An investigation of causality between insider transactions and stock returns

Prior research shows that corporate insiders engage in profitable transactions by trading securities of their own firms. The main purpose of this study is to examine whether insider transactions and stock returns have causality relationships at the firm level for a sample of 2,521 firms during the period 1988 to 1998. We find a large impact of stock returns on subsequent insider transactions at both the aggregate and firm levels. The impact appears to be negative which suggests that insiders buy after stock price decreases and sell after stock price increases. Our findings on the predictive content of insider transactions for subsequent stock returns are primarily consistent with prior literature. We observe a positive but weak relationship between insider transactions and future stock returns.     

The influence and predictive powers of mixed-frequency individual stock sentiment on stock returns

In this article, we construct mixed-frequency individual stock sentiment using MIDAS model. We first investigate the influence power of mixed-frequency individual stock sentiment on excess returns. The results indicate that the higher the frequency of individual stock sentiment is, the better it explains the variation of excess returns, that mixed-frequency individual stock sentiment, especially mixed high-frequency sentiment, exerts greater influence on excess returns than the same frequency one and that the mixed-frequency sentiment has a stronger explanatory power to the variation of excess returns than size factor, book-to-market factor, profitability factor and investment factor do. Then, we study the predictive content of mixed-frequency individual stock sentiment. The results show that the higher the frequency of individual stock sentiment is, the better the forecast performs. Moreover, by comparing the corresponding statistics in influence and predictive power models, we find that the influence power of mixed-frequency individual stock sentiment is more significant than its predictive power.     

The VIX,the variance premium and stock market volatility

We decompose the squared VIX index, derived from US S&P500 options prices, into the conditional variance of stock returns and the equity variance premium. We evaluate a plethora of state-of-the-art volatility forecasting models to produce an accurate measure of the conditional variance. We then examine the predictive power of the VIX and its two components for stock market returns, economic activity and financial instability. The variance premium predicts stock returns while the conditional stock market variance predicts economic activity and has a relatively higher predictive power for financial instability than does the variance premium.     

The asymmetric predictability of high-yield bonds

This study examines the relationship between the high-yield bonds market and the stock market and indicates that stock returns lead high-yield bond returns. Specifically, this study further shows that this lead–lag relationship is more solid during bear market periods since a downward trend in the stock market implies a high likelihood of the exercise of the equity put in short position embedded in a high-yield bond at maturity. We also conducted out-of-sample forecast using a VAR model, an AR model and naïve estimation during bear market and non-bear market periods. Our results demonstrate that high-yield bond returns are better predicted by a VAR model that includes past stock returns than by an AR model or naive estimation during bear market periods, but such is not the case during non-bear market periods.     

Liquidity,earnings management,and stock expected returns

This paper examines the relationships among liquidity, earnings management, and stock expected returns by using a sample of Chinese listed firms to investigate 22,022 firm–year observations from 1998 to 2018. Our study reveals that an increase in stock liquidity is associated with a decrease in the degree of earnings management. This result is robust to the use of alternative measures when endogeneity concerns are controlled for. Moreover, the findings indicate that the stock liquidity component of earnings management is positively associated with future stock returns in Chinese firms. Our results reveal that the stock liquidity component of short-termism in managerial decisions plays a critical role in determining future stock returns.     

Nonlinear effects of monetary policy on stock returns in a smooth transition autoregressive model

This paper employs smooth transition autoregressive (STAR) models to investigate the nonlinear effect of monetary policy on stock returns. The change in the Federal funds rate is used as an endogenous measure of monetary policy, and the growth rate of industrial production is also considered in the model. Our results show that the relationship between the monetary policy and excess returns on stock prices is positive and nonlinear. A decrease in the Federal funds rate causes a larger increase in excess returns if excess stock returns are located in the extreme low excess returns regime.     

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.     

Forecasting the direction of the US stock market with dynamic binary probit models

Several empirical studies have documented that the signs of excess stock returns are, to some extent, predictable. In this paper, we consider the predictive ability of the binary dependent dynamic probit model in predicting the direction of monthly excess stock returns. The recession forecast obtained from the model for a binary recession indicator appears to be the most useful predictive variable, and once it is employed, the sign of the excess return is predictable in-sample. The new dynamic “error correction” probit model proposed in the paper yields better out-of-sample sign forecasts, with the resulting average trading returns being higher than those of either the buy-and-hold strategy or trading rules based on ARMAX models.     

The momentum and reversal effects of investor sentiment on stock prices

In this paper, we illustrate the real function relationship between the stock returns and change of investor sentiment based on the nonparametric regression model. The empirical results show that when the change of investor sentiment is moderate, the stock return is positively correlated with the change of investor sentiment, presenting an obvious momentum effect. However, the stock return is negatively correlated with the change of investor sentiment if the change of investor sentiment is dramatic, presenting significant reversal effects. Moreover, the degree of reversal effect caused by extremely optimistic sentiment is greater than that driven by extremely pessimistic sentiment, which shows a significant asymmetry. Our findings offer a partial explanation for financial anomalies such as the mean reversion of stock returns, the characteristic of slow rise and steep fall in China's stock market and so on.     

Explaining movements in UK stock prices

We examine movements in aggregate UK stock prices by decomposing the variance of unexpected real stock returns into components due to revisions in expectations of future dividends, discount rates, and the covariance between the two. The contribution of news about future discount rates is about four times that of news about future dividends, with no significant covariance between them. Our analysis of excess returns uncovers a positive covariance between news about dividends and news about real interest rates. Since these two elements have opposite effects on current stock prices, their combined effect is negligible. Persistence in expected returns, as well as predictability, are found to be important in explaining stock price movements.     

