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This study aims to investigate whether the zero-investment portfolio strategy generates higher excess returns for all listed companies in the Stock Exchange of Thailand (SET) or ESG100 stocks. The study period is from January 2016 to December 2020, a total of 60 months. The dividend yield is employed for categorizing the stock into value and growth stocks. The strategy of buying value stocks and short-selling growth stocks is then applied. The results show that investing using the zero-investment portfolio strategy can generate higher returns in an investment portfolio that consists of ESG100 stocks than in an investment portfolio that consists of all stocks in the SET. The optimal holding periods for investing in portfolios that consist of stocks in the SET are 6 months, 9 months, and 12 months, and the optimal holding periods for a portfolio that consists of ESG100 stocks is 6 months. To explain excess returns of stocks in the SET, the Fama and French (2015) five-factor model is employed. There is no relation between risk factors and excess returns for the holding period of 6 months and 12 months. However, excess return is found to have a negative relation with the market risk premium factor for a 9-month holding period. The excess returns of ESG100 stocks are also inversely correlated with investment factors for a holding period of 6 months.

Using a unique dataset of ownership structure for all stocks listed on the Stockholm Stock Exchange in Sweden, we examine different degrees of institutional holdings in Swedish firms during the bear market of 2000 to 2002. We find that examination by institutional investor domicile reveals that both Swedish and foreign institutions increase their equity holdings, although the increase by foreign institutions is proportionately higher, (individuals reduce their equity holdings). We find evidence that foreign and domestic institutional investors exhibit different preferences for excess returns and standard deviations in excess returns when we control for firm size; excess return is associated with changes in foreign institutional holdings while higher standard deviation in excess return is associated with the change in domestic institutional holdings. Both types of institutions are sensitive to liquidity and trading factors, causing portfolio realignment.

The purpose of this paper is to provide an analysis of the relationship between expected risk premia on property stocks and some major macroeconomic risk factors as reflected in the general business and financial conditions

Employs a three‐step estimation strategy (principal component analysis, GARCH (1,1) and GMM) to model the macroeconomic risk variables (GDP growth, INDP growth, unexpected inflation, money supply, interest rate and exchange rate) and relate them to the first and second moments on property stock excess returns of four major markets, namely, Singapore, Hong Kong, Japan and the UK. Macroeconomic risk is measured by the conditional volatility of macroeconomic variables.

The expected risk premia and the conditional volatilities of the risk premia on property stocks are time‐varying and dynamically linked to the conditional volatilities of the macroeconomic risk factors. However there are some disparities in the significance, as well as direction of impact in the macroeconomic risk factors across the property stock markets. Consequently there are opportunities for risk diversification in international property stock markets.

Results help international investors and portfolio managers deepen their understanding of the risk‐return relationship, pricing of macroeconomic risk as well as diversification implications in major Asia‐Pacific and UK property stock markets. Additionally, policy makers may play a role in influencing the expected risk premia and volatility on property stock markets through the use of macroeconomic policy.

Aims to investigate whether the level and volatility of interest rates affect the excess returns of major Asian listed property markets within a time‐varying risk framework.

A three‐factor model is employed with excess return volatility, interest rate level and interest rate volatility as its factors. The generalized autoregressive conditionally heteroskedasticity in the mean (GARCH‐M) analyzes are undertaken on monthly excess returns of property stock indexes for the period 1987‐2003.

Property stocks are generally sensitive to changes in the long‐term and short‐term interest rates and to a lesser extent, their volatility. Moreover, there are disparities in the magnitude as well as direction of sensitivities in interest rate level and volatility across the listed property markets and under different market conditions. Overall, results indicate changes in the ARCH parameter, risk premia, volatility persistence and interest rate level and volatility effects before and after the 1997 Asian financial crisis. However, these noted changes are not uniform and depend on the individual listed property markets.

The findings enhance investors' understanding in financial asset pricing and complement existing evidence in international real estate. With the increasing significance of property stocks as real estate investment vehicles for international investors to gain property exposure in Asia and internationally, the paper is timely and provides the basis for more advanced research in international real estate investment strategies and capital asset pricing.

This study aims to determine whether the stock holdings of equity mutual funds are informative for predicting future stock performance in the Chinese market. It is a puzzle that actively managed mutual funds underperform passive benchmarks, whereas retail investors still delegate investment decisions to the fund managers. The present study sheds light on whether mutual fund managers possess security selection skills in their top ten holdings.

By regression analysis and portfolio sorting, this study focuses on 830 Chinese A-share stocks in the industry research reports from the Guotai Junan Securities Company. It collects mutual fund's top ten holdings data from the Wind Financial Terminal between 2019Q1 and 2021Q1. As robustness checks, the result holds for the fixed-effect model, an additional measure of ranks in the top ten holdings, the predictability test based on the confusion matrix and two stage least square (2SLS) regression.

The authors find that the top ten holdings by equity mutual funds are informative for predicting stock performance and can provide valuable information for investors to support their decision-making.

The findings of this study provide insightful guidance for retail investors in making investment decisions and support the hypothesis that active fund management adds value.

Firstly, the authors find that the top ten holdings of Chinese mutual funds show significantly positive signals for future stock excess returns, indicating the selection skills of fund managers. Secondly, the above positive relationship exhibits a diminishing marginal effect with more funds holding this stock. Thirdly, the authors find that the predictability horizon of the number of overweighing funds is up to three quarters and then diminishes in the fourth quarter. Finally, investors have a 59% prediction accuracy for the whole stock sample and an 85% precision conditional on the predicted positive subsample to outperform the market. The authors also address the endogeneity and reverse causality issues.

