Real-time macroeconomic data and ex ante stock return predictability

We report results on the ex ante predictability of monthly excess stock returns in Germany using real-time and revised macroeconomic data. Our real-time macroeconomic data cover the period 1994-2005. We report that the contribution of real-time macroeconomic data to ex ante stock return predictability is similar to that of revised macroeconomic data. Moreover, the performance of an investor who had to rely on noisy real-time macroeconomic data would have been similar to the performance of an investor who had access to revised macroeconomic data.     

Predicting bear and bull stock markets with dynamic binary time series models

Despite the voluminous empirical research on the potential predictability of stock returns, much less attention has been paid to the predictability of bear and bull stock markets. In this study, the aim is to predict U.S. bear and bull stock markets with dynamic binary time series models. Based on the analysis of the monthly U.S. data set, bear and bull markets are predictable in and out of sample. In particular, substantial additional predictive power can be obtained by allowing for a dynamic structure in the binary response model. Probability forecasts of the state of the stock market can also be utilized to obtain optimal asset allocation decisions between stocks and bonds. It turns out that the dynamic probit models yield much higher portfolio returns than the buy-and-hold trading strategy in a small-scale market timing experiment.     

Modeling and measuring intraday overreaction of stock prices

We introduce a model for stock prices consisting of a fundamental price process and a news impact curve, which allows for either overreaction, underreaction, or correct response to changes of the fundamental value. We further develop statistics based on OHLC data, which separately measure upside and downside overreaction. The distribution of these statistics under the hypothesis of correct response and fundamental prices following Brownian motions is used to derive tests for upside and downside overreaction. We show that more realistic and frequently used fundamental price processes with correct response leave the distribution of the test statistics widely unaffected or lead to conservative tests. Empirical application to different stock markets provides strong evidence for intraday overreaction, particularly to bad news. The economic significance of the discrimination induced by the proposed statistics is further illustrated by analyzing the performance of a simple buy on bad news strategy.     

Disagreement and asset prices

How do differences of opinion affect asset prices? Do investors earn a risk premium when disagreement arises in the market? Despite their fundamental importance, these questions are among the most controversial issues in finance. In this paper, we use a novel data set that allows us to directly measure the level of disagreement among Wall Street mortgage dealers about prepayment speeds. We examine how disagreement evolves over time and study its effects on expected returns, return volatility, and trading volume in the mortgage-backed security market. We find that increased disagreement is associated with higher expected returns, higher return volatility, and larger trading volume. These results imply that there is a positive risk premium for disagreement in asset prices. We also show that volatility in and of itself does not lead to higher trading volume. Instead, only when disagreement arises in the market is higher uncertainty associated with more trading. Finally, we are able to distinguish empirically between two competing hypotheses regarding how information in markets gets incorporated into asset prices. We find that sophisticated investors appear to update their beliefs through a rational expectations mechanism when disagreement arises.     

Nonlinear mean reversion in stock prices

This paper provides new evidence on the time-series predictability of stock market returns by introducing a test of nonlinear mean reversion. The performance of extreme daily returns is evaluated in terms of their power to predict short- and long-horizon returns on various stock market indices and size portfolios. The paper shows that the speed of mean reversion is significantly higher during the large falls of the market. The parameter estimates indicate a negative and significant relation between the monthly portfolio returns and the extreme daily returns observed over the past one to eight months. Specifically, in a quarter in which the minimum daily return is −2% the expected excess return is 37 basis points higher than in a month in which the minimum return is only −1%. This result holds for the value-weighted and equal-weighted stock market indices and for each of the size decile portfolios. The findings are also robust to different sample periods, different indices, and investment horizons.     

Predicting stock returns: A regime-switching combination approach and economic links

This paper introduces a regime-switching combination approach to predict excess stock returns. The approach explicitly incorporates model uncertainty, regime uncertainty, and parameter uncertainty. The empirical findings reveal that the regime-switching combination forecasts of excess returns deliver consistent out-of-sample forecasting gains relative to the historical average and the Rapach et al. (2010) combination forecasts. The findings also reveal that two regimes are related to the business cycle. Based on the business cycle explanation of regimes, excess returns are found to be more predictable during economic contractions than during expansions. Finally, return forecasts are related to the real economy, thus providing insights on the economic sources of return predictability.     

