single and multiple risk factors in the egyptian stock market

Afro-Asian J. Finance and Accounting, Vol. 3, No. 3, 2013 195

Copyright © 2013 Inderscience Enterprises Ltd.

Single and multiple risk factors in the Egyptian stock market

Mohammed Fawzy Omran School of Business, Nile University, Smart Village, km28 Cairo/Alexandria Desert Road, Cairo, 12577, Egypt E-mail: [email protected] E-mail: [email protected]

Abstract: The return-risk trade-off of the 100 stocks contained in the Egyptian EGX100 index is examined. The Egyptian stock market has an average free float of only 45%. It is estimated that 50% of trading in the free float stocks is dominated by large investors, and local and international fund managers. The market suffers from low turnovers and more recently long periods of trade suspension after the political unrest of January 25th, 2011. The study finds that a serial correlated returns model is more suitable to estimate returns for low free float stocks in Egypt. However, it is unlikely that this serial correlation could lead to above average trading profits since it is a reflection of ownership concentration in a small illiquid market. The market offers diversification benefits due to its low correlation with major world indices. However, trade suspension due to political instability is an extra risk that could negate diversification benefits.

Keywords: risk-return trade-off; Egyptian stock markets; illiquidity; free floats; Egypt.

Reference to this paper should be made as follows: Omran, M.F. (2013) ‘Single and multiple risk factors in the Egyptian stock market’, Afro-Asian J. Finance and Accounting, Vol. 3, No. 3, pp.195–207.

Biographical notes: Mohammed Fawzy Omran is Professor of Finance and Director of EMBA at the School of Business, Nile University (NU) in Cairo, Egypt. Before joining NU, he served at the Universities of Stirling and Heriot-Watt in the UK. He is a Chartered Financial Analyst (CFAI, USA) since 2001. He has published in some of the well-respected refereed journals in the USA and Europe. His publications appeared in the Quarterly Review of Economics and Finance, the Statistician, Applied Mathematical Finance, Advances in Econometrics and Applied Economics.

1 Introduction

The Egyptian stock market suffers from illiquidity and the concentration of stocks’ ownership among a few powerful individuals or entities. Although the turnover ratio of the Egyptian stocks has improved from 22% in 1996 to 52% in 2006, the ratio is still far lower than the most liquid markets in the world. McMillan and Thupayagale (2009)

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report turnover ratios of 89% and 94% for the UK-FTSE100 and the USA-S&P500, respectively1. The Egyptian stock market capitalisation rose from $14.2 bn in 2006 to $93.5 bn in 2006. The market came third in Africa in terms of market capitalisation in 2006. South Africa and Nigeria stock markets came first and second in Africa in 2006 in terms of market capitalisations which reached $585.6 bn for South Africa and $125.3 bn for Nigeria. The market capitalisations of the three major stock markets in Africa are small if compared with market capitalisations of UK-FTSE100 and USA-S&P500 which were $3,654 bn and $12,727 bn, respectively2. Irving (2005) points out that ownership in African stock markets in general is dominated by a small number of large conglomerates. The limited base of stocks’ ownership in African markets in addition to the small size of those markets would certainly limit liquidity. Sadik et al. (2004) report that the controlling shareholders of the stock markets in the Middle East and North Africa (MENA) region belong to the political elites or influential institutions. Azzam (2010) finds that the payout ratio is lower for Egyptian companies that are controlled by majority shareholders. The majority shareholders, who typically manage directly or through a proxy, prefer to increase their consumption rather than paying dividends. McMillan and Thupayagale (2009) argue that the returns generating mechanism for African stocks markets, including Egypt, should exhibit longer memory than in more matured markets in the UK and the USA due to low turnovers and high illiquidity. The weak form efficiency of the efficient market hypothesises implies that stock returns are identically and independently distributed variables (see Smith et al., 2002). For a market to exhibit weak form efficiency, its returns generating process must not show any type of memory at least in its mean.

The objective of the current paper is to study serial correlation in the single index model for returns on individual stocks that make up the EGX100 index. The hypothesis is that higher serial correlation is associated with low free float and vice versa. Low free float impacts liquidity for the stock and hence information may take longer to be reflected in the stock price causing high returns’ serial correlation. However, this high serial correlation is unlikely to lead to profitable trades since it is due to stock illiquidity. The study is different from this of McMillan and Thupayagale (2009) in two aspects. First, we examine the returns for individual stocks making up the EGX100 index while their study concentrated on the index only. Secondly, we concentrate on the mean process of the single index model for individual stocks while their study was a joint estimation of the mean and volatility of the index.

