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Infrequent Rebalancing, Return Autocorrelation, and Seasonality

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Abstract

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.

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... These authors provide detailed economic motivation and intuition behind the given mechanism of short-term reversal. 5 We use the structure of stock prices derived in the above models without its micro-economical inference to consider its implications for measuring stock returns. ...
... Furthermore, aggregation of stock allocations across investors of a given type makes their total allocation become a macro state variable which affect the stock prices. A more rigorous explanation of this conclusion based on equilibrium analysis could be found, for example, in Vayanos and Wooley (2013), Isaenko (2015), and Bogousslavsky (2016). ...
... The limitations of this assumption are discussed at the end of this section. The linearity of the stock price is proven in Vayanos and Wooley (2013), Bogousslavsky (2016), and Isaenko (2022) in a general equilibrium framework and results from normality of the dividends and endowment processes and a constant absolute risk aversion of investors. Consequently, we set: where β Y and β N are positive, while ...
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We consider a linear model of stock returns derived from equilibrium analysis and study how trading strategy of a marginal investor affects the relation between estimates of the moments of stock returns and measuring frequency. Subject to the impact of the stock allocations of the marginal investor on the stock price, the rate of her trading to the optimal allocations and the amount of idiosyncratic risk, the estimates of the moments of stock returns may significantly change with measuring frequency. This change for the standard deviation of the conditional Sharpe ratio is in tens of times stronger than for unconditional moments of stock returns in the case when the marginal investor converges to her optimal allocation within a few days or weeks.
... Gao et al. (2018) conjecture that one of the biggest sources of this intraday momentum effect is the overnight accumulation of information. If this conjecture holds, we hypothesise that the strength of the ITSM effect should be affected by the liquidity provision at the market open (Bogousslavsky, 2016), the information arrival process, i.e. whether new information comes as a shock or is slowly perceived (Da et al., 2014), clarity of the economic implications for new information (Daniel and Titman, 1999;Zhang, 2006), and cultural differences (Chui et al., 2010). Our evidence from both the cross-section and time series 4 Chapter 1. Introduction shows that the ITSM effect is stronger when liquidity is low, volatility is high, and new information is discrete. ...
... Gao et al. (2018) assert that the ITSM effect originates from the overnight information accumulation and suggest two possible explanations. The first explanation is the infrequent trading behavior of investors that has been documented both empirically and theoretically (Bogousslavsky, 2016;Duffie, 2010;Heston et al., 2010;Rakowski and Wang, 2009). The model by Bogousslavsky (2016) suggests that infrequent traders who absorb a liquidity shock by taking a sub-optimal position will have the intention to unload the sub-optimal position at the next active period, causing another liquidity shock that is in the same direction as the original one. ...
... The first explanation is the infrequent trading behavior of investors that has been documented both empirically and theoretically (Bogousslavsky, 2016;Duffie, 2010;Heston et al., 2010;Rakowski and Wang, 2009). The model by Bogousslavsky (2016) suggests that infrequent traders who absorb a liquidity shock by taking a sub-optimal position will have the intention to unload the sub-optimal position at the next active period, causing another liquidity shock that is in the same direction as the original one. Based on this model, we hypothesize that ITSM is associated with market liquidity provision. ...
Thesis
The thrust of this thesis is to shed light on the intraday predictability of stock returns and its association with market microstructure and behavioural biases of traders. The first essay looks into an intraday effect of return continuation, namely intraday time series momentum (ITSM), in an international setting. Employing high-frequency trading data, we show that ITSM is economically sizeable and statistically significant both in- and out-of-sample in most of the 16 developed markets in our sample. To obtain a deeper understanding of the drivers behind the phenomenon, we propose four hypotheses based on existing theories of market microstructure and investor behaviour. We empirically test the hypotheses in both cross-sectional and time series dimensions, finding that ITSM is stronger when liquidity is low, volatility is high, and new information is discrete. The evidence suggests that the ITSM is driven by both market microstructure and behavioural factors. In the second essay, we turn our attention to the intraday cross-sectional predictability of stock returns, again in an international setting. Portfolio sorts and Fama-Macbeth regressions show that the first half-hour return and the first half-hour volatility have strong cross-sectional predictability on the last half-hour return, both economically and statistically. Portfolios that exploit the predictability of these two intraday characteristics produce positive and statistically significant alphas when regressed against passive benchmarks, suggesting remarkable economic gains. A comparison of our crosssectional portfolios and a strategy based on the intraday time series momentum (ITSM) shows that our strategies provide extra benefit to ITSM. This chapter contributes to the recent growing literature on intraday return predictability and asset pricing. Finally, the third essay is concerned with the dynamic overnight-intraday return relationship and intraday investor heterogeneity. We find that there exists a significant reversal effect at the market open that converts to the momentum documented in Gao et al. (2018) at the market close. More importantly, we show that the significance of the opening reversal is almost entirely from days with negative overnight returns whereas that of the closing momentum is mainly from days with positive overnight return days The asymmetric overnight-intraday return relationship on the two types of days implies heterogeneity in intraday traders. A closer examination of the opening reversal shows that the effect is stronger on days with larger overnight volatility and trade size, and over periods of financial crisis, recessions, and greater uncertainty. Practically, we document strong economic significance of strategies that are based on the opening reversal.
... We also find that the more illiquid the crude oil market is, the stronger the predictability of the third half-hour returns, which is consistent with the liquidity provision argument made by Bogousslavsky (2016) and Elaut et al. (2018). Hence, we conclude that EIA announcements contribute to intraday momentum because they attract more informed traders, and the period surrounding these news releases is often associated with a reduction in liquidity. ...
... Hence, trading in the same direction as the first halfhour can yield a positive return in the last half-hour (Gao et al., 2018). Second, the model of liquidity provision suggests that, at the beginning of a trading session, temporary order imbalances may arise as market participants react to news released overnight (Bogousslavsky, 2016). Hence, during the first half-hour of trading, liquidity providers supply liquidity to earn the bid-ask spread. ...
