The Journal of Finance

Can Tax‐Loss Selling Explain the January Seasonal in Stock Returns?

Published: 12/01/1986   |   DOI: 10.1111/j.1540-6261.1986.tb02534.x

K. C. CHAN

This paper analyzes the tax‐loss selling hypothesis as an explanation of the January seasonal in stock returns and argues that rational tax‐loss selling implies little relation between the January seasonal and the long‐term loss. Empirical results show that the January seasonal is as strongly related to the long‐term loss as it is to the short‐term loss. The evidence is inconsistent with a model that explains the January seasonal by optimal tax trading.

Macroeconomic Influences and the Variability of the Commodity Futures Basis

Published: 06/01/1993   |   DOI: 10.1111/j.1540-6261.1993.tb04727.x

WARREN BAILEY, K. C. CHAN

We provide evidence that the spread between commodity spot and futures prices (the basis) reflects the macroeconomic risks common to all asset markets. The basis of many commodities is correlated with the stock index dividend yield and corporate bond quality spread. Explanatory power is related to exposure to macroeconomic fluctuations: about 40 percent of the variation in the basis of a portfolio of commodities with high business cycle sensitivity is explained by the stock and bond yields. Further diagnostics indicate that these associations are largely due to the presence of risk premiums, rather than spot price forecasts, in the basis.

An Unconditional Asset‐Pricing Test and the Role of Firm Size as an Instrumental Variable for Risk

Published: 06/01/1988   |   DOI: 10.1111/j.1540-6261.1988.tb03941.x

K. C. CHAN, NAI‐FU CHEN

In an intertemporal economy where both risk (stock beta) and expected return are time varying, the authors derive a linear relation between the unconditional beta and the unconditional return under certain stationarity assumptions about the stochastic process of size‐portfolio betas. The model suggests the use of long time periods to estimate the unconditional portfolio betas. The authors find that, after controlling for the betas thus estimated, a firm‐size proxy, such as the logarithm of the firm size, does not have explanatory power for the averaged returns across the size‐ranked portfolios.

Structural and Return Characteristics of Small and Large Firms

Published: 09/01/1991   |   DOI: 10.1111/j.1540-6261.1991.tb04626.x

K. C. CHAN, NAI‐FU CHEN

We examine differences in structural characteristics that lead firms of different sizes to react differently to the same economic news. We find that a small firm portfolio contains a large proportion of marginal firms‐firms with low production efficiency and high financial leverage. We construct two size‐matched return indices designed to mimic the return behavior of marginal firms and find that these return indices are important in explaining the time‐series return difference between small and large firms. Furthermore, risk exposures to these indices are as powerful as log(size) in explaining average returns of size‐ranked portfolios.

Does Money Explain Asset Returns? Theory and Empirical Analysis

Published: 03/01/1996   |   DOI: 10.1111/j.1540-6261.1996.tb05212.x

K. C. CHAN, SILVERIO FORESI, LARRY H. P. LANG

A cash‐in‐advance model of a monetary economy is used to derive a money‐based CAPM (M‐CAPM), which allows us to implement tests of asset pricing restrictions without consumption data. A test as in Fama and MacBeth of the model suggests that the money betas have some explanatory power for the cross‐sectional variation of expected returns; however, the model is rejected using conditional information. Consistent with our predictions, estimates of the curvature parameter are lower than those of the consumption CAPM (C‐CAPM) and pricing errors of the M‐CAPM tend to be smaller than those of the C‐CAPM.

An Empirical Comparison of Alternative Models of the Short‐Term Interest Rate

Published: 07/01/1992   |   DOI: 10.1111/j.1540-6261.1992.tb04011.x

K. C. CHAN, G. ANDREW KAROLYI, FRANCIS A. LONGSTAFF, ANTHONY B. SANDERS

We estimate and compare a variety of continuous‐time models of the short‐term riskless rate using the Generalized Method of Moments. We find that the most successful models in capturing the dynamics of the short‐term interest rate are those that allow the volatility of interest rate changes to be highly sensitive to the level of the riskless rate. A number of well‐known models perform poorly in the comparisons because of their implicit restrictions on term structure volatility. We show that these results have important implications for the use of different term structure models in valuing interest rate contingent claims and in hedging interest rate risk.

