521/2). This relationship suggests that we can empirically estimate the volatility s as the standard deviation of a series of daily observations of dYt = ln( Xt+dt/Xt ) for t = 0, dt, 2dt, …, using the relationship s = D[ dY ] (2521/2). If data are available only once per month, i.e. 12 times per year, then the standard deviation of the monthly observations will be s/(12)1/2. Similarly, if the data are only annual, then the standard deviation of the annual observations will be simply s. If time is measured in trading days, rather than years, and observations are still made daily, then dt = 1 day, and the daily volatility is D[ dYt ] = D[ s dBt ] = s(dt)1/2 = s. This demonstrates why it is so important to pay close attention to the units in which time is measured, as well as the number of observations per unit of time. It has become customary in the Mechanical Investing community to measure volatility with a statistic known as the Geometric Standard Deviation (GSD), which is defined as the exponential of the annual volatility: GSD = exp[ s ]. By convention, CAGR and GSD figures are reported in “percentage” terms, where the following relationships apply: CAGR% = 100( CAGR – 1 ), GSD% = 100( GSD – 1 ). To summarize, when setting out to measure volatility or growth, three decisions need to be made in advance: (a) the units in which time is measured, (b) the number of observations per time unit, and (c) whether the result is to be given in instantaneous or annualized form. Confusion can be avoided only when all three decisions are made with total clarity.

Simulations of Investment Growth and Volatility

The colored histogram shown in Figure 1 is based on 25,000 independent runs of a simulated investment whose growth rate and volatility are both very high (CAGR% = 100, GSD% = 100). The effect of the high volatility is very clear in this figure: some investors may easily see a return in excess of 600%, while others may lose over 60%. Consequently, their initial uncertainty as to the outcome of their investments is very large indeed. The simulation itself was based on the stochastic difference equation dXt = (e?dt)Xt dt + sXt dBt, where ? = ln( 2 ) / 252 = 0.002751, s = ln( 2 ) / 2521/2 = 0.043664, and dt = 1 day. Each simulation was run for 252 days from a starting value of $10. The above stochastic difference equation works very well as a simulation of the true SDE, which cannot be simulated due to the infinitesimal time step dt, but only because the given time step dt = 1 is reasonably small compared to the duration of the simulation, 252 trading days. When the time step is a significant fraction of the total duration, then another simulation method must be used. For example, suppose that we need to simulate an investment growth process over 10 years, with a time step of dt = 1 year. In this case we simulate the logarithm of the process, Yt = ln Xt, and use dYt ~ N[ (? – A½ s2)dt, s2dt ]. In either case, the simulation requires the generation of a series of high-quality independent normally-distributed random numbers. The typical built-in random number generator that is supplied with most computer languages is not of high enough quality to support the simulation of stochastic differential equations. Experience with the simulations that were performed to test the results in this essay suggests the observed volatility will be substantially less than theory predicts when a deficient random number generator is used. One solution for this problem is to hand-code a custom random number generator using, for example, the portable Ran1 algorithm described in Numerical Recipes in C by William Press et al, published 1988 by Cambridge University Press. The Ran1 algorithm uses one linear congruential generator to produce the high-order bits of the result, a second to produce the low-order bits, and a third to shuffle the sequence of output numbers to remove periodicities.

The Sampling Variation of Growth Statistics

When an empirical statistic such as the CAGR is measured on a series of independent samples of data, we observe a distribution of values for the statistic. To treat this sampling varation mathematically, we consider each datum to be a random variable, and the estimator to be a transformation of those random variables. Thus the estimator itself is a random variable, and its theoretical variance can be used to estimate the sampling variation of the statistic in the real world. In the case of the growth rate, the estimator is constructed from an average of normally-distributed random variables. As one might expect, the sampling distribution of a growth rate ? depends strongly on the volatility s of the growth process. Here is a formula for the limits of the 95% confidence interval for the estimated CCGR, denoted here by r: r A± (t0.025,s) s/?1/2 The notation ta,? refers to the point x on Student’s t distribution with &nu degrees of freedom, such that P{ t > x } = a. When calculating r from N observations, the degrees of freedom are just &nu = N-1. When &nu > 30, the approximation ta,? = Za can be used, where the notation Za refers to the point on the standard normal distribution such that P{ Z > x } = a. For the 95% confidence interval, Z0.025 = 1.96.


