This book is an ambitious effort by three well-known and
 well-respected scholars to fill an acknowledged void in the
 literature—a text covering the burgeoning field of empirical finance.
 As the authors note in the preface, there are several excellent books
 covering financial theory at a level suitable for a Ph.D. class or as
 a reference for academics and practitioners, but there is little or
 nothing similar that covers econometric methods and applications.
 Perhaps the closest existing text is the recent addition to the Wiley
 Series in Financial and Quantitative Analysis. written by Cuthbertson
 (1996). The major difference between the books is that Cuthbertson
 focuses exclusively on asset pricing in the stock, bond, and foreign
 exchange markets, whereas Campbell, Lo, and MacKinlay (henceforth CLM)
 consider empirical applications throughout the field of finance,
 including corporate finance, derivatives markets, and market
 microstructure. The level of anticipation preceding publication
 can be partly measured by the fact that at least three reviews
 (including this one) have appeared since the book arrived. Moreover,
 in their reviews, both Harvey (1998) and Tiso (1998) comment on the
 need for such a text, a sentiment that has been echoed by numerous
 finance academics.

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The econometrics of financial markets

This study investigates whether marketwide liquidity is a state variable important for asset pricing. We find that expected stock returns are related cross-sectionally to the sensitivities of returns to fluctuations in aggregate liquidity. Our monthly liquidity measure, an average of individual-stock measures estimated with daily data, relies on the principle that order flow induces greater return reversals when liquidity is lower. From 1966 through 1999, the average return on stocks with high sensitivities to liquidity exceeds that for stocks with low sensitivities by 7.5 percent annually, adjusted for exposures to the market return as well as size, value, and momentum factors. Furthermore, a liquidity risk factor accounts for half of the profits to a momentum strategy over the same 34-year period.

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Liquidity Risk and Expected Stock Returns

We provide a model that links an asset's market liquidity - i.e., the ease with which it is traded - and traders' funding liquidity - i.e., the ease with which they can obtain funding. Traders provide market liquidity, and their ability to do so depends on their availability of funding. Conversely, traders' funding, i.e., their capital and the margins they are charged, depend on the assets' market liquidity. We show that, under certain conditions, margins are destabilizing and market liquidity and funding liquidity are mutually reinforcing, leading to liquidity spirals. The model explains the empirically documented features that market liquidity (i) can suddenly dry up, (ii) has commonality across securities, (iii) is related to volatility, (iv) is subject to “flight to quality¶, and (v) comoves with the market, and it provides new testable predictions. Keywords: Liquidity Risk Management, Liquidity, Liquidation, Systemic Risk, Leverage, Margins, Haircuts, Value-at-Risk, Counterparty Credit Risk

Market liquidity and funding liquidity

Complex dynamical systems, ranging from ecosystems to financial markets and the climate, can have tipping points at which a sudden shift to a contrasting dynamical regime may occur. Although predicting such critical points before they are reached is extremely difficult, work in different scientific fields is now suggesting the existence of generic early-warning signals that may indicate for a wide class of systems if a critical threshold is approaching.

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Early-warning signals for critical transitions

We assume that the instantaneous riskless rate reverts towards a central tendency which in turn, is changing stochastically over time. As a result, current short-term rates are not" sufficient to predict future short-term rates movements, as would be the case if the central" tendency was constant. However, since longer-maturity bond prices incorporate information" about the central tendency, longer-maturity bond yields can be used to predict future short-term" rate movements. We develop a two-factor model of the term-structure which implies that a" linear combination of any two rates can be used as a proxy for the central tendency. Based on" this central-tendency proxy, we estimate a model of the one-month rate which performs better" than models which assume the central tendency to be constant.

A Unified Theory of Underreaction, Momentum Trading and Overreaction in Asset Markets

This paper investigates the relationship between aggregate stock market trading volume and the serial correlation of daily stock returns. For both stock indexes and individual large stocks, the first-order daily return autocorrelation tends to decline with volume. The paper explains this phenomenon using a model in which risk-averse "market makers" accommodate buying or selling pressure from "liquidity" or "noninformational" traders. Changing expected stock returns reward market makers for playing this role. The model implies that a stock price decline on a high-volume day is more likely than a stock price decline on a low-volume day to be associated with an increase in the expected stock return.

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Trading Volume and Serial Correlation in Stock Returns

A model of competitive stock trading is developed in which investors are heterogeneous in their information and private investment opportunities and rationally trade for both informational and noninformational motives. I examine the link between the nature of heterogeneity among investors and the behavior of trading volume and its relation to price dynamics. It is found that volume is positively correlated with absolute changes in prices and dividends. I show that informational trading and noninformational trading lead to different dynamic relations between trading volume and stock returns.

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A Model of Competitive Stock Trading Volume

This paper examines the illiquidity of corporate bonds and its asset-pricing implications. Using transactions data from 2003 to 2009, we show that the illiquidity in corporate bonds is substantial, significantly greater than what can be explained by bid–ask spreads. We establish a strong link between bond illiquidity and bond prices. In aggregate, changes in market-level illiquidity explain a substantial part of the time variation in yield spreads of high-rated (AAA through A) bonds, overshadowing the credit risk component. In the cross-section, the bond-level illiquidity measure explains individual bond yield spreads with large economic significance.

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The Illiquidity of Corporate Bonds

Technical analysis, also known as "charting," has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis-the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and we apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution-conditioned on specific technical indicators such as head-and-shoulders or double-bottoms-we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value.

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Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation

This paper presents a dynamic asset-pricing model under asymmetric information. Investors have different information concerning the future growth rate of dividends. They rationally extract information from prices as well as dividends and maximize their expected utility. The model has a closed-form solution to the rational expectations equilibrium. We find that existence of uninformed investors increases the risk premium. Supply shocks can affect the risk premium only under asymmetric information. Information asymmetry among investors can increase price volatility and negative autocorrelation in returns. Less-informed investors may rationally behave like price chasers.

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Jiang Wang

Papers

Trading Volume and Serial Correlation in Stock Returns

A Model of Competitive Stock Trading Volume

The Illiquidity of Corporate Bonds

Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation

A model of intertemporal asset prices under asymmetric information