The study has five sections with the introduction in Section 1. Section 2 reviews the literature on the characteristics of the Egyptian stock market. Section 3 describes the dataset and provides some relevant descriptive statistics. Section 4 presents the methodology and the empirical results. Section 5 concludes the study.

2 Literature review

The Egyptian stock market has two linked exchanges, one in Alexandria established in 1888 and the other in Cairo established in 1903. The two exchanges are integrated and investors have access to stocks listed on each exchange. The two exchanges are governed by the same board and share the same regulations and execution systems. The Egyptian stock market suffers from some past historical issues. The nationalisation of private

Single and multiple risk factors in the Egyptian stock market 197

equities by the socialist governments of Egypt in the ‘50s and ‘60s of the last century had a negative impact on the development of the Egyptian stock market. The stock market never closed its doors during that period but very few trades took place. The nationalised industries had a majority government ownership. The minority shares were traded in the stock market but its trading was limited due to investors’ mistrust of the system. The situation changed slightly in the late ‘70s when the Egyptian government started to encourage foreign direct investments and encouraged the private sector to play a larger role in economic development. However, the stock market did not develop much till the late 1980s and early 1990s. In the 1990s the Egyptian government, encouraged by the international monetary fund (IMF), embarked on a series of privatisation attempts that led to the opening up of the market to local and foreign investment.

A new capital market law issued in 1992 established an independent regulatory agency [Capital Market Authority (CMA)], strengthened investors rights, and improved transparency through better financial disclosure requirements. This led to a rapid growth in the individual and institutional participation in the stock market. Mecagni and Sourial (1999) report that turnover ratios improved from 6.7% in 1990 to 34.2% in 1997. They also report that two-thirds of trading value in 1997 was concentrated in 25 shares which is less than 4% of total listed shares. Mecagni and Sourial (1999) report that many companies list their shares not for raising capital but to take benefit of certain tax exemption on paid up capital. The tax law gives exemption on the income generated on paid up capital up to the tax free deposit rate determined by the Central Bank of Egypt. The delisting rules of the stock exchange allow for infrequent trading, requiring very few trades per year.

The late ‘90s witnessed massive privatisation in many sectors especially in the telecommunications, pharmaceuticals, and food industries (see Shinnawy and Handoussa, 2003). The privatisation pace and the opening up of the market for foreign direct investments and the modernisation of the regulatory systems have led to a high growth in the gross domestic products during the period from 2005 and 2011. The Egyptian stock market started to move away from the coffee shop atmosphere and started to look as a serious business stock exchange. However, the Egyptian political unrest of the January 25th 2011 had a negative impact on the Egyptian stock market as it led to its closure for a long period of time. The market closed its doors on January 27, 2011 after losing 16% of its market capitalisation in two sessions on January 26 and 27, 2011. Trade was suspended till March 23, 2011. One reason put forward to explain the long suspensions is the pressure exerted by local investors on the stock market board not to reopen the market for the fear of realising larger losses as foreign fund managers start to liquidate their positions (see Wigglesworth, 2011).

The Egyptian stock market faced the prospect of being withdrawn from the computation of the worldwide stock indices if the closure period exceeded 40 non-trading days. Although, the market opened just before reaching the 40 non-trading days, the long closure period added too much uncertainty to the market which led to massive sell out by investors. Although, the stock market is slowly going back into business, there are some problems inherited in the system which still need solutions. Many of the listed companies’ common stocks are controlled by large shareholders. This leads to a low free float. Only 45% of the common stocks available in the top 100 companies in Egypt are available for public trade. It is estimated that 50% of the 45% freely floating stocks are owned by local and international fund managers (see Wigglesworth, 2011).

198 M.F. Omran

Wigglesworth (2011) estimates that about 23% of the total market capitalisation is owned by international fund managers. International fund managers hold common stocks in Egypt due to their low correlations with major world indices. Tolikas (2011) calculated the correlations using 60 months of data of S&P/IFC Global indices between Egypt on one hand and each of S&P500, FTSE100 and NIKKEI225 on the other hand. The correlation coefficients between Egypt and each of S&P500, FTSE100, and NIKKEI225 were 0.09, 0.21, and 0.20, respectively. These low correlations provide diversification benefits. However, this implies that trading by international fund managers under normal circumstances could be scheduled and planned as a balance for their portfolios. Accordingly, liquidity for individual investors could be worse as a result.

