Intraday time series reversal
Yasuhiro Iwanaga* and Ryuta Sakemoto†
26 November 2025
Abstract
This study investigates intraday time series reversal that the overnight return predicts the
first half-hour return for U.S. market indices. Intraday reversal strengthens during periods
of high volatility, including the global financial crisis and the early phase of the COVID19 pandemic. In contrast, it weakened after the 2010s and during the later phase of the
pandemic. These findings suggest that intraday reversal is associated with the presence
of opposing investor clienteles—overnight buying by retail investors and daytime selling
by arbitrageurs. Moreover, we do not observe intraday reversal for other ETFs that track
international market indices. Finally, we propose investment strategies based on intraday
reversal and present that they are profitable for U.S market indices but not for
international market indices.
Keywords: Intraday reversal, Intraday momentum, Global Financial Crisis, COVID-19,
Retail investors
JEL codes: G01, G1, G14
*
Tamagawa University, College of Business Administration, Tokyo, Japan. Email:
iwanagay@bus.tamagawa.ac.jp
†
(Corresponding author) Faculty of Economics and Business, Hokkaido University,
Kita 9 Nishi 7, Kita Ward, Sapporo, Hokkaido, 060-0809, Japan.
Email: rsakemoto@econ.hokudai.ac.jp
Data availability: The authors do not have permission to share data.
Conflict of interest: We have no conflicts of interests to disclose.
Funding: This work was supported by Japan Society for the Promotion of Science KAKENHI
Grant Number 24K16398.
1
1.Introduction
The predictability of time series momentum is one of the most important market features
for investors. For instance, Moskowitz et al. (2012) find that past one- to 12-month returns
predict future returns across asset classes, and Georgopoulou and Wang (2017) observe
the same pattern in emerging markets. Moreover, predictability of cross-sectional reversal
was observed by De Bondt and Thaler (1985) and Jegadeesh (1990). The recent work of
Mehat and Schmeling (2022) finds that short-term cross-sectional momentum and
reversal depend on share turnover. Although previous studies focus on monthly formation
and holding periods, recent research has extended its focus to shorter time frames. The
influential work of Gao et al. (2018) employs S&P 500 exchange-traded fund (ETF) data
and reveals that the first half-hour return predicts the last half-hour return due to
infrequent portfolio rebalancing (Bogousslavsky, 2016) and late-informed traders. This
intraday momentum is also observed in foreign exchange rates (Elaut et al., 2018),
individual stocks (Gao et al., 2019), commodity futures (Jin et al., 2020), international
equity indices (Li et al., 2022), Bitcoin (Shen et al., 2022), and VIX futures (Huang et al.,
2023).1 Furthermore, Baltussen et al. (2021) identify another pattern: returns from the
previous market close to the last 30 minutes are strongly linked to the last half-hour
returns across equity, bond, currency, and commodity futures markets.
1
Another strand of literature reports that adopting machine learning techniques leads to
predictability of intraday returns (Huddleston et al., 2023; Aleti et al., 2025). Moreover, Chen
(2021) observes that round-time trades are associated with the intraday return predictability.
Xu et al. (2025) find that the U.S. last half-hour return predicts the first half-hour returns on
other international stock market.
2
In contrast to these studies, we investigate intraday reversal that the overnight
return predicts the first half-hour return.2 Early work on intraday reversal was conducted
by Fung et al. (2000) and Grant et al. (2005), while the empirical evidence in the U.S.
market is mixed. Intraday reversal builds on the literature concerning opposing investor
clienteles. Lou et al. (2019) decompose daily close-to-close returns into overnight and
intraday components and find that stocks with higher overnight returns tend to yield lower
intraday returns. Berkman et al. (2012) and Akbas et al. (2022) interpret daytime reversal
as being associated with overnight buying by retail investors and daytime selling by
arbitrageurs.3 Moreover, Bogousslavsky (2021) argues that institutional investors who
trade as arbitrageurs face requirements of high margin call and lending fee during the
overnight period and, therefore, prefer to trade during daytime.
Our main findings are as follows. First, we find that the overnight return predicts
the first half-hour return for the S&P 500 ETF. Second, this predictability strengthens
during periods of market stress, aligning with findings in the intraday momentum
literature, such as Gao et al. (2018) and Li et al. (2022). Our findings suggest that periods
2
We define the overnight return using the close price at 4:00 p.m. on day t−1 and the open
price at 9:30 a.m. on day t. The time stamp is based on Eastern Standard Time, which is
standard in the literature (e.g., Gao et al., 2018).
3
Retail investors were traditionally considered as uninformed investors (e.g., Barber and Odean,
2000). However, recent studies provide empirical evidence that retail investors are not entirely
uninformed, and some can earn positive profits using private information (Kaniel et al., 2012;
Boehmer et al., 2021) and liquidity provision (Barrot et al., 2016).
3
of high market stress create greater arbitrage opportunities. 4 Third, predictability
weakened after the 2010s, consistent with the rise of retail trading driven by the
development of trading tools such as mobile apps and robo-advisors. For instance, the
Amundi Research Center (2023) reports that the share of retail trading in the U.S. equity
market increased from 10% in 2010 to 18% in 2023. These advancements have altered
retail investors’ behavior and increased heterogeneity among retail investors, resulting in
a decline in intraday reversal. Fourth, the early phase of the COVID-19 pandemic
enhances the profitability of intraday reversal due to high volatility, while the later phase
does not. These results suggest that more retail investors tend to trade during the daytime
due to remote work. Fifth, intraday reversal is also observed for other ETFs that track U.S.
market indices, though not for those that track international market indices. Finally, our
investment strategies based on intraday reversal signals are profitable for U.S. market
indices, but not for the other international market indices.
Our first contribution is to extend the intraday momentum literature, such as Gao
et al. (2018) and Baltussen et al. (2021), by incorporating the concept of opposing investor
clienteles observed by Berkman et al. (2012), Lou et al. (2019), Akbas et al. (2022), and
Kallinterakis and Karaa (2023). Heston et al. (2010) identify intraday reversal in crosssectional returns between the current and the next half-hour, and Baltussen et al. (2024)
report cross-sectional intraday reversal during the last half-hour of the trading day.
Moreover, Kang et al. (2022) propose two different mechanisms that generate cross-
4
Ranaldo and Santucci de Magistris (2022) also reveal that periods of high market stress lead
to greater mispricing in a high frequency currency market context.
4
sectional intraday reversal. In contrast, our study focuses on time-series reversal
occurring during the first-half hour after the market opens. Understanding of intraday
market mechanisms is substantial for day traders who close their positions within the
same trading day. Our study also differs from that of Berkman et al. (2012), Lou et al.
