The Monday effect revisited: An alternative testing approach

This paper questions traditional approaches for testing the Monday effect of stock returns. We propose an alternative, multiple hypothesis testing approach based on the closure test principle which controls the multiple type I error. We consider the US, the UK and the German stock markets and test Monday related pairwise comparisons of daily expected stock returns, while the probability of committing any type I error is always kept smaller than a prespecified level α, for each combination of true null hypotheses. Overall, the new testing approach supports previous findings of a Monday effect for the 1970s and 1980s, in particular for the US and Germany, while it suggests that the Monday effect has vanished in the 1990s and 2000s in all three markets. The comparison of the closure test procedure, the traditional multiple t-test and the Bonferroni test, a classical multiple test procedure, shows that traditional testing may result in spurious significance while the Bonferroni test may sometimes be too conservative.     

The Specification and Power of the Sign Test in Measuring Security Price Performance: Comments and Analysis

In a 1980 paper, Brown and Warner claim that when applied to stock returns, the nonparametric sign test is misspecified and lacking in power. We show that this claim is incorrect and stems from confounding the mean and median of a distribution and from not correcting for the different natural levels of significance. After restating Brown and Warner's results, we find that in general the sign test appears as powerful and well-specified as the t-test; and when applied to market-adjusted returns and market- and risk-adjusted returns methodologies, the sign test appears more powerful than the t-test.     

Consumer expectations and short-horizon return predictability

Lettau and Ludvigson [Lettau, M., Ludvigson, S, 2001. Consumption, aggregate wealth and expected stock returns. Journal of Finance 56, 815–849] argue that fluctuations from the equilibrium ratio of consumption to wealth (cây) reflect changing expectations of asset returns and document significant short-horizon predictability based on cây. This paper further explores the role of consumer expectations in modeling time variation of expected equity returns by considering two measures of consumer expectations: (i) consumer behavior as reflected in cây, and (ii) a more-direct measure of expectations captured by the Index of Consumer Sentiment (ICS). We report strong regression-based evidence of return predictability based on cây, which remains evident even after accounting for various sources of estimation risk. However, the regression-based evidence of predictability does not necessarily imply that shifts in aggregate consumption and the components of aggregate wealth give rise to economically significant investment signals. The survey-based measure of expectations (ICS) is shown to complement the behavioral measure (cây) but has no apparent stand-alone predictive value in forecasting equity returns.     

Predictable Stock Returns: The Role of Small Sample Bias

Predictive regressions are subject to two small sample biases: the coefficient estimate is biased if the predictor is endogenous, and asymptotic standard errors in the case of overlapping periods are biased downward. Both biases work in the direction of making t-ratios too large so that standard inference may indicate predictability even if none is present. Using annual returns since 1872 and monthly returns since 1927 we estimate empirical distributions by randomizing residuals in the VAR representation of the variables. The estimated biases are large enough to affect inference in practice, and should be accounted for when studying predictability.     

When does the dividend–price ratio predict stock returns?

If the dividend–price ratio becomes I(1) while stock returns are I(0), the unbalanced predictive regression makes the predictability test more likely to indicate that the dividend–price ratio has no predictive power. This might explain why the dividend–price ratio evidences strong predictive power during one period, while it exhibits weak or no predictive power at other times. Using international data, this paper demonstrates that the dividend–price ratio generally has predictive power for stock returns when both are I(0). However, this paper also shows that the dividend–price ratio loses its predictive power when it becomes I(1). The results are shown to be robust across countries.     

Personal taxes and the time variation of stock returns – evidence from the UK

A potential explanation for the time-series predictability of market returns with dividend yields is the differential tax treatment of capital gains and dividends. This article investigates to what extent the predictability of long-horizon returns can be explained by this tax effect. Assuming that after-tax expected returns are constant, we test the derived relationships between pretax returns and lagged dividend yields with UK data. We also compare the predictability of before-tax long-horizon returns with that of after-tax returns. The results indicate that the tax treatment of dividends does not significantly contribute towards the predictability of stock returns.     

Predictability of currency carry trades and asset pricing implications

This paper studies the time series predictability of currency carry trades, constructed by selecting currencies to be bought or sold against the US dollar, based on forward discounts. Changes in a commodity index, currency volatility and, to a lesser extent, a measure of liquidity predict in-sample the payoffs of dynamically re-balanced carry trades, as evidenced by individual and joint p-values in monthly predictive regressions at horizons up to six months. Predictability is further supported through out-of-sample metrics, and a predictability-based decision rule produces sizable improvements in the Sharpe ratios and skewness profile of carry trade payoffs. Our evidence also indicates that predictability can be traced to the long legs of the carry trades and their currency components. We test the theoretical restrictions that an asset pricing model, with average currency returns and the mimicking portfolio for the innovations in currency volatility as risk factors, imposes on the coefficients in predictive regressions.     