... In Panel C of Table 5, we also observe the importance of market liquidity for the predictability of the third half-hour returns. Specifically, a positive relationship with the end-of-day returns is observed when illiquidity of third half-hour returns is high, i.e., liquidity is an important driver of the predictability of the third half-hour returns, consistent with the model of liquidity provision (Bogousslavsky, 2016;Cohen andFrazzini, 2008, Elaut et al., 2018). Therefore, the predictability of third half-hour returns can be attributed to both informed trading and liquidity provision. ...
... The empirical findings are supported by two theoretical explanations, the model of infrequent portfolio rebalancing (Bogousslavsky, 2016) and later-informed investors (Cushing and Madhavan). The theoretical model of Bogousslavsky (2016) shows that some investors balance their portfolios infrequently due to slow-moving capital, which leads to the positive correlation between the predictive half-hour return earlier in the day and its last half-hour counterpart the same day. ...
... The empirical findings are supported by two theoretical explanations, the model of infrequent portfolio rebalancing (Bogousslavsky, 2016) and later-informed investors (Cushing and Madhavan). The theoretical model of Bogousslavsky (2016) shows that some investors balance their portfolios infrequently due to slow-moving capital, which leads to the positive correlation between the predictive half-hour return earlier in the day and its last half-hour counterpart the same day. The other theoretical model that can explain our results involves the presence of late-informed investors who access and process market information more slowly than others, and thus have to wait to trade at the end of the trading day (Cushing and Madhavan, 2000). ...
... In this section, we provide a brief discussion about the economic mechanism driving the intraday predictability by referring to previous literature such as Gao et al. (2018) and Zhang et al. (2019). The first theoretical explanation relates to the infrequent portfolio rebalancing model of Bogousslavsky (2016), which demonstrates theoretically that some traders tend to delay their portfolio rebalancing till the market close instead of trading instantaneously when the profitable signal is released, which results in the positive correlation. Important research by Duffie (2010) emphasizes the impacts of slow-moving capital and infrequent decisions made by investors. ...
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Using high-frequency data of crude oil, gold, and silver exchange-traded funds (ETFs) and their related volatility indices, we analyse patterns of intraday return predictability, also called intraday momentum, in each market. We find that intraday return predictability exists in all the markets, but the patterns of predictability differ for each market, with different half-hour returns, not necessarily the first half-hour returns of the trading day, exhibiting significant predictability for their last half-hour counterparts, depending on the specific market. The intraday return predictability is stronger on days of higher volatility and larger jumps. Substantial economic value can be generated by a market timing strategy which is constructed upon the intraday momentum, in all the markets under study. Possible theoretical explanations for the intraday return predictability are infrequent portfolio rebalancing investors and late-informed investors.
... The empirical findings can be theoretically explained by infrequent portfolio rebalancing (e.g., Bogousslavsky, 2016) and the presence of late-informed investors (e.g., Baker and Wurgler, 2006;Cohen and Frazzini, 2008;Huang et al., 2015). We also compare the intraday trading volume pattern in the crude oil market with that in the equity markets, documented by Gao et al. (2018) and Zhang et al. (2019a). ...
... contribute most predictability. One possible explanation is that in oil market intraday momentum is more closely related to day trading liquidity provision (e.g., Elaut et al., 2018), while in Chinese 8 agricultural and metal commodity markets it is more likely to be affected by the behaviour of informed traders (e.g., Bogousslavsky, 2016). The different sources of prediction highlight the distinct mechanisms among various markets and the future direction for full-round research. ...
... The first explanation is based on the model of infrequent portfolio rebalancing proposed by Bogousslavsky (2016). Due to slow-moving capital (e.g., Duffie, 2010), some investors could delay their rebalancing orders until near market close, resulting in an intraday momentum, with the same trading direction between the first and last half-hour returns. ...
Article
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Intraday return predictability has firstly been identified in the equity markets, and we extend the analysis to the crude oil market by using high-frequency United States Oil Fund data from 2006 to 2018. We find a different intraday prediction pattern in the oil market, where only the first half-hour returns positively predict the last half-hour returns. A market timing strategy based on the findings generates substantial profits. We further decompose the first half-hour return into the overnight and the open half-hour components, and find that the former contains more predictive information. Economic mechanisms of the infrequent portfolio rebalancing and the presence of late-informed investors explain our findings. Notably, unlike equity markets, the oil market exhibits a unique intraday trading volume pattern, caused by the release of two routine oil inventory announcements. However, the information contained in the inventory announcements does not offer predictability to the last half-hour returns.
... In addition, there exists a representative investor in the economy whose stock allocation, N t , also affects the stock price. It is shown in Isaenko (2013) and Bogousslavsky (2016) that the stock price becomes a function of stock allocations due to delays in these allocations when investors cannot trade fast enough to follow economic news. It is assumed that the stock price is affected by the two factors linearly: ...
... Next, we consider an example based on empirical observations of cross-sectional stock returns reported in Bogousslavsky (2016). We use the formula for the autocorrelation coefficient ...
... Bogousslavsky (2016) to estimate parameters κ Y , ξ, and θ. This panel shows the autocorrelation coefficients of daily returns for 20 lags. ...
Article
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We consider a simple model of stock returns with price overreaction derived from equilibrium analysis and study how this overreaction affects the relation between estimates of stock returns and measuring frequency. It is shown in agreement with the existing empirical literature that the presence of short–term overreaction in a stock price could make estimates of stock’s beta strongly depend on the frequency of time series. The model predicts that the standard deviation of the conditional Sharpe ratio also strongly depend on this frequency, while this dependence is even more pronounced for the standard deviation of the conditional risk premium
... Delays in capital allocations lead to a presence of a shortterm reversal (or overreaction) of stock returns which is commonly observed among many stocks and causes prices to bounce back from information shocks within a few days, weeks, or months. 2 Consequently, changing measuring frequency from daily to weekly and then to quarterly is expected to influence estimates of statistical moments of stock returns. Furthermore, a broad interpretation of economic origin of delays in capital allocations expands the existing intuition behind this influence. ...
