Stock Performance of Socially Responsible Companies

Background: As financial professionals including policy-makers tend to base decisions on research performed using large machine-readable financial databases, the accuracy of the financial data provided by database companies has a direct impact on the quality of their decisions. Objectives: The objective of this study was to examine data errors in the DataGuide and KisValue databases which are both primary sources of stock prices and return data for Korea Exchange securities in Korea. This article also discussed the methodological implications of erroneous data on monthly stock returns in empirical studies on Korean financial markets. Methods: A cross-checking technique was used in this study. Results: The results suggest that there are material discrepancies between the DataGuide and KisValue databases in monthly stock returns, most of which are attributable to the mishandling of split events and of missing values. The results also indicate that DataGuide provides a more reliable service than KisValue in terms of monthly stock returns. Conclusion: The results show that extreme monthly returns resulting from serious data errors in the DataGuide and KisValue databases may be enough to sharply change the properties of monthly stock return distributions and to over- or underestimate long-term abnormal stock returns.

This study examines the impact of federal funds rate on monthly stocks return of the United States of America. The study made use of secondary data from 31st January 1980 to 31st December 2009 gotten from Fred Economic Data and Economic Research Federal Reserve Bank of St. Louis and the Ordinary Least Square Method was applied to perform the analysis using Eviews 9.0. The findings of this study reveal that before the crisis, the rate of interest significantly predicted monthly stock return while during the crisis; the rate of interest did not significantly predict monthly stock return. In addition, the growth rate of industrial production significantly predicted monthly stock return with while FFR did not significantly predict monthly stock return. Likewise, change in FFR significantly predicted monthly stock return while the growth rate of industrial production did not significantly predict monthly stock return.

This research is to recognize the accuracy of CAPM models in predicting the stock return of rural stocks and conventional stock at Jakarta Islamic Index and Indonesian Stock Exchange. Variable of this research are JII and LQ45 stock return, Beta, Risk free, and Market return. The accuracy of CAPM models is measured by standart deviation and t test. The population of this research is all monthly stock return JII and LQ45 already go public at Indonesian Stock Exchange. Whereas the sample used is the monthly stock return of 11 JII companies and 13 LQ45 companies during 2007 – 2012. The result of this research showes that the CAPM model is accurate in predicting the stock return JII and LQ45.Keywords: LQ45. JII, Stock Return, Beta, Risk free, Market return, and CAPM

The goal of this research was to examine the longitudinal impact of ISO 9000 certification on business performance. The study compared the monthly stock returns and variability of the returns of ISO 9000 certified versus non-ISO 9000 certified firms traded on the Istanbul Stock Exchange (ISE) from January 1997 to September 2005. The study also compared the stock returns and the variability of the returns for the firms certified by Turkish agency vs. foreign certifying agency. Using annual two-year, three-year, and four-year averages of the monthly stock returns, the results indicated that ISO 9000 certified firms generally had higher returns and lower variability of returns than non-ISO 9000 firms. Moreover, the comparisons of the longitudinal means covering successive years showed that ISO 9000 certified firms consistently had higher means and lower variances, some of which were statistically significant. Finally, the certifying agency (Turkish or foreign) and the length of time with ISO 9000 certification had little effect on the stock performance and variability of the returns.

This abstract was created post-production by the JFI Editorial Board. This study has examined the power and type I error rate of four methods of testing for regression parameter changes when applied to detecting beta changes in monthly stock return series. The study used simulated stock return series with known betas, error variances, beta change dates, and error term distributions. In summary, it appears to be nearly impossible to detect or find the location of small or moderate beta changes in monthly stock return series. This suggests that the market model parameter changes reported by Hays and Upton (1986) are most likely not beta changes. However, they find of market model nonstationarity in almost all of the stocks in their sample-far more than this study finds. This suggests that if non-normality of stock returns accounts for the results obtained by Hays and Upton, the Stable Paretian 1.95 distribution does not adequately explain monthly stock returns.

