Choosing the right stock for investment is usually a hard and a difficult decision for investment and portfolio managers as the action of selecting a stock to buy is not that easy due to the different perspectives that need to be evaluated before taking the action of purchasing a stock. The decision to purchase stock includes the necessary evaluation of several perspectives, this leads to Data Envelopment Analysis (DEA) which is considered a multi-criteria decision-making tool that can select the most efficient stocks from a large combination of stocks. In this chapter, the usefulness of Data Envelopment Analysis as an efficiency measurement tool used for ideal stock selection and portfolio construction is presented several studies are presented which examines the effect of corporate action announcements on share prices and trading activities. This subject has encouraged researchers to publish many theoretical and empirical studies for more than 40 years. In literature, the majority of studies found that corporate action announcements such as dividend, capital increase, and earnings per share (EPS) announcements, have strong effects on the share prices and trading activities, and that these effects appear in a predictable manner around critical dates; and therefore, it is important for the trading desk in any firm to have access to accurate and timely data on such corporate actions in order to conduct proper and more efficient trading strategies. However, a more careful look at literature reveals a vast amount of contradictions in the empirical studies done on corporate action announcement. This chapter tackles the background overview of these studies and shows the contradictions between them. This chapter starts with a brief about Kuwait economy then an overview about the Kuwait Stock Exchange, its regulation and the trading rules in Kuwait Stock Exchange. Then in the following part 2.4, the concept of stock selection is discussed via highlighting the validity of different methods used for stock selection .Part 2.5 gives an overview about Data Envelopment Analysis, its history, models and its strengths and limitations. This chapter ends with part 2.6 that shows previous researches that examined the use of Data Envelopment Analysis as a useful tool for stock selection.
Kuwait is considered a geographically small country (ranked number 157 worldwide), but with a high crude oil reserves of about 102 billion barrels (9% of world reserves), petroleum accounts for 50% of GDP, 95% of export incomes. Kuwaiti government estimation is to increase oil production to 4 million barrel/day by 2020. High oil prices was the reason behind the ability of Kuwait to bypass the economic crisis, where in 2008 it reported a 10 successive years of budget surpluses before reporting a budget deficit in 2009. In 2009 the Kuwaiti government allocated $140 billion for a five year plan in order to diversify its income via attracting more investment, and increasing the private sector participation in the economy.(CIA world-factbook, 2010)
TheA Kuwait Stock ExchangeA was established by law in 1977 and it is considered as the first and largest stock exchanges in theA gulfA region. Since 1977 KSE went through many changes until it was organized by an Amiri decree on 1983, where many regulations and decisions where taken by the ministry of commerce, and KSE committee to come up with the rules that can comply with the international standards in order to enhance the performance of the stock market in Kuwait. In 1990 after the Iraqi invasion KSE postponed its work till the liberation of Kuwait in 1992, and then in 1995 it became the most active market in the Arab World after the adoption of an automated trading system. In year 2000 was the start of foreigners’ participation in KSE via owning shares of Kuwaiti shareholding companies. James (2007) By the end 2007 KSE was ranked number 38 within the world largest stock market capitalisation with about $188 billion. (Economist, 2010) While in 2009 it was ranked number 34 with a market capital of $96 billion. (CIA world-factbook, 2010) In 2002 KSE contained 77 listed companies ,while now in 2010 KSE contains 229 company distributed on 10 sectors which are the banking sector ( 9 companies),investment sector (51 company), insurance sector ( 7 companies), real estate sector ( 39 companies), industrial sector ( 28 companies ), services sector ( 60 companies), food sector ( 6 companies), non Kuwaities sector ( 14 company), mutual fund sector ( 1 company) and the parallel market sector ( 14 company) . (Kuwait Stock Exchange, 2010)
Upon these rules in 2007 In order to protect the rights of investors KSE Committee took the decision No. (4) for the Year 2007 which states that all listed companies in the KSE must organize their general assembly meeting at the end of each company’s financial year within a period of 45 days from the KSE committee approval date on its annual financial statements, where all the companies must distribute the cash and share dividends to shareholders in a period of 10 working days after the approval taken within the company’s general assembly meeting. (Kuwait Stock Exchange, 2010)