One of the most important risk measures is beta which measures a particular stock sensitivity to general move in the market portfolio. Theoretically, the market portfolio is a portfolio that includes all available investments in an economy. However, the most widely used model is the single index model which relates the rate of return on individual stocks to the rate of return on the market index (see Omran, 2007). The low free float, low turnover, and concentration of ownership in the EGX100 could induce serial autocorrelations in the single index model. Bekaert and Harvey (1997) show that returns autocorrelations are higher in emerging markets than in developed markets.

Omran (forthcoming) examined the issues of the cross sectional probability distributions of the 50 most actively traded stocks in Egypt. Omran (2001) results confirm with theoretical expectation. For example, the probability distributions of short-term debt and bondholders returns are found to be much less risky than the probability distribution of shareholders equity returns. This is consistent with economic theory since shareholders are the residual owners of the firm. In other words, the results of Omran (forthcoming) indicate that the most active stocks in Egypt have statistical characteristics that are not different from reported results on international markets in the literature.

The major problems of the Egyptian stock market remain in lack of liquidity, low turnover, and concentration of ownership. Levine (1991) argues that liquid stock markets encourage long term commitment to capital projects as they give the early investors assurance that they can easily sell their shares in case they need to exit from these long term investments.

Smith et al. (2002) found that the Egyptian stock market has witnessed massive turnover of around 60% in annual growth in the period between 1988 and 1997. However, Omran (2007) examined turnover and activity during shorter periods of time and found that the turnover ratios are much lower in shorter time periods than that reported for longer time periods in Smith et al. (2002). Omran (2007) found that only 11.8% of the stocks quoted during March 2001 to October 2001 could be considered active.

Ang et al. (2011) find that investors’ risk aversion tends to increase in an illiquid market. This in turn leads to under-investment in liquid and illiquid risky stocks due to high uncertainty with regard to the ability of trading an illiquid stock. According to Ang et al. (2011), since the waiting time to liquidate the illiquid risky stocks is random, investors would also reduce their holdings of liquid risky stocks to meet their liquidity demands. In other words, if the liquidity from the liquid assets drops to zero, investors are not sure of the next opportunity they can trade their illiquid assets. Therefore, investors will reduce their allocations not only to illiquid assets but also to liquid assets.


Nagasayu (2003) argues that less developed markets should exhibit longer memory in their returns generating process due to problems such as lack of liquidity and weak regulatory frameworks. McMillan and Thupayagale (2009) studied 2982 daily returns observations on the Egyptian Hermes Financial Index starting from 27 July 1995. Their results indicate that volatility persistence in Egypt is of the explosive type indicating that a shock to volatility in one period will lead to even greater volatility in the next period.

The Egyptian market seems to suffer from the following shortcomings. Before the political unrest of January 25, 2011, the Egyptian stock market suffered from lack of liquidity, low turnovers, low free float, and concentration of ownership in the available free float. After the political unrest of January 25, 2011, the market closure for long time period till March 23, 2011 made it difficult for investors to trade. Investors who wished to liquidate their positions could not do that. The inability to trade would certainly deepen the inherited problems of the market and will increase the required risk premium. These problems will most likely manifest themselves in significant autocorrelations especially in low free float stocks. The issue of whether low free float stocks exhibit higher order returns autocorrelations in the single index model is the main concern of the current study.

3 Data description and descriptive statistics

The current study focuses on the 100 stocks in the EGX100 during the period from 3 January 2008 to 14 December 2009. Only ninety one companies are used in the analysis since nine companies were excluded for exceeding 10% limit of no daily trade imposed in the current study. There are 479 observations for each of the ninety one companies. The EGX100 was introduced on August 2nd, 2009 and was calculated backward starting from January 1st, 2006. The index measures changes in closing prices without taking into account market capitalisations and free float. Daily closing stock prices for the 100 companies that make up the EGX100 along with the closing values for the EGX100 were collected from Bloomberg Professional for the period from 3 January 2008 to 14 December 2009.

Table 1 contains summary statistics for the data divided by industry. The table contains the number of stocks per each industry classification, the average return during the period, beta3, and the percentage of free float. The free float statistics were obtained on 14 December 2009 from Bloomberg website (http://www.bloomberg.com/ markets/stocks/)4.