Over the 17 year period 1986 through 2002, the RRS189 monthly 5-stock screen had an observed CAGR% of 42 and GSD%(D) of 55. What is the 95% confidence interval for the CAGR%? First we calculate an estimate r for the CCGR, namely r = ln( 1.42 ) = 0.3507. The degrees of freedom are &nu = (17)(12)-1 = 203. This is comfortably higher than 30, allowing us to use the normal approximation Z0.025 = 1.96 in place of the t-statistic. Our estimate for the instantaneous volatility is s = ln( 1.55 ) = 0.4383, with time measured in years. Thus the limits of the 95% confidence interval are 0.3507 A± (1.96)(0.4383)/2031/2 = 0.3507 A± 0.0603. Converting each limit to a CAGR, using the relationship CAGR = exp[ ? ], we get: Lower limit for CAGR = exp[ 0.3507 – 0.0603 ] = 1.34, Upper limit for CAGR = exp[ 0.3507 + 0.0603 ] = 1.51. We conclude that the 95% confidence interval for the CAGR% of RRS189 runs between 34 and 51, with 42 as the best point estimate. It may be argued by some that an observed CAGR is not random at all, because it represents reality, and therefore it is inappropriate to calculate a confidence interval around the observed CAGR. While this is superficially true, it misses the point of carrying out this calculation. We are not actually interested in what the true rate of return might have been for that particular investment, because we already know exactly what it was. Instead, we want the answer to a different question: “How much volatility-induced variation should we expect in point estimates of the CAGR, across an ensemble of independent similar investments with the same CAGR and GSD?” The answer to that question is what the confidence interval provides. It is important to remember that sampling variation is just one source of the variation that can occur in the estimation of a statistic. Systematic errors such as might be caused by changes in the way the variable is measured, or structural changes in the process itself, can contribute large amounts of additional variability. Even typographical errors in data transcription, whenever they occur, add to the observed variability. Therefore, sampling variation must be seen as a lower bound for the total variability of any statistic.

The Sampling Variation of Volatility Statistics

In the case of volatility, the estimator is constructed from a sum of squared normally-distributed random variables. For this reason, the sampling distribution of s2 is closely related to the Chi-square distribution. Here is a formula for the limits of the 95% confidence interval for the estimated variance s2 around the unknown true variance s2: s2(?/A) < s2 < s2(?/B), where s2 is the estimated variance, s2 is the unknown true variance, A = ?2(a/2, &nu) and B = ?2(1-a/2, &nu). The notation ?2(a, &nu) refers to the point x on the Chi-square distribution with &nu degrees of freedom, such that P{ ?2 > x } = a. When calculating s2 from T observations, the degrees of freedom are just &nu = T-1. Here is a summary table of the some useful Chi-square points for various lengths of backtests, with a = 5%:


American is falling down so sharply. But no body can understand the reason of fall down in American Market. World’s biggest economy system will be collapsed so sharp, no body has thought earlier. But stop, look carefully, American economy is not collapsing . It is just a full stop of a age. American economy has been reached on top of its heights when computer technology and software technology were making new history daily. Most of American business were based on development of software technology. But there are every end of every starting. The age of computer science and technology has touched the highest peak of its development. There is nothing so much new to do in this field now. The population of America is approx 3 Bl. Here supply has more then its demand. After 9/11 attack America has bring strict rule for comer to America. So the population of consumer has become constant even the production has increased in these years. But after a limit the demand has became lower than its supply. The same situation has faced by Great Britain after over the Industrial Revolution which has began in Great Britain during the 1700s In the large scale many industries has shut down their company due to supply more then demand. In the result they has searched the other market in foreign for export their product. This act has became result of Imperialism over the world. After First World War America has faced the fall down due to heavy unemployment in America. All American soldiers who were engaged in world war has became unemployed after over the first world war. That time there was no any new invention has became in which they could be work . Inflation was touching the new high daily. This uneployment and inflation has became the result of second world war. I think that we are facing same situation such a over a age of new inventions. We can accept that America and other countries will find any new field for great opportunity for job. Without new opportunity we can not hope for recover for down fall in American and World economy market.