A momentum trading strategy based on the low frequency component of the exchange rate

In this paper, we develop a momentum trading strategy based on the low frequency trend component of the spot exchange rate. Using kernel regression and the high-pass filter of Hodrick and Prescott [Hodrick, R., Prescott, E., 1997. Post-war US business cycles: An empirical investigation. Journal of Money, Credit and Banking 29, 1–16], we recover the non-linear trend in the monthly exchange rate and use short-term momentum in this to generate buy and sell signals. The low frequency momentum trading strategy offers greater directional accuracy, higher returns and Sharpe ratios, lower maximum drawdown and less frequent trading than traditional moving average rules. Moreover, unlike traditional moving average rules, the performance of the low frequency momentum trading strategy is relatively robust across different time periods. The low frequency momentum trading strategy is also robust to the choice of smoothing parameter (in the case of the HP filter) and the distribution and bandwidth parameter (in the case of kernel regression) over a wide range of values.     

Return sign forecasts based on conditional risk: Evidence from the UK stock market index

Recent theoretical works have found a link between return sign forecastability and conditional volatility. This paper compares the predictive performance of the conditional country risk and the conditional residual risk in forecasting the direction of change in the return on the UK stock market index. The conditional country risk and the conditional residual risk are estimated using the bivariate BEKK-GARCH technique and the direction of change in the UK stock market index is modelled using the binary logit approach. Both the in-sample and the out-of-sample predictions suggest that, as a predictor, the conditional residual risk is superior to the conditional country risk. Our findings support the residual risk model while contradicting the traditional capital asset pricing model (CAPM). Moreover, our tactical asset allocation simulations show that when the conditional residual risk is used in conjunction with multiple-threshold trading strategies to guide the investment decisions, the actively managed portfolio achieves greater returns than the return on a buy and hold portfolio.     

Efficient tests of stock return predictability

Conventional tests of the predictability of stock returns could be invalid, that is reject the null too frequently, when the predictor variable is persistent and its innovations are highly correlated with returns. We develop a pretest to determine whether the conventional t-test leads to invalid inference and an efficient test of predictability that corrects this problem. Although the conventional t-test is invalid for the dividend–price and smoothed earnings–price ratios, our test finds evidence for predictability. We also find evidence for predictability with the short rate and the long-short yield spread, for which the conventional t-test leads to valid inference.     

Overnight information and stochastic volatility: A study of European and US stock exchanges

This paper provides a comprehensive evaluation of the predictive ability of information accumulated during nontrading hours for a set of European and US stock indexes. We introduce a stochastic volatility model, which conditions on lagged overnight information, distinguishes between the nontrading periods of weeknights, weekends, holidays and long weekends, and allows for an asymmetric leverage effect on the impact of overnight news. We implement Bayesian methods for estimation and ranking of the empirical models, and find two key results: (i) there is substantial predictive ability in financial information accumulated during nontrading hours; and (ii) the performance of stochastic volatility models improves considerably by separating the asymmetric impact of positive and negative news made available over weeknights, weekends, holidays and long weekends.     

On the reversal of return and dividend growth predictability: A tale of two periods

A disconcerting, albeit generally accepted, finding is that aggregate stock returns are predictable by dividend yield but dividend growth is unpredictable. I show that part of this lack of dividend growth predictability stems from how dividend growth is constructed. I then show a dramatic reversal of predictability in the 134 years during 1872–2005: stock returns are largely unpredictable in the first seven decades, but become predictable in the postwar period; dividend growth is strongly predictable in the prewar years but this predictability disappears in the postwar years. New evidence on the predictability of long-run returns and dividend growth is also shown.     

On the role of the estimation error in prediction of expected shortfall

In the estimation of risk measures such as Value at Risk and Expected shortfall relatively short estimation windows are typically used rendering the estimation error a possibly non-negligible component. In this paper we build upon previous results for the Value at Risk and discuss how the estimation error comes into play for the Expected Shortfall. We identify two important aspects where it may be of importance. On the one hand there is in the evaluation of predictors of the measure. On the other there is in the interpretation and communication of it. We illustrate magnitudes numerically and emphasize the practical importance of the latter aspect in an empirical application with stock market index data.     

Forecasting EUR–USD implied volatility: The case of intraday data

This study models and forecasts the evolution of intraday implied volatility on an underlying EUR–USD exchange rate for a number of maturities. To our knowledge we are the first to employ high frequency data in this context. This allows the construction of forecasting models that can attempt to exploit intraday seasonalities such as overnight effects. Results show that implied volatility is predictable at shorter horizons, within a given day and across the term structure. Moreover, at the conventional daily frequency, intraday seasonality effects can be used to augment the forecasting power of models. The type of inefficiency revealed suggests potentially profitable trading models.     