The data period was a declining one in terms of prices worldwide after the severe credit crunch of the 2008. The average daily returns were –0.08% for all sectors for the period from January 3, 2008 to December 14, 2009. Out of the seventeen industry sectors, only two sectors had positive daily average returns, the food and beverages and oil and gas. Industry sectors average betas lie in the range from 0.40 (oil and gas) and 1.39 (real estate). The overall average beta for all industries is 1.09. The oil and gas sector had the highest daily average return of 0.13% despite having the lowest beta of 0.40. The percentage free float ranges from 2.91% (pharmaceutical) to 99% (retail). The average free float for all sectors is 45.10%.

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Table 1 Descriptive statistics per industry

Sector Number Return Beta Free float

Financial services 7 –0.11% 1.04 59.58% Household products 10 –0.06% 1.33 55.43% Industrial 11 –0.13% 1.30 42.73% Oil and gas 2 0.13% 0.40 38.95% Basic materials 6 –0.14% 1.23 15.96% Real estate 14 –0.03% 1.39 69.42% Food and beverages 12 0.05% 1.36 59.74% Banks 5 –0.18% 0.98 56.17% Construction 10 –0.02% 1.27 47.25% Chemicals 4 –0.12% 1.13 56.54% Travel and leisure 2 –0.20% 1.16 31.38% Media 1 –0.16% 0.99 20.21% Pharmaceutical 1 –0.02% 1.28 2.91% Retail 1 –0.03% 0.79 98.99% Telecommunications 3 –0.07% 0.72 31.17% Utilities 1 –0.13% 1.11 19.42% Technology 1 –0.18% 1.03 60.89% Average 91 –0.08% 1.09 45.10%

4 Methodology and empirical results

The study aims at answering the question of whether low free float stocks tend to have a higher serial correlation in the single index model. To explore the relationship between beta and free float, the 91 companies were ranked from lowest to largest according to free float. Only 87 companies were used as the free floats were not available for four companies. Ten portfolios were formed from lowest to largest free floats. Table 2 has the portfolios ranked into percentiles, number of stocks in each portfolio, average return, betas, and free float. Table 2 Average return, beta, free float for ten portfolios ranked from lowest to largest

according to free float

Quantile Number Average Beta Free float

10th 8 –0.10% 1.19 5.03% 20th 9 –0.09% 1.02 16.09% 30th 9 –0.14% 1.13 23.26% 40th 9 –0.15% 1.17 29.46% 50th 9 –0.11% 1.30 37.75% 60th 9 –0.08% 1.16 49.08% 70th 9 0.02% 1.11 65.48% 80th 9 0.02% 1.34 87.68% 90th 9 –0.07% 1.30 98.98% 100th 7 –0.08% 1.26 99.35%


The lowest free float stocks, the 10th percentile portfolio, have a free float of 5% and a beta of 1.19. The second lowest 20th percentile portfolio has a free float of 16% and a beta of 1.02. The largest free float portfolio has a free float of 99.35% and beta of 1.26. The 90th percentile portfolio has a free float of 99% and a beta of 1.30. Stocks with lower free float have smaller betas than stocks with larger free floats. The lack of liquidity in low free float stocks should be reflected in higher serial correlation in returns for those stocks compared with large free float stocks. The hypothesis is that information takes longer time to be reflected in stock prices for low free float as compared with large free float. Therefore, lagged returns on the EGX100 index should be included in modelling returns for low free flow stocks.

Equation (1) assumes that stock returns for each individual company is affected by three days lag. The objective of the current study is not to determine how many lags are required to optimally model stock returns. If there were no serial correlations in the three lags, a conclusion of no serial correlation cannot be deduced since higher lag orders were not examined. However, if there were significant serial correlations in the first three lags, then there is serial correlation in the process which could be of higher order than the first three lags5.

3

0 ,1

t m j m t j tj

r r r e−

=

= + + +∑α β β (1)

Equation (1) represents a multiple regression equation where rt represents the continuously compounded returns on the individual stock at time t where t goes from 1 to 479, α is a constant, rm is the continuously compounded return on the EGX100, β0 is the parameter estimate for the sensitivity of changes in stock’s returns in response to changes in the EGX100. βj, where j goes from one to three, are parameter estimates of the past changes in the EGX100 returns, rm,t–j refer to lagged market returns on EGX100, and finally et represent the error terms. The single index model as used in Omran (2007) implies that all βj’s are equal to zeros.