This paper examines the effect of sustainability performance of European corporations on their stock performance, measured as the average monthly stock return from 1996 to 2001. The econometric analysis is based on common empirical asset pricing models, particularly on the multifactor model according to Fama and French (1993, Journal of Financial Economics, 33:3–56). The consideration of sustainability performance is two-fold: The average sustainability performance of the industry in which a corporation operates and the relative sustainability performance of a corporation within a given industry. The main result is that the average environmental performance of the industry has a significantly positive influence on the stock performance. In contrast, the average social performance of the industry has a significantly negative influence. The variables of the relative environmental or social performance of a corporation within a given industry have no significant effect on the stock performance. As a by-product, the econometric analysis implies that some results of Fama and French (1993, 1996, The Journal of Finance, LI (1):55–84) regarding the risk factors of the multifactor model need not hold true for different observation periods, for different stock markets, and for the use of single stocks (instead of portfolios).

This paper examines the effect of sustainability performance of European corporations on their stock performance, measured as the average monthly stock return from 1996 to 2001. The econometric analysis is based on common empirical asset pricing models, particularly on the multifactor model according to Fama and French (1993, Journal of Financial Economics, 33:3–56). The consideration of sustainability performance is two-fold: The average sustainability performance of the industry in which a corporation operates and the relative sustainability performance of a corporation within a given industry. The main result is that the average environmental performance of the industry has a significantly positive influence on the stock performance. In contrast, the average social performance of the industry has a significantly negative influence. The variables of the relative environmental or social performance of a corporation within a given industry have no significant effect on the stock performance. As a by-product, the econometric analysis implies that some results of Fama and French (1993, 1996, The Journal of Finance, LI (1):55–84) regarding the risk factors of the multifactor model need not hold true for different observation periods, for different stock markets, and for the use of single stocks (instead of portfolios).

Investment practitioners and the empirical finance literature make extensive use of monthly stock returns, where a monthly return is based on the change in stock price between one particular day of the calendar month—the reference day—and the corresponding day of the following month. We show that the choice of reference day seriously affects estimates of the properties of monthly returns, including their means, medians, variances, correlations, and betas. We find these effects both in individual stocks and in market indexes. Our evidence indicates the effects are generally unsystematic and are caused by sampling variation but are sufficiently pervasive and serious to suggest that studies that use estimates of the properties of monthly returns as inputs, and portfolio decisions based on such estimates, should be tested for robustness against different reference days.

The focus of this study was to examine the relationship between market risk and stock return of different markets of insurance, namely, conventional insurance and takaful. In order to estimate the market better with care, the variance of the stock return was examined as well. Earlier studies in determining the relationship between risk and return of insurer stock return employed the assumption that variance is constant over time. However, empirical evidence rejected the notion of a constant better and pointed to changing risk premium and returns variability over time. As a consequence, this study employed CAPM-GARCH (1,1) in order to estimate the market return coefficient and took the time varying volatility into consideration. Data for monthly stock return for Syarikat Takaful Malaysia as representative of the Takaful market, Hong Leong Finance Group as representative of the conventional insurance market and Kuala Lumpur Composite Index as market return were obtained from Bursa Malaysia. The time period of study was from February 1997 to February 2008. The results showed that there are clusters of volatile movements in both companies stock return with intervening periods of relative stability. This implied that GARCH is the appropriate model for insurance stock return. The results also proved that for market movements, the effect varies across these two firms. These results were expected since each company will have a different exposure towards the fluctuation of market movements. Key words: risk, return, CAPM-GARCH, conventional insurance, takaful

This study investigates machine learning on influential factors on the correlation of the stock return movements between US firms and their Chinese suppliers, given that the overall correlation has already been proven in the previous study. The present investigation was conducted by performing econometrical analysis and applying a variety of regression models to US firms' stock returns (independent variable) and their Chinese suppliers' stock returns (dependent variable) with various factors involved. From the results, it was evident that industry differences are a factor that caused variations in the degree of stock return correlations. Furthermore, firm size and stock trading volume yielded positive impacts on the significance of the correlation. Subsequently, predicated on said findings, a prediction model was generated using the Random Forest machine learning approach. Using US customer firms' monthly stock return data, as well as the three aforementioned factors, the monthly stock return value of their Chinese supplier firms can be predicted.