Importance of skewness in decision making: Evidence from the Indian stock exchange

In this paper our goal is to examine the importance of skewness in decision making, in particular on investor utility. We use time-series daily data on sectoral stock returns on the Indian stock exchange. We test for sectoral stock return predictability using commonly used financial ratios, namely, the price-to-book, dividend yield and price-earnings. We find strong evidence of predictability. Using this evidence of predictability, we forecast sectoral stock returns for each of the sectors in our sample, allowing us to devise trading strategies that account for skewness of returns. We discover evidence that accounting for skewness leads not only to higher utility compared to a model that ignores skewness, but utility is sector-dependent.     

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.     

On the distribution of government bond returns: evidence from the EMU

This paper assesses the statistical distribution of daily EMU bond returns for the period 1999–2012. The normality assumption is tested and clearly rejected for all European countries and maturities. Although skewness plays a minor role in this departure from normality, it is mainly due to the excess kurtosis of bond returns. Therefore, we test the Student’s t, skewed Student’s t, and stable distribution that exhibit this feature. The financial crisis leads to a structural break in the time series. We account for this and retest the alternative distributions. A value-at-risk application underlines the importance of our findings for investors. In sum, excess kurtosis in bond returns is essential for risk management, and the stable distribution captures this feature best.     

Stock price synchronicity and analyst coverage in emerging markets

This paper examines the relation between the stock price synchronicity and analyst activity in emerging markets. Contrary to the conventional wisdom that security analysts specialize in the production of firm-specific information, we find that securities which are covered by more analysts incorporate greater (lesser) market-wide (firm-specific) information. Using the R² statistics of the market model as a measure of synchronicity of stock price movement, we find that greater analyst coverage increases stock price synchronicity. Furthermore, after controlling for the influence of firm size on the lead–lag relation, we find that the returns of high analyst-following portfolio lead returns of low analyst-following portfolio more than vice versa. We also find that the aggregate change in the earnings forecasts in a high analyst-following portfolio affects the aggregate returns of the portfolio itself as well as those of the low analyst-following portfolio, whereas the aggregate change in the earnings forecasts of the low analyst-following portfolio have no predictive ability. Finally, when the forecast dispersion is high, the effect of analyst coverage on stock price synchronicity is reduced.     

Tests of the multiperiod two-parameter model

Although investors face multiperiod decision problems, there are conditions under which the results of the one-period two-parameter model apply period by period. In addition to the assumptions made in the development of the two-parameter model itself (a perfect capital market, investor risk aversion, and normal distributions of one-period portfolio returns), the critical assumption in a multiperiod context is that, for any t, returns on portfolio assets from t?1 to t are independent of stochastic elements of the state-of-the-world at time t that affect investor tastes for given levels of wealth to be obtained at t.One such element of the state-of-the-world is the nature of investment opportunities to be available at t. For example, if the level of expected returns on investment portfolios to be available at time t is uncertain at time t?1, and if the returns from t?1 to t on some investment assets are more strongly related to the level of expected returns at t than returns on other assets, then the former assets are better vehicles for hedging against the level of expected returns at t. This can affect the demands for assets and their prices in such a way that the simple results of the one-period two-parameter model do not hold.The empirical tests of this paper reveal no evidence of measurable relationships between the returns on portfolio assets from t?1 to t and the level of expected returns to be available at t. Indeed, in our opinion there is no reliable evidence that the level of expected returns changed during the 1953–1972 period.     

Does country risks predict stock returns and volatility? Evidence from a nonparametric approach

We use the k-th order nonparametric causality test at monthly frequency over the period of 1984:1–2015:12 to analyze whether aggregate country risk, and its components (economic, financial and political) can predict movements in stock returns and volatility of eighty-three developed and developing economies. The nonparametric approach controls for the existing misspecification of a linear framework of causality, and hence, the weak evidence of causality obtained under the standard Granger tests cannot be relied upon. When we apply the nonparametric test, we find that, while there is no evidence of predictability of squared stock returns barring one case, at times, there are nearly 50 percent of the countries where the aggregate risks and its components tend to predict stock returns and realized volatility.     

