A study of cross-industry return predictability in the Chinese stock market

We investigate cross-industry return predictability for the Shanghai and Shenzhen stock exchanges, by constructing 6- and 26- industry portfolios. The dominance of retail investors in these markets, in conjunction with the gradual diffusion of information hypothesis provide the theoretical background that allows us to employ machine learning methods to test for cross-industry predictability. We find that Oil, Telecommunications and Finance industry portfolio returns are significant predictors of other industries. Our out-of-sample forecasting exercise shows that the OLS post-LASSO estimation outperforms a variety of benchmarks and a long–short trading strategy generates an average annual excess return of 13%.     

International stock return predictability under model uncertainty

This paper examines return predictability when the investor is uncertain about the right state variables. A novel feature of the model averaging approach used in this paper is to account for finite-sample bias of the coefficients in the predictive regressions. Drawing on an extensive international dataset, we find that interest-rate related variables are usually among the most prominent predictive variables, whereas valuation ratios perform rather poorly. Yet, predictability of market excess returns weakens substantially, once model uncertainty is accounted for. We document notable differences in the degree of in-sample and out-of-sample predictability across different stock markets. Overall, these findings suggest that return predictability is neither a uniform, nor a universal feature across international capital markets.     

In-sample vs. out-of-sample tests of stock return predictability in the context of data mining

We undertake an extensive analysis of in-sample and out-of-sample tests of stock return predictability in an effort to better understand the nature of the empirical evidence on return predictability. We find that a number of financial variables appearing in the literature display both in-sample and out-of-sample predictive ability with respect to stock returns in annual data covering most of the twentieth century. In contrast to the extant literature, we demonstrate that there is little discrepancy between in-sample and out-of-sample test results once we employ out-of-sample tests with good power. While conventional wisdom holds that out-of-sample tests help guard against data mining, Inoue and Kilian [Inoue, A., Kilian, L., 2004. In-sample or out-of-sample tests of predictability: which one should we use? Econometric Reviews 23, 371–402.] recently argue that in-sample and out-of-sample tests are equally susceptible to data mining biases. Using a bootstrap procedure that explicitly accounts for data mining, we still find that certain financial variables display significant in-sample and out-of-sample predictive ability with respect to stock returns.     

Long-term adjusted volatility: Powerful capability in forecasting stock market returns

We develop the long-term adjusted volatility (LV_ADJ) by removing the interference information of short-term volatility from the simple long-term volatility and examine the role of LV_ADJ in the predictability of stock market returns. Using a sample from January 2000 to December 2019 and considering 19 popular predictors, LV_ADJ positively predicts the next-month returns of S&P 500 and the univariate model with LV_ADJ has the best forecasting performance with adjusted in-sample r-squared of 3.825%, out-of-sample r-squared of 3.356%, return gains of 5.976%, CER gains of 4.708 and Sharpe ratio gains of 0.394. The predictive information of LV_ADJ is independent of that obtained from the 19 popular predictors. Furthermore, we find that LV_ADJ also has predictive power for long-term (3–12 months) stock returns, and can forecast returns of industry portfolios and characteristic portfolios.     

Stock return predictability and model uncertainty

We use Bayesian model averaging to analyze the sample evidence on return predictability in the presence of model uncertainty. The analysis reveals in-sample and out-of-sample predictability, and shows that the out-of-sample performance of the Bayesian approach is superior to that of model selection criteria. We find that term and market premia are robust predictors. Moreover, small-cap value stocks appear more predictable than large-cap growth stocks. We also investigate the implications of model uncertainty from investment management perspectives. We show that model uncertainty is more important than estimation risk, and investors who discard model uncertainty face large utility losses.     

Federal Reserve Monetary Policy and Industry Stock Returns

Recent studies identify stock return patterns associated with changes in Federal Reserve monetary policy. We find that these return patterns prevail across sixteen industry stock indices. However, significant cross-industry variation exists as the apparel industry exhibits mean annual returns that are 50% higher under an expansive Fed policy than under a restrictive policy, while the same return difference for the oil industry is only 20%. This cross-industry variation suggests that monetary conditions may be used by investors to estimate different expected returns across industries. Furthermore, the findings support the view that monetary considerations should be considered in ex ante asset pricing models such as the CAPM.     