Trading in KSE regular market is characterized by 2 main issues the first one is the ability of only trading shares in the form of units ranging from 500 shares till 80000 shares and the second is the limitation of the stock price fluctuation during a day trade where the main guide of the following rules is the price of the stock in the market as according to the price share the investor is obligated to buy and sell shares in form of units where the share prices can fluctuate 5 pricing units daily according to its category. (Kuwait Stock Exchange, 2010) Stock Price (Fils) Value Unit Unit Change (Fils) Max Daily Change (5 Units) 0.5:50 80,000 0.5 5×0.5 = 2.5 Fils 51:100 40,000 1 5×1 = 5 Fils 102:250 20,000 2 5×2 = 10 Fils 255:500 10,000 5 5×5 = 25 Fils 510:1000 5,000 10 5×10 = 50 Fils 1020:2520 2,500 20 5×20 = 100 Fils 2520:5000 1,000 20 5×20 = 100 Fils 5050:9050 500 50 50X5 = 250 Fils
Ideal stock selection is the goal of each portfolio manger in order to reach the optimum combination of stocks to form an investment portfolio that yields the best results in terms of ROI and to increase the value of the portfolio. Michael & Yan-Leung (1998) investigated the practice of investment management in Hong Kong regarding stock selection as a 142 investment managers from several categories were asked to rank the importance of fundamental analysis, technical analysis and portfolio analysis as methods for stock selection, results showed that fundamental and technical analyses comes first followed by portfolio analysis.” Michael, et al. (1998) This was relevant with the survey done by Carter and Van Auken (1990) over 185 portfolio managers in the United States as the result showed that fundamental analysis was ranked number one followed by technical analysis and in the third rank came the portfolio analysis. Carter, et al. (1990) Several studies and researches have been done in order to evaluate these strategies. Starting with the random stock selection Hsin-Hung Chen (2008) outline Jensen’s (1968) “New evidence on size and price-to-book effects in stock returns” demonstrated that fund managers in financial service industry generally failed to outperform a random selection of stocks. (Jensen, 1968, in Hsin-Hung Chen, 2008). Ion & Elena (2010) studied portfolio analysis as a strategy for stock selection via examining the efficiency of investing the whole capital in one sector and the efficiency of investing the capital in a diversified portfolio where the results showed that the portfolios based on stocks from one sector showed a higher return than portfolios based on stocks from diversified sectors. Ion & Elena (2010) Lukas Menkhoff (2010) concluded in his survey study about the use of technical analysis as a stock selection tool by fund managers via “analyzing survey evidence from 692 fund managers in five countries, the vast majority of whom rely on technical analysis. At a forecasting horizon of weeks, technical analysis is the most important form of analysis and up to this horizon it is thus more important than fundamental analysis. Technicians are as experienced as educated, as successful in their career and largely just as overconfident in decision-making as others. However, technical analysis is somewhat more popular in smaller asset management firms. What we find most significant is the relation of technical analysis with the view that prices are heavily determined by psychological influences.” Lukas Menkhoff (2010) Going through the fundamental analysis based strategy which is defined as “a method of evaluatingA a securityA that entailsA attempting to measureA its intrinsic value by examining related economic, financial and other qualitative and quantitative factors.” (Investopedia, 2010) Many studies done on evaluating the efficiency of this strategy upon them is the research study of Jane & Stephen (1989) which resulted in that if “an extensive financial statement analysis is done to the data from financial statements it is possible to predict future stock returns as this fundamental measure captures equity values that are not reflected in stock prices.” Jane & Stephen (1989) The use of data envelopment analysis in order to analyse multiple financial ratios in order to identify the most efficient stocks will be discussed in sector number 2.5
Efficiency, defined as the competency in performance, was always the goal of any productive person, firm or any other entity as efficiency can classify any unit and categorizes it in the top of the rank if it is highly efficient or at the bottom of the rank if it is inefficient. Data envelopment analysis represents one of the most widely used tools to measure the efficiency as it was described by Charnes, Cooper, & Rhodes (1978) as a ‘mathematical programming model applied to observational data that provides a new way of obtaining empirical estimates of relations – such as the production functions and/or efficient production possibility surfaces – that are cornerstones of modern economics’ (Charnes, et al., 1978).