Websites of NSE, BSE, RBI, SEBI, CSO, Bharti Airtel, DLF, HUL, ICICI, Infosys, L&T, Reliance Infrastructure, RIL, money.rediff, money control, google etc.


Arestis, P., P.O. Demetriades and K.B. Luintel (2001)”Financial Development and Economic Growth: The Role of Stock Markets”, Journal of Money, Credit and Banking, 33(2):16-41. Bilson, C.M., Brailsford, T.J. and Hooper,V.J. (1999)”Selecting Macroeconomic Variables as Explanatory Factors of Emerging Stock Market Returns”. Working Paper. Bollerslev, T (1986) Generalized Autoregressive Conditional Heteroskedasticity, Journal of Economterics, 31(1):307-327. Bonnici, M., (1997),’Letter of Transmittal’ Minister of Finance & Commerce, Malta. Campbell, J (1996) Consumption and the Stock Market: Interpreting InternationalA  Experience”, NBER Working Paper, 5610. Capital Markets Development Authority. Published Annual Reports. Engle, R. F (1982) Autoregressive Conditional Heteroscadasticity with Estimates of the Variance of the U.K. Inflation, Econometrica, 50(3):987-1008. Krainer, J (2002)”Stock Market Volatility”, FRBSF Economic Letter, Western Banking, 2002-32, pp1-4. Levine, R and S. Zervos (1996)”Stock Market Development and Long-Run Growth”, World Bank Economic Review, 10(1):323-339. Ludvigson, S and C. Steindel (1999)”How Important is the Stock Market Effect on Consumption”Federal Reserve Bank of New York Economic Policy Review, 5(1):29-51. Poterba, J. M (2000)”Stock Market Wealth and Consumption”, Journal of Economic Perspectives, 14(2):99-118. Starr-McCluer, M (1998)”Stock Market Wealth and Consumer Spending”, Board of Governors of the Federal Reserve System, Finance and Economics Discussion Paper Series, 98/20. South Pacific Stock Exchange. Published Annual Reports. Zuliu, H (1995)”Stock market Volatility and Corporate Investment”, IMF Working Paper, 95/102. [1] Arestis, P., P.O. Demetriades and K.B. Luintel (2001)”Financial Development and Economic Growth: The Role of Stock Markets”, Journal of Money, Credit and Banking, 33(2):16-41. [2] Bilson, C.M., Brailsford, T.J. and Hooper,V.J. (1999)”Selecting Macroeconomic Variables as Explanatory Factors of Emerging Stock Market Returns”. Working Paper. [3] Bollerslev, T (1986) Generalized Autoregressive Conditional Heteroskedasticity, Journal of Economterics, 31(1):307-327. [4] Bonnici, M., (1997),’Letter of Transmittal’ Minister of Finance & Commerce, Malta. [5] Campbell, J (1996) Consumption and the Stock Market: Interpreting International Experience”, NBER Working Paper, 5610. [6] Capital Markets Development Authority. Published Annual Reports. [7] Engle, R. F (1982) Autoregressive Conditional Heteroscadasticity with Estimates of the Variance of the U.K. Inflation, Econometrica, 50(3):987-1008. [8] Krainer, J (2002)”Stock Market Volatility”, FRBSF Economic Letter, Western Banking, 2002-32, pp1-4. [9] Levine, R and S. Zervos (1996)”Stock Market Development and Long-Run Growth”, World Bank Economic Review, 10(1):323-339. [10] Ludvigson, S and C. Steindel (1999)”How Important is the Stock Market Effect on Consumption”Federal Reserve Bank of New York Economic Policy Review, 5(1):29-51. [11] Poterba, J. M (2000)”Stock Market Wealth and Consumption”, Journal of Economic Perspectives, 14(2):99-118. [12] Starr-McCluer, M (1998)”Stock Market Wealth and Consumer Spending”, Board of Governors of the Federal Reserve System, Finance and Economics Discussion Paper Series, 98/20. [13] South Pacific Stock Exchange. Published Annual Reports. [14] Zuliu, H (1995)”Stock market Volatility and Corporate Investment”, IMF Working Paper, 95/102.

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