... A good proxy for state variable N could be an aggregate allocation of financial intermediaries who often behave as a marginal investor. 8 It is shown in Bogousslavsky (2016) and Isaenko (2020) that the stock price becomes a function of stock allocations due to delays in these allocations when investors cannot trade fast enough to follow economic news. It is assumed that the stock price is affected by the two factors linearly: ...
... Next, we consider an example based on empirical observations of cross-sectional stock returns reported in Bogousslavsky (2016). We use the formula for the autocorrelation coefficient ...
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We show that the presence of short-term overreaction in a stock price could make estimates of the conditional moments of stock returns be strongly affected by the frequency of time series. This conclusion implies that the measures built on stock returns, such as the conditional Sharpe ratio, its standard deviation, stock's conditional alpha and beta could also be strong functions of measuring frequency.
... It is hypothesized that the demonstrated significant predictability of intraday market returns through time is driven by slow-moving trader capital, i.e., by infrequent portfolio rebalancing (Bogousslavsky [40] and Duffie [101]), at a high frequency. If some traders rebalance their portfolios infrequently, they may be slow to incorporate shocks to individual stock returns into the aggregate market, particularly when traders face severe volatility or illiquidity. ...
... Gao et al. [133] demonstrate that the first half-hour return of the SPY market exchange-traded fund (ETF) predicts the last half-hour return. Bogousslavsky [40] theoretically establishes that seasonality in intraday returns may be caused by traders' infrequent rebalancing. Chinco, Clark-Joseph and Ye [74] use a LAS model on the cross-section of NYSE lagged returns to show that one-minute returns are predictable. ...
Thesis
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Three essays in financial asset pricing are given; one concerning the partial differential equation (PDE) pricing and hedging of a class of continuous/generalized power mean Asian options, via their (optimal) Lie point symmetry groups, leading to practical pricing formulas. The second presents high-frequency predictions of S&P 500 returns via several machine learning models, statistically significantly demonstrating short-horizon market predictability and economically significantly profitable (beyond transaction costs) trading strategies. The third compares profitability between these [(mean) ensemble] strategies and Asian option Δ-hedging, using results of the first. Interpreting bounds on arithmetic Asian option prices as ask and bid values, hedging profitability depends largely on securing prices closer to the bid, and settling midway between the bid and ask, significant profits are consistently accumulated during the years 2004-2016. Ensemble predictive trading the S&P 500 yields comparatively very small returns, despite trading much more frequently. The pricing and hedging of (arithmetic) Asian options are difficult and have spurred several solution approaches, differing in theoretical insight and practicality. Multiple families of exact solutions to relaxed power mean Asian option pricing boundary-value problems are explicitly established, which approximately satisfy the full pricing problem, and in one case, converge to exact solutions under certain parametric restrictions. Corresponding hedging parameters/Greeks are derived. This family consists of (optimal) invariant solutions, constructed for the corresponding pricing PDEs. Numerical experiments explore this family behaviorally, achieving reliably accurate pricing. The second chapter studies intraday market return predictability. Regularized linear and nonlinear tree-based models enjoy significant predictability. Ensemble models perform best across time and their return predictability realizes economically significant profits with Sharpe ratios after transaction costs of 0.98. These results strongly evidence that intraday market returns are predictable during short time horizons, beyond that explainable by transaction costs. The lagged constituent returns are shown to hold significant predictive information not contained in lagged market returns or price trend and liquidity characteristics. Consistent with the hypothesis that predictability is driven by slow-moving trader capital, predictability decreased post-decimalization, and market returns are more predictable midday, on days with high volatility or illiquidity, and during financial crises.
... Our paper is closely tied to a literature that started with Grossman and Miller (1988) in which market makers smooth nonsynchronous trading demands due to inattentive investors. Recent inattention papers, such as Duffie (2010) and Bogousslavsky (2016), include attention heterogeneity that increases the need 7 Since we have market-maker inventories and retail trades, institutional trades are defined by a market-clearing constraint. Lakonishok, Shleifer, and Vishny (1992) measure the size of the institutional imbalance and its relation to current price movements. ...
... for intertemporal smoothing. 9 Bogousslavsky (2016) shows that the inattentive investors can explain regularities in stock return autocorrelation patterns. Our model differs from these papers in a number of key ways. ...
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We identify long-lived pricing errors through a model in which inattentive investors arrive stochastically to trade. The model's parameters are structurally estimated using daily NYSE market-maker inventories, retail order flows, and prices. The estimated model fits empirical variances, autocorrelations, and cross-autocorrelations among our three data series from daily to monthly frequencies. Pricing errors for the typical NYSE stock have a standard deviation of 3.2 percentage points and a half-life of 6.2 weeks. These pricing errors account for 9.4%, 7.0%, and 4.5% of the respective daily, monthly, and quarterly idiosyncratic return variances.
... The predictive inability of liquidity variables provides evidence to the contrary. Alternatively, could the predictability be caused by intraday momentum, as motivated by Heston, Korajczyk, and Sadka (2010) and Gao, Han, Li, and Zhou (2018), and formally modeled by Bogousslavsky (2016)? This explanation also seems unlikely given that price trend variables fail to improve model predictability. ...
... Gao, Han, Li, and Zhou (2018) demonstrate that the first half-hour return of the SPY market exchange-traded fund (ETF) predicts the last half-hour return. Bogousslavsky (2016) 3 Gu, Kelly, and Xiu (2020b) demonstrate that price trend and liquidity have the strongest predictive ability. theoretically establishes that seasonality in intraday returns may be caused by traders' infrequent rebalancing. ...