Equation (1) is estimated for each stock from the 87 stocks during the period from January 3, 2008 to December 14, 2009. The sample went down from 100 companies since nine companies exceeded the 10% limit of no daily trade and four companies were excluded due to lack of data on their free float. There are 479 daily returns included in the regression. Table A1 in the Appendix has the results. Beta in the table refers to the beta estimated using the single index model. Beta(0), beta(1), beta(2), beta(3) columns in the table correspond to β0, β1, β2, and β3, in equation one respectively. The next column entitled significant states which parameter estimate is significant at the 5% level. For example, ‘0’ indicates that moving from the single index model of beta(0) to multiple index model with three lagged did not add much information. On the other hand ‘1, 2, 3’ indicate a long memory in terms of stock’s responsiveness to changes in market index. The next column is the difference in adjusted R-squared. It is the difference between adjusted R-squared of the lagged model and adjusted R-squared of the single index model. Higher differences in adjusted R-squared reflect extra information content added by the lagged model. The next column entitled ‘rank’ has a count of how many parameter estimates are significant in the lagged model. It takes a value between one and four. One refers to the case where the single index model is sufficient while four refers to the need for the maximum of three lags. It is important to note that for each company in our

202 M.F. Omran

sample, beta(0) was statistically significant at the 5% level. Accordingly, a rank of one indicates that the single index model is sufficient.

To help in summarising the information presented in the appendix, a simple statistic was devised. For each of the ranks representing the number of significant lags, the median float was calculated. The median was chosen over the average to avoid problems with the small sample size at each rank and the possibility of outliers. Table 3 has the results.

Table 3 Median free floats for each of the four ranks along with the number of stocks in each rank

Rank: 1 (sufficiency of the single index model)

Median free float: 0.54

Number of companies in the rank: 39

Rank: 2 (the need for extra one lag)



Rank: 3 (the need for extra two lags)



Rank: 4 (the need for extra three lags)



According to Table 3, rank one which refers to the sufficiency of the single index model has 39 stocks. This represents 45% of the 87 stocks studied. The median free float for rank one is 54%. Rank 2 which refers to the need of one extra lag to be estimated in addition to the single index model has 33 stocks representing 38% of the sample. The median float for rank 2 is 42%. Rank 3 which refers to the need of two extra lags to be estimated in addition to the single index model has 11 stocks representing 13% of the sample. The median float for rank 3 is 31%. Rank 4 which refers to the need of three extra lags to be estimated in addition to the single index model has four stocks representing 4% of the sample. The median float for rank four is 23%. Clearly, the smaller the float rate the higher the need for more lags to be included in the estimation. However, the single index model is sufficient for 45% of the stocks. One lagged model is sufficient for 38% of the stocks while two lags model is sufficient for 13% of the stocks. Only 4% of the stocks require three lags in addition to the single index model. The results are in line with those of Tooma (2011). Tooma (2011) studied the returns on stocks of the five most actively traded stocks in Egypt during the period between January 3, 1994 and December 31, 2001. Tooma (2011) tested for the significance of up to four lags of serial correlations and found that two lags were sufficient to model the returns generating process.


5 Conclusions

The study examined the relationship between the free float and the single index model in the Egyptian stock market during the period from 3 January 2008 to 14 December 2009. The study concentrated on 87 stocks that were included in the EGX100 stock index which represent the most actively traded stocks in Egypt. Common stocks with low free float requires extra lags of market information, as measured in terms of returns to the EGX100 index, to be added to the single index model. Stocks with low free float required extra lagged information on the market index than stocks with high free float. While the Egyptian stock market provides diversification benefits to international fund managers due to its low correlation with major worldwide indices, the stock market has major liquidity problems that could lead to large swings in stock prices. The suspension of trade between January 27, 2011 and March 23, 2011 (due to the political unrest that started on January 25, 2011) added extra uncertainty to the market. According to Wigglesworth (2011), it is estimated that international fund managers had about $16 bn trapped in the market during this period. This represented 23% of the market capitalisation of $67 bn as of January 27, 2011. Although the Egyptian stock market offers diversification benefits due to its low correlation with major world indices, these benefits are at risk because of possible trade suspension due to political instability.

Acknowledgments

I would like to thank Professor D.K. Malhotra and two anonymous referees for their constructive help and comments.

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Working paper, Columbia University, available at http://www2.gsb.columbia.edu/faculty/ aang/papers/APW-110513.pdf (accessed on 27 February 2012).

Azzam, I. (2010) ‘The impact of institutional ownership and dividend policy on stock returns and volatility: evidence from Egypt’, International Journal of Business, Vol. 15, No. 4, pp.443–458.