Market Timing And Model Uncertainty: An Exploratory Study For The Swiss Stock Market

We use statistical model selection criteria and Avramov's (2002) Bayesian model averaging approach to analyze the sample evidence of stock market predictability in the presence of model uncertainty. The empirical analysis for the Swiss stock market is based on a number of predictive variables found important in previous studies of return predictability. We find that it is difficult to discard any predictive variable as completely worthless, but that the posterior probabilities of the individual forecasting models as well as the cumulative posterior probabilities of the predictive variables are time-varying. Moreover, the estimates of the posterior probabilities are not robust to whether the predictive variables are stochastically detrended or not. The decomposition of the variance of predicted future returns into the components parameter uncertainty, model uncertainty, and the uncertainty attributed to forecast errors indicates that the respective contributions strongly depend on the time period under consideration and the initial values of the predictive variables. In contrast to AVRAMOV (2002), model uncertainty is generally not more important than parameter uncertainty. Finally, we demonstrate the implications of model uncertainty for market timing strategies. In general, our results do not indicate any reliable out-of-sample return predictability. Among the predictive variables, the dividend-price ratio exhibits the worst external validation on average. Again in contrast to AVRAMOV (2002), our analysis suggests that the out-of-sample performance of the Bayesian model averaging approach is not superior to the statistical model selection criteria. Consequently, model averaging does not seem to help improve the performance of the resulting short-term market timing strategies.     

The Gilt-Equity Yield Ratio and the Predictability of UK and US Equity Returns

A number of financial variables have been shown to be effective in explaining the time-series of aggregate equity returns in both the UK and the US. These include, inter alia , the equity dividend yield, the spread between the yields on long and short government bonds, and the lagged equity return. Recently, however, the ratio between the long government bond yield and the equity dividend yield – the gilt-equity yield ratio – has emerged as a variable that has considerable explanatory power for UK equity returns. This paper compares the predictive ability of the gilt-equity yield ratio with these other variables for UK and US equity returns, providing evidence on both in-sample and out-of-sample performance. For UK monthly returns, it is shown that while the dividend yield has substantial in-sample explanatory power, this is not matched by out-of sample forecast accuracy. The gilt-equity yield ratio, in contrast, performs well both in-sample and out-of-sample. Although the predictability of US monthly equity returns is much lower than for the UK, a similar result emerges, with the gilt-equity yield ratio dominating the other variables in terms of both in-sample explanatory power and out-of-sample forecast performance. The gilt-equity yield ratio is also shown to have substantial predictive ability for long horizon returns.     

The Gilt-Equity Yield Ratio and the Predictability of UK and US Equity Returns

A number of financial variables have been shown to be effective in explaining the time-series of aggregate equity returns in both the UK and the US. These include, inter alia , the equity dividend yield, the spread between the yields on long and short government bonds, and the lagged equity return. Recently, however, the ratio between the long government bond yield and the equity dividend yield – the gilt-equity yield ratio – has emerged as a variable that has considerable explanatory power for UK equity returns. This paper compares the predictive ability of the gilt-equity yield ratio with these other variables for UK and US equity returns, providing evidence on both in-sample and out-of-sample performance. For UK monthly returns, it is shown that while the dividend yield has substantial in-sample explanatory power, this is not matched by out-of sample forecast accuracy. The gilt-equity yield ratio, in contrast, performs well both in-sample and out-of-sample. Although the predictability of US monthly equity returns is much lower than for the UK, a similar result emerges, with the gilt-equity yield ratio dominating the other variables in terms of both in-sample explanatory power and out-of-sample forecast performance. The gilt-equity yield ratio is also shown to have substantial predictive ability for long horizon returns.     

Global industry portfolios and short-term predictability of returns: Is it there?

This paper explores the degree of success of a large set of active trading rules that have been popularized in the literature on the short-term predictability of returns in equity and foreign exchange markets by extending the scope of research in three dimensions: global portfolios, industry portfolios, and exclusive versus inclusive portfolios. Our results show that after adjusting for (1) the impact of nonsynchronous prices in the reported closing index levels which causes spurious autocorrelations in returns, (2) data snooping bias caused by searching through a large number of possible trading strategies in order to find a few that yield superior in-sample performance, and (3) transaction costs that reduce any profits from active trading, the risk-adjusted profits generated by short-term trend chasing trading rules are generally not statistically significant and the hypothesis of no outperformance of trading rules over either buy-and-hold or risk-free benchmark return cannot be rejected in most industries. Such findings favor short-term market efficiency and are hardly comforting for active traders.     

Predicting the bear stock market: Macroeconomic variables as leading indicators

This paper investigates whether macroeconomic variables can predict recessions in the stock market, i.e., bear markets. Series such as interest rate spreads, inflation rates, money stocks, aggregate output, unemployment rates, federal funds rates, federal government debt, and nominal exchange rates are evaluated. After using parametric and nonparametric approaches to identify recession periods in the stock market, we consider both in-sample and out-of-sample tests of the variables’ predictive ability. Empirical evidence from monthly data on the Standard & Poor’s S&P 500 price index suggests that among the macroeconomic variables we have evaluated, yield curve spreads and inflation rates are the most useful predictors of recessions in the US stock market, according to both in-sample and out-of-sample forecasting performance. Moreover, comparing the bear market prediction to the stock return predictability has shown that it is easier to predict bear markets using macroeconomic variables.     