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The question, of how predictable are intraday market returns, is answered by conducting the largest study of such returns, using state-of-the-art machine learning models trained on lagged returns to forecast five-minute S&P 500 exchange-traded fund returns. Lasso, elastic nets, and random forests are consistently found to be the best-performing models, yielding an out-of-sample R 2 of 0.24% from 2001 to 2016. This predictability translates to economically significant profits: A market-timing strategy using random forest predictions earns 58% (8%) annualized returns before (after) transaction costs, and Sharpe ratio equal to 1.86 (0.73). Combined are found to outperform individual model predictions, yielding an out-of-sample R 2 of 0.26% and after-transaction cost return of 10% with a Sharpe ratio of 1.20. This strong predictability of intraday market returns provides evidence against market efficiency over short time horizons. These empirical findings suggest an investigation into the economic mechanisms driving such short-horizon predictability. JEL Classification: G14, G17, C45, C55
... Researchers (e.g. Bogousslavsky, 2016;Hendershott, Menkveld, Praz, & Seasholes, 2018) have developed theoretical limited-attention models in which the market consists of attentive and inattentive investors. In these models, with liquidity shocks, inattentive investors' trades prolong pricing errors and cause positive return autocorrelation. ...
... These results are predicted by the limited-attention models (e.g. Bogousslavsky, 2016). ...
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Moods affect investors’ attention, memory, and capacity to process information. Inattentive investors delay the price adjustment process, thus leading to a positive autocorrelation of asset returns. In this study, I investigate the relationship between weather-induced moods and stock-return autocorrelation in the Stock Exchange of Thailand from January 2, 1991, to December 29, 2017. Only good moods contribute significantly to return autocorrelation.
... More recently, Gao et al. (2018) extend the momentum literature to the field of intraday highfrequency and uncover a striking intraday time-series predictability in the US S&P 500 exchange-traded fund market. That is, the first half-hour return positively predicts the last halfhour return within the same trading day. 3 They argue that the intraday momentum is consistent with both the (Bogousslavsky 2016) model of infrequent portfolio rebalancing and the model of late-informed trading near the market close. ...
... Bogousslavsky 2016) model of infrequent portfolio rebalancing and theGao et al. (2018) model of late-informed trading near the market close. As is explained in the introduction section, China adopts a unique " + 1 trading rule", which prevents investors from selling stocks bought on the same day.This unique trading rule, however, goes counter to the settings of the infrequent portfolio rebalancing model, as traders who hold an excess position in the asset could not rebalance at the intraday frequency. ...
Article
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Based on high-frequency firm-level data, this paper uncovers new empirical patterns on intraday momentum in China. First, there exists a strong intraday momentum effect at the firm level. Second, the intraday predictability stems mainly from the overnight component rather than the opening half-hour component, which is consistent with the microstructure features of the Chinese market. Third, the intraday predictability attenuates (strengthens) following large positive (negative) informational shocks, implying a striking asymmetric reaction by market participants. Finally, we document that late-informed traders are relatively less experienced or skillful. Overall, the empirical results lend support to the model of late-informed trading.
... More recently, Gao et al. (2018) extend the momentum literature to the field of intraday highfrequency and uncover a striking intraday time-series predictability in the US S&P 500 exchange-traded fund market. That is, the first half-hour return positively predicts the last halfhour return within the same trading day. 3 They argue that the intraday momentum is consistent with both the (Bogousslavsky 2016) model of infrequent portfolio rebalancing and the model of late-informed trading near the market close. ...
... Bogousslavsky 2016) model of infrequent portfolio rebalancing and theGao et al. (2018) model of late-informed trading near the market close. As is explained in the introduction section, China adopts a unique " + 1 trading rule", which prevents investors from selling stocks bought on the same day.This unique trading rule, however, goes counter to the settings of the infrequent portfolio rebalancing model, as traders who hold an excess position in the asset could not rebalance at the intraday frequency. ...
Article
Full-text available
Based on high-frequency firm-level data, this paper uncovers new empirical patterns on intraday momentum in China. First, there exists a strong intraday momentum effect at the firm level. Second, the intraday predictability stems mainly from the overnight component rather than the opening half-hour component, which is consistent with the microstructure features of the Chinese market. Third, the intraday predictability attenuates (strengthens) following large positive (negative) informational shocks, implying a striking asymmetric reaction by market participants. Finally, we document that late-informed traders are relatively less experienced or skilful. Overall, the empirical results lend support to the model of late-informed trading.
... 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). ...
Article
The low correlation between commodities and traditional assets, particularly after the crash of the equity market in the year 2000, is seemingly a major factor influencing global investors' appetite to embrace commodities as a profitable alternative financial asset. In this paper, we critically and selectively provide the knowledge map of the connectedness of commodity markets based on the scientific articles published on the Web of Science (WoS). In doing this, we group the literature survey based on notable commodity markets and provide an overview of the empirical literature based on single‐ and cross‐commodity markets. The key finding of the literature survey is that there is connectedness within and across commodity markets, with evidence of time variations triggered largely by global financial crises. In addition, from 144 articles over the last two decades (1990–2021), significant conceptual clusters and networks arise, which suggest a close density of networks in terms of the keyword clusters, keyword plus co‐occurrences, country collaborations, and journal co‐citations. Furthermore, there are significant conceptual clusters that cover the association of connectedness type, commodity market, type of statistical analysis, association of major energy shocks, futures market, co‐movement, and association of transmission in stock and gold markets. Our analysis, therefore, suggests, among other things, the need for future research to analyze the pricing of pollution credits as the newest commodity market. This helps economic actors, investors, and policymakers have a better understanding of the dynamic behavior of commodity prices.
... According to Li, Sakkas, and Urquhart (2021), intraday trends may be due to a mixture of market microstructure and behavioral factors. Trends may be caused by infrequent traders impacting the market when liquidity is low (Bogousslavsky 2016). They may be due to some investors being slow to process information and later overreacting in what Da, Gurun and Warachka (2014) Overconfident investors may also trade on their own beliefs and willfully ignore certain news. ...