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.     

Sentiment and stock prices: The case of aviation disasters

Behavioral economic studies reveal that negative sentiment driven by bad mood and anxiety affects investment decisions and may hence affect asset pricing. In this study we examine the effect of aviation disasters on stock prices. We find evidence of a significant negative event effect with an average market loss of more than $60 billion per aviation disaster, whereas the estimated actual loss is no more than $1 billion. In two days a price reversal occurs. We find the effect to be greater in small and riskier stocks and in firms belonging to less stable industries. This event effect is also accompanied by an increase in the perceived risk: implied volatility increases after aviation disasters without an increase in actual volatility.     

Dynamic factor Value-at-Risk for large heteroskedastic portfolios

We propose a methodology that can efficiently measure the Value-at-Risk (VaR) of large portfolios with time-varying volatility and correlations by bringing together the established historical simulation framework and recent contributions to the dynamic factor models literature. We find that the proposed methodology performs well relative to widely used VaR methodologies, and is a significant improvement from a computational point of view.     

A multi-horizon comparison of density forecasts for the S&P 500 using index returns and option prices

We compare density forecasts of the S&P 500 index from 1991 to 2004, obtained from option prices and daily and 5-min index returns. Risk-neutral densities are given by using option prices to estimate diffusion and jump-diffusion processes which incorporate stochastic volatility. Three transformations are then used to obtain real-world densities. These densities are compared with historical densities defined by ARCH models. For horizons of two and four weeks the best forecasts are obtained from risk-transformations of the risk-neutral densities, while the historical forecasts are superior for the one-day horizon; our ranking criterion is the out-of-sample likelihood of observed index levels. Mixtures of the real-world and historical densities have higher likelihoods than both components for short forecast horizons.     

An advanced perspective on the predictability in hedge fund returns

This paper advances the research on the predictability in hedge fund returns, using a broad set of risk factors within a variety of different prediction models. Accounting for the fact that returns are non-normally distributed, heteroscedastic and time-varying in their exposure to pervasive economic risk factors, we advocate a non-parametric backward elimination regression approach. The interdependencies between the monthly changes of envisaged risk factors and the subsequent hedge fund returns remain remarkably stable in terms of the observed direction of impact. Thus, taking into account the specific characteristics of this asset class, we find strong evidence of its return predictability.     

A careful re-examination of seasonality in international stock markets: Comment on sentiment and stock returns

In questioning Kamstra, Kramer, and Levi’s (2003) finding of an economically and statistically significant seasonal affective disorder (SAD) effect, Kelly and Meschke (2010) make errors of commission and omission. They misrepresent their empirical results, claiming that the SAD effect arises due to a “mechanically induced” effect that is non-existent, labeling the SAD effect a “turn-of-year” effect (when in fact their models and ours separately control for turn-of-year effects), and ignoring coefficient-estimate patterns that strongly support the SAD effect. Our analysis of their data shows, even using their low-power statistical tests, there is significant international evidence supporting the SAD effect. Employing modern, panel/time-series statistical methods strengthens the case dramatically. Additionally, Kelly and Meschke represent the finance, psychology, and medical literatures in misleading ways, describing some findings as opposite to those reported by the researchers themselves, and choosing selective quotes that could easily lead readers to a distorted understanding of these findings.     

Persistence characteristics of the Chinese stock markets

Using advanced signal processing, this paper identifies the lack of ergodicity, stationarity, independence and the degree of persistence of the Shanghai (SHI) stock market and Shenzhen A shares (SZI) and B shares (SZBI), before and after the various deregulations and reregulations. Their lack of stationarity and ergodicity are ascribed to (1) the initial interventions in these stock markets by the Chinese government by imposing various daily price change limits, and (2) the changing trading styles, after the Chinese government left these equity markets to develop by themselves. The SHI, SZI, and SZBI are moderately persistent with Hurst exponents slightly greater than the Fickian 0.5 of the Geometric Brownian Motion. These stock markets were considerably more persistent before the deregulations, but they now behave more like Geometric Brownian Motions, i.e., efficiently. Thus, the Chinese stock markets are gradually and properly being integrated into one Chinese stock market. Our results are consistent with similar empirical findings from Latin American, European, and other Asian emerging financial markets.