The effects of economic policy uncertainty on stock market returns: Evidence from Brazil

This article investigates the effects of economic policy uncertainty on the Brazilian stock market. We link excess returns and dividend growth rates to the economic policy uncertainty index of Baker et al. (2016) and other control variables. In recent years, Brazil has experienced political tensions, which affected its economic policy. Therefore, this country is the most suitable environment to test the hypothesis that this measure of economic policy uncertainty has an informational content not wholly reflected in the usual constructs of economic uncertainty and economic distress. Our results show that economic policy uncertainty (i) correlates negatively with current excess stock returns; (ii) correlates positively with future excess stock returns, showing itself to be a good predictor of future performance of the stock market; (iii) is not significantly related to future dividend growth rates; and (iv) anticipates changes in discount rates.

Consumption, aggregate wealth and expected stock returns: a quantile cointegration approach

This paper empirically examines the long-run relationship between consumption, asset wealth and labor income (i.e., cay) in the United States through the lens of a quantile cointegration approach. The advantage of using this approach is that it allows for a nonlinear relationship between these variables depending on the level of consumption. We estimate the coefficients using a Phillips–Hansen type fully modified quantile estimator to correct for the presence of endogeneity in the cointegrating relationship. To test for the null of cointegration at each quantile, we apply a quantile CUSUM test. Results show that: (i) consumption is more sensitive to changes in labor income than to changes in asset wealth for the entire distribution of consumption, (ii) the elasticity of consumption with respect to labor income (asset wealth) is larger at the right (left) tail of the consumption distribution than at the left (right) tail, (iii) the series are cointegrated around the median, but not in the tails of the distribution of consumption, (iv) using the estimated cay obtained for the right (left) tail of the distribution of consumption improves the long-run (short-run) forecast ability on real excess stock returns over a risk-free rate.

Can Multistep Nonparametric Regressions Beat Historical Average in Predicting Excess Stock Returns?

Several economic and financial variables are said to have predictive power over excess stock returns. Empirically there is little consensus among academics, whether these variables have predictive power or not. Results are often sensitive to the econometric model of choice. The econometric models can produce biased results due to the high degree of persistence in predictive variables. Apart from high persistence, the relationship between stock return and the predictive variable may also be misspecified in the model. In order to address possible non-linearities and endogeneity between the residuals and persistent independent variables in predictive regressions, multi-step non-parametric and semiparametric regressions are explored in this paper. In these regressions, the conditional mean and the residuals are estimated separately and then added to obtain the predicted excess stock returns. Goyal and Welch's (2008) predictive variables are used to predict excess S&P 500 returns. The predictive performance of both in-sample and out-of-sample of the two proposed models are compared with the historical average, Ordinary Least Squares (OLS) and non-parametric regressions. The performance of the models is evaluated using Root Mean Squared Errors (RMSEs). The explored models, particularly the two-step nonparametric model, outperform the compared models in-sample. Out-of-sample several variables are found to have predictive ability.

Shareholder Engagement on Environmental, Social, and Governance Performance

We study behind-the-scenes investor activism promoting environmental, social, and governance (ESG) improvements by means of a proprietary dataset of a large international, socially responsible activist fund. We examine the activist’s target selection, forms of engagement, impact on ESG performance, drivers of success, and effects on the targets’ operations and value creation. Target firms are typically large and visible, perform well, and have high liquidity (stock turnover) and low ESG performance. Engagement induces ESG rating adjustments: firms with poor ex ante ESG ratings experience a ratings increase after complying with the activist’s demands, whereas firms with high ex ante ESG ratings experience a ratings decrease following the revelation of their ESG problems. Activism that is focused on environmental and social issues is more likely to succeed if targets are ESG-sensitive (i.e., they have a strong ex ante ESG profile). Successful engagements boost targets’ sales. Risk-adjusted excess stock returns (with four-factor adjustment and relative to a matched sample of non-engaged firms) of successful engagements outperform those of unsuccessful engagements by 2.7%. Results are especially strong for firms with low ex ante ESG scores. Specifically, targeted firms in the lowest ex ante ESG quartile outperform matched peers by 7.5% in the year after the end of the engagement. Our results thus suggest that the activism regarding corporate social responsibility generally improves ESG practices and corporate sales and is profitable to the activist. Taken together, we provide direct evidence that ethical investing and strong financial performance, both from the activist’s and the targeted firm’s perspective, can go hand-in-hand together.