Institutional ownership and return predictability across economically unrelated stocks

We document strong weekly lead-lag return predictability across stocks from different industries with no customer-supplier linkages (economically unrelated stocks). Between 1980 and 2010, the industry-neutral long-short hedge portfolio earns an average of over 19 basis points per week. This predictability is related to common institutional ownership and is distinct from previously documented lead-lag effects. Common institutional ownership is a complementary rather than a substitute explanation for return predictability. Information linkages are enough to induce return predictability among stocks in the same industry, but economically unrelated stocks exhibit return predictability only when they have common institutional owners. Our findings suggest that institutional portfolio reallocations can induce return predictability among otherwise unrelated stocks.     

Quantiles of the realized stock–bond correlation and links to the macroeconomy

This paper adopts quantile regressions to scrutinize the realized stock–bond correlation based upon high frequency returns. The paper provides in-sample and out-of-sample analysis and considers factors constructed from a large number of macro-finance predictors well-known from the return predictability literature. Strong in-sample predictability is obtained from the factor quantile model. Out-of-sample the quantile factor model outperforms benchmark models.     

When does investor sentiment predict stock returns?

We examine the asymmetry in the predictive power of investor sentiment in the cross-section of stock returns across economic expansion and recession states. We test the implication of behavioral theories and evidence that the return predictability of sentiment should be most pronounced in an expansion state when investors' optimism increases. We segregate economic states according to the NBER business cycles and further implement a multivariate Markov-switching model to capture the unobservable dynamics of the changes in the economic regime. The evidence suggests that only in the expansion state does sentiment perform both in-sample and out-of-sample predictive power for the returns of portfolio formed on size, book-to-market equity ratio, dividend yield, earnings-to-price ratio, age, return volatility, asset tangibility, growth opportunities, and 11 widely documented anomalies. In a recession state, however, the predictive power of sentiment is generally insignificant.     

INSIDER DEMAND AND INDUSTRY TRENDS

We investigate the time‐series patterns in industry return predictability conditioned on insider demand from 1996 to 2016. Current insider demand within an industry is positively associated with higher future industry returns. This relation is primarily driven by the buy side of insider trades and is more pronounced during periods of economic recession and high market volatility where uncertainty and information asymmetry are relatively high. Our results are consistent with the notion that corporate insiders have an informational advantage that can be used as an indicator for industry portfolio selection.     

The impact and role of COVID-19 uncertainty: A global industry analysis

The novel 2019 coronavirus (COVID-19) has resulted in uncertainty that permeates every aspect of life and business. In this study we undertake a comprehensive analysis of the impact of COVID-19 related uncertainty on global industry returns and volatility using a sample of 68 global industries and Google Trends search data to measure COVID-19 related uncertainty. The results indicate that COVID-19 related uncertainty negatively impacts returns on all industries and generally leads to higher volatility. We interpret these findings as uncertainty related to the future financial performance of firms and emerging opportunities for some industries. Certain industries are more resilient than others and increased uncertainty is not only necessarily associated with industries that experienced the largest negative returns. We also find that new factors emerged in the return generating process during the COVID-19 period. We show that despite an uncertain climate, some industries performed well, yielding positive cumulative abnormal returns that at times are greater than during the pre-COVID-19 period. The implications of our findings for investors are discussed.     

Stock Return Predictability: A Bayesian Model Selection Perspective

Attempts to characterize stock return predictability have resultedin little consensus on the important conditioning variables,giving rise to model uncertainty and data snooping fears. Weintroduce a new methodology that explicitly incorporates modeluncertainty by comparing all possible models simultaneouslyand in which the priors are calibrated to reflect economicallymeaningful information. Our approach minimizes data snoopinggiven the information set and the priors. We compare the priorviews of a skeptic and a confident investor. The data implyposterior probabilities that are in general more supportiveof stock return predictability than the priors for both typesof investors.     

Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model

Within a VAR based intertemporal asset allocation model we explore the effects on return predictability and optimal asset allocation of adjusting VAR parameter estimates for small-sample bias. We apply a simple and easy-to-use analytical bias formula instead of bootstrap or Monte Carlo bias-adjustment. Regarding return predictability we show that bias-adjustment in the multivariate setup can yield very different results than in the univariate case. Furthermore, bias-correcting the VAR parameters has both quantitatively and qualitatively important effects on the optimal portfolio choice. For intermediate values of risk-aversion, the intertemporal hedging demand for bonds and stocks is heavily affected by the bias-correction. Utility calculations also show large effects of bias-adjustment, both in-sample and out-of-sample.     

Tactical Industry Allocation and Model Uncertainty

We use Bayesian model averaging to analyze industry return predictability in the presence of model uncertainty. The posterior analysis shows the importance of inflation and earnings yield in predicting industry returns. The out‐of‐sample performance of the Bayesian approach is, in general, superior to that of other statistical model selection criteria. However, the out‐of‐sample forecasting power of a naive i.i.d. forecast is similar to the Bayesian forecast. A variance decomposition into model risk, estimation risk, and forecast error shows that model risk is less important than estimation risk.