Time-varying Return Predictability Research Articles

Technology shocks and stock returns: A long-term perspective

Using patent data dating as far back as 1870, we compute local and global technology shocks. United States data reveal strong evidence of in-sample predictability particularly at longer horizons and during economic expansions, principally driven by global technology factors. We also discover strong evidence of time-varying predictability for the United States. We find that the global technology shock is a stronger time-varying predictor of stock returns, predicting returns in as many as 41% of sub-samples of data. Using OECD data for 11 countries, we find evidence of time-varying return predictability for seven countries; however, in-sample and long horizon predictability are, in general, weak. • Historical patent data are used to compute local and global technology shocks. • United States data reveal strong evidence of in-sample predictability. • Predictability is found only at longer horizons and during economic expansions. • Much stronger evidence of time-varying predictability for United States is found. • Seven of the 11 OECD countries reveal strong time-varying return predictability.

Time-Varying Return Predictability and Adaptive Behavior in The U.S. Commodity Markets During COVID-19

The study investigates the time-varying efficiency of the four most commonly traded international commodities from the U.S. Chicago Board of Options Exchange (CBOE) over a more extended period as well as during COVID-19. The study also explores how adaptive behavior of returns induces profitable opportunities in the commodity markets. Daily returns of commodity indices (gold, silver, oil, metal) are divided into subsamples of six years, to apply a battery of linear/nonlinear tests. The study uncovers the linear and nonlinear serial dependence in returns from commodities and finds evidence of time-varying volatility, thus consistent with the Adaptive Market Hypothesis over the full sample period. Moreover, returns from all the commodities are highly volatile and predictable during COVID-19.

Time-varying return predictability and adaptive markets hypothesis: Evidence on MIST countries from a novel wild bootstrap likelihood ratio approach

Time-varying return predictability and adaptive markets hypothesis: Evidence on MIST countries from a novel wild bootstrap likelihood ratio approach

Understanding time-varying short-horizon predictability✰

Time-varying return predictability should stem either from time-varying dividend growth predictability or from time-varying dividend yield autocorrelation. The decomposition results show that the bulk of the variation in return predictability is due to its negative correlation with time-varying dividend yield autocorrelation, which is in turn positively correlated with investor sentiment. Using a linear regression based on these results, we confirm a significantly negative relation between investor sentiment and stock return predictability, after controlling for macroeconomic factors, dividend growth, and structural breaks. These findings suggest that investor sentiment is an important determinant of time-varying short-horizon predictability.

Are Stock Markets Adaptive? Evidence from US, Hong Kong and India

The study tests the adaptive market hypothesis for the US (Dow Jones and S&P 500), Hong Kong (Hang Seng) and Indian (BSE Sensex) stock markets by testing the 20 years of daily and the weekly data for the return predictability. The indices exhibit the time varying realized risk premia, and the time varying risk return characteristic (Sharpe ratio), indicating that risk preferences are adaptive to the changing market conditions. We also test the time varying return predictability by applying the Ljung - Box test and the Chow - Denning heteroscedasticity consistent variance ratio test to the fixed length moving sub-sample windows of 100, 200, 300 and 500 days and 25, 50 and 100 weeks. We find the evidence of the changing return predictability for all the indices and for all the sub-sample window sizes. Despite the significant correlation between the log returns of the indices, barring Dow Jones and S&P 500, for the weekly data, the periods of the return predictability of the indices do not coincide. For the daily data, the return predictability for the US and the Indian stock markets coincided during the sub-prime crisis of 2008. The heteroscedasticity consistent Chow - Denning statistic is a better measure for comparing the relative market efficiency between the stock markets. This statistic is robust to the sub-sample window size and data frequency i.e. weekly or daily data. This statistic indicates that the periods of return predictability are sparse and hence an active investment management could add value by implementing appropriate trading strategies in the periods of the return predictability. For the period considered, the relative efficiency of Indian stock market is comparable to that of the US stock market.

Variance risk in aggregate stock returns and time-varying return predictability

This paper introduces a new out-of-sample forecasting methodology for monthly market returns using the variance risk premium (VRP) that is both statistically and economically significant. This methodology is motivated by the ‘beta representation,’ which implies that the market risk premium is related to the price of variance risk by the variance risk exposure. Hence, when the slope of the contemporaneous regression of market returns on variance innovation is larger, future returns are more sharply related to the current VRP. Also, predictions are more accurate when market returns are highly correlated to variance shocks.

Time-Varying Return Predictability in the Chinese Stock Market

China’s stock market is the largest emerging market in the world. It is widely accepted that the Chinese stock market is far from efficiency and it possesses possible linear and nonlinear dependencies. We study the predictability of returns in the Chinese stock market by employing the wild bootstrap automatic variance ratio test and the generalized spectral test. We find that the return predictability vary over time and a significant return predictability is observed around market turmoils. Our findings are consistent with the Adaptive Markets Hypothesis (AMH) and have practical implications for market participants and policy makers. A predictability index can be constructed for each asset, which might help warn a crisis is in store, ease the development of the ongoing bubble, and stabilize the market.

Time-varying return predictability in South Asian equity markets

Time-varying return predictability in four South Asian stock markets is examined using the wild-bootstrapped automatic variance ratio test and price delay measures. Strong evidence of predictability is found in aggregate market and size-sorted portfolio returns. The cross-sectional variation in return predictability is inversely related to firm size and trading frequency, while the time variation in return predictability is related to market conditions—the level of equity market development, liquidity, volatility, automation of trading mechanism and financial crises. These results strongly corroborate Lo's (2004) adaptive market hypothesis, and are robust to controlling for thin trading, changes in data frequency, and use of alternative return predictability measures.