(2019), Akbas et al. (2022), and Kallinterakis and Karaa (2023), as we propose an intraday
reversal trading strategy and assess structural changes in market dynamics, including the
global financial crisis (GFC) and the COVID-19 pandemic.
We also contribute to the COVID-19 pandemic literature. The pandemic had
significant effects on stock markets as governments restricted commercial activity (Baker
et al., 2020) and lowered firms’ growth expectations (Gormsen and Koijen, 2020). More
retail investors participated in the stock markets through online retail brokerage platforms
such as Robinhood (Welch, 2022) and provided liquidity during this period (Ozik et al.,
2021). 5 This period differs from other market stress events, as it involved both
heightened market uncertainty and a surge in retail trading. The former strengthens
intraday reversal, while the latter weakens it, as more retail investors trade during the
daytime. Our findings indicate that the impact of heightened market uncertainty
dominates, reinforcing intraday reversal.
5
Welch (2022) presents empirical evidence that Robinhood users tend to trade large market value
firms, influencing the price of the S&P 500 ETF. Welch (2022) also indicates that the aggregation
of Robinhood users’ positions leads to a positive alpha, whereas Barber et al. (2022) and Ardia et
al. (2025) demonstrate that these users tend to invest in high-attention stocks, with increases in
their buying activity leading to negative returns. Eaton et al. (2022) find that Robinhood investors
employ momentum trading styles, while other retail investors adopt reversal trading styles.
5
The rest of the paper is organized as follows: Section 2 describes our data, Section 3
presents our main results, Section 4 conducts further analysis, and Section 5 provides the
conclusion.
2.Data
In this study, we primarily focus on the SPDR S&P 500 ETF Trust (SPY), one of the
largest ETFs listed on the U.S. market by market capitalization, which is traded between
9:30 a.m. and 4:00 p.m. Eastern Standard Time. SPY has served as a principal subject of
analysis in previous literature, such as Gao et al. (2018), Rosa (2022), and Kallinterakis
and Karaa (2023).6 Moreover, we employ nine additional ETFs in the robustness section.
See Table A1 and Section 4. Intraday data on one-minute prices and trading volumes are
obtained from LSEG Tick History. Our main analysis employs mid prices, while we
confirm that our results are robust when using bid-ask prices.7 Our main sample period
spans from January 2, 1996, to December 31, 2024, and the total number of observations
is 7,188. We explore the effects of major macroeconomic dates, obtaining historical
release dates for gross domestic product (GDP) and consumer price index (CPI) data from
the Federal Reserve Bank of St. Louis and historical release dates for the Federal Open
Market Committee (FOMC) minutes from the Federal Reserve Board.
To investigate intraday market dynamics, we follow Baltussen et al. (2021) and
define the following five intervals.
6
Summary statistics are reported in Table A2.
7
See Table A6.
6
(i)
Overnight (ON): From the market close on day 𝑡 −1 to the market open on day
𝑡.
(ii)
First half hour (FH): From the market opening on the day 𝑡 to the first 30
minutes.
(iii)
Middle of the day (M): From the end of the first 30 minutes until one hour before
the market closes.
(iv)
Second-to-last half hour (SLH): From one hour before the market closes
to the last 30 minutes.
(v)
Last half hour (LH): From the last 30 minutes until the market closes on day 𝑡.
Figure 1 summarizes these intervals. Based on these definitions, we calculate returns for
each interval as follows:
𝑃
𝑃
𝑃
𝑃
𝑅𝑂𝑁,𝑡 = 𝑃 𝑜,𝑡 − 1, 𝑅𝐹𝐻,𝑡 = 𝑃𝑓ℎ,𝑡 − 1, 𝑅𝑀,𝑡 = 𝑃𝑙𝑜ℎ,𝑡 − 1, 𝑅𝑆𝐿𝐻,𝑡 = 𝑃 𝑙ℎ,𝑡 − 1,
𝑐,𝑡−1
𝑜,𝑡
𝑓ℎ,𝑡
𝑙𝑜ℎ,𝑡
(1)
𝑃
𝑅𝐿𝐻,𝑡 = 𝑃 𝑐,𝑡 − 1,
𝑙ℎ,𝑡
where 𝑃𝑜,𝑡 indicates the open price on day 𝑡, 𝑃𝑐,𝑡−1 represents the close price on day
𝑡 − 1, 𝑃𝑓ℎ,𝑡 denotes the last price in the first 30 minutes on day 𝑡, 𝑃𝑙𝑜ℎ,𝑡 stands for the
price one hour before the market closes, and 𝑃𝑙ℎ,𝑡 is the first price in the last 30 minutes
before the market closes.
Adopting these intervals, the previous studies uncover important findings. For
instance, the information contained in 𝑅𝑂𝑁,𝑡 + 𝑅𝐹𝐻,𝑡 predicts 𝑅𝐿𝐻,𝑡 in stock markets
(Gao et al., 2018; Li et al., 2022), while the information from 𝑅𝑂𝑁,𝑡 + 𝑅𝐹𝐻,𝑡 + 𝑅𝑀,𝑡 +
𝑅𝑆𝐿𝐻,𝑡 forecasts 𝑅𝐿𝐻,𝑡 in stock futures markets (Baltussen et al., 2021). In contrast, we
7
investigate the relationship between 𝑅𝑂𝑁,𝑡 and 𝑅𝐹𝐻,𝑡 .
3.Empirical results
3.1 Intraday reversal
We begin by investigating whether 𝑅𝑂𝑁,𝑡 predicts 𝑅𝐹𝐻,𝑡 in the S&P 500 ETF market.
Following Gao et al. (2018) and Baltussen et al. (2021), we run the following regression:
𝑅𝐹𝐻,𝑡 = α + β𝑅𝑂𝑁,𝑡 + 𝑢𝑖,𝑡
(2)
where α and β represent estimated parameters and 𝑢𝑖,𝑡 indicates an error term. For
comparison, we replace 𝑅𝐹𝐻,𝑡 with 𝑅𝑀,𝑡 , 𝑅𝑆𝐿𝐻,𝑡 , and 𝑅𝐿𝐻,𝑡 , and repeat the estimation in
Equation (2).
Table 1 presents empirical evidence that 𝑅𝑂𝑁,𝑡 is negatively associated with
𝑅𝐹𝐻,𝑡 , indicating a return reversal, which we refer to as intraday reversal. This relationship
is stronger than the other three cases based on 𝑅 2 𝑠, and the value of 3.8% is relatively
higher than those found in intraday momentum studies (Gao et al., 2018; Li et al., 2022).8
The absolute magnitude of the coefficient is 9.2, which is comparable to U.S. intraday
momentum literature (Gao et al., 2018) and higher than that for other countries’ equity
indices (Li et al., 2022). The standard deviation of 𝑅𝑂𝑁,𝑡 is approximately 0.69, and a
one standard deviation change in 𝑅𝑂𝑁,𝑡 leads to a 6.3% change in 𝑅𝐹𝐻,𝑡 . 9 We also find
8
Note that these previous studies predict 𝑅𝐿𝐻,𝑡 , whereas our study forecasts 𝑅𝐹𝐻,𝑡 , and
therefore the high 𝑅 2 does not necessarily indicate that our model outperforms that of Gao et
al. (2018).
