Why long horizons? A study of power against persistent alternatives

This paper studies tests of predictability in regressions with a given AR(1) regressor and an asset return dependent variable measured over a short or long horizon. The paper shows that when there is a persistent predictable component in the return, an increase in the horizon may increase the R² statistic of the regression and the approximate slope of a predictability test. Monte Carlo experiments show that long-horizon regression tests have serious size distortions when asymptotic critical values are used, but some versions of such tests have power advantages remaining after size is corrected.     

Measuring the predictable variation in stock and bond returns

Recent studies show that when a regression model is used toforecast stock and bond returns, the sample R² increases dramaticallywith the length of the return horizon. These studies argue,therefore, that long-horizon returns are highly predictable.This article presents evidence that suggests otherwise. Long-horizonregressions can easily yield large values of the sample R²,even if the populations R² is smaller or zero. Moreover, long-horizonregressions with a small or zero population R² can produce t-ratiosthat might be interpreted as evidence of strong predictability.In general, the analysis provides little support for the viewthat long-horizon returns are highly predictable.     

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.     

The SIML Estimation of Integrated Covariance and Hedging Coefficient Under Round-off Errors,Micro-market Price Adjustments and Random Sampling

For estimating the integrated volatility and covariance by using high frequency data, Kunitomo and Sato (Math Comput Simul 81:1272–1289, 2011; N Am J Econ Finance 26:289–309, 2013) have proposed the separating information maximum likelihood (SIML) method when there are micro-market noises. The SIML estimator has reasonable finite sample properties and asymptotic properties when the sample size is large when the hidden efficient price process follows a Brownian semi-martingale. We shall show that the SIML estimation is useful for estimating the integrated covariance and hedging coefficient when we have round-off errors, micro-market price adjustments and noises, and when the high-frequency data are randomly sampled. The SIML estimation is consistent, asymptotically normal in the stable convergence sense under a set of reasonable assumptions and it has reasonable finite sample properties with these effects.     

Testing for Serial Correlation in the Presence of Stochastic Volatility

This paper develops a regression-based testing procedure for serial correlation in the presence of stochastic volatility. The asymptotic distribution of the test is derived, and the finite sample properties are investigated. Monte Carlo results shows that the test is reliable in terms of both size and power performances, when the underlying process is a log-linear stochastic volatility. Moreover, the test is superior to Woolridge's (1991) robust LM tests in terms of size in finite sample. Serial correlation tests were conducted for nominal returns of ten exchange rates, and indicated that there is a strong evidence of serial correlation for Yen/Dollar exchange rates.     

The predictability of aggregate returns on commodity futures

This paper provides evidence that aggregate returns on commodity futures (without the returns on collateral) are predictable, both in-sample and out-of-sample, by various lagged variables from the stock market, bond market, macroeconomics, and the commodity market. Out of the 32 candidate predictors we consider, we find that investor sentiment is the best in-sample predictor of short-horizon returns, whereas the level and slope of the yield curve have much in-sample predictive power for long-horizon returns. We find that it is possible to forecast aggregate returns on commodity futures out-of-sample through several combination forecasts (the out-of-sample return forecasting R² is up to 1.65% at the monthly frequency).     

Models of asset returns: changes of pattern from high to low event frequency

In this paper we have analysed asset returns of the New York Stock Exchange and the Helsinki Stock Exchange using various time-independent models such as normal, general stable, truncated Lévy, mixed diffusion-jump, compound normal, Student t distribution and power exponential distribution and the time-dependent GARCH(1, 1) model with Gaussian and Student t distributed innovations. In order to study changes of pattern at different event horizons, as well as changes of pattern over time for a given event horizon, we have analysed high-frequency or short-horizon intraday returns up from 20 s intervals to a full trading day, medium-frequency or medium-horizon daily returns and low-frequency or long-horizon returns with holding period up to 30 days. As for changes of pattern over time, we found that for medium-frequency returns there are relatively long periods of business-as-usual when the return-generating process is well-described by a normal distribution. We also found periods of ferment, when the volatility grows and complex time dependences tend to emerge, but the known time dependences cannot explain the variability of the distribution. Such changes of pattern are also observed for high-frequency or short-horizon returns, with the exception that the return-generating process never becomes normal. It also turned out that the time dependence of the distribution shape is far more prominent at high frequencies or short horizons than the time dependence of the variance. For long-horizon or low-frequency returns, the distribution is found to converge towards a normal distribution with the time dependences vanishing after a few days.     

Mean reversion in annual earnings and its implications for security valuation

This article documents the long-horizon mean reverting character of annual earnings and tests the implications of such mean reversion for security valuation. First, both theory-based and nonparametric measures of earnings persistence decrease as the estimation order increases, revealing 40 percent less long-horizon persistence than expected under the commonly used random walk model. Second, the return responses to the earnings shocks are more closely related across firms to the higher-order measures of persistence that reflect significant long-horizon mean reversion. Third, the persistence measure derived from classical valuation theory outperforms the generic measure in explaining the return responses. Taken as a whole, these results provide evidence for significant mean reversion in the higher-order properties of earnings and for the stock market incorporating these properties in a manner consistent with classical valuation theory.     