Article
This study reviews the empirical and theoretical evidenced about intraday trends. The paper shows the benefits of intraday trend-following and how to allocate into the strategy within a multi-asset portfolio.
... The results reinforce those in prior studies that indicate an important role of economic fundamentals in explaining the U.S. stock returns (e.g., Rapach and Wohar 2006;Chang et al. 2019). They also highlight strong autocorrelations in the daily U.S. stock returns, consistent with the literature of short-term stock return behavior (e.g., Avramov et al. 2006;Bogousslavsky 2016). ...
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The effect of COVID-19 on stock market performance has important implications for both financial theory and practice. This paper examines the relationship between COVID-19 and the instability of both stock return predictability and price volatility in the U.S over the period January 1st, 2019 to June 30th, 2020 by using the methodologies of Bai and Perron (Econometrica 66:47–78, 1998. 10.2307/2998540 ; J Appl Econo 18:1–22, 2003. 10.1002/jae.659 ), Elliot and Muller (Optimal testing general breaking processes in linear time series models. University of California at San Diego Economic Working Paper, 2004), and Xu (J Econ 173:126–142, 2013. 10.1016/j.jeconom.2012.11.001 ). The results highlight a single break in return predictability and price volatility of both S&P 500 and DJIA. The timing of the break is consistent with the COVID-19 outbreak, or more specifically the stock selling-offs by the U.S. senate committee members before COVID-19 crashed the market. Furthermore, return predictability and price volatility significantly increased following the derived break. The findings suggest that the pandemic crisis was associated with market inefficiency, creating profitable opportunities for traders and speculators. Furthermore, it also induced income and wealth inequality between market participants with plenty of liquidity at hand and those short of funds.
... (3) 11 Assume that an investor trades ∆N shares over time interval dt at speed u. Then her trading loss is αnu ∆N which becomes infinitely large if the stock holding has a diffusion term. ...
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This paper studies the effects that delay in capital allocations in the stock market and high short-term trading incentives have on returns of this market. We report that capital inertia makes the Sharpe ratio and the volatility of the stock returns many times higher than in an economy with no capital delays. Furthermore, in agreement with empirical literature, the stock price displays short-term overreaction and high volatility of the conditional Sharpe ratio.
... Analyzing ETF price dynamics, Gao et al. (2017) (GHLZ hereafter) provide empirical evidence showing that the first half-hour return predicts positively the last half-hour return. Theoretically, the market intraday momentum is consistent with an infrequent rebalancing mechanism (Bogousslavsky, 2016) and with the presence of late-informed traders in the market. Extending GHLZ model by exploring the relationship between intraday stock market returns and intraday sentiment, Sun et al. (2016) (SNS hereafter) find that the change in investor sentiment has predictive value for the intraday market returns. ...
Thesis
The massive increase in the availability of data generated everyday by individuals on the Internet has made it possible to address the predictability of financial markets from a different perspective. Without making the claim of offering a definitive answer to a debate that has persisted for forty years between partisans of the efficient market hypothesis and behavioral finance academics, this dissertation aims to improve our understanding of the price formation process in financial markets through the use of Big Data analytics. More precisely, it analyzes: (1) how to measure intraday investor sentiment and determine the relation between investor sentiment and aggregate market returns, (2) how to measure investor attention to news in real time, and identify the relation between investor attention and the price dynamics of large capitalization stocks, and (3) how to detect suspicious behaviors that could undermine the in-formational role of financial markets, and determine the relation between the level of posting activity on social media and small-capitalization stock returns. The first essay proposes a methodology to construct a novel indicator of investor sentiment by analyzing an extensive dataset of user-generated content published on the social media platform Stock-Twits. Examining users’ self-reported trading characteristics, the essay provides empirical evidence of sentiment-driven noise trading at the intraday level, consistent with behavioral finance theories. The second essay proposes a methodology to measure investor attention to news in real-time by combining data from traditional newswires with the content published by experts on the social media platform Twitter. The essay demonstrates that news that garners high attention leads to large and persistent change in trading activity, volatility, and price jumps. It also demonstrates that the pre-announcement effect is reduced when corrected newswire timestamps are considered. The third essay provides new insights into the empirical literature on small capitalization stocks market manipulation by examining a novel dataset of messages published on the social media plat-form Twitter. The essay proposes a novel methodology to identify suspicious behaviors by analyzing interactions between users and provide empirical evidence of suspicious stock recommendations on social media that could be related to market manipulation. The conclusion of the essay should rein-force regulators’ efforts to better control social media and highlights the need for a better education of individual investors.
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We consider a linear model of stock returns derived from equilibrium analysis and study how trading strategy of a marginal investor affects the relation between estimates of the moments of stock returns and measuring frequency. Subject to the impact of the stock allocations of the marginal investor on the stock price, the rate of her trading to the optimal allocations and the amount of idiosyncratic risk, the estimates of the moments of stock returns may significantly change with measuring frequency. This change for the standard deviation of the conditional Sharpe ratio could be in tens of times stronger than for unconditional moments of stock returns.
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This paper studies the out‐of‐sample performance of the intraday momentum strategy where the overnight return predicts the return of the last half‐hour of trading. The predictability disappears in the out‐of‐sample period. A Markov‐switching model endogenously identifies two distinct regimes and suggests that the predictability depends on the strength of the signal. Hence, assessing return predictability in calendar time may lead to false conclusions when anomalies feature time‐varying returns. The paper documents that understanding the return dynamics is important for an effective strategy implementation. A strategy with thresholds delivers higher returns than a strategy that is always active.
