We find support for a negative relation between conditional expected monthly return and conditional variance of monthly return, using a GARCH-M model modified by allowing (1) seasonal patterns in volatility, (2) positive and negative innovations to returns having different impacts on conditional volatility, and (3) nominal interest rates to predict conditional variance. Using the modified GARCH-M model, we also show that monthly conditional volatility may not be as persistent as was thought. Positive unanticipated returns appear to result in a downward revision of the conditional volatility whereas negative unanticipated returns result in an upward revision of conditional volatility. THE TRADEOFF BETWEEN RISK and return has long been an important topic in asset valuation research. Most of this research has examined the tradeoff between risk and return among different securities within a given time period. The intertemporal relation between risk and return has been examined by several authors-Fama and Schwert (1977), French, Schwert, and Stambaugh (1987), Harvey (1989), Campbell and Hentschel (1992), Nelson (1991), and Chan, Karolyi, and Stulz (1992), to name a few. This paper extends that research.

https://faculty.washington.edu/ezivot/econ589/GJRJOF1993.pdf

On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks

SEQUELS ARE RARELY AS good as the originals, so I approach this review of the market efficiency literature with trepidation. The task is thornier than it was 20 years ago, when work on efficiency was rather new. The literature is now so large that a full review is impossible, and is not attempted here. Instead, I discuss the work that I find most interesting, and I offer my views on what we have learned from the research on market efficiency.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1540-6261.1991.tb04636.x

Efficient Capital Markets: II

Asset pricing and the bid-ask spread

Abstract This paper studies the effect of the bid-ask spread on asset pricing. We analyze a model in which investors with different expected holding periods trade assets with different relative spreads. The resulting testable hypothesis is that market-observed expexted return is an increasing and concave function of the spread. We test this hypothesis, and the empirical results are consistent with the predictions of the model.

Asset pricing and bid-ask spread

Risk measurement when shares are subject to infrequent trading

In recent years there has been a proliferation of empirical studies documenting unexpected or anomalous regularities in security rates of return. In addition to the widely studied relation between firm size and rate of return,1 these include seasonal regularities related to the time of the day [Harris (1986)], the day of the week [see Ball and Bowers (1986), Cross (1973), French (1980), Gibbons and Hess (1981), Jaffe and Westerfield (1985), Keim and Stambaugh (1984), and Lakonishok and Levi (1982)], the time of the month [Ariel (1987)], and the turn of the year [see Haugen and Lakonishok (1988), Jones, Pearce, and Wilson (1987), Lakonishok

Are Seasonal Anomalies Real? A Ninety-Year Perspective

The authors develop a dynamic model of market-making incorporating inventory and information effects. The marketmaker is both a dealer and an investor, quoting prices that induce mean reversion in inventory toward targets determined by portfolio considerations. The authors test the model with inventory data from a New York Stock Exchange specialist. Specialist inventories exhibit slow mean reversion, with a half-life of over forty-nine days, suggesting weak inventory effects. However, after controlling for shifts in desired inventories, the half-life falls to seven and three-tenths days. Further, quote revisions are negatively related to specialist trades and are positively related to the information conveyed by order imbalances. Copyright 1993 by American Finance Association.

An Analysis of Changes in Specialist Inventories and Quotations

A Bayesian model of intraday specialist pricing

Capital gains taxes create incentives to trade. Our major finding is that turnover is higher for winners (stocks, the prices of which have increased) than for losers, which is not consistent with the tax prediction. However, the turnover in December and January is evidence of tax-motivated trading; there is a relatively high turnover for losers in December and for winners in January. We conclude that taxes influence turnover, but other motives for trading are more important. We were unable to find evidence that changing the length of the holding period required to qualify for long-term capital gains treatment affected turnover. INVESTORS RESPONDING TO THE tax incentives in the capital gains laws should produce predictable patterns in the volume of trading in a stock, but not necessarily in its price. This study uses past price patterns as a proxy to measure the incentives to sell or avoid selling a stock and observes trading volumes to estimate the extent to which investors respond to these incentives. Past price patterns may influence trading volumes for other (non-tax related) reasons as well. These are also considered. We find that past prices influence current incentives to trade through both tax and non-tax motives. Winners tend to have higher abnormal volumes than losers. This would be expected if non-tax-related motives were predominant. However, the influence of tax-related motives on trading volume is shown by seasonal variations in the relationship between past price changes and turnover. As predicted by tax-induced trading theories, the volume of losers is higher than normal in December, and the volume of winners is higher than normal in January. We found no evidence that trading volume was affected by changes in the length of the holding period required to qualify for long-term capital gains. Investors' reactions to capital gains taxes are important for several reasons. First, a comprehensive understanding of investor behavior would include both price and volume. Understanding the relationship between taxes and volume is a fruitful and convenient method of contributing to this subject. The resource allocation consequences of capital gains taxes are controversial in part because

https://www.jstor.org/stable/pdfplus/2328239.pdf

Seymour Smidt

Papers

Are Seasonal Anomalies Real? A Ninety-Year Perspective

An Analysis of Changes in Specialist Inventories and Quotations

A Bayesian model of intraday specialist pricing

Volume for Winners and Losers: Taxation and Other Motives for Stock Trading

The capital budgeting decision