Stock return forecasting: Some new evidence

This paper makes three contributions to the literature on forecasting stock returns. First, unlike the extant literature on oil price and stock returns, we focus on out-of-sample forecasting of returns. We show that the ability of the oil price to forecast stock returns depends not only on the data frequency used but also on the estimator. Second, out-of-sample forecasting of returns is sector-dependent, suggesting that oil price is relatively more important for some sectors than others. Third, we examine the determinants of out-of-sample predictability for each sector using industry characteristics and find strong evidence that return predictability has links to certain industry characteristics, such as book-to-market ratio, dividend yield, size, price earnings ratio, and trading volume.     

Accurate minimum capital risk requirements: A comparison of several approaches

In this paper we estimate, for several investment horizons, minimum capital risk requirements for short and long positions, using the unconditional distribution of three daily indexes futures returns and a set of short and long memory stochastic volatility and GARCH-type models. We consider the possibility that errors follow a t-Student distribution in order to capture the kurtosis of the returns’ series. The results suggest that accurate modelling of extreme observations obtained for long and short trading investment positions is possible with an autoregressive stochastic volatility model. Moreover, modelling futures returns with a long memory stochastic volatility model produces, in general, excessive volatility persistence, and consequently, leads to large minimum capital risk requirement estimates. Finally, the models’ predictive ability is assessed with the help of out-of-sample conditional tests.     

Predicting dividends in log-linear present value models

In a present value model, high dividend yields imply that either future dividend growth must be low, or future discount rates must be high, or both. While previous studies have largely focused on the predictability of future returns from dividend yields, dividend yields also strongly predict future dividends, and the predictability of dividend growth is much stronger than the predictability of returns at a one-year horizon. Inference from annual regressions over the 1927–2000 sample imputes over 85% of the variation of log dividend yields to variations in dividend growth. Point estimates of the predictability of both dividend growth and discount rates are stronger when the 1990–2000 decade is omitted.     

Predicting firm level stock returns: Implications for asset pricing and economic links

We examine the predictive ability of stock price ratios, stock return dispersion and distribution measures for firm level returns. Analysis typically focusses on market level returns, however, for the underlying asset pricing model to hold, firm-level predictability should be present. Additionally, we examine the economic content of predictability by considering whether the predictive coefficient has the theoretically correct sign and whether it is related to future output growth. While stock returns reflect investor expectations regarding future economic conditions, they are often too noisy to act as predictor. We use the time-varying predictive coefficient as it reflects investor confidence in the predictive relation. Results suggest that a subset of stock price ratios have predictive power for individual firm stock returns, exhibit the correct coefficient sign and has predictive power for output growth. Each of these ratios has a measure of fundamentals divided by the stock price and has a positive relation with stock returns and output growth. This implies that as investors expect future economic conditions to improve and earnings and dividends to rise, so expected stock returns will increase. This supports the cash flow channel as the avenue through which stock return predictability arises.     

Oil price dynamics,macro-finance interactions and the role of financial speculation

What is the role of financial speculation in determining the real oil price? We find that while macroeconomic shocks have been the main real oil price upward driver since mid-1980s, financial shocks have sizably contributed since early 2000s as well, and at a much larger extent since mid-2000s. Even though financial shocks contribute 44% out of the 65% real oil price increase over the period 2004–2010, the third oil price shock is a macro-finance episode: macroeconomic shocks actually largely account for the 2007–2008 oil price swing. While we then find support to the demand side view of real oil price determination, we however also find a much larger role for financial shocks than previously noted in the literature.     

Predicting the equity market with option-implied variables

We comprehensively analyze the predictive power of several option-implied variables for monthly S&P 500 excess returns and realized variance. The correlation risk premium (CRP) and the variance risk premium (VRP) emerge as strong predictors of both excess returns and realized variance. This is true both in- and out-of-sample. Our results also reveal that statistical evidence of predictability does not necessarily lead to economic gains. However, a timing strategy based on the CRP leads to utility gains of more than 5.03% per annum. Forecast combinations provide stable forecasts for both excess returns and realized variance, and add economic value.     

Inference in asset pricing models with a low-variance factor

This paper concerns with the effects of including a low-variance factor in an asset pricing model. When a low-variance factor is present, the commonly applied Fama–MacBeth two-pass regression procedure is very likely to yield misleading results. Local asymptotic analysis and simulation evidence indicate that the risk premiums corresponding to all factors are very likely to be unreliably estimated. Moreover, t- and F-statistics are less likely to detect whether the risk premiums are significantly different from zero. We recommend Kleibergen’s (2009)FAR statistic when there is a low-variance factor included in an asset pricing model.