Vincent Bogousslavsky's research while affiliated with Boston College and other places

Publications (10)

Article
Full-text available
News mostly drive overnight returns, whereas investors' trading mostly drives intraday returns. We use this fact to test theories of momentum and reversal with a sample of intraday and overnight returns spanning 1926 to 2019. Portfolios formed on past intra-day returns display short-term reversal and momentum without long-term reversal. In contrast...
Article
I investigate cross-sectional variation in stock returns over the trading day and overnight to shed light on what drives asset pricing anomalies. Margin requirements are higher overnight, and lending fees are typically charged only on positions held overnight. Such institutional constraints and overnight risk incentivize arbitrageurs who trade on m...
Article
We investigate the impact of an exogenous trading glitch at a high-frequency market-making firm on standard measures of stock liquidity (spreads, price impact, turnover, and depth) and institutional trading costs (implementation shortfall and volume-weighted average price slippage). Stocks in which the firm accumulates large long (short) positions...
Article
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 positi...
Article
Using a thirty-year sample of intraday returns on U.S. stocks, I show that asset pricing anomalies accrue over the day in radically different ways. Size and illiquidity premia are realized in the last thirty minutes of trading. Furthermore, the turnover of small stocks relative to that of large stocks spikes around the close. This evidence can be e...
Article
I investigate seasonalities in a set of well-known anomalies in the cross-section of U.S. stock returns. A January seasonality goes beyond a size effect and strongly affects most anomalies, which can even switch sign in January. Return seasonality exists outside of January depending on the month of the quarter. Small stocks earn abnormally high ave...
Article
A model of infrequent rebalancing generates return autocorrelation patterns that are consistent with empirical evidence from intraday returns, as well as new evidence from daily returns. Contrary to similar frictionless economies, the model can produce positive return autocorrelation. Return volatility and correlation increase exponentially with th...

Citations

... Finally, this paper is also related to existing work aiming to understand the difference between intraday and overnight returns (Cliff, Cooper, and Gulen, 2008;Berkman et al., 2012;Lou, Polk, and Skouras, 2019;Hendershott, Livdan, and Ro¨sch, 2020;Bogousslavsky, 2021). While these papers examine intraday versus overnight differences in average factor returns, my paper focuses on explaining the price fluctuation in factors. ...
... In Table 3, we report volume-weighted average price (VWAP) slippage from investor order submission to final (trade) execution (see, for example, Bogousslavsky et al. 2021). For investor buy orders (sell orders are multiplied by − 1), VWAP slippage is the share weighted order execution price minus the realized VWAP over the order execution period, divided by the realized VWAP over the order execution period. ...
... (2018) found that stocks with more ETF ownership are more volatile than otherwise similar securities, and they argued that the volatility arising from ETF trading represents a nondiversifiable source of risk, at least in the short term. In similar analyses using higher-frequency data, Goldman Sachs (2019) and Bogousslavsky and Muravyev (2019) found that the effect is concentrated near the close of daily trading sessions, and they argued it may be due to the concentration of ETF portfolio trades at that time. In theoretical work, Bhattacharya and O'Hara (2018) ...
... We denote here statistical moments as p (1) The value and sign of correlations depend on the duration of the averaging time interval Δ and that important dependence should be investigated. Let us underline that correlations (A.5) between trade volume U and square of price p 2 determine relations between trade volume and volatility studies (Ito and Lin, 1993;Brock and LeBaron, 1995;Ciner and Sackley, 2007;Bogousslavsky and Collin-Dufresne, 2019), but correlations (A.5) are determined by the market-based price probability (2.12; 2.14; 2.19). ...
... With the rise of computer based trading and increasing availability of high frequency data, a deep understanding of how intraday information might be used for cross-sectional forecasting has aroused great interest of academics and practitioners. For example, Lou et al. (2019), and Bogousslavsky (2021) study the relationship between intraday information and traditional anomalies that have been observed in the cross-section at the monthly level. In contrast, Chapter 3 zooms further into the intraday cross-sectional predictability. ...
... Xu et al. (2020) postulate that there are two theoretical explanations for the interconnectivity of the commodity market. The first relates portfolio rebalancing model of Bogousslavsky (2016), which indicates theoretically that certain traders choose to delay portfolio rebalancing until the market closure instead of trading immediately when a successful signal is delivered, resulting in a positive correlation. The second pertains to the slow-moving capital and the presence of investors who make decisions infrequently, as proposed by Duffie (2010). ...