Changes in oil prices predict stock market returns worldwide. In our thirty year sample of monthly returns for developed stock markets, we find statistically significant predictability for twelve out of eighteen countries as well as for the world market index. Results are similar for our shorter time series of emerging markets. We find no evidence that our results can be explained by time varying risk premia. Even though oil price shocks increase risk, investors seem to underreact to information in the price of oil: a rise in oil prices does not lead to higher stock market returns, but drastically lowers returns. For instance, an oil price shock of one standard deviation (around 10 percent) predictably lowers world market returns by one percent. Oil price changes also significantly predict negative excess returns. Our findings are consistent with the hypothesis of a delayed reaction by investors to oil price changes. In line with this hypothesis the relation between monthly stock returns and lagged monthly oil price changes becomes substantially stronger once we introduce lags of several trading days between monthly stock returns and lagged monthly oil price changes.

This study is to determine the accuracy of the CAPM model in predicting 100 compass stock returns listed on the Indonesia Stock Exchange for the period 2013-2017. The variables of this study are 100 stock compass returns, Beta, Risk-Free, and Market return. The accuracy of the CAPM model is measured by standard deviation and t-test. The population of this research is all the monthly stock returns of the compass 100 stock index have gone public on the Indonesia Stock Exchange. While the sample used is a monthly stock return of 58 compass 100 companies from 2013 - 2017. The results of this study indicate that the CAPM model is accurate in predicting 100 stock compass returns.

The study investigates the joint market efficiency hypothesis of the OPEC countries by obtaining monthly stock price data from seven OPEC countries from January, 2005 to April, 2016. The study confirms the risk-return tradeoff in the OPEC stock markets. While most relationships are positive only a pair of country shows strong negative association Results of both parametric and nonparametric tests indicate that all OPEC members’ monthly stock return, except Qatar, are not weak-efficient. This implies that not all OPEC stock markets are efficient. Meanwhile, the study finds that current monthly stock return of one country member can be predicted using the historical monthly price movement of another OPEC member. As a whole, the monthly stock price of OPEC countries are not jointly weak efficient. Recommendations were offered based on these important discoveries.

The aim of this paper is to examine the validity of the four-factor asset pricing as a comparison the standard Fama-French three factor model using U.S. monthly stock return data from period January 1963 to December 2010. Monthly stock return are constructed into 25 portfolio while the four-factor model includes the market factor (beta), the size factor (SMB), the book-to-market factor (HML), and the ‘momentum’ factor (MOM) which represents winners minus losers in terms of returns. Time series regressions following Fama and French (1993) are employed which includes the three-factor model as well as the four-factor model. Results indicated that the four-factor model to some extent have significant capability in explaining the variations in average excess stock return which consistent with Carhart (1997). R2 from the four-factor model is just slightly higher than the three factor model yet it provides indicative for the robustness of the model. Meanwhile, the January seasonals are also able to be absorbed by the risk factors including the market, SMB, HML, and MOM. Since the four-factor model seems capable in explaining the variation of the stock returns then application of this model in emerging markets may provide guidance for investor in understanding the market condition.

The aim of this paper is to examine the validity of the four-factor asset pricing as a comparison the standard Fama-French three factor model using U.S. monthly stock return data from period January 1963 to December 2010. Monthly stock return are constructed into 25 portfolio while the four-factor model includes the market factor ( beta ), the size factor (SMB), the book-to-market factor (HML), and the ‘momentum’ factor (MOM) which represents winners minus losers in terms of returns. Time series regressions following Fama and French (1993) are employed which includes the three-factor model as well as the four-factor model. Results indicated that the four-factor model to some extent have significant capability in explaining the variations in average excess stock return which consistent with Carhart (1997). R 2 from the four-factor model is just slightly higher than the three factor model yet it provides indicative for the robustness of the model. Meanwhile, the January seasonals are also able to be absorbed by the risk factors including the market, SMB, HML, and MOM. Since the four-factor model seems capable in explaining the variation of the stock returns then application of this model in emerging markets may provide guidance for investor in understanding the market condition.