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We introduce a portfolio friction in a two-country DSGE model where investors face a constant probability to make new portfolio decisions. The friction leads to a more gradual portfolio adjustment to shocks and a weaker portfolio response to changes in expected excess returns. We apply the model to monthly data for the US and rest of the world for equity portfolios. We show that the model is consistent with a broad set of evidence related to portfolios, equity prices and excess returns for an intermediate level of the friction. The evidence includes portfolio inertia, limited sensitivity to expected excess returns, a significant impact of financial shocks, excess return predictability, and asset price momentum and reversal.
Article
Studies focusing on the U.S. stock markets argue that the short-term reversal is related to liquidity provision. We test the implications of the theory of liquidity provision to investigate the profitability of reversal trading strategies in the Chinese markets. We find highly robust and puzzling patterns that are mainly inconsistent with findings in the U.S. markets and liquidity provision. There is a momentum based on the most recent day instead of reversal, only few specific days drive the profitability of the reversal strategies, and even these profits do not depend on liquidity constraints as liquidity provision theory implies.
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This paper reveals that the rest-of-day return has positively significant prediction on the last 30-minutes return in the Chinese SSE 50 exchange-traded fund market. Its predictive power is economically significant and will decay in the next three days. Moreover, it reveals the relationship between intraday return prediction and short gamma hedging demand from option market makers, demonstrating that intraday hedging could be postponed till the end of a trading day. Intraday momentum caused by rebalancing traders and information investors cannot be completely offset by option market makers on days with positive net gamma exposure. Our results are robust to alternative exchange-traded fund index option and its underlying asset.
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Recently portfolio choice has become an important element of many DSGE open economy models. Yet, a substantial body of evidence is inconsistent with standard frictionless portfolio choice models. In this paper we introduce a quadratic cost of changes in portfolio allocation into a two-country DSGE model. We investigate the level of portfolio frictions most consistent with the data and the impact of portfolio frictions on asset prices and net capital flows. We find the portfolio friction accounts for (i) micro evidence of portfolio inertia by households, (ii) macro evidence of the price impact of financial shocks and related disconnect of asset prices from fundamentals, (iii) a broad set of moments related to the time series behavior of saving, investment and net capital flows, and (iv) other phenomena relating to excess return dynamics. Financial and saving shocks each account for close to half of the variance of net capital flows.
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From 2010 to 2015, a group of traders illegally accessed earnings information before their public release by hacking several newswire services. We use this scheme as a natural experiment to investigate how informed investors select among private signals and how efficiently financial markets incorporate private information contained in trades into prices. We construct a measure of qualitative information using machine learning and find that the hackers traded on both qualitative and quantitative signals. The hackers’ trading caused 15% more of the earnings news to be incorporated in prices before their public release. Liquidity providers responded to the hackers’ trades by widening spreads.
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This paper analyzes the statistical impact of COVID-19 on the S&P500 and the CSI300 intraday momentum. This study employs an empirical method, that is, the intraday momentum method used in this research. Also, the predictability of timing conditional strategies is also used here to predict the intraday momentum of stock returns. In addition, this study aims to estimate and forecast the coefficients in the stock market pandemic crisis through a robust standard error approach. The empirical findings indicate that the intraday market behavior an unusual balanced; the volatility and trading volume imbalance and the return trends are losing overwhelmingly. The consequence is that the first half-hour return will forecast the last half-hour return of the S&P500, but during the pandemic shock, the last half-hour of both stock markets will not have a significant impact on intraday momentum. Additionally, market timing strategy analysis is a significant factor in the stock market because it shows the perfect trading time, decides investment opportunities and which stocks will perform well on this day. Besides, we also found that when the volatility and volume of the S&P500 are both at a high level, the first half-hour has been a positive impact, while at the low level, the CSI300 has a negative impact on the last half-hour. In addition, this shows that the optimistic effect and positive outlook of the stockholders for the S&P500 is in the first half-hours after weekend on Monday morning because market open during the weekend holiday, and the mentality of every stockholder’s indicate the positive impression of the stock market.
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Conducting, to our knowledge, the largest study ever of 5-min equity market returns using state-of-the-art machine learning models trained on the cross-section of lagged market index constituent returns, we show that regularized linear models and nonlinear tree-based models yield significant market return predictability. Ensemble models perform the best across time and their predictability translates into economically significant Sharpe ratios of 0.98 after transaction costs. These results provide strong evidence that intraday market returns are predictable during short time horizons, beyond what can be explained by transaction costs. Furthermore, we show that constituent returns hold significant predictive information that is not contained in market returns or in price trend and liquidity characteristics. Consistent with the hypothesis that predictability is driven by slow-moving trader capital, predictability decreased post-decimalization, and market returns are more predictable during the middle of the day, on days with high volatility or illiquidity, and in financial crisis periods.
Article
A higher frequency of positive overnight returns followed by negative trading day reversals during a month suggests a more intense daily tug of war between opposing investor clienteles, who are likely composed of noise traders overnight and arbitrageurs during the day. We show that a more intense daily tug of war predicts higher future returns in the cross section. Additional tests support the conclusion that, in a more intense tug of war, daytime arbitrageurs are more likely to discount the possibility that positive news arrives overnight and thus overcorrect the persistent upward overnight price pressure.
Article
Purpose To capture the last hour momentum over the intraday session, the authors develop a trading strategy for the exchange-traded fund (ETF) that is effective because of the T +0 trading rule. This strategy generates annualized excess return of 9.673%. Design/methodology/approach In this study, the authors identify a last hour momentum pattern in which the sixth (seventh) half-hour return predicts the next half-hour return by employing high frequency 2012–2017 data from the China Securities Index (CSI) 300 and its ETF. Findings Overall, both the predictability and the trading strategy are statistically and economically significant. In addition, the strategy performs more strongly on high volatility days, high trading volume days, high order-imbalance days and days without economic news releases than on other days. Originality/value Noise trading, late-information trading, infrequent rebalancing and disposition effects from retail investors may account for this phenomenon.