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Irving, J. (2005) ‘Regional integration of stock exchanges in Eastern and Southern Africa: progress and prospects’, IMF Working Paper WP/05/122, International Monetary Fund, Washington, DC.

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McMillan, D.G. and Thupayagale, P. (2009) ‘The efficiency of African equity markets’, Studies in Economics and Finance, Vol. 26, No. 4, pp.275–292.

Mecagni, M. and Sourial, M.S. (1999) ‘The Egyptian stock market: efficiency tests and volatility effects’, IMF Working Paper, WP/99/48.

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Omran, M.F. (2007) ‘An analysis of the capital asset pricing model in the Egyptian stock market’, The Quarterly Review of Economics and Finance, Vol. 46, No. 5, pp.801–812.

Omran, M.F. (forthcoming) ‘The characteristics of the probability distributions of economic value added and financial accounting ratios for listed companies in Egypt’, International Journal of Economics and Accounting.

Sadik, A., Bolbol, A. and Omran, M. (2004) ‘The Arab economy: between reality and hopes’, Al Mustaqbal Al Arabi, No. 299, pp.29–60.

Shinnawy, A. and Handoussa, H. (2003) ‘Case studies of foreign direct investment in Egypt’, No. 2, DRC working papers, Centre for New and Emerging Markets, London Business School.

Smith, G., Jefferis, K. and Ryoo, H. (2002) ‘African stock markets: multiple variance ratio tests of random walks’, Applied Financial Economics, 12, pp.475–484.

Tolikas, K. (2011) ‘The rare event risk in African emerging stock markets’, Managerial Finance, Vol. 37, No. 3, pp.275–294.

Tooma, E. (2011) ‘The magnetic attraction of price limits’, International Journal of Business, Vol. 16, No. 1, pp.35–50.

Wigglesworth, R. (2011) ‘Egyptian bourse angers foreign investors’, Financial Times, February 16 2011, available at http://www.ft.com/cms/s/0/62f8318e-39e2-11e0-8dba-00144feabdc0.html (accessed on 1 March 2012).

Notes 1 Turnover ratio is defined as the value of trading to the market capitalisation of the market. 2 The turnover ratios for Egypt, UK and USA stock markets major indices and market

capitalisations for Egypt, South Africa, Nigeria, USA and UK are obtained from McMillan and Thupayagale (2009), Table 2, p.278.

3 Beta is obtained by running a single regression where the returns on the individual stock are the dependent variable and the returns on the EGX100 are the independent variable. Beta is the coefficient estimate of the parameter of the independent variable.

4 The code for each company should be entered in the search window. The code is available in Table A1 in the Appendix. For example, ENGC, the code for the first company in Table A1 in the Appendix, will be entered in the search space. Please note that the download of the daily data was obtained from Bloomberg Professional which requires subscription. Past historical data on free float among other statistics are also available on Bloomberg Professional.

5 A five order lag was also examined to account for the five days per week of trading. However, the results were not significant beyond the first three lags. Due to reporting constraints, only the results of the first three lags are reported. Our results are in line with those of Tooma (2011) on the five most actively traded stocks in Egypt. Tooma (2011) found that two lags were sufficient than four lags as the third and fourth lags turned out to be insignificant.


Appendix

Table A1 Betas for the lagged model, parameter significance at 5%, difference in adjusted R-squared between multi betas model and single beta model, rank in terms of how many parameters are significant, and free float

Beta Beta (0)

Beta (1)

Beta(2)

