Indicator selection and stock return predictability

We propose a momentum-determined indicator-switching (MDIS) strategy, simple and effective, to improve the predictability of stock returns, which can effectively select predictors. Empirical results indicate that the stock return forecasts generated by the MDIS strategy are statistically and economically significant. And we find that super long-term momentum of predictability (SMoP) exists in predictive factors. That is, in a long period of time in the past, the best predictor among a series of factors has best prediction ability in the future. We also design restricted momentum-determined indicator-switching (RMDIS) strategy when considering economic constrain. It is robust for the prediction performance of this strategy using a series of extension and robustness test. Success of the RMDIS strategy is also seen in using technical indicators to forecast stock returns.     

Price effects of steel commodities on worldwide stock market returns

We analyze the price effects of steel commodities on stock market returns in emerging and developed economies. These commodities have recently attained increased media exposure due to the rise in the U.S. steel import tariffs, which pose the threat of reducing global demand for steel products and, consequently, lowering prices abroad. However, little has been investigated on the impact of steel commodity prices on worldwide stock market returns. By performing structural VAR and GARCH techniques on a weekly-frequency time series from 2002 to 2015, we find positive and statistically significant effects of linear and non-linear steel commodity price shocks on real stock returns in the commodity markets. In the highly diversified financial markets such as U.S. and Germany, real stock returns do not significantly respond to steel commodity price shocks, although we find highly significant positive responses from developed economies such as Australia, Japan and South Korea. Results are robust to different model specifications. Our evidence suggests that higher tariffs on steel imports represent a larger disadvantage to commodity markets which are more largely impacted by steel commodity prices. We provide economic policy implications based on recent literature.     

Testing for linear and nonlinear granger causality in the stock price-volume relation: Korean evidence

In this paper, linear and nonlinear Granger causality tests are used to examine the dynamic relationship between daily Korean stock returns and trading volume. We find evidence of significant bidirectional linear and nonlinear causality between these two series. ARCH-ype models are used to examine whether the nonlinear causal relations can be explained by stock returns and volume serving as proxies for information flow in the stochastic process generating volume and stock returns respectively. After controlling for volatility persistent in both series and filtering for linear dependence, we find evidence of nonlinear bidirectional causality between stock returns and volume series. The finding of strong bidirectional stock price-volume causal relationships implies that knowledge of current trading volume improves the ability to forecast stock prices. This evidence is not supportive of the efficient market hypothesis. Another finding is that the nonlinear relationship is sensitive to institutional, organizational, and structural factors. The results of this study should be useful to regulators, practitioners and derivative market participants whose success precariously depends on the ability to forecast stock price movements.     

Predictability of tail risks of Canada and the U.S. Over a Century: The role of spillovers and oil tail Risks☆

We examine the tail risk spillovers between Canada and U.S. stock markets using over a century data, and also account for the roles of tail risks of other advanced economies (France, Germany, Japan, Italy, Switzerland, and the UK) and oil-market tail risk. We use the “best” tail risk measure obtained from different variants of the Conditional Autoregressive Value at Risk (CAViaR) model developed by Engle and Manganelli (2004) in the predictive model and compare its performance with that of an AR(1) benchmark model. We find strong evidence of risk spillovers between the two stock markets. We find contrasting evidence for the predictability of oil-market tail risk, with positive predictability in case of the net oil exporter and negative in case of the net oil importer. Further results using tail risks of other advanced economies (combined) support possible diversification potential for Canadian stocks in the presence of market risks of advanced economies other than the U.S. Our findings have implications for investors and are robust to various out-of-sample forecast horizons, alternative data frequencies, data splits, and 1% and 5% VaRs.     

Lagged order flows and returns: A longer-term perspective

In this paper, we consider the large-sample relation between returns and lagged order flows over horizons of up to 2 months. The analysis is motivated by work in market microstructure which suggests that the effects of inventory control on stock returns should be discernible over horizons longer than those considered in the literature. We begin our analysis by developing a simple model of inventory effects in the presence of public information. Using mid-quote return data, we then find some evidence of return predictability using order flows, even after controlling for lagged returns, which is consistent with our theoretical setting. The relation is present only for negative imbalances and is stronger in large firms rather than small ones. Overall, the analysis is consistent with the notion that inventory control effects span several weeks.     