Article
This paper documents intraday time-series momentum in Taiwanese exchange-traded funds, as evidenced by the predictive relationship between the last half-hour return and the first three half-hour returns. A market timing trading strategy that uses trading signals from the second (third) half-hour return outperforms the benchmarks, earning a market-adjusted return of 5.33% (5.27%) per annum. Institutional and foreign investors’ order imbalances over the last half-hour determine concurrent returns and positively respond to early-morning returns, while the predictive effect of the first half-hour return on the last half-hour return disappears after controlling for institutional and foreign investors’ trading behavior. Collectively, we show that institutional and foreign investors’ late-informed trading contributes to intraday time-series momentum.
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Inclusion of jump component in the price process has been a long debate in finance literature. In this paper, we identify and characterize jump risks in the Canadian stock market using high-frequency data from the Toronto Stock Exchange. Our results provide a strong evidence of jump clustering – about 30% of jumps occur within first 30 minutes of trading hours, and about 25% of jumps are due to the overnight returns. While average intraday jump is negative, jumps induced by overnight returns bring a cancellation effect yielding average size of the jumps to zero. We show that the economic significance of jump component in volatility forecasting is significant but nominal. Our results further demonstrate that market jumps and overnight returns bring significant changes in systematic risk of stocks. From a cross-sectional perspective, while the average effect of market jumps on the beta is not significantly different from zero, the average effect of overnight returns is statistically significant. Overall, our results suggest that systematic risk induced by the market jumps could be hedged by combining value stocks and growth stocks in a portfolio whereas the systematic risk induced by overnight returns can not be hedged even with a well diversified portfolio.
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Hedging short gamma exposure requires trading in the direction of price movements, thereby creating price momentum. Using intraday returns on over 60 futures on equities, bonds, commodities, and currencies between 1974 and 2020, we find strong market intraday momentum everywhere. The return during the last 30 minutes before the market close is positively predicted by the return during the rest of the day (from previous market close to the last 30 minutes). The predictive power is economically and statistically highly significant, and reverts over the next days. We provide novel evidence that links market intraday momentum to the gamma hedging demand from market participants such as market makers of options and leveraged ETFs.
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The objective of this paper is to show that the proposal by Froot and Thaler (1990) of delayed portfolio adjustment can account for a broad set of puzzles about the relationship between interest rates and exchange rates. The puzzles include: i) the delayed overshooting puzzle; ii) the forward discount puzzle (or Fama puzzle); iii) the predictability reversal puzzle; iv) the Engel puzzle (high interest rate currencies are stronger than implied by UIP); v) the forward guidance exchange rate puzzle; vi) the absence of a forward discount puzzle with long-term bonds. These results are derived analytically in a simple two-country model with portfolio adjustment costs. Quantitatively, this approach can match all targeted moments related to these puzzles.
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We identify a component of monetary policy news that is extracted from high-frequency changes in risky asset prices. These surprises, which we call “risk shifts”, are uncorrelated, and therefore complementary, to risk-free rate surprises. We show that (i) risk shifts capture the lion’s share of stock price movements around FOMC announcements; (ii) that they are accompanied by significant investor fund flows, suggesting that investors react heterogeneously to monetary policy news; and (iii) that price pressure amplifies the stock market response to monetary policy news. Our results imply that central bank information effects are overshadowed by short-term dynamics stemming from investor rebalancing activities and are likely to be more difficult to identify than previously thought.
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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 mispricing to reduce their positions before the end of the day. Consistent with this intuition, a mispricing factor earns positive returns throughout the day but performs poorly at the end of the day. This pattern strengthens in the second half of the sample and is shared by several well-known anomalies.
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We examine intraday time series momentum (ITSM) in an international setting by employing high-frequency data of 16 developed markets. We show that ITSM is economically sizable and statistically significant both in- and out-of-sample in most countries. Based on theories of investor behavior, we propose and test four hypotheses to reveal the source of ITSM profitability. We document both in the cross-section and time series dimension that ITSM is stronger when liquidity is low, volatility is high, and new information is discrete. Overall, our results suggest that the ITSM is driven by both market microstructure and behavioral factors.
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We investigate whether the market intraday momentum reported by Gao et al. (2018) is observed in the Australian context. First, we use US data to validate our empirical method, documenting the same statistically significant positive relationship between first and last half-hour market returns that were reported by Gao et al. (2018). Despite this, our analysis using Australian data yields no statistically significant results and, as such, provides little evidence of intraday momentum in this market. Subsequent analyses suggest that the relatively small number of daily trades in the Australian market might explain our finding.
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This study investigates the multi‐scale inter‐temporal capital asset pricing model (ICAPM). We focus upon differences across timescales since they represent heterogeneities of investors in markets. This study employs a wavelet approach to decompose return data into multiple timescales. Furthermore, we impose a same risk‐aversion parameter constraint into all portfolios, which is proposed by Engle and Bali who show that the constraint provides a reasonable equity risk premium at a daily frequency. We observe positive relations between the expected returns on portfolios and the covariance of the market at a daily frequency, while these relations change as timescales increase. We find that a negative risk–return relation, which might be related to a correction process of overreaction at an approximately weekly frequency (2–16 days). The strongest positive relation is observed at an approximately monthly frequency (16–32 days). Monthly portfolio re‐balances are widely used and might impact stock market return patterns. The equity risk premium in the longer frequency ranges from 8.64 to 11.10%. Our results are robust after controlling for macroeconomic variables, market implied volatility and test portfolios. Moreover, we investigate size and value factors and reveal that the risk premia disappear in the longer frequency, which suggests that ICAPM is satisfied.
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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 increase (decrease) by about 4% during the glitch and become substantially more illiquid. It takes one day for prices and spread-based liquidity measures to revert. Institutional trading costs, however, remain significantly higher for more than one week. Both liquidity measures are also weakly correlated outside the glitch period, suggesting they capture different aspects of liquidity.