Beta(3) Significant Diff RSQ Rank Free float

ENGC 1.34 1.35 –0.07 0.18 –0.22 0, 3 0.38% 2 0.98% BIOC 1.28 1.34 –0.40 0.32 –0.02 0, 1, 2 3.58% 3 2.91% ECMI 1.13 1.13 –0.01 –0.02 0.12 0 –0.52% 1 3.34% ALCN 1.45 1.45 –0.05 0.21 –0.06 0, 2 1.23% 2 3.98% IRON 1.45 1.46 –0.13 0.17 0.01 0, 2 0.39% 2 5.69% EGAL 0.95 0.92 0.09 0.11 0.01 0 –0.15% 1 6.41% SUGR 1.20 1.24 –0.27 0.10 –0.14 0, 1 1.28% 2 7.60% IRAX 0.72 0.75 –0.16 0.08 –0.01 0, 1 1.56% 2 9.36% NASR 1.20 1.20 –0.05 0.16 0.08 0 0.58% 1 11.03% RAKT 1.87 1.92 –0.08 –0.33 –0.10 0, 2 0.99% 2 11.22% HDBK 0.77 0.77 –0.02 –0.03 –0.08 0 –0.12% 1 11.45% NCCW 1.33 1.32 –0.02 0.18 0.03 0 –0.39% 1 12.00% EGAS 1.11 1.20 –0.66 0.65 –0.21 0, 1, 2, 3 11.31% 4 19.42% AMOC 0.47 0.49 –0.13 0.09 0.00 0, 1, 2 1.78% 3 19.69% CIEB 0.83 0.87 –0.21 0.12 –0.14 0, 1, 2, 3 2.31% 4 19.80% ETEL 0.64 0.66 –0.13 –0.01 –0.14 0, 1, 3 1.73% 3 20.00% MPRC 0.99 0.97 0.06 0.18 –0.14 0, 2, 3 1.45% 3 20.21% AUTO 1.20 1.17 0.21 –0.19 0.03 0, 1, 2 1.43% 3 20.37% DCRC 1.20 1.17 0.11 0.16 –0.03 0, 2 0.75% 2 21.80% HELI 1.32 1.33 –0.02 0.06 –0.18 0, 3 0.60% 2 21.88% RTVC 1.16 1.15 0.02 0.03 0.03 0 –0.45% 1 22.44% SKPC 0.78 0.78 –0.01 0.00 0.00 0 –0.36% 1 22.55% SVCE 1.25 1.25 –0.02 0.10 –0.10 0 0.09% 1 24.24% IDEA 1.38 1.41 –0.19 0.13 0.04 0 0.09% 1 24.95% ORWE 0.58 0.57 0.03 0.03 –0.06 0 –0.45% 1 25.22% MICH 1.31 1.31 0.09 –0.21 0.06 0, 2 1.47% 2 25.94% CANA 1.08 1.11 –0.18 0.30 –0.25 0, 1, 2, 3 2.86% 4 27.00% MOSC 1.42 1.44 –0.28 0.14 0.09 0, 1 0.50% 2 27.50% EMOB 0.53 0.56 –0.14 0.02 –0.04 0, 1 0.13% 2 27.65% AIVC 0.72 0.74 –0.18 0.33 –0.29 0, 1, 2, 3 6.33% 4 27.99% UASG 1.49 1.46 0.05 0.26 –0.07 0, 2 0.83% 2 29.00% CCRS 1.63 1.63 –0.01 –0.02 –0.04 0 –0.25% 1 30.00% ASCM 1.26 1.24 0.12 0.16 –0.08 0, 1, 2 1.53% 3 31.32% ESRS 1.14 1.13 0.21 –0.29 –0.06 0, 1, 2 2.77% 3 31.79%

206 M.F. Omran

Table A1 Betas for the lagged model, parameter significance at 5%, difference in adjusted R-squared between multi betas model and single beta model, rank in terms of how many parameters are significant, and free float (continued)

Beta Beta (0)

Beta (1)

Beta(2)