Non-linear long horizon returns predictability: evidence from six south-east Asian markets

Whilst the existence of long-horizon returns predictability has been a recurrent theme in empirical finance research, extant work has focused almost exclusively on the US, and more recent work has cast doubt over the validity of such potential predictability due to non-stationarity and serial correlation in the data. The present paper examines long-horizon returns predictability in six South-East Asian markets in the context of unit root tests conducted under corrections for serial correlation and heteroscedasticity. The analysis conducted also extends the investigation of long-horizon predictability to the non-linear setting, and examines whether any detected non-linear predictability is consistent with behavioural approaches to asset pricing which emphasise the role of noise traders. The results obtained suggest the following conclusions. First, long-horizon predictability is present in each of the six South-East Asian markets considered. Second, whilst forecast power increases with horizon, for the majority of series it is maximised for forecast horizons of between 12 and 48 months. Third, non-linear predictability is reported for all series, suggesting that positive and negative values of the (demeaned) dividend yield impart different levels of predictability for returns at different forecast horizons. However, there is no consistency in the pattern of non-linearity reported, and whilst the observed patterns are consistent with noise traders models for some of the markets considered, this is not true of all the markets considered. Nevertheless, the non-linear dynamics detected are suggestive of potential market inefficiencies in all six cases.     

Executive option exercises and financial misreporting

Several recent papers document that the magnitude of potential gains from stock-based compensation is positively related to the likelihood of misreporting. In a sample of firms that announce restatements of their financial statements from 1997 to 2002, we examine whether managers realize these potential gains occurring from their accounting choices. After controlling for diversification needs and stock price impact, we find no significant evidence of higher option exercises by executives in the misreported years. However, for firms that are more likely to have made deliberate aggressive accounting choices, we find significant evidence of higher option exercises. For these firms, option exercises are higher by 20–60% in comparison to industry and size matched nonrestating firms. Options exercises by executives are also increasing in the magnitude of the restatement as captured by the effect of the restatement on net income. These higher option exercises tend to be more pervasive and are not just confined to the CEO and CFO of the firm.     

Time-varying risk premia and the cost of capital: An alternative implication of the Q theory of investment

Evidence suggests that expected excess stock market returns vary over time, and that this variation is much larger than that of expected real interest rates. It follows that a large fraction of the movement in the cost of capital in standard investment models must be attributable to movements in equity risk-premia. In this paper we emphasize that such movements in equity risk premia should have implications not merely for investment today, but also for future investment over long horizons. In this case, predictive variables for excess stock returns over long-horizons are also likely to forecast long-horizon fluctuations in the growth of marginal Q, and therefore investment. We test this implication directly by performing long-horizon forecasting regressions of aggregate investment growth using a variety of predictive variables shown elsewhere to have forecasting power for excess stock market returns.     

Cross-sectional tests of the CAPM and Fama–French three-factor model

Grouping does not produce a wide range of betas. Consequently, cross-sectional tests of the CAPM are bound to lack power. This paper provides a simple way to alleviate the problem by repackaging the data with zero-weight portfolios. When the CAPM is true and the data are repackaged, simulation shows that the average values of the intercept and slope converge to their true values more rapidly and there are striking increases in R² and the power of the tests. Empirical results are dramatically different in datasets with and without the zero-weight portfolios.     

Testing for explosive bubbles in the presence of autocorrelated innovations

We analyze an empirically important issue with recursive right-tailed unit root tests for bubbles in asset prices. First, we show that serially correlated innovations, which is a feature that is present in most financial series used to test for bubbles, can lead to severe size distortions when using either fixed or automatic (based on information criteria) lag-length selection in the auxiliary regressions underlying the test. Second, we propose a sieve-bootstrap version of these tests and show that this results in tests which control size well across a number of simulation designs both with and without highly autocorrelated innovations. We also find that these improvements in size come at a relatively low cost for the power of the tests. Finally, we apply the bootstrap tests on the housing market of OECD countries, and generally find much weaker evidence of housing bubbles compared to existing evidence.     

EXCESS RETURNS AND RISK AT THE LONG END OF THE TREASURY MARKET: AN EGARCH-M APPROACH

In this paper we model weekly excess returns of ten-year Treasury notes and long-term Treasury bonds from 1968 through 1993 using an exponential generalized autoregressive conditional heteroskedasticity in mean (EGARCH-M) approach. The results indicate the presence of conditional heteroskedasticity and a strong tendency for the ex-ante volatility of excess returns to increase more following negative excess return innovations compared with positive innovations of equal magnitude. In addition, increases in ex-ante volatility are associated in some subperiods with rising excess returns on longer-term instruments, although the slope of the yield curve and lagged excess returns generally remain significant predictors of excess returns.     

