Mark J. Kamstra

We analyze the flow of money between mutual fund categories, finding strong evidence of seasonality in investor risk aversion. Aggregate investor flow data reveal an investor preference for safe mutual funds in autumn and risky funds in spring. During September alone, outflows from equity funds average $13 billion, controlling for previously documented flow determinants (e.g., capital-gains overhang). This movement of large amounts of money between fund categories is correlated with seasonality in investor risk aversion, consistent with investors preferring safer (riskier) investments in autumn (spring). We find consistent evidence in Canada and also in Australia, where seasons are offset by 6 months.

Many assume that in the short run, annuity prices promptly and efficiently respond to changes in interest rates. Using a unique database of quotes, we show this is not the case. Prices are less sensitive to changes in rates than expected, and responses are asymmetric. Prices react more rapidly and with greater sensitivity to an increase than to a decrease in rates. The results are robust, but there is a small degree of heterogeneity in the responses of different insurance companies. When rates increase, larger firms are slightly quicker to improve prices. The opposite is true when rates decline. In sum, we show that the microstructure of annuity dynamics is more complicated than (simply) adding mortality credits to bond yields.

We document a novel and striking annual cycle in the U.S. Treasury market, with a variation in mean monthly returns of over 80 basis points from peak to trough. We show that this seasonal Treasury return pattern does not arise due to macroeconomic seasonalities, seasonal variation in risk, the weather, cross-hedging between equity and Treasury markets, conventional measures of investor sentiment, seasonalities in the Treasury market auction schedule, seasonalities in the Treasury debt supply, seasonalities in the FOMC cycle, or peculiarities of the sample period considered. Rather, the seasonal pattern in Treasury returns is significantly correlated with a proxy for variation in investor risk aversion across the seasons, and a model based on that proxy is able to explain more than sixty percent of the average seasonal variation in monthly Treasury returns. The White (2000) reality test confirms that the correlation between returns and the proxy for seasonal variation in investor risk aversion cannot be easily dismissed as the simple result of data snooping.

We investigate an asset pricing model with preferences cycling between high risk aversion and low EIS in fall/winter and the reverse in spring/summer. Calibrating to consumption data and allowing plausible preference parameter values, we produce returns that match observed equity and Treasury returns across the seasons: risky returns are higher and risk-free returns are lower or stable in fall/winter, and they reverse in spring/summer. Further, risky returns vary more than risk-free returns. A novel finding is that both EIS and risk aversion must vary seasonally to match observed returns. Further, the degree of necessary seasonal change in EIS is small. (JEL E44, G11, G12)

We show that lenders make price concessions for the right to resell loans and reveal a strong countervailing association between the ex ante probability of loan resale and the initial loan spreads. We disentangle the side effects (reduced monitoring) from the benefits (enhanced liquidity) brought by the secondary loan resales. The average net impact of simultaneously reducing the probability of the presence of resale constraint and raising the probability of resale across the full sample is to lower spreads by 14 basis points. On balance, the secondary loan market provides clear benefits to the issuers of debt.

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.

In a 2011 reply to our 2010 comment in this journal, Berument and Dogen maintained their challenge to the existence of the negative daylight-saving effect in stock returns reported by Kamstra, Kramer, and Levi in 2000. Unfortunately, in their reply, Berument and Dogen ignored all of the points raised in the comment, failing even to cite the Kamstra, et al. comment. Berument and Dogen continued to use inappropriate estimation techniques, over-parameterized models, and low-power tests and perhaps most surprisingly even failed to replicate results they themselves reported in their previous paper, written by Berument, Dogen, and Onar in 2010. The findings reported by Berument and Dogen, as well as by Berument, Dogen, and Onar, are neither well-supported nor well-reasoned. We maintain our original objections to their analysis, highlight new serious empirical and theoretical problems, and emphasize that there remains statistically significant evidence of an economically large negative daylight-saving effect in U.S. stock returns. The issues raised in this rebuttal extend beyond the daylight-saving effect itself, touching on methodological points that arise more generally when deciding how to model financial returns data.