The Journal of Finance

Trading and Returns under Periodic Market Closures

Published: 03/31/2007   |   DOI: 10.1111/0022-1082.00207

Harrison Hong, Jiang Wang

This paper studies how market closures affect investors' trading policies and the resulting return‐generating process. It shows that closures generate rich patterns of time variation in trading and returns, including those consistent with empirical findings: (1) U‐shaped patterns in the mean and volatility of returns over trading periods, (2) higher trading activity around the close and open, (3) more volatile open‐to‐open returns than close‐to‐close returns, (4) higher returns over trading periods than over nontrading periods, (5) more volatile returns over trading periods than over nontrading periods. It also shows that closures can make prices more informative about future payoffs.

Trading Volume: Implications of an Intertemporal Capital Asset Pricing Model

Published: 01/11/2007   |   DOI: 10.1111/j.1540-6261.2006.01005.x

ANDREW W. LO, JIANG WANG

We derive an intertemporal asset pricing model and explore its implications for trading volume and asset returns. We show that investors trade in only two portfolios: the market portfolio, and a hedging portfolio that is used to hedge the risk of changing market conditions. We empirically identify the hedging portfolio using weekly volume and returns data for U.S. stocks, and then test two of its properties implied by the theory: Its return should be an additional risk factor in explaining the cross section of asset returns, and should also be the best predictor of future market returns.

Implementing Option Pricing Models When Asset Returns Are Predictable

Published: 03/01/1995   |   DOI: 10.1111/j.1540-6261.1995.tb05168.x

ANDREW W. LO, JIANG WANG

The predictability of an asset's returns will affect the prices of options on that asset, even though predictability is typically induced by the drift, which does not enter the option pricing formula. For discretely‐sampled data, predictability is linked to the parameters that do enter the option pricing formula. We construct an adjustment for predictability to the Black‐Scholes formula and show that this adjustment can be important even for small levels of predictability, especially for longer maturity options. We propose several continuous‐time linear diffusion processes that can capture broader forms of predictability, and provide numerical examples that illustrate their importance for pricing options.

The Illiquidity of Corporate Bonds

Published: 05/23/2011   |   DOI: 10.1111/j.1540-6261.2011.01655.x

JACK BAO, JUN PAN, JIANG WANG

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.

Noise as Information for Illiquidity

Published: 07/26/2013   |   DOI: 10.1111/jofi.12083

GRACE XING HU, JUN PAN, JIANG WANG

We propose a market‐wide liquidity measure by exploiting the connection between the amount of arbitrage capital in the market and observed “noise” in U.S. Treasury bonds—the shortage of arbitrage capital allows yields to deviate more freely from the curve, resulting in more noise in prices. Our noise measure captures episodes of liquidity crises of different origins across the financial market, providing information beyond existing liquidity proxies. Moreover, as a priced risk factor, it helps to explain cross‐sectional returns on hedge funds and currency carry trades, both known to be sensitive to the general liquidity conditions of the market.

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

Published: 12/17/2002   |   DOI: 10.1111/0022-1082.00265

Andrew W. Lo, Harry Mamaysky, Jiang Wang

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.

The Dark Side of Circuit Breakers

Published: 02/23/2024   |   DOI: 10.1111/jofi.13310

HUI CHEN, ANTON PETUKHOV, JIANG WANG, HAO XING

Market‐wide circuit breakers are trading halts aimed at stabilizing the market during dramatic price declines. Using an intertemporal equilibrium model, we show that a circuit breaker significantly alters market dynamics and affects investor welfare. As the market approaches the circuit breaker, price volatility rises drastically, accelerating the chance of triggering the circuit breaker—the so‐called “magnet effect,” returns exhibit increasing negative skewness, and trading activity spikes up. Our empirical analysis supports the model's predictions. Circuit breakers can affect overall welfare negatively or positively, depending on the relative significance of investors' trading motives for risk sharing versus irrational speculation.

The Price Impact and Survival of Irrational Traders

Published: 01/20/2006   |   DOI: 10.1111/j.1540-6261.2006.00834.x

LEONID KOGAN, STEPHEN A. ROSS, JIANG WANG, MARK M. WESTERFIELD

Milton Friedman argued that irrational traders will consistently lose money, will not survive, and, therefore, cannot influence long‐run asset prices. Since his work, survival and price impact have been assumed to be the same. In this paper, we demonstrate that survival and price impact are two independent concepts. The price impact of irrational traders does not rely on their long‐run survival, and they can have a significant impact on asset prices even when their wealth becomes negligible. We also show that irrational traders' portfolio policies can deviate from their limits long after the price process approaches its long‐run limit.