The quantilogram: With an application to evaluating directional predictability

We propose a new diagnostic tool for time series called the quantilogram. The tool can be used formally and we provide the inference tools to do this under general conditions, and it can also be used as a simple graphical device. We apply our method to measure directional predictability and to test the hypothesis that a given time series has no directional predictability. The test is based on comparing the correlogram of quantile hits to a pointwise confidence interval or on comparing the cumulated squared autocorrelations with the corresponding critical value. We provide the distribution theory needed to conduct inference, propose some model free upper bound critical values, and apply our methods to S&P500 stock index return data. The empirical results suggest some directional predictability in returns. The evidence is strongest in mid range quantiles like 5–10% and for daily data. The evidence for predictability at the median is of comparable strength to the evidence around the mean, and is strongest at the daily frequency.     

Time-frequency causality and dependence structure between crude oil,EPU and Chinese industry stock: Evidence from multiscale quantile perspectives

This article investigates the time-frequency causality and dependence structure of Chinese industry stock returns on crude oil shocks and China's economic policy uncertainty (EPU) across quantiles over the period from January 2001 to June 2021. We use wavelet-based decomposition series to establish a multiscale causality-in-quantiles test and a quantile-on-quantile regression approach to reveal the complicated relationships involving crude oil, EPU and stock returns. Our empirical results are as follows: First, the predictability of crude oil and EPU on industry stock returns is significantly strong under extreme market conditions. Second, the explanatory ability of EPU on industry stock returns in the long term is stronger than EPU’s ability to explain short term returns. Third, the impacts of crude oil and EPU on industry stock returns remain remarkably asymmetric across quantile levels. Finally, nonenergy-intensive industries are also affected by crude oil shocks, but less than energy-intensive industries. Overall, these empirical findings can provide implications for policymakers to stabilize stock markets and investors to hedge the potential risks from crude oil and EPU.     

On economic uncertainty,stock market predictability and nonlinear spillover effects

This paper uses a k-th order nonparametric Granger causality test to analyze whether firm-level, economic policy and macroeconomic uncertainty indicators predict movements in real stock returns and their volatility. Linear Granger causality tests show that whilst economic policy and macroeconomic uncertainty indices can predict stock returns, firm-level uncertainty measures possess no predictability. However, given the existence of structural breaks and inherent nonlinearities in the series, we employ a nonparametric causality methodology, as linear modeling leads to misspecifications thus the results cannot be considered reliable. The nonparametric test reveals that in fact no predictability can be observed for the various measures of uncertainty i.e., firm-level, macroeconomic and economic policy uncertainty, vis-à-vis real stock returns. In turn, a profound causal predictability is demonstrated for the volatility series, with the exception of firm-level uncertainty. Overall our results not only emphasize the role of economic and firm-level uncertainty measures in predicting the volatility of stock returns, but also presage against using linear models which are likely to suffer from misspecification in the presence of parameter instability and nonlinear spillover effects.     

Economic policy uncertainty,oil price volatility and stock market returns: Evidence from a nonlinear model

This paper investigates the nonlinear relationship between economic policy uncertainty, oil price volatility and stock market returns for 25 countries by applying the panel smooth transition regression model. We find that oil price volatility has a negative effect on stock returns, and this effect increases with economic policy uncertainty. Furthermore, there is pronounced heterogeneity in responses. First, oil-exporting countries whose economies depend more on oil prices respond more strongly to oil price volatility than oil-importing countries. Second, stock returns of developing countries are more susceptible to oil price volatility than that of developed countries. Third, crisis plays a crucial role in the relation between oil price volatility and stock returns.     

