The liquidity, performance and investor preference of socially responsible investments

PurposeThe purpose of the paper is to analyze socially responsible investment (SRI) asset performance compared to traditional assets using the MSCI KLD 400 Index. The authors examine the required return that investors expect to maintain their holdings in SRI stock and whether SRI stocks can be used for diversification during financial crises.Design/methodology/approachThe authors examine SRI stocks' liquidity from the MSCI KLD 400 index, encompassing all environmental, social and governance (ESG) factor investments over 25 years, from 1990 until 2019. The authors test whether sorting portfolios based on their excess return, liquidity and volatility can explain the difference in SRI and non-SRI stocks' returns and then examine the global financial crisis' (GFC) impact on excess returns for SRI and non-SRI assets.FindingsThe authors find a significant difference in liquidity and volatility between SRI and non-SRI stocks and that SRI stocks perform better during financial crises. The results suggest a possible general investor preference to invest in non-SRI stocks despite our findings that SRI stocks tend to withstand financial risk better than non-SRI stocks. The authors find that long-term investors may be willing to forego short-term gains to reduce their overall risk exposure during crises.Originality/valueSRI is gaining international popularity as an alternative investment that includes ratings based on ESG factors. Previous studies provide mixed results of whether SRI stocks outperform conventional stocks. In addition, there is limited research examining the liquidity and volatility of SRI assets. The authors compare the differences between SRI and non-SRI stocks in terms of excess return, volatility and liquidity and compare the liquidity of SRI and non-SRI stocks during the financial crisis.

An Exploration of Realized Volatility and Returns in the Chinese Stock Market

This paper uses high-frequency stock index data to construct realized volatilities for the Chinese stock market and applies in-sample and out-of-sample to test the predictive power of realized volatility on Chinese stock market returns. The empirical results show that realized volatility can significantly predict the excess return of the Chinese stock market in the next month, and the in-sample and out-of-sample regression models are positive, and the out-of-sample The p-value of the regression model is significant. And after controlling for a range of other stock predictor variables, we find that the regression coefficient of realized volatility is still significant, and we find that after adding realized volatility, the in-sample adj- increases with the inclusion of realized volatility, suggesting that realized volatility does have components that are not explained by other economic variables. Also based on a different construction method, the realized variance still has significant predictive power after averaging the realized variance. After combining two different realized variance indicators, the predictive power is still better. In terms of economic interpretation, this paper finds that the predictive power of realized variance on stock returns is through influencing the turnover rate (market trading activity), which in turn influences stock market returns. We find that realized volatility has a significant effect on the turnover rate, and when we use realized volatility to predict the turnover rate, which in turn predicts the excess return, we find that the coefficient is highly significant, indicating that realized volatility can indeed cause changes in excess return by affecting the turnover rate.

Financing Constraints, Risk of Stock Price Crash and Impact of New Crown Pneumonia

Taking Shanghai and Shenzhen A stock listed companies as a sample, this paper uses the event research method to test the effect of financing constraints and stock price crash risk on the cumulative excess return (CAR) of enterprises under the impact of the epidemic situation. The results show that the risk of stock price collapse plays a negative role in the transmission channel of the impact of the new crown epidemic on the enterprise's cumulative excess return, and the financing constraint intensifies the negative effect. Further, the heterogeneity analysis based on firm size and ownership found that in SMEs and non-state-owned enterprises, the effect of increased financing constraints is more significant Is significant. Finally, this paper puts forward some suggestions from the perspective of relevant regulatory departments and enterprises themselves.

The Month-of-the-Year Effect in the European, American, Australian and Asian Markets

This paper examines the existence of the month-of-the-year effects in four different continents, namely Europe, Asia, America, and Oceania. Nine indexes were analyzed in order to verify differences between monthly returns from January 1990 to December 2013, followed by an examination of the January effect, Halloween effect, and the October effect, testing for statistical significance using an OLS linear regression in order to verify whether those effects offer consistent opportunities for investors. Investors with globally diversified portfolios benefit from the Halloween effect, with a 1.2% average monthly excess return in winter and spring, while the pre-dotcom-bubble period had a better performance than the post-dotcom-bubble period. In the global post-dotcom-bubble period, there is statistical evidence for 1.60% and 1% lower average monthly returns in January (the January effect) and in months other than October (the October effect), respectively, contradicting the literature. The dotcom bubble seems to be responsible for the January effect differing from what might otherwise have been expected in the later period. There is no consistent and clear impact on continental incidence. The Halloween effect is revealed to be a fruitful strategy in the FTSE, DAX, Dow Jones, BOVESPA, and N225 indexes taken one-by-one. The January effect excess average return was only statistically significative for the pre-dotcom-bubble period for globally diversified portfolios. This paper contributes to a wider global and comparable view upon month-of-the-year effect.