9
The standard deviation results are available upon request.
8
that 𝑅𝑂𝑁,𝑡 predicts 𝑅𝐿𝐻,𝑡 , consistent with the findings of Gao et al. (2018).10 However,
the coefficient indicates 6.4, which is lower than that reported by Gao et al. (2018). This
decline in intraday momentum effects is also reported by Rosa (2022).
We further explore whether lagged returns such as 𝑅𝐹𝐻,𝑡−1 , 𝑅𝑀,𝑡−1 , 𝑅𝑆𝐿𝐻,𝑡−1 ,
and 𝑅𝐿𝐻,𝑡−1 , are linked to 𝑅𝐹𝐻,𝑡 . Columns (b) to (e) in Table 2 demonstrate that these
lagged returns do not predict 𝑅𝐹𝐻,𝑡 . Moreover, column (f) shows that 𝑅𝑂𝑁,𝑡 remains
statistically significant at the 1% level after controlling for these variables. These results
indicate that our findings differ from those of Heston et al. (2010), who provide empirical
evidence that cross-sectional returns in the current half-hour predict returns in the same
time interval on the next day.
3.2 Effects of volatility and trading volume
Next, we examine whether volatility and trading volume impact intraday reversal. Gao et
al. (2018) find that intraday momentum tends to be stronger during periods of high
volatility and high trading volume. This is because high market uncertainty leads to
stronger market trends, as suggested by Zhang (2006). Following Gao et al. (2018), we
split the sample into two sub-samples based on the first 30 minutes’ volatility or trading
volume and estimate Equation (2) for each sub-sample.11
Panel A of Table 3 reports the estimation results for periods of high and low
10
See Table 16 in Gao et al. (2018).
11
As volume increases over time, we follow Gao et al. (2018) and define a high-volume period
for each year using the volume in the first 30 minutes.
9
volatility. We observe that high-volatility periods are associated with stronger reversal in
terms of both the magnitude of β and 𝑅 2 values. The absolute magnitude of β during
high volatility periods is 9.8, compared to 5.3 during low volatility periods. The latter
result is much smaller than the full-sample period in Table 2. The standard deviation of
𝑅𝑂𝑁,𝑡 is 0.91 and 0.37, respectively. 12 A one standard deviation change in 𝑅𝑂𝑁,𝑡 leads
to an 8.9% change in 𝑅𝐹𝐻,𝑡 during high volatility periods, while a 2.0% change during
low volatility periods. Our results are associated with the work of Ranaldo and Santucci
de Magistris (2022), who demonstrate that periods of high market stress lead to greater
mispricing. Berkman et al. (2012) and Akbas et al. (2022) report that daytime reversal is
primarily driven by overnight trading by retail investors. Our results suggest that retail
investors exert a strong influence on open prices, creating significant arbitrage
opportunities for institutional investors during daytime trading sessions. This leads to a
larger magnitude of intraday reversal. This interpretation aligns with the assumption of
Gao et al. (2018), who note that some institutional investors trade in the first half-hour of
daytime trading. Panel B of Table 3 presents the results for periods of high and low
volume. We observe a similar pattern to that seen in the volatility results in Panel A, while
the effect is more pronounced when sorting by volatility.
3.3 Subsample analysis
Having recognized the importance of market conditions, we focus on the effects of the
GFC from 2007 to 2009. Gao et al. (2018) and Li et al. (2022) report that intraday
12
The standard deviation results are not reported in Table 3 but available upon request.
10
momentum in stock markets was more pronounced during the GFC. Moreover, Huang et
al. (2023) observe that intraday momentum in the VIX futures market weakened during
the same period. Following Gao et al. (2018), we define the GFC period as spanning from
December 2, 2007, to June 30, 2009 and split the sample into two subsamples: the GFC
period and the period excluding the GFC.
Columns (a) and (b) in Table 4 present that intraday reversal was stronger during
the GFC period. The absolute magnitudes of β and 𝑅 2 increase from 7.8 to 15.3 and
from 2.8% to 9.1%, respectively. Our results suggest that retail investors’ risk perceptions
varied during the GFC, leading to an increase in trading activities, as demonstrated by
Hoffmann et al. (2013) and Baig et al. (2022). These factors create additional arbitrage
opportunities.
Next, to evaluate the persistence of intraday reversal, we divide the entire sample
into the first half (January 1996 – December 2009) and the second half (January 2010 –
December 2024). Columns (c) and (d) in Table 4 display that intraday reversal weakened
in the second half-period. This result is partially related to the effects of the GFC, while
columns (b) and (d) indicate that the second half result was weaker even compared to the
period excluding the GFC.13 These findings suggest the presence of an additional factor
contributing to the weakening of intraday reversal. One possible explanation is the
evolution of retail investors’ trading behavior due to technological advancements, such as
mobile apps and robo-advisors. Early literature, such as Barber and Odean (2000),
characterized retail investors as uninformed traders. However, recent studies suggest the
13
Rosa (2022) also reports that intraday momentum weakens after 2014.
11
opposite. For instance, Boehmer et al. (2021) adopt data from 2010 to 2015 and reveal
that some retail investors act as informed traders. Eaton et al. (2022) employ data from
2019 to 2021 and find that some retail investors contribute to market quality
improvements. These changes may have reduced arbitrage opportunities for institutional
investors, leading to a decline in intraday reversal.
3.4 Effects of the COVID-19 pandemic
This section examines whether the COVID-19 pandemic influenced intraday reversal.
The pandemic had two opposing impacts. First, the shift to remote work encouraged retail
investor activities during the daytime, reducing arbitrage opportunities for institutional
investors. For instance, Ozik et al. (2021) provide empirical evidence that retail investors
supplied liquidity during the lockdown. Second, the pandemic led to an increase in market
volatility (e.g., Baker et al., 2020). Higher volatility yielded more arbitrage opportunities,
enhancing intraday reversal, as discussed in Section 3.3.