Priced risk and asymmetric volatility in the cross section of skewness

We investigate the sources of skewness in aggregate risk factors and the cross section of stock returns. In an ICAPM setting with conditional volatility, we find theoretical time series predictions on the relationships among volatility, returns, and skewness for priced risk factors. Market returns resemble these predictions; however, size, book-to-market, and momentum factor returns are not always consistent with our predictions. We find evidence that size and book-to-market may be priced post-crisis but not in the decade before. Momentum does not appear priced by our test. We link aggregate risk and skewness to individual stocks and find empirically that the risk aversion effect manifests in individual stock skewness. Additionally, we find several firm characteristics that explain stock skewness. Smaller firms, value firms, highly levered firms, and firms with poor credit ratings have more positive skewness.     

Geopolitical risk and stock market volatility in emerging markets: A GARCH – MIDAS approach

In this study, we examine the connection between geopolitical risk (GPR) and stock market volatility in emerging economies. Our motivation for this study is premised on the need to assess both the predictability and the associated economic gains in relation to the subject in order to offer more useful insights to investors and practitioners. To the best of our knowledge, this is the first study that jointly considers these objectives. Consequently, we employ the GARCH-MIDAS framework which accommodates mixed data frequencies thereby circumventing information loss or any associated bias. We find that emerging stock market volatility responds more positively to geopolitical risks although the act-related GPR index offers better out-of-sample forecasts than the threat-related GPR. We also find that accounting for global economic factors in the predictability analysis is crucial for robust outcomes. Finally, we provide some utility gains of including GPR in the predictive model of stock market volatility while also highlighting some useful implications of our findings for investment and policy decisions.     

Adaptive market hypothesis: The story of the stock markets and COVID-19 pandemic

Since the level of markets’ information efficiency is key to profiteering by strategic players, Shocks; such as the COVID-19 pandemic, can play a role in the nature of markets’ information efficiency. The martingale difference and conditional heteroscedasticity tests are used to evaluate the Adaptive form of market efficiency for four (4) major stock market indexes in the top four affected economies during the COVID-19 pandemic (USA, Brazil, India, and Russia). Generally, based on the martingale difference spectral test, there is no evidence of a substantial change in the levels of market efficiency for the US and Brazilian stock markets in the short, medium, and long term. However, in the long term, the Indian stock markets became more information inefficient after the coronavirus outbreak while the Russian stock markets become more information efficient. Intuitively, these affect the forecastability and predictability of these markets’ prices and/or returns. Thereby, informing the strategic and trading actions of stock investors (including arbitrageurs) towards profit optimization, portfolio asset selection, portfolio asset adjustment, etc. Similar policy implications are further discussed.     

A Simple Approach for Diagnosing Instabilities in Predictive Regressions

We introduce a method for detecting the presence of structural breaks in the parameters of predictive regressions linking noisy variables such as stock returns to persistent predictors such as valuation ratios. Our approach relies on the least squares‐based squared residuals of the predictive regression and is straightforward to implement. The distributions of the two test statistics we introduce are shown to be free of nuisance parameters, valid under dependent errors, already tabulated in the literature and robust to the degree of persistence of the chosen predictor. Our proposed method is subsequently applied to the predictability of US stock returns.     

Investor protection and value creation in cross‐border M&As by emerging economies

Previous research investigating cross‐border M&As (CBM&As) by emerging economies (EEs) provided contrasting evidence on the value enhancement role of investor protection rules. We conduct a new empirical study to address the issue with an accurate sample selection of bidders from more homogeneous developing countries and transactions on developed countries only. Our analysis over the 1997–2012 period on a sample of M&A deals by companies from Brazil, Russia, India, China, and South Africa (BRICS) does not provide evidence that better institutional standards in the destination country are rewarded by the local stock market. We find that foreign governance quality is not associated with positive excess stock returns around the announcement date. Rather, these returns are affected by firm‐specific and deal‐specific factors, such as the relative deal size, the listed status of the target company, and the acquirer size. Comparison with other studies on excess returns for emerging markets (including BRICs) suggests that the results could be driven at least partially by country choice.     

Predictability in international stock returns using currency fluctuations and forward rate forecasts

We find that currency risk, specifically dollar exchange rate risk, is a determinant in firm stock returns worldwide. Firms exposed to various dollar exchange rate risks worldwide exhibit strong differences in expected returns, and firms with previously high sensitivity to their home country’s exchange rate fluctuation subsequently outperform during the following six to twelve months. This effect is robust across countries, time, exchange rate policies, and macroeconomic environments. We find that information in currency forward rates provides additional, useful information when predicting future returns of these currency-sensitive firms, and dynamic, state-space estimation of currency forward rate term structures complements the predictability.     