The Pricing of Oil and Gas: Some Further Results

The Hotelling Valuation Principle (HVP) implies that the unit value of an exhaustible natural resource can be written as a function of its current price, net of extraction costs; other variables such as interest rates have no additional explanatory power. The results of earlier tests using data from 1979–1981 strongly support the HVP. This paper presents a series of follow-up tests using time-series cross-section data covering the period August 1981 to December 1983. Because the variance of petroleum prices in this period was substantially less than in the earlier period, the follow-up sample proved generally noninformative. The sample also contains some observations on oil and gas royalty trusts. Tests of the HVP using these trust data yielded generally satisfactory results, although—given the limited sample size—the results must be viewed with caution.     

Credit policy and its heterogeneous effects on green innovations

We use the issuance of a new credit policy in China—the Green Credit Guideline (Guideline)—as a unique event and investigate its heterogenous effects on various green technology innovations for different firms in the world’s largest emerging economy. We find that all firms experience a significant increase in green technology innovations after the issuance of the Guideline. Companies with more considerable legitimacy deficiency in environmental compliance are more responsive to the Guideline. The larger increase in green technology innovations for companies with more legitimacy deficiency in environmental compliance is mainly driven by their higher productivity in applicational technology innovations after the Guideline was issued. All firms experience significant improvements in the effectiveness of investments, which is an important channel for increasing green technology innovations. Local legal system development and law enforcement efforts, firms’ political connections, and state ownership are essential mediating factors for green technology innovations, especially for firms with more legitimacy deficiency in environmental compliance. The Guideline positively affects both firm value and reputation, indicating that the new credit policy promotes impact investing.     

When does the tone of earnings press releases matter?

The tone of a firm's financial disclosure is increasingly used as a variable in panel data regressions to predict future performance and explain investors' reaction at earnings announcement. We investigate when tone is informative, and argue that the informativeness of tone increases with the information asymmetry between firms and investors. Using a sample of over 50,000 earnings press releases of about 1800 U.S. public firms between 2004 and 2015, we find that firm growth, size, age, complexity and forecast inaccuracy are key drivers of tone informativeness. The effect is economically significant, since, compared to the reference case of a transparent firm, we find that the slope coefficient of tone doubles or even quadruples in panel data regressions when the firm operates in an environment with high information asymmetry.     

Long-horizon event studies and event firm portfolio weights: Evidence from U.K. rights issues re-visited

Empirical results, in long-horizon event studies, are sensitive to whether equal- or value-weighting schemes are used to form event firm portfolios. In this paper we propose, as a first step, an evaluation of the economic value to investing in an equal-weighted and a value-weighted event firm portfolio prior to the event study. Using tests for mean-variance spanning we find in our data a significant improvement in an investor's investment opportunity set to investing in an equal-weighted portfolio. We then re-visit the long-run post-offer performance for rights issuers in the U.K. where we find, as in other studies, differences in both the magnitude and statistical significance of abnormal performance for value-weighted and equal-weighted event firm portfolios. We then use the results from our first step to provide an economic rationale for interpreting our results. A general conclusion we draw is that it may be useful to first ascertain, in long-horizon event studies, the economic value of portfolio weighting schemes as this can then provide some guidance to the use of a specific portfolio weighting scheme and thereby to interpretation of often conflicting results.     

Log-normal continuous cascade model of asset returns: aggregation properties and estimation

Multifractal models and random cascades have been successfully used to model asset returns. In particular, the log-normal continuous cascade is a parsimonious model that has proven to reproduce most observed stylized facts. In this paper, several statistical issues related to this model are studied. We first present a quick, but extensive, review of its main properties and show that most of these properties can be studied analytically. We then develop an approximation theory in the limit of small intermittency λ²???1, i.e. when the degree of multifractality is small. This allows us to prove that the probability distributions associated with these processes possess some very simple aggregation properties across time scales. Such a control of the process properties at different time scales allows us to address the problem of parameter estimation. We show that one has to distinguish two different asymptotic regimes: the first, referred to as the ‘low-frequency asymptotics’, corresponds to taking a sample whose overall size increases, whereas the second, referred to as the ‘high-frequency asymptotics’, corresponds to sampling the process at an increasing sampling rate. The first case leads to convergent estimators, whereas in the high-frequency asymptotics, the situation is much more intricate: only the intermittency coefficient λ² can be estimated using a consistent estimator. However, we show that, in practical situations, one can detect the nature of the asymptotic regime (low frequency versus high frequency) and consequently decide whether the estimations of the other parameters are reliable or not. We apply our results to equity market (individual stocks and indices) daily return series and illustrate a possible application to the prediction of volatility and conditional value at risk.     

The Role of Financial Market Structure and the Trade Elasticity for Monetary Policy in Open Economies

Imperfect international risk sharing and exchange rate volatility matter for how monetary policy should optimally be conducted in an open economy through affecting policymakers’ terms of trade considerations. I study these motives for a classical and long‐standing question in international monetary economics: the size of potential gains from international policy coordination. In a relatively standard model I allow for various degrees of risk sharing by considering different assumptions on international financial markets, and a large region for the crucial parameter of the trade elasticity. When incomplete markets give rise to high volatility of international prices and poor risk sharing—such as in Corsetti, Dedola, and Leduc (2008) —gains from policy coordination are an order of magnitude larger than previous studies, working under the assumptions of complete markets, suggest.