Data Envelopment Analysis (DEA) is considered a recent manner of evaluating the performance or the efficiency of a group of units or entities called Decision Making Units (DMUs).In the last few years DEA was used to evaluate the performance of different types of DMUs such as health care organisations, military units, schools, firms and countries. Cooper, Seiford & Zhu (2004) sited that “DEA has also been used to supply new insights into activities (and entities) that have previously been evaluated by other methods. For instance, studies of benchmarking practices with DEA have identified numerous sources of inefficiency in some of the most profitable firms – firms that had served as benchmarks by reference to this (profitability) criterion – and this have provided a vehicle for identifying better benchmarks in many applied studies.” (Cooper, et al., 2004). What makes DEA different from other methods is that it is firstly based on building frontiers and not on central tendencies and secondly its minimal need for assumptions, due to these differences, DEA shows a superior perfection in defining efficiency or in explaining why one DMU is more efficient than another DMU which is achieved via a direct way without the extensive need of assumptions required by other methods as with linear and nonlinear regression models. Relative efficiency in DEA is neglecting the need of taking into consideration a pre-measurement of relative importance to any input or output “Definition 1 (Efficiency – Extended Pareto-Koopmans Definition): Full (100%) efficiency is attained by any DMU if and only if none of its inputs or outputs can be improved without worsening some of its other inputs or outputs.” (Cooper, et al., 2004). This definition is replaced by Definition 2 because in the majority of the cases the efficiency theoretical possible levels are unknown. “Definition 2 (Relative Efficiency): A DMU is to be rated as fully (100%) efficient on the basis of available evidence if and only if the performances of other DMUs does not show that some of its inputs or outputs can be improved without worsening some of its other inputs or outputs.” (Cooper, et al., 2004). Here it is important to mention that this definition is sparing two needs firstly is the need of weights to show the relative importance of the different inputs or outputs and secondly is the need of noticing the formal relations that are supposed to exist between inputs and outputs.
It was in the mid 50’s where the first approach to DEA was developed by Farrell (1957) as he was in need to create a better way to evaluate efficiency and productivity this need raised after his unsuccessful tries to simultaneously use the measurements of several inputs in efficiency measurement as he came up with an analytical approach that could solve the problem. (Cooper, et al., 2004). After Farrell studies several models and methods was developed where the first DEA model named CCR model referred to Charnes, Cooper, and Rhodes (1978) which raised in response to the thesis efforts of Edwardo Rhodes Under the supervision of W.W. Cooper, this thesis was to be directed to evaluate educational programs for disadvantaged students in a series of large scale studies undertaken in U.S. Rhodes secured access to the data being processed for that study, the data base was sufficiently large so that issues of degrees of freedom, etc., were not a serious problem despite the numerous input and output variables used in the study. Since the initial study by Charnes, Cooper, and Rhodes some 2000 articles have appeared in the literature. See Cooper, Seiford and Tone (2000). See also G. Tavares (2003). Such rapid growth and widespread (and almost immediate) acceptance of the methodology of DEA is testimony to its strengths and applicability. Researchers in a number of fields have quickly recognized that DEA is an excellent methodology for modeling operational processes, and its empirical orientation and minimization of a priori assumptions has resulted in its use in a number of studies involving efficient frontier estimation in the nonprofit sector, in the regulated sector, and in the private sector. At present, DEA actually encompasses a variety of alternate (but related) approaches to evaluating performance. Extensions to the original CCR work have resulted in a deeper analysis of both the “multiplier side” from the dual model and the “envelopment side” from the primal model of the mathematical duality structure. Properties such as isotonicity, nonconcavity, economies of scale, piecewise linearity, Cobb-Douglas loglinear forms, discretionary and nondiscretionary inputs, categorical variables, and ordinal relationships can also be treated through DEA. Actually the concept of a frontier is more general than the concept of a “production function” which has been regarded as fundamental in economics in that the frontier concept admits the possibility of multiple production functions, one for each DMU, with the frontier boundaries consisting of “supports” which are “tangential” to the more efficient members of the set of such frontiers.
BCC The BCC model is one of the most commonly used DEA models. It is credited to Banker, Charnes, and Cooper. This model differs from the CCR model in that it exhibits variable returns to scale rather than constant returns to scale. CCR Perhaps the most commonly used DEA model originating with Charnes, Cooper, and Rhodes. This model exhibits constant returns to scale. Book pdf
Inputs and outputs Inputs are the resources used by a DMU in achieving its goals. Inputs are “bads” in that increasing levels of an input while holding everything else constant should generally result in a lower efficiency score. Outputs have the opposite property. Examples of DEA inputs might include the number of staff assigned to a team or capital expenditures in networking. Outputs might be lines of code or reduced computing time. Orientation DEA models often have two important but underappreciated variations based on the orientation of the model. An input-oriented model primarily focuses on input reduction while an output-oriented primarily model focuses on output augmentation. Returns to scale Two of the most common returns to scale assumptions are constant and variable. Constant returns to scale (or CRS) implies that doubling each of the inputs used by a DMU should double each of the outputs. Variable returns to scale (or VRS) implies that doubling each of the inputs used by a DMU does not necessarily double each of the outputs. weight restrictions DEA normally does not place any restrictions on the relative trade-offs between the inputs or the trade-offs between the outputs. This can lead to unrealistic or extreme trade-offs. Various weight restriction techniques can be applied to overcome this.
