Southbound capital flows and stock return predictability

ABSTRACTIn this article, we document the asymmetric role that the US stock market plays in the international predictability of excess stock returns during recession and expansion periods. Most of the positive evidence accrues during the periods of recessions in the United States. During the expansions, there is only a limited evidence supporting the importance of lagged US returns in predictability of stock returns in 10 industrialized countries.

Are stock markets really efficient? Evidence of the adaptive market hypothesis

Using an intangible intensity factor that is orthogonal to the Fama–French factors, we compare the role of intangible investment in predicting stock returns over the periods 1963–1992 and 1993–2022. For 1963–1992, intangible investment is weak in predicting stock returns, but for 1993–2022, the predictive power of intangible investment becomes very strong. Intangible investment has a significant impact not only on the MTB ratio (Fama–French high minus low [HML] factor) but also on operating profitability (OP) (Fama–French robust minus weak [RMW] factor) when forecasting stock returns from 1993 to 2022. For intangible asset‐intensive firms, intangible investment is the main predictor of stock returns, rather than MTB ratio and profitability. Our evidence suggests that intangible investment has become an important factor in explaining stock returns over time, independent of other factors such as profitability and MTB ratio.

Predicting stock returns by classifier ensembles

Recession is known to cause an increase in stock return predictability. The COVID-19 pandemics had not only resulted in sickness and loss of life but also plunged global economy into recession prompting governments to come up with measures to combat the disease. This paper first confirms that return predictability increased due the spread of COVID-19 using data from 41 countries on variations of popular predictors. Furthermore, it shows that government responses to COVID-19 alleviated the pandemic, the recession and reduced the return predictability with varying impact for different government measures. However, cases and deaths from COVID-19 which should have intensified the recession were found to have an insignificant impact on the return predictability.

Portfolio choice with stochastic interest rates and learning about stock return predictability

Human capital measures and stock return predictability: Macroeconomic versus microeconomic approaches

Stock Return Predictability with Financial Ratios: Evidence from PSX 100 Index Companies

This study is based on the rapid development of the investment world. Hence, most investors have limited abilities and strategies in investing, therefore it takes important methods and concepts to assess the level of profit from an investment with various existing risk factors. This study focuses on the comparison of the accuracy between the Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT) methods in predicting stock returns of LQ 45 index companies for the 2016-2020 period. This study aimed to determine which method was more appropriate in predicting the company's stock returns. The accuracy of the CAPM and APT methods was measured by using the Mean Absolute Deviation (MAD). The Independent Sample T-test was used to determine whether there was a difference in accuracy between the CAPM and APT methods in predicting stock returns. The results of this study show that by using the calculation of the Mean Absolute Deviation, the APT method is more accurate than the CAPM method in predicting stock returns. The MAD APT value (0.000246) is smaller than the MAD CAPM value (0.003739) and based on data processing from the Independent Sample T-test shows that H0 is rejected, which means that there is a significant difference in accuracy between the CAPM and APT models particularly in predicting the company's stock return.

Predicting stock returns and volatility using consumption-aggregate wealth ratios: A nonlinear approach

Studies have shown that small stock returns can be partially predicted by the past returns of large stocks (cross-correlations), while a larger body of literature has shown that macroeconomic variables can predict future stock returns. This paper assesses the marginal contribution of cross-correlations after controlling for predictability inherent in lagged macroeconomic variables. Macroeconomic forecasting models generate trading rule profits of up to 0.431% per month, while the inclusion of cross-correlations increases returns to 0.516% per month. Such results suggest that cross-correlations may serve as a proxy for omitted macroeconomic variables in studies of stock market predictability. Macroeconomic variables are more important than cross-correlations in forecasting small stock returns and encompassing tests suggest that the small marginal contribution of cross-correlations is not statistically significant. Copyright © 2001 by John Wiley & Sons, Ltd.

We compare the performance of the linear regression model, which is the current standard in science and practice for cross-sectional stock return forecasting, with that of machine learning methods, i.e., penalized linear models, support vector regression, random forests, gradient boosted trees and neural networks. Our analysis is based on monthly data on nearly 12,000 individual stocks from 16 European economies over almost 30 years from 1990 to 2019. We find that the prediction of stock returns can be decisively improved through machine learning methods. The outperformance of individual (combined) machine learning models over the benchmark model is approximately 0.6% (0.7%) per month for the full cross-section of stocks. Furthermore, we find no model breakdowns, which suggests that investors do not incur additional risk from using machine learning methods compared to the traditional benchmark approach. Additionally, the superior performance of machine learning models is not due to substantially higher portfolio turnover. Further analyses suggest that machine learning models generate their added value particularly in bear markets when the average investor tends to lose money. Our results indicate that future research and practice should make more intensive use of machine learning techniques with respect to stock return prediction.

There exist inconsistencies in which financial ratio predicts stock return. This research aims to find empirical evidence of predictive powers of several financial ratios (Return on Equity (ROE), Earnings per Share (EPS), Debt to Equity Ratio (DER), Dividend Yield (DY), and Price/Earnings Ratio (PER)) in predicting Stock Return. Samples used in this study are 8 non-bank companies that are listed in KOMPAS100 stock index from 2009-2020. Results of this study shows ROE and DY is able to predict Stock Return, while EPS, DER, and PER does not show significant predictive ability.

PurposeThis paper aims to investigate the performance of various return forecasting variables and methods in Saudi Arabia and Malaysia. The authors document that market excess returns in Saudi Arabia are predicted by changes in oil prices, the dividend yield and inflation, whereas the equity premium in Malaysia is predicted only by the US market excess returns. In both countries, the authors find that the diffusion index is the best forecasting method and stock return predictability is stronger in expansions than in recessions. To interpret the findings, the authors perform two tests. The empirical results suggest irrational pricing in Malaysia and rationally time-varying expected returns in Saudi Arabia.Design/methodology/approachThe authors apply the state-of-the-art in-sample and out-of-sample forecasting techniques to predict stock returns in Saudi Arabia and Malaysia.FindingsThe Saudi equity premium is predicted by oil prices, dividend yield and inflation. The Malaysian equity premium is predicted by the US market excess returns. In both countries, the authors find that the diffusion index is the best forecasting method. In both countries, predictability is stronger in expansions than in recessions. The tests suggest irrational pricing in Malaysia and rationality in Saudi Arabia.Practical implicationsThe empirical results have some practical implications. The fact that stock returns are predictable in Saudi Arabia makes it possible for policymakers to better evaluate future business conditions, and thus to take appropriate decisions regarding economic and monetary policy. In Malaysia, the results of this study have interesting implications for portfolio management. The fact that the Malaysian market seems to be inefficient suggests the presence of strong opportunities for sophisticated investors, such as hedge and mutual funds.Originality/valueFirst, there are no papers that have studied the return predictability in Saudi Arabia in spite of its importance as an emerging market. Second, the methods that combine all predictive variables such as the diffusion index or the kitchen sink methods have not been implemented in emerging markets. Third, this paper is the first study to deal with time-varying short-horizon predictability in emerging countries.

The small-cap effect in the predictability of individual stock returns