Pengaruh fama-french five factor model terhadap excess return pada perusahaan LQ 45 tahun 2014-2019

Penelitian ini bertujuan untuk mengetahui pengaruh masing-masing variabel Fama-French Five Factor Model yaitu premi risiko, ukuran perusahaan, book to market ratio, profitabilitas dan investasi terhadap Excess return pada perusahaan LQ 45 tahun 2014-2019. Terdapat 23 perusahaan yang terdaftar dalam indeks LQ 45 secara berturut-turut dari tahun 2014-2019 dari total 60 perusahaan yang terdaftar pada LQ 45 tahun 2014-2019 dengan menggunakan teknik pengambilan sampel purposive sampling. Penelitian ini adalah penelitian kuantitatif dengan menggunakan metode analisis regresi linear berganda. Hasil penelitian ini menunjukkan bahwa terdapat pengaruh yang signifikan antara premi risiko, ukuran perusahaan serta book to market ratio. Sedangkan untuk variabel profitabilitas dan investasi tidak berpengaruh terhadap Excess Return pada perusahaan LQ 45 tahun 2014-2019. Hasil penelitian juga menunjukkan bahwa variabel premi risiko, ukuran perusahaan, book to market ratio, profitabilitas serta investasi berpengaruh secara simultan terhadap excess return pada perusahaan LQ 45 tahun 2014-2019 yaitu sebesar 48,1%. Disarankan untuk peneliti selanjutnya menggunakan variabel lain atau menambah variabel lain agar dapat menghasilkan nilai R Square yang lebih besar atau lebih kuat, dan menguji model estimasi yang lain seperti CAPM atau APT.

Risk-Return Analysis on Optimum Portfolio Selection of Islamic Stocks

<p><em>This study aims to determine the optimum portfolio category and analyze the risk-return on a formed portfolio. Data was taken from eighteen listed companies indexed by Jakarta Islamic Index during 2015-2018. Stock returns are calculated based on the closing price at the end of each month in the period. Sharia Certificate of Bank Indonesia is a proxy of risk-free return, while the market return is measured by the value of the Jakarta Islamic Index. Stocks are sorted by the value of excess return to beta (ERB) from highest to lowest, and to obtain optimal stock portfolio candidates, and the ERB value must be compared with the cut-off rate value. Seven issuers qualify for forming the optimum portfolio of shares. The results show that the optimum portfolio return is greater than the expected return and the expected risk-free return. When compared between individual stock returns and portfolio stock returns, some individual stocks provide higher returns than portfolio returns. However, the risk of individual shares was also higher than the risk of the portfolio. This finding proves that risk can be reduced optimally in Islamic stocks selection by forming an optimum portfolio.</em></p>

Factor Investment: Evaluating Persistence Effect for Investment Performance and Sustainability Exposure

This research includes two separate studies. The first study is devoted to evaluating the persistence effect by analyzing performances of portfolios ranked based on previous performances under various factor models. The result shows that the shorter the holding period, the stronger the predictability and that the Multi-factor model has the highest explaining power for the excess return regarding the underlying factors. The second study is devoted to exploring how sustainable investing influences alpha by introducing a new sustainable factor to reflect the premium due to exposure to sin industries. The study result shows that there is no significant alpha associated with sustainable investing and that there is no significant return differential between funds that have high/low exposure to the sustainable factor.

Investigating the Dynamics of Exchange Traded Funds Across the Bear and Bull Markets: Evidence from Indian Equity ETFs

The present study attempts to examine the tracking ability of Indian equity exchange traded funds (ETFs) across the bearish and bullish market regimes. Also, ETFs’ sensitivity to their respective underlying indices across the two market conditions is examined so as to gain an insight into the differences in risk exposure under the two regimes using DBM. The results found that the tracking error (TE) of ETFs varies across the two market regimes with it higher during the bullish regime. At the same time, ETFs’ responsiveness to their underlying indices is found to be higher during the bearish market regime, which justifies the existence of lower TE during the bearish regime. NIFTYBEES, KOTAKNIFTY and BANKBEES emerged to be the top three performers in terms of tracking efficiency. Further, NIFTYBEES, BANKBEES and JUNIORBEES are reported to provide significantly positive excess returns during the bullish regime. As such, investors considering investment in equity ETFs can opt for the top performing funds where they also stand a chance to earn excess return (in few cases). Also, it is observed the beta coefficients of ETFs varied significantly from unity. It suggests that the ETFs and their respective underlying indices are not subject to similar systematic risk.