C. Strengths of Data Envelopment Analysis DAE is considered an excellent technique when used in the right position; its excellence comes from its ability of dealing with multiple inputs and outputs, it doesn’t require to unify the units between inputs and outputs and that each DMU can be compared against a combination of other DMUs Although it considered a powerful tool it still have its limitations that must be kept in mind in order to decide either to use or not to use DEA Since DEA is an extreme point technique, noise (even symmetrical noise with zero mean) such as measurement error can cause problems. DEA is good at estimating “relative” efficiency of a DMU, but it converges very slowly to “true” efficiency. In other words, it can tell you how well you are doing compared to your peers but not compared to a “theoretical maximum.” Since DEA is a nonparametric technique, statistical hypothesis tests are difficult and are the focus of ongoing research. Since a standard formulation of DEA creates a separate linear program for each DMU, large problems can be computationally intensive. Naive implementations of DEA using off-the-shelf linear programming packages can result in computational problems. I have frequently seen this with respect to the Excel Solver and poorly scaled data. This has improved in recent versions of Excel (Excel 2000’s Solver seems to be much more robust), but the prevalence of degeneracy and potential for cycling are still cause for concern. Book pdf
This section will highlight on the previous researches that assessed DEA as a selection tools used by portfolio managers in their investment decisions.
The first research done to assess the use of DEA models in stock selection and to compare the performances of the portfolios constructed by DEA analysis versus stock market indices was carried out by Hsin-Hung Chen (2008). In his study Hsin-Hung Chen used two DEA models the CCR and BCC models to evaluate the efficiency of the firms listed in the Taiwan Stock Exchange to construct portfolios by selecting stocks with high efficiency from the listed stocks, where the return rates of the portfolios constructed by DEA models and market indices were compared via empirical data analysis. In this study Hsin-Hung Chen used average equity, average asset, and sales cost as inputs for the DEA models and he used revenues, operating profit and net income as outputs for the DEA models where the software DEA-Frontier was used to solve the DEA models. “Hsin-Hung Chen used the historical financial ratios and stock prices of the firms listed in eight major industries on the Taiwan Stock Exchange as the empirical data, where stocks are selected by DEA methods for portfolio construction. The empirical data used in this study covers the period from the second quarter of 2004 to the second quarter of 2007. Based on the financial data of the second quarter of 2004, stocks are selected and portfolios are constructed then the performances of these portfolios in the next quarter (the third quarter of 2004) are compared with the average returns of all stocks in the eight major industries. From the second quarter of 2004 to the first quarter of 2007, the same procedure is repeated to construct portfolios and compare their performances with average industry stock returns in the next quarter. As a conclusion of the research portfolios constructed by DEA models demonstrated good ability to create noticeably superior returns. The BCC portfolios achieved superior returns of 6.90 per cent, 3.48 per cent, 6.51 per cent and the CCR portfolios achieved superior returns of 5.86 per cent, 4.16 per cent, 5.72 per cent for year 1, year 2 and year 3, respectively.” Another research was done by Ana Lopes (2008) “DEA investment strategy in the Brazilian stock market”, this research assessed a multi-period investment strategy applied to the Brazilian stock market using DEA models to select efficient stocks where price to earnings ratio, beta, and return volatility for each stock where the inputs and earnings per share, and the last 12, 36, and 60 month return where the outputs. “To be included in the sample the stock should belong to the IBrX-100 index (the Sao Paulo Stock Exchange value-weighted index) at the beginning of each of the 22 quarters along the period of Jan/2001 to Jun/2006. Stocks considered to be efficient were selected to make up a portfolio at the beginning of a quarter. In each of the 22 quarters DEA-portfolio was composed by an investment of the same proportion for each efficient stock so the portfolio was equally weighted. The acquisition of the stocks on the first day of a quarter and the sale on the last day of the same quarter was simulated. For the calculation of the return for each stock, the closing price on the first and last day of the quarter was used. The same procedure was adopted for calculating the IBrX100 index returns.” The research results showed that during the 22 quarters the portfolio constructed via DEA performed much better than the IBrX-100 index. Lopes, Ana, Edgar Lanzer, Marcus Lima, and Newton da Costa, Jr., (2008) “DEA investment strategy in the Brazilian stock market.” Economics Bulletin, Vol. 13, No. 2 pp. 1-10 2.7 The Case of Kuwait
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