Short-Term Exuberance and Long-Term Stability: A Simultaneous Optimization of Stock Return Predictions for Short and Long Horizons

The fundamental interest of investors in econometric modeling for excess stock returns usually focuses either on short- or long-term predictions to individually reduce the investment risk. In this paper, we present a new and simple model that contemporaneously accounts for short- and long-term predictions. By combining the different horizons, we exploit the lower long-term variance to further reduce the short-term variance, which is susceptible to speculative exuberance. As a consequence, the long-term pension-saver avoids an over-conservative portfolio with implied potential upside reductions given their optimal risk appetite. Different combinations of short and long horizons as well as definitions of excess returns, for example, concerning the traditional short-term interest rate but also the inflation, are easily accommodated in our model.

How Does Google Search Affect the Stock Market? Evidence from Indian Companies

Google search data has received considerable attention for its predictive ability in various social and economic outcomes. In the arena of investments, a surge in online searches indicates an enhanced interest of investors, particularly retail, in that company. In this article, we have examined the association between Google search and stock prices in a sample of Indian companies. The results suggest that an increase in Google search is positively related to future excess stock returns, liquidity and volatility. The positive influence of Google search on stock prices, however, is temporary and reverses in the next week. We further show that the market sentiment moderates the interconnection between Google searches and future excess stock returns. The findings are in consonance with the ‘price pressure hypothesis’ of Barber and Odean (2008, Review of Financial Studies, 21(2), 785–818).

Empirical Test of Fama and French Three-Factor Model in the Egyptian Stock Exchange

We test the empirical validity of the three-factor model of Fama and French in the Egyptian Stock Exchange (EGX) using monthly excess stock returns of 50 stocks listed on the EGX from January 2014 to December 2018. Our findings do not support Fama and French three-factor model, where the coefficient of the beta was insignificant. The “SBM” coefficient and the “HML” coefficient were equal to zero and insignificant, which confirms the absence of the small firm effect and book-to-market ratio effect in the market. We conclude that there is no relation between expected return and Fama-French risk factors.

Liquidity Risks and Asset Pricing: Evidence from Developed and Emerging Markets

The study examines the liquidity adjusted capital asset pricing model in developed and emerging markets. Amihud measure is used to compute market liquidity. Innovations in Amihud ratio are generated through the autoregressive process to avoid autocorrelation in illiquidity data series. Decile portfolios based on illiquidity cost are formulated for each stock market. Liquidity adjusted betas are calculated at the portfolio level and then stocks as test assets have been used in the regression stage. Panel regression with fixed effect has been employed on LCAPM specifications for explaining the excess stock returns of developed and emerging markets during a period July 2005- June 2017. The findings of the study support that individual and aggregate liquidity risk price in stock markets except for Pakistan. The results of the study suggest that investors institutional or individual should consider liquidity risks for assessing the worth of assets.

Multilayer Network Risk Factor Pricing Model

This paper proposes a multilayer network risk factor pricing model to depict the impact of interactions between stocks on excess stock returns by constructing the network risk factor based on the stock multilayer network and introducing it to the traditional three-factor pricing model. According to China’s stock market data, we find that compared with the traditional three-factor model, the multilayer network risk factor pricing model can achieve higher fitting degree. Meanwhile, the multilayer network risk factor has a significant positive impact on the excess stock returns in most cases.