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Review of Quantitative Finance and Accounting

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04-07-2020 | Original Research

Asymmetrical impacts from overnight returns on stock returns

Authors: Alex YiHou Huang, Ming-Che Hu, Quang Thai Truong

Published in: Review of Quantitative Finance and Accounting | Issue 3/2021

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Abstract

This paper documents significant relationship between overnight returns and future stock returns in the long-term where high averages of overnight returns lead to low future stock returns, with formation periods ranging from 1 month to 1 year. On the other hand, variations in overnight returns lead to different reactions of future stock returns, depending on the levels of past return performances and stabilities of momentum effects. Return reversals are strongest for stocks with extreme past returns. When momentum effects are volatile, higher variations of overnight returns lead to higher future stock returns. When momentum effects are stable, lower variations of overnight returns lead to higher future stock returns for stocks with extreme positive past returns; for stocks that perform worst in the past few months, the two variables have a non-linear relationship. A set of sample sorting criteria according to above relationship are found to significantly enhance the profitability of momentum trading strategy.

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Analyses of Aboody et al. (2018) are consistent with the idea that retail investors are net buyers of attention-grabbing stocks and also with the work of Barclay and Hendershott (2003), which documents a significant and inefficient price discovery of trading after hours.

In particular, Gervais et al. (2001) find that the unusual trading volume increases the visibility of stock and cause the demand from the investor. Lou (2014) shows that firm manager increase advertising spending to attract the attention of investors, leading increase of retail buying increase and abnormal returns. For another example, using attention-grabbing announcements of the upcoming earnings date, Chapman (2018) documents that high investor attention leads to positive return, more online searches, and higher trading volume.

To make a few examples: volatility clustering in Bollerslev (1986), leverage effects in Nelson (1991) and Bekaert and Wu (2000), asymmetrical dependencies of Glosten et al. (1993), mean reversion process in Eraker et al. (2003), and feedback theory of Wu (2001).

Both market on open (MOO) and limit on open (LOO) orders are acceptable where MOO orders seek to purchase shares at the current market price at the time the market opens.

The data stream is published every five minutes until 9:00 a.m. at one-minute intervals from 9:00 a.m. to 9:20 a.m. and every 15 s for the last ten minutes prior to the market opening.

In the same spirit, the open prices of stocks on NASDAQ are determined through an auction process known as the “opening cross", where buyers and sellers place offers and counter-offers until prices match, resulting in a trade.

Lou et al. (2019) provide similar observation and argue that institutions tend to initiate trades throughout the day and particularly at the close while individuals are more likely to initiate trades at the open. Such a result is also consistent with the narrative of how these two classes of investors approach equity trading. Professional investors tend to trade during the day, particularly near the close, taking advantage of the relatively high liquidity at that time. Conversely, individuals may be more likely to evaluate their portfolios in the evening after work and thus may tend to initiate trades that execute when markets open.

For example, Weinbaum (2009) categorizes market participants based on levels of risk preferences and shows that investors with different risk preferences trade in the stock market with different set of strategies. Such risk-based heterogeneity increases return volatility and leads to the leverage effects. For another instance, Adrian (2009) finds that the relations between arbitrage trading strategies and return volatility depend on information transparency of the asset. Specifically, the author argues that when stock return in the future is expectable such as a known drift of dividend, the risk-neutral activities of arbitrageurs would lower return volatility. On the other hand, when little uncertainty is faced, contrarian strategies will be taken by arbitrageurs and lead to higher price volatility.

Same with the averages, the standard deviations of overnight returns are composed as the sample standard deviations of daily overnight returns in the past J months.

Please see Hong and Stein (1999), Zhang (2006), and Huang (2012) for examples of heterogeneities across investors who observe different components of information at different points in time. Grinblatt and Han (2005) show that investors hold their losing stocks longer than they should have, suggesting price under-reaction to information, and Huang (2012) show that information asymmetry has been a key factor affecting future stock returns and that the impact behaves differently across losers and winners stocks.

Such classification of decile rank in 6 months is analogous to the conventional momentum grouping where group of P1 (P10) is often referred as the losers (winners) stocks.

Most of the correlations in the table are statistically significant for stocks with extreme returns. Appendix 2 presents these correlations with t-statistics in brackets for groups P1 and P10.

The regression results with different frequencies (3-, 9, and 12-months) in past stock returns, A-OR, and SD-OR reveal very similar findings with ones in Table 6 and are available upon request.

The phenomenon was particularly notable during the 2008 financial crisis where the P10–P1 profits of (6, 6) momentum investment suffered an average loss of 70.35% per month between October 2008 and February 2009.

Here, \( \sigma_{t} \) is the standard deviation of daily returns of momentum profits in the previous 6 months, and \( \sigma_{target} \) is set as annualized volatility of 12%. This time-series factor ranges from 0.13 to 2.00, and its average is 0.90. This factor is applied as a weight to scale long-short momentum investment, and since the strategy is self-financed, such scaling would not lead to extra costs.

The abnormal returns are computed as the differences of raw returns over CRSP equal weighted market index returns. Table 8 provides outcomes of non-overlapping portfolios for simplicity in comparing the performances across samples. Outcomes of profits by overlapping portfolios (Jegadeesh and Titman 1993) provide similar results and are excluded due to space limitation but available upon request.

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Title: Asymmetrical impacts from overnight returns on stock returns
Authors: Alex YiHou Huang
Ming-Che Hu
Quang Thai Truong
Publication date: 04-07-2020
Publisher: Springer US
Published in: Review of Quantitative Finance and Accounting / Issue 3/2021
Print ISSN: 0924-865X
Electronic ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-020-00911-y

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