To disentangle these effects, we estimate the following regression:
𝑅𝐹𝐻,𝑡 = α + 𝛽1 𝑅𝑂𝑁,𝑡 + 𝛽2 𝐷1 + 𝛽3 𝐷2 + 𝛽4 𝐷3 + 𝛽5 𝑅𝑂𝑁,𝑡 × 𝐷1
(3)
+ 𝛽6 𝑅𝑂𝑁,𝑡 × 𝐷2 + 𝛽7 𝑅𝑂𝑁,𝑡 × 𝐷3 + 𝑢𝑖,𝑡
where α, 𝛽1 , ⋯ , 𝛽7 represent estimated parameters, 𝐷1 stands for a dummy variable
that equals one if day t is in the GFC period and zero otherwise, 𝐷2 denotes a dummy
variable that equals one if day t is in Pandemic period I (January 21, 2020 – May 7, 2020)
and zero otherwise, and 𝐷3 indicates a dummy variable that equals one if day t is in
Pandemic period II (May 8, 2020 – December 31, 2020) and zero otherwise. Pandemic
12
period I refers to the beginning of the pandemic, with its terminal date corresponding to
the end of the U.S. lockdown (Ozik et al., 2021). During this period, the U.S. stock market
experienced a sharp decline (e.g., Gormsen and Koijen, 2020). In contrast, coronavirus
cases and deaths increased during the Pandemic period II, as noted by Huang et al. (2023),
whereas the stock market began to recover due to fiscal stimuli and monetary policy
actions (Gormsen and Koijen, 2020; Ramelli and Wagner, 2020).
Column (a) in Table 5 presents that the estimated parameters for the GFC dummy
and the cross term are statistically significant at the 5% and 10% levels, respectively. Both
parameters are negative, which confirms the findings in Table 4. Note that column (a) in
Table 4 employs only GFC period data, while column (a) in Table 5 suggests that the GFC
enhanced daily reversal even when using all data. Column (b) in Table 5 reports that the
estimated parameter for the cross-term between 𝑅𝑂𝑁,𝑡 and the Pandemic period I dummy
is statistically significant. The negative parameter value indicates that intraday reversal
strengthened during the Pandemic period I. This result suggests that large market shocks
are substantial to generate intraday reversal.
Column (c) in Table 5 displays that Pandemic period II is not linked to intraday
reversal. Although this period saw increased coronavirus cases and deaths, the stock
market was supported by fiscal and monetary policies, and few arbitrage opportunities
were observed. Moreover, individual traders tended to trade during the daytime due to
remote work, which also weakened intraday reversal. This pattern is consistent with the
subsample results in Table 4, which indicate that intraday reversal was weaker in the latter
half of the period.
Finally, column (d) in Table 5 adopts all three dummy variables, as in Equation
13
(3). We confirm the previous findings, the market turmoil during the GFC and the early
phase of the pandemic strengthened intraday reversal. Our results suggest that increases
in market volatility and arbitrage opportunities dominated the effects of retail investor
activities during lockdown and remote work. Huang et al. (2023) report that the GFC and
the pandemic had heterogeneous impacts on intraday momentum in the VIX futures
market, while our findings display that both crises influenced intraday reversal. This
difference may be related to the relatively lower trading volume of VIX futures during
the GFC compared to the S&P 500 ETF.
4.Further analysis
4.1 Other time frames
Our analysis of intraday reversal focuses on the first 30 minutes after the market opens.
Gao et al. (2018) and Huang et al. (2023) report that intraday momentum is not sensitive
to specific time frames. Following Hua et al. (2023), we employ returns over the first 15,
45, and 60 minutes and repeat the estimation in Equation (2). Table A3 reports that
intraday reversal is not sensitive to the choice of time frame, which aligns with findings
in the intraday momentum literature.
4.2 Signs of overnight returns
This section explores whether intraday reversal depends on the sign of the overnight
return. Gao et al. (2018) find that intraday momentum weakens when 𝑅𝑂𝑁,𝑡 + 𝑅𝐹𝐻,𝑡 < 0.
They interpret this finding that the cost of holding a short position is high, which reduces
arbitrage activities, as shown by Abreu and Brunnermeier (2002). We split the sample
14
based on the sign of 𝑅𝑂𝑁,𝑡 and estimate Equation (2). Table A4 reports that intraday
reversal is stronger when the sign is negative. Our findings also support the view that
holding short positions is costly.
4.3 Effects of U.S. macroeconomic announcement
This section examines whether U.S. macroeconomic announcements impact intraday
reversal. Gao et al. (2018) and Huang et al. (2023) find that intraday momentum
strengthens on U.S. macroeconomic announcement days. Additionally, Xiao et al.
(2020) focus on jumps during trading periods and find that scheduled macroeconomic
announcements lead to investor overreaction, resulting in reversal during the next 60
minutes. We focus on the release of the following three variables: the minutes of the
FOMC, GDP, and CPI. The FOMC minutes are released regularly at 2:15 p.m., with
meetings held eight times per year. GDP and CPI are released at 8:30 a.m. each month
before the market opens. To assess the effects of macroeconomic announcements, we
follow Huang et al. (2023) and estimate the following regression model:
𝑅𝐹𝐻,𝑡 = α + 𝛽1 𝑅𝑂𝑁,𝑡 + 𝛽2 𝐷𝑚 + 𝛽3 𝑅𝑂𝑁,𝑡 × 𝐷𝑚 + 𝑢𝑖,𝑡
(4)
where α, 𝛽1 , 𝛽2 , and 𝛽3 represent estimated parameters, and 𝐷𝑚 denotes a dummy
variable that equals one if day t has a macroeconomic announcement and zero otherwise.
Table A5 reports that macroeconomic announcements do not have a strong impact on
intraday reversal, contrasting with the findings of Gao et al. (2018) and Huang et al.
(2023). These results suggest that intraday reversal is driven by a different mechanism
from intraday momentum.
15
4.4 Other ETFs
This section explores whether intraday reversal is observed across different ETFs.
Following Gao et al. (2018), we employ nine additional ETFs with high trading volumes,
all of which are traded in the U.S. market. These ETFs include alternative stock indices,
international equity indices, and sector indices. The list of ETFs and their summary
statistics are provided in Tables A1 and A2 of Online Appendix.
Panel A of Table 6 presents that ETFs tracking U.S. alternative indices, such as
NASADAQ 100, Russel 2000, and Dow Jones Industrial Average, exhibit intraday
reversal. In contrast, Panel B demonstrates that ETFs tracking international equity indices
do not indicate a clear pattern. Gao et al. (2018), Baltussen et al. (2021), and Li et al.
(2022) observe intraday momentum across international stock markets, driven by
infrequent portfolio rebalancing, late-informed trading, and gamma hedging demand. Our
findings differ from these studies and suggest that intraday reversal is associated with a
different mechanism—specifically, overnight buying by retail investors and daytime
selling by arbitrageurs, as proposed by Berkman et al. (2012) and Akbas et al. (2022).