The authors provide a comparative analysis between emerging and developed financial markets in terms of the effects of political risks on stock market returns and volatility. The authors also examine whether this impact depends on the nature of political risks. Therefore, this study aims to detect which financial markets are the most profitable and the riskiest in terms of political risks.

The authors investigate the impact of political risks on the excess stock market return and its conditional volatility using the generalized ARCH model for a sample of 46 developed and emerging markets over a period ranging from 1995 to 2019. In order to test how the nature of political risks affects equity excess returns and volatility differently in different markets, the authors employ (1) a composite political risk score, (2) the four subgroups of political risks as defined by Bekaert et al. (2005, 2014) and (3) the individual dimensions of political risks.

The findings indicate that the composite political risk is priced into both stock markets. The effect of political risks is positive for excess returns and negative for volatility. The authors show that the political risk leads to more volatility in developed markets. Nevertheless, the effect of individual components varies according to the market category.

The authors provide a framework for predicting market returns and volatility using changes in the political risk of the country. The findings help investors make investment decisions based on the political decisions of governments. In other words, investors should consider political uncertainty when determining their expected earnings.

The authors engage monthly panel data methodology in terms of the political risk stock market relationship. In addition, the authors consider recent and very long data covering the period 1995–2019. Furthermore, this study combines three various political risk measures, and both equity returns and volatility.

This study aims to draw on a less explored predictor – the average correlation of pairwise returns on industry portfolios – to predict stock market returns (SMRs) in the USA.

This study uses the average correlation approach of Pollet and Wilson (2010) and predicts the SMRs in the USA. The model is estimated using monthly data for a long time horizon, from July 1963 to December 2018, for the portfolios comprising 48 Fama-French industries. The model is extended to examine the effects of a longer lag structure of one-month to four-month lags and to control for the effects of a number of variables – average variance (AV), cyclically adjusted price-to-earnings ratio (CAPE), term spread (TS), default spread (DS), risk-free rate returns (R_f) and lagged excess market returns (R_s).

The study finds that the two-month lagged average correlation of returns on individual industry portfolios, used individually and collectively with financial predictors and economic factors, predicts excess returns on the stock market in an effective manner.

The methodology and results are of interest to academics as they could further explore the use of average correlation to improve the predictive powers of their models.

Market practitioners could include the average correlation in their asset pricing models to improve the predictions for the future trend in stock market returns. Investors could consider including average correlation in their forecasting models, along with the traditional financial ratios and economic indicators. They could adjust their expected returns to a lower level when the average correlation increases during a recession.

The finding that recession periods have effects on the SMRs would be useful for the policymakers. The understanding of the co-movement of returns on industry portfolios during a recession would be useful for the formulation of policies aimed at ensuring the stability of the financial markets.

The study contributes to the literature on three counts. First, the study uses industry portfolio returns – as compared to individual stock returns used in Pollet and Wilson (2010) – in constructing average correlation. When stock market becomes more volatile on returns, the individual stocks are more diverse on their performance; the comovement between individual stock returns might be dominated by the idiosyncratic component, which may not have any implications for future SMRs. Using the industry portfolio returns can potentially reduce such an effect by a large extent, and thus, can provide more reliable estimates. Second, the effects of business cycles could be better identified in a long sample period and through several sub-sample tests. This study uses a data set, which spans the period from July 1963 to December 2018. This long sample period covers multiple phases of business cycles. The daily data are used to compute the monthly and equally-weighted average correlation of returns on 48 Fama-French industry portfolios. Third, previous studies have often ignored the use of investors’ sentiments in their prediction models, while investors’ irrational decisions could have an important impact on expected returns (Huang et al., 2015). This study extends the analysis and incorporates investors’ sentiments in the model.

The purpose of this paper is to investigate whether the investor attention using the Google search volume index (GSVI) can be used to forecast stock returns. The authors also find the answer to whether the “price pressure hypothesis” would hold true for the Indian stock market.

The authors employ a more recent fully balanced panel data for the period from July 2012 to Jun 2017 (260 weeks) of observations for companies of NIFTY 50 of the National Stock Exchange in the Indian stock market. The authors are motivated by Tetlock (2007) and Bijl et al. (2016) to employ regression approach of econometric estimation.

The authors find that high Google search volumes lead to positive returns. More precisely, the high Google search volumes predict positive and significant returns in the subsequent fourth and fifth weeks. The GSVI performs as an useful predictor of the direction as well as the magnitude of the excess returns. The higher quantiles of the GSVI have corresponding higher excess returns. The authors notice that the domestic investor searches are correlated with higher excess returns than the worldwide investor searches. The findings imply that the signals from the search volume data could be of help in the construction of profitable trading strategies.

To the best of the authors knowledge, no paper has examined the relationship between Google search intensity and stock-trading behavior in the Indian stock market. The authors use a more recent data for the period from 2012 to 2017 to investigate whether search query data on company names can be used to predict weekly stock returns for individual firms. This study complements the prior studies by investigating the relationship between search intensity and stock-trading behavior in the Indian stock market.

This paper empirically investigates the link between expected returns on stocks and a set of variables that describe the general state of economic activity. The model relates the first and second conditional moments on stock excess returns to the conditional variances and covariances of a set of prespecified macroeconomic factors. The estimation results suggest that industrial production growth, inflation, and short‐term interest rates help explain the behavior over time of expected excess returns on stocks.

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.