The Journal of Finance publishes leading research across all the major fields of finance. It is one of the most widely cited journals in academic finance, and in all of economics. Each of the six issues per year reaches over 8,000 academics, finance professionals, libraries, and government and financial institutions around the world. The journal is the official publication of The American Finance Association, the premier academic organization devoted to the study and promotion of knowledge about financial economics.
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Search results: 3.
Infrequent Rebalancing, Return Autocorrelation, and Seasonality
Published: 08/04/2016 | DOI: 10.1111/jofi.12436
VINCENT BOGOUSSLAVSKY
A model of infrequent rebalancing can explain specific predictability patterns in the time series and cross‐section of stock returns. First, infrequent rebalancing produces return autocorrelations that are consistent with empirical evidence from intraday returns and new evidence from daily returns. Autocorrelations can switch sign and become positive at the rebalancing horizon. Second, the cross‐sectional variance in expected returns is larger when more traders rebalance. This effect generates seasonality in the cross‐section of stock returns, which can help explain available empirical evidence.
Liquidity, Volume, and Order Imbalance Volatility
Published: 05/24/2023 | DOI: 10.1111/jofi.13248
VINCENT BOGOUSSLAVSKY, PIERRE COLLIN‐DUFRESNE
We examine the dynamics of liquidity using a comprehensive sample of U.S. stocks in the post‐decimalization period. Motivated by a continuous‐time inventory model, we compute a high‐frequency measure of order imbalance volatility to proxy for the inventory risk faced by liquidity providers. We show that high‐frequency order imbalance volatility is an important driver of liquidity and explains the often positive time‐series relation between spread and volume for large stocks, which seems to run counter to most theoretical models. Furthermore, order imbalance volatility is priced in the cross‐section of stock returns.
Informed Trading Intensity
Published: 02/27/2024 | DOI: 10.1111/jofi.13320
VINCENT BOGOUSSLAVSKY, VYACHESLAV FOS, DMITRIY MURAVYEV
We train a machine learning method on a class of informed trades to develop a new measure of informed trading, informed trading intensity (ITI). ITI increases before earnings, mergers and acquisitions, and news announcements, and has implications for return reversal and asset pricing. ITI is effective because it captures nonlinearities and interactions between informed trading, volume, and volatility. This data‐driven approach can shed light on the economics of informed trading, including impatient informed trading, commonality in informed trading, and models of informed trading. Overall, learning from informed trading data can generate an effective informed trading measure.