Refining the bootstrap method of stochastic dominance analysis: The case of the January effect

This study investigates the effect of sample size and population distribution on the bootstrap estimated sampling distributions for stochastic dominance (SD) test statistics. Bootstrap critical values for Whitmore's (1978) second- and third-degree stochastic dominance test statistics are found to vary with both data sample size and variance of the population distribution. The results indicate the parametric nature of the statistics and suggest that the bootstrap method should be used to estimate a sampling distribution each time a new data sample is drawn. As an application of the bootstrap method, the January small firm effect is examined. The results conflict with the SD results of others, and indicate that not all investors would prefer to hold just a portfolio of small capitalization firms in January.     

Retail investors exonerated: the case of the January effect

We dispel the belief that the January effect is due to retail investor trading. Previous studies suggest that retail investors, affected by behavioural biases and disproportionally invested in small capitalization stocks, are the source of the January effect. Furthermore, the literature regards retail investor trading and the tax‐loss selling hypothesis as essentially the same explanation. We separate tax implications and market capitalization to show that retail traders are not the cause of the January effect. Our study is an important direct test of whether retail trading causes market anomalies.     

The other January effect: International, style, and subperiod evidence

We report international, style, and subperiod evidence for the other January effect (OJE) documented in Cooper et al. [2006. The other January effect. Journal of Financial Economics 82, 315–341]. When examining the OJE in 22 countries starting as early as 1801, we find that the spread between 11-month returns following positive and negative Januarys does tend to be positive. However, the spreads are rarely statistically significant and the returns of other calendar months exhibit similar subsequent 11-month return spreads. Further, the international OJE spreads and the OJE spreads in disaggregate U.S.-style portfolios are more related to the U.S. market-level January return, rather than the respective country-specific or portfolio-specific January return. Finally, the OJE is weaker over the 1975–2006 post-discovery period than over the 1940–1974 pre-discovery period. Our evidence indicates that the OJE is primarily a U.S. market-level-based phenomenon that has diminished over time, which suggests a ‘temporary anomaly’ interpretation.     

The Other January Effect: international evidence

This paper investigates the predictive power of stock market returns in January for the subsequent 11 months’ returns across 19 countries, thereby contributing to the literature on stock market seasonalities. Only 2 out of 19 countries’ stock markets exhibit a robust Other January Effect. In the light of this evidence, we conclude that the Other January Effect is not an international phenomenon.     

Institutional Investors, Analyst Following, and the January Anomaly

Average stock returns for small, low stock price firms are higher in January than for the rest of the year. Two explanations have received a great deal of attention: tax-loss selling and gamesmanship. This paper documents that seasonality in returns is not a phenomenon observed only for small firms' stock or those with low prices. Strong seasonality in excess returns is reported for a sample of widely followed firms. Sample firms have unusually low excess returns in January and returns adjust upward over the year. These results are consistent with the gamesmanship hypothesis, but not the tax-loss-selling hypothesis.     

Institutional Investors, Analyst Following, and the January Anomaly

Average stock returns for small, low stock price firms are higher in January than for the rest of the year. Two explanations have received a great deal of attention: tax-loss selling and gamesmanship. This paper documents that seasonality in returns is not a phenomenon observed only for small firms' stock or those with low prices. Strong seasonality in excess returns is reported for a sample of widely followed firms. Sample firms have unusually low excess returns in January and returns adjust upward over the year. These results are consistent with the gamesmanship hypothesis, but not the tax-loss-selling hypothesis.     

On Unmodeled Breaks in the Turn of the Year,Turn of the Month,and January Effects

We produce convincing new evidence that the turn of the year (TOY), turn of the month (TOM), and January effects are critically dependent on the sample period over which they are estimated. The TOY effect is significant in the value‐weight portfolio from 1962 to 1997. It becomes insignificant in the medium‐size portfolio after 1994 and in the equal‐weight and low‐size portfolios after 1997. The TOM effect becomes insignificant in the value‐weight and high‐size portfolios after 1978, in the equal‐weight and medium‐size portfolios after 1997, and in the low‐size portfolio after 1998. January effects are significant in some subperiods but not others.     

The pricing of firms with expected losses/profits: The role of January

We examine the role of January in the relation between expected losses/profits and future stock returns. We predict and find that the relation between expected losses/profits and future returns reverses from the usual positive relation in non‐January months to a negative one in January. The reverse January relation is consistent across sample years, is observed in the United States and international markets, and is incremental to other variables associated with January returns. At least part of the reverse January relation is explained by tax‐loss selling. Further analysis shows that the reverse January relation results in a temporary price drift away from fundamental value. In other words, we find that abnormal positive (negative) future returns do not always indicate past under(over)valuation. Overall, our results illustrate the importance of controlling for the effect of January when examining how investors price expected losses/profits.     

January Anomalies: Implications for the Market's Incorporation of News

We examine the responses of five interest rate instruments to the release of macroeconomic announcements to determine whether January returns behave differently from returns in other months when information is released. Our results suggest that in all instruments, returns in January are less sensitive to macroeconomic news, compared with other months. This is true even though the number and type of announcements are much the same in January as in other months. The instruments examined feature important differences in liquidity, maturity, credit risk, and other institutional differences, suggesting that our evidence is robust.     

The Inverted-U hypothesis for the effect of uncertainty on investment: Evidence from UK firms

Abstract

This paper offers the first attempt to test the inverted-U hypothesis for the effect of uncertainty on investment, implied by a number of recent theoretical studies, using a panel of UK firms. It is found that the effect of uncertainty on corporate investment is indeed approximated by an inverted-U shaped relationship: at low levels of uncertainty the effect is positive, but it becomes negative at high levels of uncertainty. This result represents the first empirical verification of the hypothesis with respect to UK firms.     