Some components of information related to international equity indices emerge overnight,
and arbitrage activities are less active for these ETFs.
We also assess the effects of the GFC and the COVID-19 pandemic for these
ETFs using Equation (3). Panel A of Table 7 presents that the coefficient of 𝐷1 and that
of 𝑅𝑂𝑁,𝑡 × 𝐷1 are negative and statistically significant, indicating that the GFC
strengthened intraday reversal for ETFs tracking U.S. indices. We observe that the late
phase of the pandemic did not affect intraday reversal, which is consistent with the results
16
for the S&P 500 ETF. Additionally, Panel B displays that the coefficient of 𝑅𝑂𝑁,𝑡 × 𝐷2
suggests that the early phase of the pandemic had positive impacts on ETFs tracking
international stock indices.
4.5 Trading strategy
This section explores whether intraday reversal leads to a profitable trading strategy.
Following Gao et al. (2018) and Baltussen et al. (2021), we consider the following trading
strategy:
𝑅𝐹𝐻,𝑡 ,
if
𝑅𝑂𝑁,𝑡 < 0
(5)
−𝑅𝐹𝐻,𝑡
otherwise.
Gao et al. (2018) and Baltussen et al. (2021) take positions during the last half hour, while
𝜂(𝑟) = {
we construct a position during the first half hour. Our benchmark strategy always takes a
long position (Baltussen et al., 2021).
Panel A of Table 8 reports that intraday reversal strategies outperform the
benchmark strategies for the U.S market indices. For instance, the average annual return
difference between intraday reversal and benchmark strategies for the SPY is 8.2%, which
is statistically significant at the 1% level and comparable to the results in Gao et al.
(2018).14 The Sharpe ratio ranges from 0.95 to 1.51, and these values are similar to those
reported by Gao et al. (2018). Figure 2 illustrates the cumulative return for this strategy.
14
Even after accounting for transaction costs, we confirm that the return difference for the SPY
remains of the same magnitude (8.5%). See Table A6. We do not report the other indices’
results with transaction costs because LSEG Tick History sometimes reports unreasonable bidask spreads.
17
Panel C demonstrates that the intraday reversal strategy also creates positive returns for
the sector indices. In contrast, Panel B exhibits that the average returns are negative for
the international indices. These findings are consistent with in-sample regression results
in Table 6.
5.Conclusion
This study extends the literature on intraday momentum (e.g., Gao et al., 2018; Baltussen
et al., 2021) and investigates intraday time series reversal. We find that the overnight
return predicts the first half-hour return for the S&P 500 ETF. We attribute this market
pattern to the presence of opposing investor clienteles—specifically, overnight buying by
retail investors and daytime selling by arbitrageurs—as proposed by Berkman et al.
(2012), Lou et al. (2019), and Akbas et al. (2022). Supporting this interpretation, intraday
reversal weakened after the 2010s, driven by retail traders benefiting from the
development of trading tools. Furthermore, we find that intraday reversal remained weak
during the COVID-19 pandemic period, except during episodes of temporarily
heightened volatility. These findings suggest that the shift to remote work facilitated
increased daytime trading by retail investors.
Intraday reversal exhibits both similarities to and differences from intraday
momentum. High-volatility periods and negative overnight returns strengthen intraday
reversal, which is consistent with the intraday momentum literature (e.g., Gao et al. ,2018).
However, in contrast to prior intraday momentum studies (Gao et al., 2018; Baltussen et
al., 2021; Li et al., 2022), intraday reversal appears weak for international stock indices.
We acknowledge that we cannot access direct transaction data for retail and institutional
18
investors. Directly assessing the opposing investor clientele hypothesis for intraday
reversal is a topic for future research.
19
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23
Table 1 In-sample predictability of the overnight return
Intercept
βON
R2 (%)
FH
M
SLH
LH
0.16
0.34
0.49
-0.62
(0.45)
(0.42)
(1.27)
(-1.28)
-9.19 ***
3.32
1.53
6.39 ***
(-5.16)
(1.52)
(1.10)
(2.60)
3.77
0.08
0.09
1.24
Notes: This table reports time series regression results for the S&P 500 ETF. We run the
following regressions: 𝑅𝑖,𝑡 = α + β𝑅𝑂𝑁,𝑡 + 𝑢𝑖,𝑡 where 𝑅𝑖,𝑡 indicates 𝑅𝐹𝐻,𝑡
𝑅𝑀,𝑡 , 𝑅𝑆𝐿𝐻,𝑡 , and 𝑅𝐿𝐻,𝑡 , respectively. ON indicates Overnight, FH denotes the First half
an hour, M stands for the Middle of the day, SLH represents the Second-to-last half an
hour, and LH indicates the Last half an hour. The Newey and West (1987) robust tstatistics are reported in parentheses. The adjusted 𝑅 2 (%) is reported in the last row. ∗,
∗∗, and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
The sample period spans from January 2, 1996 to December 31, 2024.
24
Table 2 Regression with lagged variables
Intercept
βON(t)
(a)
(b)
(c)
(d)
(e)
(f)
0.16
-0.09
-0.08
-0.09
-0.08
0.20
(0.45)
(-0.24)
(-0.23)
(-0.24)
(-0.24)
(0.56)
-9.19 ***
-8.81 ***
(-5.16)
(-4.95)
βFH(t-1)
-4.07
-2.56
(-1.28)
βM(t-1)
(-0.93)
-0.59
-0.33
(-0.53)
(-0.33)
βSLH(t-1)
0.75
1.42
(0.30)
(0.67)
βLH(t-1)
2
R (%)
3.77
0.15
0.00
-0.01
-0.52
-2.48
(-0.18)
(-1.00)
-0.01
3.50
Notes: This table reports time series regression results for the S&P 500 ETF. We run the
following regressions: 𝑅𝐹𝐻,𝑡 = α + β𝑅𝑖,𝑡 + 𝑢𝑖,𝑡 where 𝑅𝑖,𝑡 indicates 𝑅𝑂𝑁,𝑡 , 𝑅𝐹𝐻,𝑡−1 ,
𝑅𝑀,𝑡−1, 𝑅𝑆𝐿𝐻,𝑡−1 , and 𝑅𝐿𝐻,𝑡−1. ON indicates Overnight, FH denotes the First half an hour,
M stands for the Middle of the day, SLH represents the Second-to-last half an hour, and
LH indicates the Last half an hour. The Newey and West (1987) robust t-statistics are
reported in parentheses. The adjusted 𝑅 2 (%) is reported in the last row. ∗, ∗∗, and ∗∗∗
indicate statistical significance at the 10%, 5%, and 1% levels, respectively. The sample
period spans from January 2, 1996 to December 31, 2024.