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

This study examines the adaptive market hypothesis in the S&P500, FTSE100, NIKKEI225 and EURO STOXX 50 by testing for stock return predictability using daily data from January 1990 to May 2014. We apply three bootstrapped versions of the variance ratio test to the raw stock returns and also whiten the returns through an AR-GARCH process to study the nonlinear predictability after accounting for conditional heteroscedasticity through the BDS test. We evaluate the time-varying return predictability by applying these tests to fixed-length moving subsample windows and also examine whether there is a relationship between the level of predictability in stock returns and market conditions. The results show that there are periods of statistically significant return predictability, but also episodes of no statistically significant predictability in stock returns. We also find that certain market conditions are statistically significantly related to predictability in certain markets but each market interacts differently with the different market conditions. Therefore our findings suggest that return predictability in stock markets does vary over time in a manner consistent with the adaptive market hypothesis and that each market adapts differently to certain market conditions. Consequently our findings suggest that investors should view each market independently since different markets experience contrasting levels of predictability, which are related to market conditions.

How predictable are precious metal returns?

This paper provides strong evidence of time-varying return predictability of three precious metals from January 1987 to September 2014. We use three variations of the variance ratio test, the nonlinear Brock, Dechert and Schieinkman test as well as the Hurst exponent to evaluate the time-varying return predictability of precious metals to reduce the risk of spurious results. Our full sample results report mixed findings where some tests indicate significant predictability while some suggest no predictability. However through a time-varying procedure, we show that each precious metal market goes through periods of significant predictability as well as periods of unpredictability. Therefore this finding suggests that return predictability does vary over time and is not a static, all-or-nothing condition and therefore is consistent with the adaptive market hypothesis. We also show that platinum is the most predictable of the three precious metals and silver the least predictable, which may be of great to investors who include precious metals in their investment portfolios.

The Adaptive Market Hypothesis and Stock Return Predictability: Evidence from Major Stock Indices

This study examines the adaptive market hypothesis of the S&P500, FTSE100, NIKKEI225 and EURO STOXX 50 by testing for stock return predictability using daily data from January 1990 to May 2014. We apply three bootstrapped versions of the variance ratio test to the raw stock returns and also whiten the returns through an AR-GARCH process to study the nonlinear predictability after accounting for conditional heteroscedasticity through the BDS test. We evaluate the time-varying return predictability by applying these tests to fixed length moving subsample windows. We also examine whether there is a relationship between the level of predictability in stock returns and market conditions. The results show that there are periods of significant return predictability, but also episodes of no significant predictability in stock returns. We also find that certain market conditions are significantly related to predictability in certain markets but each market interacts differently with the different market conditions. Therefore our findings suggest that significant return predictability in stock markets does vary over time in a manner consistent with the adaptive market hypothesis and that each market adapts differently to certain market conditions.

A Closer Look at Return Predictability of the US Stock Market: Evidence from a Panel Variance Ratio Test

This paper examines return predictability of the U.S. stock market using portfolios sorted by size, book-to-market ratio, and industry. A novel panel variance ratio test is proposed and employed to evaluate time-varying return predictability from 1964 to 2011. It is found that the stock returns have been highly predictable from 1964 to 1996, except for a period around the 1987 stock market crash. After 1997, the stock returns have been unpredictable overall. At a disaggregated level, size and technology have been the major contributors to cross-sectional differences in informational efficiency.

Exchange-rate return predictability and the adaptive markets hypothesis: Evidence from major foreign exchange rates

This study examines return predictability of major foreign exchange rates by testing for martingale difference hypothesis (MDH) using daily and weekly nominal exchange rates from 1975 to 2009. We use three alternative tests for the MDH, which include the wild bootstrap automatic variance ratio test, generalized spectral test, and Dominguez–Lobato consistent tests. We evaluate time-varying return predictability by applying these tests with fixed-length moving sub-sample windows. While exchange rate returns are found to be unpredictable most of times, we do observe a number of episodes of statistically significant return predictability. They are mostly associated with the major events such as coordinated central bank interventions and financial crises. This finding suggests that return predictability of foreign exchange rates occurs from time to time depending on changing market conditions, consistent with the implications of the adaptive markets hypothesis.

Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data

This paper provides strong evidence of time-varying return predictability of the Dow Jones Industrial Average index from 1900 to 2009. Return predictability is found to be driven by changing market conditions, consistent with the implication of the adaptive markets hypothesis. During market crashes, no statistically significant return predictability is observed, but return predictability is associated with a high degree of uncertainty. In times of economic or political crises, stock returns have been highly predictable with a moderate degree of uncertainty in predictability. We find that return predictability has been smaller during economic bubbles than in normal times. We also find evidence that return predictability is associated with stock market volatility and economic fundamentals.

Stock Return Predictability and the Adaptive Markets Hypothesis: Evidence from Century Long U.S. Data

We study return predictability of the Dow Jones Industrial Average indices from 1900 to 2009. We find strong evidence that time-varying return predictability is driven by changing market conditions, consistent with the implications of the adaptive markets hypothesis. During market crashes, no return predictability is observed, but an extreme degree of uncertainty is associated with return predictability. During fundamental economic or political crises, stock returns have been highly predictable with a moderate degree of uncertainty. During economic bubbles, return predictability and its uncertainty have been smaller than normal times.