A quantile-copula approach to dependence between financial assets

This article unveils the dependence structure between United States stock prices, crude oil prices, exchange rates, and U.S. interest rates. In particular, we employ linear and nonlinear estimation methods, such as quantile regression and the quantile-copula approach. Over the 1998–2017 period, we find that there is a positive relationship between the dollar value and the S&P 500 stock price, with the exception of the lower and upper tails of the stock return distribution. Further evidence is obtained on the dependence structure between other asset returns. The stock returns are negatively related to oil prices but positively to U.S. interest rates. Our results highlight the way that financial assets are linked, which have implications for risk management and monetary policy.     

Spillovers and directional predictability between international energy commodities and their implications for optimal portfolio and hedging

This study sheds a new light on the dependence and the directional predictability between eight major energy price returns, using the Cross-Quantilogram (CQ) and the Partial CQ (PCQ) analysis. The energy prices cover the time series for the U.S. natural gas and seven internationally traded crude oil types. The results reveal a significant directional predictability running from most of energy commodities returns to the OPEC basket and the very light Tapis crude oil returns. However, the quantile predictability in both directions is enabled only for the relations between the light Brent and the light WTI, and between the OPEC basket and the Malaysian Tapis. The time-varying predictability analysis reveals that there is a significant upper quantile dependence between these international energy commodities. Finally, we find that the TAPIS can be a good hedging vehicle for other energy markets. These findings may be instructive for both policymakers (in terms of financial stability) and market participants (in terms of performance).     

Asymmetric volatility spillover in the Tokyo stock exchange

This paper examines volatility transfers between size-based stock indexes from the Tokyo Stock Exchange. We use a bivariate EGARCH model to test for volatility spillover effects between large- and small-cap stock indexes. We find an asymmetric volatility spillover from large-cap stock returns to small-cap returns, but not vice versa. We also find a small-firm January effect, but not a June seasonality, in either large-and small-cap stock returns. Instead, we find that the conditional correlation between large- and small-cap indexes is time-varying, showing a tendency to increase during the month of June.(JEL G12, G15)     

Return interval,dependence structure,and multivariate normality

We focus on changes in the multivariate distribution of index returns stemming purely from varying the return interval, assuming daily to quarterly returns. Whereas long-tailedness is present in daily returns, we find that, in agreement with a well-established idea, univariate return distributions converge to normality as the return interval is lengthened. Such convergence does not occur, however, for multivariate distributions. Using a new method to parametrically model the dependence structure of stock index returns, we show that the persistence of a dependence structure implying negative asymptotic dependence in return series is the reason for the rejection of multivariate normality for low return frequencies.     

Model‐free evaluation of directional predictability in foreign exchange markets

We examine directional predictability in foreign exchange markets using a model‐free statistical evaluation procedure. Based on a sample of foreign exchange spot rates and futures prices in six major currencies, we document strong evidence that the directions of foreign exchange returns are predictable not only by the past history of foreign exchange returns, but also the past history of interest rate differentials, suggesting that the latter can be a useful predictor of the directions of future foreign exchange rates. This evidence becomes stronger when the direction of larger changes is considered. We further document that despite the weak conditional mean dynamics of foreign exchange returns, directional predictability can be explained by strong dependence derived from higher‐order conditional moments such as the volatility, skewness and kurtosis of past foreign exchange returns. Moreover, the conditional mean dynamics of interest rate differentials contributes significantly to directional predictability. We also examine the co‐movements between two foreign exchange rates, particularly the co‐movements of joint large changes. There exists strong evidence that the directions of joint changes are predictable using past foreign exchange returns and interest rate differentials. Furthermore, both individual currency returns and interest rate differentials are also useful in predicting the directions of joint changes. Several sources can explain this directional predictability of joint changes, including the level and volatility of underlying currency returns. Copyright © 2007 John Wiley & Sons, Ltd.