Ken Johnston

The January Effect is not Driven by Tax Loss Selling

ositive abnormal stock returns during January have been documented and examined by numerous 1 researchers. These returns are generally shown to occur primarily for small firms and to accrue largely during the first five trading days of January.’ While various explanations have been offered to explain this phenomenon, tax loss sehng is among the most frequently cited reasons in both academic research and on Wall Street. Evidence from numerous stuhes lends some support to this explanation, in&cating that various proxies for tax loss sehng are statistically linked to abnormal January stock returns. The purpose of this study is to examine more closely the links between January returns and several indicators of tax-loss selling to see if these relationships reflect abnormal economic profits or market microstructure effects. In particular, we determine whether real abnormal returns would be available to investors who attempt to take advantage of the apparent links between abnormal January returns and three key ‘‘ind~cators~~ of tax loss sehng (large prioryear price declines, high levels of individual ownership, and small market capitabation). Since tax loss sehng caddates are most likely small stocks that have experienced dramatic price declines, they are likely to have low prices and wide bid-ask spreads. Thus, we must be careful that we do not incorrectly identi

Further evidence of the November effect

market microstructure effects as economic profits. Before we control for market microstructure effects, our tax loss trading strategy appears to be extremely profitable, generating January returns of almost 39%. After controlling for the lowest-priced stocks, however, the abnormal positive returns &sappear. Thus, a simple trading strategy that is based on prior academic research findings related to tax loss selling and January returns does not appear feasible. Even more important, our findmgs bring into question some of the conclusions reached in prior research in this area. We believe that many of the statistical links between (supposed) abnormal January returns and proxies of tax loss selling are significantly influenced by the market microstructure effects of low prices and high transaction costs. Our findings are consistent with the idea that the January effect is not driven by tax loss selhng.

A comparison of state university defined benefit and defined contribution pension plans: a Monte Carlo simulation

Tax-loss selling by individuals has long been thought to be a major factor driving the January effect. The Tax Reform Act of 1986 changed the tax-year end for mutual funds to October 31 and increased the marginal tax rate, creating a natural experiment allowing Bhabra, Dhillon, and Ramirez (1999) to empirically test the tax-selling hypothesis. They find empirical support for a postact November effect. However, a second paper by Gibson, Safieddine, and Titman (2000) finds empirical support for the November effect in only one post-act year, 1990. In this article, we respecify betas, calculate holding period returns over each tax year, construct portfolios with large, differences in mutual fund ownership, and test for the presence of a bid-ask spread bias. The empirical results offer evidence of a November effect but only in the first week of November.

Tax Loss Selling and the Contrarian Investment Strategy

Abstract This paper examines investment risk in comparing defined benefit (DB) and defined contribution (DC) plans by employing a Monte Carlo simulation. Using a bivariate normal distribution, two general types of risk are associated with a DC-plan. The first is that not enough is being earned by an allocation rule to cover DB-plan outflows. Secondly the portfolio may experience runs of losses that can’t be overcome by waiting for a better year because the money runs out. The general result is that higher stock allocations allow the higher earning potential of stocks, even if the losses are occasionally experienced, to accumulate enough wealth to see a DC portfolio match the promised benefits of a DB-plan.

Market index returns, macroeconomic variables, and tax-loss selling

Tax loss selling is examined as a possible explanation for the long term price reversal patterns associated with the contrarian investment strategy. Our empirical results are consistent with a tax effect. When we adjust for size and potential tax loss selling, the abnormally high returns previously found in January are eliminated. These results suggest that short term tax effects are associated with the longer term overreaction or contrarian hypothesis, and at this point cannot be dismissed.

Put Option Portfolio Insurance vs. Asset Allocation

This study provides the most direct macro-level test to date of the tax-loss selling hypothesis as an explanation of the January effect. By examining relationships between macroeconomic variables that should be related to tax-loss selling and market index measures of the January effect, this study provides an approach that addresses the market microstructure problems that are inherent in much of the prior research regarding tax-loss selling. This study also addresses some of the methodological and variable specification concerns in prior macro-level testing, resulting in stronger support for taxloss selling.

Exchange rates, and fundamental variables: a semi-parametric analysis of binary choice

The purpose of this study is to develop a model that uses index put options to replace fixed income securities in an individual investors portfolio. Such a portfolio would allow the investor to reduce downside risk, consistent with the rationale for the fixed income allocation, while also allowing the investor to participate to a greater degree, in any potential gains from a market upturn. Results indicate that in order for the model to be superior to a stock/bond portfolio, substantial movement in stock returns is necessary. Implications of results are discussed.

THE STATISTICAL DISTRIBUTION OF DAILY EXCHANGE RATE PRICE CHANGES: DEPENDENT VS INDEPENDENT MODELS

This study is motivated by the dearth of models that provide good out-of-sample fit for exchange rates. That is, current models of exchange rate behaviour are poor predictors of subsequent currency movements. An attempt is made to determine if the relationship between exchange rates and fundamental variables can help explain the more extreme exchange rate movements (distributional switches). Models are developed that relate fundamental economic variables to the resulting estimates based on the mixture of normal probability distributions. Parametric estimation procedures (Logit and Probit) are compared with a semi-parametric technique, maximum score estimation (MSCORE), which is relatively untested in the field of finance. The fundamental variables of these models include information on trade balances, money supply changes, interest rate changes, real economic growth, relative inflation rates and changes in stock market indexes. Classification results favour MSCORE. Implications of results and improvements in methodology are discussed.