A tug of war: Overnight versus intraday expected returns

Lou, Dong; Polk, Christopher; Skouras, Spyros

doi:10.1016/j.jfineco.2019.03.011

Cited by 223 publications

(135 citation statements)

References 69 publications

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“…They conclude that their evidence is consistent with Bogousslavsky's (2016) model, as well as a model of late-informed trading near the market close. Lou et al (2019) report that major anomaly trading strategies earn profits either entirely overnight or entirely intraday, where the profits are of the opposite sign during the opposite period. The persistence of overnight and intraday components of returns is driven by a tug-of-war between retail investors and institutional investors.…”

Section: Motivationmentioning

confidence: 99%

Option trading after the opening bell and intraday stock return predictability

Bergsma

Fodor

Singal

et al. 2019

Financial Management

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Prior literature finds that information is reflected in option markets before stock markets, but no study has explored whether option volume soon after market open has predictive power for intraday stock returns. Using novel intraday signed option‐to‐stock volume data, we find that a composite option trading score (OTS) in the first 30 min of market open predicts stock returns during the rest of the trading day. Such return predictability is greater for smaller stocks, stocks with higher idiosyncratic volatility, and stocks with higher bid–ask spreads relative to their options’ bid–ask spreads. Moreover, OTS is a significantly stronger predictor of intraday stock returns after overnight earnings announcements. The evidence suggests that option trading in the 30 min after the opening bell has predictive power for intraday stock returns.

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Section: Motivationmentioning

confidence: 99%

Option trading after the opening bell and intraday stock return predictability

Bergsma

Fodor

Singal

et al. 2019

Financial Management

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“…With respect to previous works, we chose to use the intra-day price returns. This decision was motivated by recent studies [32] where the authors decompose the returns in overnight returns and intra-day price returns. They point out that afterhours trading happens much more seldom than trading while markets are open.…”

Section: Feature Engineeringmentioning

confidence: 99%

Ensembling and Dynamic Asset Selection for Risk-Controlled Statistical Arbitrage

et al. 2021

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In recent years, machine learning algorithms have been successfully employed to leverage the potential of identifying hidden patterns of financial market behavior and, consequently, have become a land of opportunities for financial applications such as algorithmic trading. In this paper, we propose a statistical arbitrage trading strategy with two key elements: an ensemble of regression algorithms for asset return prediction, followed by a dynamic asset selection. More specifically, we construct an extremely heterogeneous ensemble ensuring model diversity by using state-of-the-art machine learning algorithms, data diversity by using a feature selection process, and method diversity by using individual models for each asset, as well models that learn cross-sectional across multiple assets. Then, their predictive results are fed into a quality assurance mechanism that prunes assets with poor forecasting performance in the previous periods. We evaluate the approach on historical data of component stocks of the S&P500 index. By performing an in-depth risk-return analysis, we show that this setup outperforms highly competitive trading strategies considered as baselines. Experimentally, we show that the dynamic asset selection enhances overall trading performance both in terms of return and risk. Moreover, the proposed approach proved to yield superior results during both financial turmoil and massive market growth periods, and it showed to have general application for any risk-balanced trading strategy aiming to exploit different asset classes.

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“…Recently, Lou et al (2019) show that the profits of time series momentum, among other strategies, are entirely due to overnight and not intraday returns. When checking whether technical profits are sensitive to return measurement errors, studies synthesize realistic trading timing through the ‘one‐day delay’ adjustment, that is, the signal observed at close t is implemented at close t + 1.…”

Section: Analysing Tf Profitsmentioning

confidence: 99%

Evaluating active investing with generic trading reactions

Zoicas-Ienciu

2020

Int. J Fin Econ

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We evaluate the performance of rules using past information to generate daily trading signals. Assuming generic trading reactions to buy and sell signals, we derive an analytic excess return that isolates commissions, interests, the impact of trading timing, and that of the benchmark's choice. The result is useful in dealing with data snooping through leverage and benchmark tweaking. We illustrate the empirical implications by examining trend‐following performance across Dow Jones Industrial Average (1927–2016) and an international sample of major equity indexes and blue‐chip stocks (1980–2016). The results show substantial, fading, non‐persistent and highly methodology‐sensitive excess returns.

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A tug of war: Overnight versus intraday expected returns

Cited by 223 publications

References 69 publications

Option trading after the opening bell and intraday stock return predictability

Option trading after the opening bell and intraday stock return predictability

Ensembling and Dynamic Asset Selection for Risk-Controlled Statistical Arbitrage

Evaluating active investing with generic trading reactions

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