Assessing the relevance of an information source to trading from an adaptive-markets hypothesis perspective

I propose a framework motivated by the Adaptive Markets Hypothesis (AMH) to analyze the relevance of a specific information source for the trading of a given security. To illustrate the applicability and advantages of this methodology, I explore the extent to which the financial statement (FS) is relevant for Credit Default Swap (CDS) trading. Specifically, I adopt a Bayesian Model Averaging approach to examine properties of the accounting metrics that enter the implied trading heuristics of the market participants. Hypothesis-testing is conducted on various horizons around the announcement dates of corporate results. The diversity of trading rules and the shift in the heuristics mix that occurred after 2008 support the AMH perspective. Overall, results show that there is a significant component of profit-motivated trading in the CDS market that relies on financial statement information, even after controlling for information transmission from alternative trading forums. Out of sample trading strategies confirm the robustness the main findings.     

Firm growth and efficiency in the banking industry: A new test of the efficient structure hypothesis

In this paper we propose a new test of the efficient structure (ES) hypothesis, which predicts that efficient firms come out ahead in competition and grow as a result. Our test has significant advantages over existing ones, because it is more direct, and can jointly test the so-called quiet-life hypothesis, which predicts that in a concentrated market firms do not minimize costs. We then apply this test to large banks in Japan. Consistent with the ES hypothesis, we find that more efficient banks become larger. We also find that market concentration reduces banks’ efficiency, which supports the quiet-life hypothesis. These findings imply that there is an intriguing growth–efficiency dynamic throughout banks’ life cycle, although our findings also suggest that the ES hypothesis dominates the quiet-life hypothesis in terms of economic impact.     

Sticky-price models and the natural rate hypothesis

A major criticism of standard specifications of price adjustment in models for monetary policy analysis is that they violate the natural rate hypothesis by allowing output to differ from potential in steady state. In this paper we estimate a dynamic optimizing business cycle model whose price-setting behavior satisfies the natural rate hypothesis. The price-adjustment specifications we consider are the sticky-information specification of Mankiw and Reis (Sticky information versus sticky prices: a proposal to replace the new Keynesian Phillips curve. Quarterly Journal of Economics 117, 1295-1328) and the indexed contracts of Christiano et al. (Nominal rigidities and the dynamic effects of a shock to monetary policy. Journal of Political Economy 113, 1-45). Our empirical estimates of the real side of the economy are similar whichever price adjustment specification is chosen. Consequently, the alternative model specifications deliver similar estimates of the U.S. output gap series, but the empirical behavior of the gap series differs substantially from standard gap estimates.     

Mean-variance cointegration and the expectations hypothesis

The present paper sheds further light on a well-known (alleged) violation of the expectations hypothesis of the term structure (EHT): the frequent finding of unit roots in interest rate spreads. We show that the EHT implies (i) that the nonstationarity stems from the holding premium, which is hence (ii) cointegrated with the spread. In a stochastic discount factor framework, we model the premium as being driven by the integrated variance of excess returns. Introducing the concept of mean-variance cointegration, we actually find cointegration relations between the conditional first and second moment of US bond data.     

The effect of earnings surprises on information asymmetry

We examine the effect of earnings surprises on changes in information asymmetry. We hypothesize and find that asymmetry is lower (higher) in the quarter following positive (negative) earnings surprises compared to firms that meet the consensus analyst earnings forecast. The relations between earnings surprises and information asymmetry are stronger when the surprises are more likely to capture investors’ attention. Examining the source of these changes, we show that decreased information search activities is the most important factor for asymmetry declining after positive surprises; for negative surprises, decreased uninformed trading plays a dominant role increasing asymmetry.     

Risk and the January effect

We use a time-series GARCH framework with the conditional variance/covariance as proxies for systematic risk to reexamine the proposition by Rozeff and Kinney (1976) and Rogalski and Tinic (1986) that the January effect may be a phenomenon of risk compensation in the month. We find no clear evidence that either conditional volatility or unconditional volatility in January is predominantly higher across the sampling years. Hence, against the proposition, the January effect is not due to risk per se. Rather, we find strong evidence that the January effect is due to higher compensation for risk in the month. This may be possible if investors have an increasing RRA utility function. Although many studies find that volatility tends to be higher in January, we find it to be period-specific and mostly in value-weighted return series, but not in equal-weighted return series. This is true both for the unconditional and conditional return volatility.     

Indirect tests of the Haugen-Lakonishok small-firm/January effect hypotheses: window dressing versus performance hedging

Equity mutual fund data from 1976–1993 is used to test hypotheses that distinguish window dressing from performance hedging. No significant difference is found pre/post 1983 in the number of funds choosing non-December fiscal year ends or in the percentage of dollars invested when comparing December/non-December fiscal year ends. Significant differences are found in both January returns for mutual funds with December/non-December fiscal year ends and in one month returns for funds with/without a fiscal year end in the previous month. Therefore, if the small-firm/January effect is portfolio manager related, performance hedging, not window dressing, is the more probable source for the “excess” returns.     

Time-varying term premium in T-bill futures rate and the expectations hypothesis

This paper examines time-varying term premium in the T-bill futures rate to determine its significance for the expectations hypothesis (EH). Similar to previous studies on the T-bill forward rates, our data reject the joint hypothesis of the EH and the rational expectations hypothesis (RE). Under the assumption of zero rational expectational error, we find a substantial variation of term premium in the futures rate over time. Furthermore, the lower bound of the expected term premium variance is significantly positive when the rational expectational error is allowed to be nonzero. These findings are inconsistent with the EH. In addition, a relatively high ratio of the lower bound of the expected term premium variance to the prediction error variance implies that the poor predictive power of the futures rate should not be attributed mainly to the market's rational expectational errors.