ECAP 1.27 1.27 0.00 0.04 –0.07 0 –0.24% 1 32.91% SWDY 1.22 1.28 –0.28 0.07 –0.04 0, 1 2.10% 2 33.13% UNIP 1.46 1.46 0.01 0.10 0.03 0 –0.46% 1 33.33% DEVE 0.93 0.92 0.02 0.10 –0.10 0 0.05% 1 35.09% KABO 1.27 1.26 –0.03 –0.01 0.02 0 –0.53% 1 36.21% SPIN 1.65 1.72 –0.32 –0.18 0.12 0, 1 2.01% 2 37.83% EGTS 1.17 1.17 –0.01 0.08 –0.10 0 0.17% 1 40.32% ETRS 1.22 1.20 0.08 0.23 –0.01 0, 2 1.06% 2 40.82% MILS 1.44 1.47 –0.26 0.07 0.10 0, 1 0.60% 2 41.03% OCDI 1.39 1.42 –0.17 0.09 0.05 0, 1 0.44% 2 42.02% OLGR 0.99 0.99 0.00 0.10 –0.16 0, 3 0.90% 2 42.35% CEFM 1.43 1.46 –0.11 0.01 0.00 0 –0.45% 1 42.80% DAPH 0.99 1.00 –0.06 0.09 –0.01 0 0.37% 1 44.57% ORTE 0.99 1.02 –0.09 0.01 –0.12 0, 3 0.62% 2 45.87% MENA 1.31 1.33 –0.12 0.16 0.01 0, 2 0.73% 2 49.08% NAHO 1.14 1.16 –0.15 0.14 0.03 0 0.32% 1 50.01% POUL 0.85 0.86 –0.09 0.12 0.02 0 –0.14% 1 54.01% ELSH 1.33 1.31 0.02 0.15 –0.02 0 0.26% 1 55.00% GGCC 1.42 1.41 0.06 0.10 –0.03 0 –0.26% 1 58.00% GMCI 0.33 0.32 –0.02 0.12 0.14 0 0.16% 1 58.22% RAYA 1.03 1.03 0.06 0.01 –0.10 0 0.05% 1 60.89% ZEOT 1.32 1.29 0.08 0.03 –0.07 0 1.59% 1 61.51% ABRD 1.49 1.47 0.03 0.16 0.05 0 0.08% 1 63.13% AREH 1.43 1.46 –0.31 0.30 0.07 0, 1, 2 1.40% 3 66.25% ACGC 1.05 1.04 –0.01 0.11 0.03 0 0.24% 1 67.79% MPCO 1.21 1.24 –0.30 0.19 –0.01 0, 1 1.20% 2 68.35% APSW 1.28 1.27 –0.02 0.08 0.09 0 0.11% 1 68.71% EKHO 0.89 0.89 –0.02 0.08 0.01 0 0.12% 1 74.47% NMPH 1.41 1.38 0.05 0.26 –0.07 0, 2 1.00% 2 75.00% LCSW 1.10 1.19 –0.48 0.13 0.05 0, 1 2.97% 2 76.30% NCGC 1.73 1.78 –0.15 –0.28 –0.02 0, 2 0.66% 2 77.22% EFIC 1.09 1.08 0.05 0.10 –0.12 0, 3 0.60% 2 78.72% AFDI 1.26 1.26 –0.04 0.17 –0.01 0 –0.13% 1 90.00% NEDA 1.26 1.25 –0.08 0.16 0.09 0 0.32% 1 96.67% OCIC 1.04 1.05 –0.04 0.10 –0.12 0, 2, 3 0.91% 3 97.69% CIRF 1.42 1.46 –0.13 –0.07 0.01 0 –0.38% 1 98.63%


Table A1 Betas for the lagged model, parameter significance at 5%, difference in adjusted R-squared between multi betas model and single beta model, rank in terms of how many parameters are significant, and free float (continued)

Beta Beta (0)

Beta (1)

Beta(2)


GIHD 1.71 1.71 0.09 –0.23 0.10 0, 2 0.74% 2 98.89% EPCO 1.43 1.44 0.03 0.13 –0.43 0, 3 1.11% 2 98.94% SMFR 1.33 1.32 0.02 0.12 –0.02 0 0.16% 1 98.96% SAUD 1.21 1.23 –0.16 0.14 –0.03 0, 1 0.43% 2 98.98% ELKA 1.06 1.05 –0.08 –0.02 0.08 0 –0.43% 1 98.99% MFSC 0.79 0.84 –0.20 0.04 –0.04 0, 1 0.20% 2 98.99% PRCL 2.02 2.14 –0.56 –0.01 –0.23 0, 1 2.12% 2 98.99% IFAP 1.36 1.33 0.12 0.11 0.07 0 1.19% 1 98.99% ELEC 1.13 1.15 –0.08 0.09 –0.06 0 0.60% 1 99.00% CSAG 1.32 1.30 –0.01 0.25 –0.03 0, 2 0.85% 2 99.00% UEGC 1.39 1.36 0.15 0.08 –0.01 0 0.01% 1 99.00% UNIT 1.53 1.60 –0.53 0.16 0.02 0, 1 3.64% 2 99.03% MNHD 1.18 1.15 0.19 –0.13 –0.07 0, 1, 2 1.40% 3 99.04% EHDR 1.55 1.54 –0.04 0.20 –0.02 0 –0.24% 1 99.07% GLAS 1.26 1.35 –0.54 0.32 –0.09 0, 1, 2 2.61% 3 99.33% COMI 0.85 0.94 –0.47 0.14 –0.10 0, 1 5.90% 2 100.00% HRHO 1.06 1.07 –0.03 0.02 –0.06 0 –0.01% 1 100.00%

single and multiple risk factors in the egyptian stock market

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