The Behavior of Stock Prices Around Institutional Trades

Published: 09/01/1995   |   DOI: 10.1111/j.1540-6261.1995.tb04053.x

LOUIS K. C. CHAN, JOSEF LAKONISHOK

All trades executed by 37 large investment management firms from July 1986 to December 1988 are used to study the price impact and execution cost of the entire sequence (“package”) of trades that we interpret as an order. We find that market impact and trading cost are related to firm capitalization, relative package size, and, most importantly, to the identity of the management firm behind the trade. Money managers with high demands for immediacy tend to be associated with larger market impact.

Institutional Equity Trading Costs: NYSE Versus Nasdaq

Published: 04/18/2012   |   DOI: 10.1111/j.1540-6261.1997.tb04819.x

LOUIS K. C. CHAN, JOSEF LAKONISHOK

We compare execution costs (market impact plus commission) on the New York Stock Exchange (NYSE) and Nasdaq for institutional investors. The differences in cost generally conform to each market's area of specialization. Controlling for firm size, trade size, and the money management firm's identity, costs are lower on Nasdaq for trades in comparatively smaller firms, while costs for trading the larger stocks are lower on NYSE. The cost differences estimated from a regression model are, however, sensitive to the choice of time period.

Momentum Strategies

Published: 12/01/1996   |   DOI: 10.1111/j.1540-6261.1996.tb05222.x

LOUIS K. C. CHAN, NARASIMHAN JEGADEESH, JOSEF LAKONISHOK

We examine whether the predictability of future returns from past returns is due to the market's underreaction to information, in particular to past earnings news. Past return and past earnings surprise each predict large drifts in future returns after controlling for the other. Market risk, size, and book–to–market effects do not explain the drifts. There is little evidence of subsequent reversals in the returns of stocks with high price and earnings momentum. Security analysts' earnings forecasts also respond sluggishly to past news, especially in the case of stocks with the worst past performance. The results suggest a market that responds only gradually to new information.

Fundamentals and Stock Returns in Japan

Published: 12/01/1991   |   DOI: 10.1111/j.1540-6261.1991.tb04642.x

LOUIS K. C. CHAN, YASUSHI HAMAO, JOSEF LAKONISHOK

This paper relates cross‐sectional differences in returns on Japanese stocks to the underlying behavior of four variables: earnings yield, size, book to market ratio, and cash flow yield. Alternative statistical specifications and various estimation methods are applied to a comprehensive, high‐quality data set that extends from 1971 to 1988. The sample includes both manufacturing and nonmanufacturing firms, companies from both sections of the Tokyo Stock Exchange, and also delisted securities. Our findings reveal a significant relationship between these variables and expected returns in the Japanese market. Of the four variables considered, the book to market ratio and cash flow yield have the most significant positive impact on expected returns.

The Stock Market Valuation of Research and Development Expenditures

Published: 12/17/2002   |   DOI: 10.1111/0022-1082.00411

Louis K. C. Chan, Josef Lakonishok, Theodore Sougiannis

We examine whether stock prices fully value firms' intangible assets, specifically research and development (R&D). Under current U.S. accounting standards, financial statements do not report intangible assets and R&D spending is expensed. Nonetheless, the average historical stock returns of firms doing R&D matches the returns of firms without R&D. However, the market is apparently too pessimistic about beaten‐down R&D‐intensive technology stocks' prospects. Companies with high R&D to equity market value (which tend to have poor past returns) earn large excess returns. A similar relation exists between advertising and stock returns. R&D intensity is positively associated with return volatility.

The Level and Persistence of Growth Rates

Published: 03/21/2003   |   DOI: 10.1111/1540-6261.00540

Louis K. C. Chan, Jason Karceski, Josef Lakonishok

Expectations about long‐term earnings growth are crucial to valuation models and cost of capital estimates. We analyze historical long‐term growth rates across a broad cross section of stocks using several indicators of operating performance. We test for persistence and predictability in growth. While some firms have grown at high rates historically, they are relatively rare instances. There is no persistence in long‐term earnings growth beyond chance, and there is low predictability even with a wide variety of predictor variables. Specifically, IBES growth forecasts are overly optimistic and add little predictive power. Valuation ratios also have limited ability to predict future growth.