The relationship between information technology capability,inventory efficiency,and shareholder wealth: A firm-level empirical analysis

Inventories represent an important strategic resource for firms, with implications for shareholder wealth. As such, firms expend considerable effort in managing their inventories efficiently. Among other factors, information technology (IT) capability can play an important role in enabling inventory efficiency and financial performance. However, insight into the chain-of-effects linking IT capability, inventory efficiency, and stock market returns and risk remains limited. In this paper, we provide a conceptual model outlining the relationships between these constructs. Next, we evaluate the model using secondary information on firms from multiple industries across the 10-year time period of 2000–2009. Our analysis confirms that firms’ IT capability plays a significant role in enhancing their inventory efficiency, which, in turn, is observed to increase stock market returns. Our results also reveal that firms’ IT capability directly reduces their stock market risk and enhances their stock market returns. Taken together, these findings, along with the conceptual model that we advance, have important research and managerial implications.     

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.     

Order imbalance and stock returns: Evidence from China

We investigate the relation between daily order imbalance and return in the Chinese stock markets of Shenzhen and Shanghai. Prior studies have found that daily order imbalance is predictive of subsequent returns. On the two Chinese exchanges we find the autocorrelation in order imbalances is similar to that of the New York Stock Exchange as reported by Chordia and Subrahmanyam [Chordia, T., & Subrahmanyam, A. (2004). Order imbalance and individual stock returns: Theory and evidence. Journal of Financial Economics, 72, 485–518]. We also find a strong contemporaneous relation between daily order imbalances and returns. However, we do not find evidence that order imbalances predict subsequent returns. We attribute the difference in predicative power to differences in trading mechanisms on the two exchanges and to differences in the share turnover rates.     

Stock returns and carry trades

This paper studies the role the stock market plays in determining the carry trade return. Evidence shows stock market returns significantly affect the carry trade returns in Australia, Mexico, Japan, the UK, Sweden, and the Eurozone. This paper further examines three channels through which stock returns affect the carry trade returns: the Balassa-Samuelson, risk-premium, and flight-to-quality effects. Our model shows that the stock market impact on the carry trade return depends on stock market volatility, and the flight-to-quality effect prevails globally. Furthermore, the introduction of the stock return significantly improves the out-of-sample forecast of the carry trade return when the domestic market is volatile.     

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.     

Stock returns and volatility: Another look

Most empirical work examining the intertemporal mean-variance relationship in stock returns has tended to use relatively simple specifications of the mean and especially of the conditional variance. We augment the information set to include economic variables that other researchers have found to be important and use GARCH-M models to explore the relation between volatility and expected stock returns. We find that the additional variables have little impact on the conditional variance and that any intertemporal relationship between volatility and stock returns is weak or unstable. Our results signal the need for theoretical models of the intertemporal volatility-return relationship, and call for further studies of the determinants of the conditional variance of stock returns.     

Alternative estimation method of earnings growth rate for PEGR strategy

The ambiguous return pattern for the PEGR (the ratio of the stock’s price/earnings to its estimated earnings growth rate) strategy has been documented in literature for the US stock markets. As stock prices and earnings per share (EPS) are objective data, earnings growth rate, however, is estimated by analyst whose method partial explains the PEGR vague return pattern. The purpose of this study is not to deny or substitute analysts’ estimation, but rather, to provide a simple and popular method, log-linear regression model, to forecast the earnings growth rate (G), and examine whether the typical PEGR effect, such as PER (price/earnings ratio) or PBR (price/book ratio) effect, exists by using our alternative estimation method. Our evidence indeed shows that returns on the lowest PEGR portfolio not only dominate over all higher PEGR portfolios, but also beat the market with stochastic dominance (SD) analysis, which is consistent with our prediction. Our results, at least, imply that using the log-linear regression model to construct the PEGR-sorted portfolios can benefit investors and the model is also a good choice for analysts in their forecasting.     

Co-determination of capital structure and stock returns—A LISREL approach: An empirical test of Taiwan stock markets

Titman and Wessels (1988) utilize a structural-equations model (LISREL) to find out the latent determinants of capital structure. Maddala and Nimalendran (1996) indicate that the problematic model specification causes the poor results in Titman and Wessels’ research. Chang, Lee, & Lee (2009) apply a Multiple Indicators and Multiple Causes (MIMIC) model to re-examine the same issue as Titman and Wessels did but found more convincing results. We extend Titman and Wessels’ research from using a single-equation approach to a multi-equations approach. In addition to the determinants of firms’ capital structure, those of stock returns are determined simultaneously. Literature indicates that a firm's capital structure may affect its stock returns (Bhandari, 1988), and the reverse is true too (Baker and Wurgler, 2002, Lucas and McDonald, 1990, Welch, 2004). Hence, a firm's determinants of its capital structure and those of its stock returns should be decided simultaneously, rather than independently. By solving the simultaneous equations, we examine the empirical relationship between the two endogenous variables: capital structure and stock returns and find out their common determinants as well. Our results show that stock returns, expected growth, uniqueness, asset structure, profitability, and industry classification are the main factors of capital structure, while the primary determinants of stock returns are leverage, expected growth, profitability, value and liquidity. The level of debt ratios and stock returns are mutually determined by the aforementioned factors and themselves.     

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.     

Business cycle asymmetry and the stock market

We present and estimate models of an asymmetric relationship between CRSP stock index returns and the U.S. unemployment rate. Based on the Akaike Information Criterion, conventional linear time series models are improved by allowing asymmetric responses. Our results show that negative stock returns are quickly followed by sharp increases in unemployment, while more gradual unemployment declines follow positive stock returns. According to our forecasting model, the unemployment rate rises by 1.12 percentage points during the 12 months after a 10 percent stock decline. Because macroeconomic forecasters have been unable to reliably predict downturns, these findings may provide a useful contribution.