25
Table 3 Regression results during different market conditions
Panel A: Realized volatility
Intercept
βON(t)
R2 (%)
Panel B: Volume
Low
High
Low
High
-0.26
0.38
0.90 **
-0.60
(-0.72)
(0.64)
(2.34)
(-0.97)
-5.34 ***
-9.82 ***
-8.75 ***
-9.30 ***
(-4.36)
(-4.96)
(-4.37)
(-4.59)
0.77
4.79
2.00
4.29
Notes: This table reports time series regression results for the S&P 500 ETF. We run the
following regressions: 𝑅𝐹𝐻,𝑡 = α + β𝑅𝑂𝑁,𝑡 + 𝑢𝑖,𝑡 . ON indicates Overnight and FH
denotes the First half an hour. We split the sample into two sub-samples based on the first
30 minutes volatility or trading volume. The Newey and West (1987) robust t-statistics
are reported in parentheses. The adjusted 𝑅 2 (%) is reported in the last row. ∗, ∗∗, and
∗∗∗ indicate statistical significance at the 10%, 5%, and 1% levels, respectively. The
sample period spans from January 2, 1996 to December 31, 2024.
26
Table 4 Subsample results
Intercept
βON
R2 (%)
(a)
(b)
(c)
(d)
GFC
Ex GFC
1996 － 2009
2010 － 2024
-4.86 **
0.39
-0.61
0.90 **
(-1.97)
(1.14)
(-1.00)
(2.25)
-15.26 ***
-7.84 ***
-12.62 ***
-5.99 *
(-4.17)
(-4.05)
(-6.43)
(-1.90)
9.07
2.82
5.79
2.01
Notes: This table reports time series regression results for the S&P 500 ETF. We run the
following regressions: 𝑅𝐹𝐻,𝑡 = α + β𝑅𝑂𝑁,𝑡 + 𝑢𝑖,𝑡 . ON indicates Overnight and FH
denotes the First half an hour. Column (a) includes the GFC period spanning from
December 2, 2007, to June 30, 2009 based on Gao et al. (2018). Column (b) employs the
data excluding the GFC period. Columns (c) and (d) split the data sample into the first
half (January 1996 – December 2009) and the second half (January 2010 – December
2024) periods. The Newey and West (1987) robust t-statistics are reported in parentheses.
The adjusted 𝑅 2 (%) is reported in the last row. ∗, ∗∗, and ∗∗∗ indicate statistical
significance at the 10%, 5%, and 1% levels, respectively. The sample period spans from
January 2, 1996 to December 31, 2024.
27
Table 5 Effects of the GFC and the COVID-19 pandemic
(a)
Intercept
βON
βD1
(b)
(c)
(d)
0.39
0.16
0.12
0.32
(1.15)
(0.43)
(0.33)
(0.92)
-7.84 ***
-8.10 ***
-9.30 ***
-6.15 ***
(-3.95)
(-5.33)
(-5.09)
(-4.32)
-5.25 **
-5.18 **
(-2.11)
(-2.26)
βD2
-4.58
-4.62
(-0.49)
(-0.61)
βD3
βON×D1
1.36
(0.65)
(0.63)
-7.42 *
-9.11 **
(-1.77)
(-2.22)
βON×D2
-7.90 *
-9.86 **
(-1.69)
(-2.23)
βON×D3
R2 (%)
1.56
4.25
4.10
2.89
-0.26
(1.02)
(-0.12)
3.77
4.72
Notes: This table reports time series regression results for the S&P 500 ETF. In column
(d), we run the following regressions: 𝑅𝐹𝐻,𝑡 = α + 𝛽1 𝑅𝑂𝑁,𝑡 + 𝛽2 𝐷1 + 𝛽3 𝐷2 + 𝛽4 𝐷3 +
𝛽5 𝑅𝑂𝑁,𝑡 × 𝐷1 + 𝛽6 𝑅𝑂𝑁,𝑡 × 𝐷2 + 𝛽7 𝑅𝑂𝑁,𝑡 × 𝐷3 + 𝑢𝑖,𝑡 , 𝐷1 stands for a dummy variable
that equals one if day t is in GFC period (December 2, 2007, to June 30, 2009) and zero
otherwise, 𝐷2 denotes a dummy variable that equals one if day t is in Pandemic period I
(January 21, 2020 – May 7, 2020) and zero otherwise, and 𝐷3 indicates a dummy
variable equals one if day t is in Pandemic period II (May 8, 2020 – December 31, 2020)
and zero otherwise. Columns (a) – (c) include one of these dummy variables. The Newey
and West (1987) robust t-statistics are reported in parentheses. The adjusted 𝑅 2 (%) is
reported in the last row. ∗, ∗∗, and ∗∗∗ indicate statistical significance at the 10%, 5%,
and 1% levels, respectively. The sample period spans from January 2, 1996 to December
31, 2024.
28
Table 6 Results of other ETFS
Panel A: Alternative indices
QQQ
Intercept
1.48 **
(2.56)
βON
-9.03 ***
(-3.10)
IWM
-1.69 ***
(-2.70)
-5.63 **
(-2.12)
2.98
2.58
R2 (%)
Panel B: International equity indices
EEM
FXI
Intercept
-2.88 ***
-1.82 ***
(-4.73)
(-3.20)
βON
-1.71 *
-1.65 *
(-1.81)
(-1.94)
0.49
R2 (%)
Panel C: Sector indices
XLF
Intercept
-0.79
(-1.03)
βON
-16.50 ***
(-5.68)
R2 (%)
8.30
1.97
DIA
-0.14
(-0.34)
-8.07 ***
(-5.16)
0.43
EFA
-1.20 ***
(-4.15)
-2.33 **
(-2.44)
VWO
-5.51 ***
(-7.80)
-1.49
(-0.84)
0.16
0.74
IYR
-5.07 ***
(-5.92)
-5.30
(-1.35)
0.22
Notes: This table reports time series regression results for nine ETFs. Panel A includes
ETFs for U.S. market indices such as NASADAQ100(QQQ), Russel 2000 (IWM), and
Dow Jones Industrial Average (DIA). Panel B includes ETF for international equity
indices such as MSCI Emerging Markets (EEM), China Large-Cap (FXI), MSCI EAFE
(EFA), and Emerging Markets (VWO). Panel C includes ETFs for sector indices such as
financial (XLF) and real estate (IYR). We run the following regressions: 𝑅𝐹𝐻,𝑡 = α +
β𝑅𝑂𝑁,𝑡 + 𝑢𝑖,𝑡 . ON indicates Overnight and FH denotes the First half an hour. The Newey
and West (1987) robust t-statistics are reported in parentheses. The adjusted 𝑅 2 (%) is
reported in the last row. ∗, ∗∗, and ∗∗∗ indicate statistical significance at the 10%, 5%,
and 1% levels, respectively. The starting date of the sample varies by ETF, as shown in
Table A2, while the end date is December 31, 2024.
29
Table 7 Effects of the GFC and the COVID-19 pandemic for other ETFs
Panel A: Alternative US indices
Intercept
βON
βD1
βD2
βD3
βON×D1
βON×D2
βON×D3
R2 (%)
Panel B: International equity indices
IWM
DIA
EEM
-1.25 **
0.28
(-2.22)
-2.67 *
(-1.69)
Panel C: Sector indices
FXI
EFA
VWO
XLF
IYR
-2.42 ***
-1.58 ***
-0.97 ***
-4.15 ***
0.62
-3.23 ***
(0.74)
(-4.66)
(-2.91)
(-3.62)
(-7.27)
(1.03)
(-4.98)
-5.86 ***
-0.12
-0.08
-1.62 **
-0.38
-9.92 ***
-1.02
(-3.07)
(-0.28)
(-0.10)
(-2.09)
(-0.34)
(-6.63)
(-0.44)
-7.58 **
-6.15 ***
-7.71 **
-1.35
-2.25
-17.92 ***
-23.93 ***
-31.41 ***
(-1.98)
(-3.21)
(-1.97)
(-0.34)
(-1.10)
(-3.26)
(-3.83)
(-3.95)
-5.57
-14.32 ***
-3.90
-10.99 *
-8.20
-4.65
-13.54
-6.13
(-0.38)
(-2.88)
(-0.58)
(-1.68)
(-1.63)
(-0.75)
(-0.89)
(-0.42)
0.64
0.59
2.76
-0.81
0.12
3.12
-2.32
-5.25
(0.14)
(0.31)
(1.10)
(-0.36)
(0.07)
(1.24)
(-0.51)
(-1.16)
-24.38 ***
-8.51 *
-2.57
-2.64
-3.14 **
-0.39
-17.37 ***
-25.04 ***
(-4.16)
(-1.75)
(-1.11)
(-1.33)
(-2.11)
(-0.12)
(-3.01)
(-3.05)
2.48
-4.88 *
-12.68 ***
-10.35 ***
-5.68 ***
-13.70 ***
-6.45 *
2.72
(0.51)
(-1.90)
(-4.16)
(-5.34)
(-3.75)
(-4.03)
(-1.84)
(0.35)
-7.16
-0.60
-3.68
-1.76
-0.87
-3.15
-0.83
2.44
(-1.35)
(-0.22)
(-1.07)
(-0.60)
(-0.50)
(-0.79)
(-0.16)
(0.20)
3.80
3.30
1.64
1.26
2.96
2.08
11.01
4.52
Notes: This table reports time series regression results for the other eight ETFs. We run
the following regressions: 𝑅𝐹𝐻,𝑡 = α + 𝛽1 𝑅𝑂𝑁,𝑡 + 𝛽2 𝐷1 + 𝛽3 𝐷2 + 𝛽4 𝐷3 + 𝛽5 𝑅𝑂𝑁,𝑡 ×
𝐷1 + 𝛽6 𝑅𝑂𝑁,𝑡 × 𝐷2 + 𝛽7 𝑅𝑂𝑁,𝑡 × 𝐷3 + 𝑢𝑖,𝑡 , 𝐷1 stands for a dummy variable that equals
one if day t is in GFC period (December 2, 2007, to June 30, 2009) and zero otherwise,
𝐷2 denotes a dummy variable that equals one if day t is in Pandemic period I (January 21,
2020 – May 7, 2020) and zero otherwise, and 𝐷3 indicates a dummy variable equals one
if day t is in Pandemic period II (May 8, 2020 – December 31, 2020) and zero otherwise.
Panel A includes ETFs for U.S. market indices such as Russel 2000 (IWM), and Dow
Jones Industrial Average (DIA). Panel B includes ETF for international equity indices
such as MSCI Emerging Markets (EEM), China Large-Cap (FXI), MSCI EAFE (EFA),
and Emerging Markets (VWO). Panel C includes ETFs for sector indices such as financial
(XLF) and real estate (IYR). We do not use the NASDAQ100 index (QQQ) due to the
short sample period. The Newey and West (1987) robust t-statistics are reported in
parentheses. The adjusted 𝑅 2 (%) is reported in the last row. ∗, ∗∗, and ∗∗∗ indicate
statistical significance at the 10%, 5%, and 1% levels, respectively. The starting date of
the sample varies by ETF, as shown in Table A2, while the end date is December 31, 2024.
30
Table 8 Result of trading strategies
Panel A: US market indices
SPY
Avg ret (%)
7.76 ***
ΔAvg ret (%)
8.15 ***
SR
1.51
Success (%)
52.20
Panel B: International equity indices
EEM
Avg ret (%)
-1.13
ΔAvg ret (%)
6.19
SR
-0.16
Success (%)
46.59
Panel C: Sector indices
XLF
Avg ret (%)
18.63 ***
ΔAvg ret (%)
22.10 ***
SR
1.89
Success (%)
50.38
QQQ
6.21 ***
3.57
0.95
50.43
IWM
9.30 ***
14.14 ***
1.13
49.76
DIA
6.58 ***
7.43 ***
1.24
50.70
FXI
-2.15 ***
2.44
-0.32
47.22
EFA
5.27 ***
8.22 ***
1.52
51.91
VWO
-3.90 **
10.07 ***
-0.51
45.39
IYR
7.50 ***
21.02 ***
0.75
50.95
Notes: This table reports the annualized return (Avg ret), the annualized excess return
(∆Avg ret), Sharpe ratio (SR) and success ratio (Success) for the market timing strategy
in Equation (5). The excess return is calculated as the return difference between intraday
reversal and always long strategies. Panel A includes ETFs for U.S. market indices such
as S&P500(SPY), NASADAQ100(QQQ), Russel 2000 (IWM), and Dow Jones Industrial
Average (DIA). Panel B includes ETF for international equity indices such as MSCI
Emerging Markets (EEM), China Large-Cap (FXI), MSCI EAFE (EFA), and Emerging
Markets (VWO). Panel C includes ETFs for sector indices such as financial (XLF) and
real estate (IYR). ∗, ∗∗, and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1%
levels, respectively. The starting date of the sample varies by ETF, as shown in Table A2,
while the end date is December 31, 2024.
31
Figure 1 Intraday time intervals
Notes: This figure illustrates the five intraday time intervals based on Baltussen et al.
(2021).
32
Figure 2 Cumulative return of the trading strategy
Notes: This figure displays cumulative returns of the trading strategy based on Equation
(4). We use the SPY and do not include trading costs. The sample period spans from
January 2, 1996 to December 31, 2024.
33
Intraday time series reversal
26 November 2025
Online Supplement, Not for Publication
34
Table A1 List of ETFs
Symbol
Name
SPY
S&P 500
QQQ
Powershare NASDAQ 100
XLF
Financial Select Sector SPDR
IWM
iShares Russell 20 0 0 ETF
DIA
Dow Jones Industrial Average ETF
EEM
iShares MSCI Emerging Markets ETF
FXI
iShares China Large-Cap ETF
EFA
iShares MSCI EAFE ETF
VWO
Emerging Markets ETF
IYR
iShares US Real Estate ETF
Notes: This table reports the list of ETFs. SPY is used in our main analysis.
35
Table A2 Summary statistics
Avg (%) Std dev (%) Skewness
Kurtosis
Max (%)
Min (%)
Inception
N
SPY
-0.38
5.19
0.29
12.08
3.62
-3.19
3-Jan-96
7,188
QQQ
2.64
6.55
0.37
6.15
4.06
-2.54
24-Mar-11
3,466
XLF
-3.46
10.05
-0.51
16.38
6.68
-8.80
23-Dec-98
6,521
IWM
-4.84
8.31
-0.03
6.53
5.18
-4.37
30-May-00
6,159
DIA
-0.85
5.32
-0.08
10.41
3.80
-3.27
21-Jan-98
6,748
EEM
-7.32
7.02
-0.06
13.54
4.75
-5.80
14-Apr-03
5,460
FXI
-4.59
6.75
0.38
8.78
4.49
-3.31
11-Oct-04
5,089
EFA
-2.95
3.48
0.84
17.78
3.21
-2.03
20-Aug-01
5,849
VWO
-13.98
7.68
0.77
26.67
7.24
-4.17
11-Mar-05
4,986
IYR
-12.71
9.68
-0.41
14.42
6.04
-7.57
20-Jun-00
6,047
Notes: This table reports the annualized mean, annualized standard deviation, skewness,
kurtosis, daily maximum, daily minimum, inception date, and total number of
observations (N) for the return on each ETF. SPY is used in our main analysis.
36
Table A3 Results of different time frames
Intercept
βON
R2 (%)
15 minutes
45 minutes
60 minutes
-0.10
-0.30
0.08
(-0.32)
(-0.66)
(0.17)
-5.94 ***
-9.47 ***
-9.67 ***
(-6.11)
(-5.08)
(-5.30)
2.70
2.56
2.21
Notes: This table reports time series regression results for the S&P 500 ETF. We run the
following regressions: 𝑅𝑖,𝑡 = α + β𝑅𝑂𝑁,𝑡 + 𝑢𝑖,𝑡 where 𝑅𝑖,𝑡 indicates the return during
the first 15, 45, and 60 minutes, respectively. ON indicates Overnight. The Newey and
West (1987) robust t-statistics are reported in parentheses. The adjusted 𝑅 2 (%) is
reported in the last row. ∗, ∗∗, and ∗∗∗ indicate statistical significance at the 10%, 5%,
and 1% levels, respectively.
37
Table A4 Signs of overnight returns
Positive
Intercept
βON
R2 (%)
Negative
-0.59
-3.39 ***
(-0.60)
(-2.78)
-5.41 **
-14.65 ***
(-2.14)
(-5.27)
0.68
6.35
Notes: This table reports time series regression results for the S&P 500 ETF. We run the
following regressions: 𝑅𝐹𝐻,𝑡 = α + β𝑅𝑂𝑁,𝑡 + 𝑢𝑖,𝑡 . ON indicates Overnight and FH
denotes the First half an hour. We split the sample into two sub-samples based on the sign
of the overnight return. The Newey and West (1987) robust t-statistics are reported in
parentheses. The adjusted 𝑅 2 (%) is reported in the last row. ∗, ∗∗, and ∗∗∗ indicate
statistical significance at the 10%, 5%, and 1% levels, respectively.
38
Table A5 Effects of U.S. macroeconomic announcement
Intercept
βON(t)
βDm
βON(t)×Dm
2
R (%)
(a)
(b)
(c)
(d)
FOMC
GDP
CPI
ALL
0.05
0.06
0.11
-0.08
(0.13)
(0.15)
(0.30)
(-0.21)
-9.27 ***
-9.13 ***
-9.46 ***
-9.43 ***
(-5.12)
(-4.86)
(-5.07)
(-4.80)
3.40 *
2.07
1.14
1.82
(1.68)
(1.27)
(0.59)
(1.59)
2.48
1.58
5.31
1.89
(0.45)
(0.38)
(1.28)
(0.58)
3.79
3.77
3.81
3.80
Notes: This table reports the effects of macroeconomic announcements on intraday
reversal. We run the following regression: 𝑅𝐹𝐻,𝑡 = α + 𝛽1 𝑅𝑂𝑁,𝑡 + 𝛽2 𝐷𝑚 + 𝛽3 𝑅𝑂𝑁,𝑡 ×
𝐷𝑚 + 𝑢𝑖,𝑡 where 𝐷𝑚 denotes a dummy variable that equals one if day t has a
macroeconomic announcement and zero otherwise. Column “ALL” includes
macroeconomic announcements of the FOMC, the GDP, and the CPI. ON indicates
Overnight and FH denotes the First half an hour. The Newey and West (1987) robust tstatistics are reported in parentheses. The adjusted 𝑅 2 (%) is reported in the last row. ∗,
∗∗, and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
39
Table A6 Effects of bid-ask spreads
Avg ret (%)
Excess ret (%)
Std dev(%)
SR
Skewness
Kurtosis Success (%)
5.16
1.51
1.31
11.91
52.20
5.19
-0.07
0.29
12.08
48.93
5.14
0.94
1.35
12.41
50.67
5.20
-0.70
0.26
11.85
47.19
Panel A: Without trading costs
η
Always long
7.76 ***
8.15 ***
(6.99)
(5.95)
-0.38
(-0.42)
Panel B: With trading costs
η
Always long
4.83 ***
8.49 ***
(4.60)
(5.60)
-3.66 ***
(-4.05)
Notes: This table reports a comparison of trading strategies using mid prices and bid-ask
prices. We report annualized mean (Avg ret), annualized standard deviation (Std dev),
Sharpe ratio (SR), skewness, kurtosis and success ratio (Success) for the market timing
strategy in Equation (5). The row of “Always long” indicates the performance of the
benchmark strategy. Panel A demonstrates the results without trading costs, which are
identical to the SPY results in Table 8, while Panel B displays the results with trading
costs using bid-ask prices. The Newey and West (1987) robust t-statistics are reported in
parentheses. ∗, ∗∗, and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% levels,
respectively. The sample period spans from January 2, 1996 to December 31, 2024.
40