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Stocks tend to earn high or low returns relative to other stocks every year in the same month (Heston and Sadka, 2008). We show these seasonalities are balanced out by seasonal reversals: a stock that has a high expected return relative to other stocks in one month has a low expected return relative to other stocks in the other months. The seasonalities and seasonal reversals add up to zero over the calendar year, which is consistent with seasonalities being driven by temporary mispricing. Seasonal reversals are economically large and statistically highly significant, and they resemble, but are distinct from, long-term reversals.
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This study presents the first attempt to examine the cross-sectional seasonality anomaly in cryptocurrency markets. To this end, we apply sorts and cross-sectional regressions to investigate daily returns on 151 cryptocurrencies for the years 2016 to 2019. We find a significant seasonal pattern: average past same-weekday returns positively predict future performance in the cross-section. Cryptocurrencies with high same-day returns in the past outperform cryptocurrencies with a low same-day return. This effect is not subsumed by other established return predictors such as momentum, size, beta, idiosyncratic risk, or liquidity.
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This paper provides insights into the current development of responsible investment in the Chinese stock market. We find that responsible investment can bring portfolio benefits to investors, and institutional investors have a holding preference for stocks in responsible investment indexes. By using a national air pollution proxy, we find that investors’ pessimistic mood on days with heavy air pollution has a negative influence on the stock return of A-shares, while stocks in responsible investment indexes display improved performance over the same time period. We use aggregated trading data to study the trading preference of Chinese retail investors on days when they are influenced by air pollution, and find that their total trading ratio shows a negative influence for both A-shares and responsible investment indexes. Moreover, there is more seller-initiated trading of the whole sample but more buyer-initiated trading of stocks in responsible investment indexes on air pollution days. This finding is consistent with the different stock return performances of these two samples. Our finding extends the studies of responsible investment to emerging markets and presents new evidence about the influence of environmental factors on trading behavior and return performance.
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This study conducts an investigation of intraday time-series momentum across four Chinese commodity futures contracts: copper, steel, soybean, and soybean meal. Our results indicate that the first half-hour return positively predicts the last half-hour return across all four futures. Furthermore, in metals markets, we find that first trading sessions with high volume or volatility are associated with the strongest intraday time-series momentum dynamics. Based on this, we propose an intraday momentum informed trading strategy that earns a return in excess of standard always long and buy-and-hold benchmarks.
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The literature documents a significantly negative average variance swap payoff (VSP) for the S&P 500 index but generally not for the constituent stocks. We show that this result is affected by biases arising from (i) an intraday momentum effect and (ii) the use of an incoherent measure of return variation. Accounting for these issues, we find stronger evidence of a significant average VSP both at the index level and also for equities. We decompose the index variance risk premium (VRP) into factors related to the VRP of equities and the correlation risk premium (CRP) and assess their predictive power for aggregate stock returns.
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How does information get revealed in decentralized markets? We test several hypotheses inspired by recent dealer‐network theory. To do so we construct an empirical map of information revelation where two dealers are connected based on the synchronicity of their quote changes. The tests, based on EUR/CHF quote data including the 2015 crash, largely support theory: strongly connected (i.e., central) dealers are more informed. Connections are weaker when there is less to be learned. The crash serves to identify how a network forms when dealers are transitioned from no‐learning to learning, that is, from a fixed to a floating rate. This article is protected by copyright. All rights reserved
Article
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 minutes 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 minutes after the opening bell has predictive power for intraday stock returns. This article is protected by copyright. All rights reserved
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Recurrent intervals of inattention to the stock market are optimal if consumers incur a utility cost to observe asset values. When consumers observe the value of their wealth, they decide whether to transfer funds between a transactions account from which consumption must be financed and an investment portfolio of equity and riskless bonds. Transfers of funds are subject to a transactions cost that reduces wealth and consists of two components: one is proportional to the amount of assets transferred, and the other is a fixed resource cost. Because it is costly to transfer funds, the consumer may choose not to transfer any funds on a particular observation date. In general, the optimal adjustment rule---including the size and direction of transfers, and the time of the next observation---is state-dependent. Surprisingly, unless the fixed resource cost of transferring funds is large, the consumer's optimal behavior eventually evolves to a situation with a purely time-dependent rule with a constant interval of time between observations. This interval of time can be substantial even for tiny observation costs. When this situation is attained, the standard consumption Euler equation holds between observation dates if the consumer is sufficiently risk averse.
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The paper proposes an additive cascade model of volatility components defined over different time periods. This volatility cascade leads to a simple AR-type model in the realized volatility with the feature of considering different volatility components realized over different time horizons and thus termed Heterogeneous Autoregressive model of Realized Volatility (HAR-RV). In spite of the simplicity of its structure and the absence of true long-memory properties, simulation results show that the HAR-RV model successfully achieves the purpose of reproducing the main empirical features of financial returns (long memory, fat tails, and self-similarity) in a very tractable and parsimonious way. Moreover, empirical results show remarkably good forecasting performance.
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A major puzzle in international finance is that high interest rate currencies tend to appreciate (forward discount puzzle). Motivated by the fact that only a small fraction of foreign currency holdings is actively managed, we calibrate a two-country model in which agents make infrequent portfolio decisions. We show that the model can account for the forward discount puzzle. It can also account for several related empirical phenomena, including that of "delayed overshooting." We also show that making infrequent portfolio decisions is optimal as the welfare gain from active currency management is smaller than the corresponding fees. (JEL F31, G11, G15)
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This paper investigates the relationship between aggregate stock market trading volume and the serial correlation of daily stock returns. For both stock indexes and individual large stocks, the first-order daily return autocorrelation tends to decline with volume. The paper explains this phenomenon using a model in which risk-averse “market makers” accommodate buying or selling pressure from “liquidity” or “noninformational” traders. Changing expected stock returns reward market makers for playing this role. The model implies that a stock price decline on a high-volume day is more likely than a stock price decline on a low-volume day to be associated with an increase in the expected stock return.
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Predictable variation in equity returns might reflect either (1) predictable changes in expected returns or (2) market inefficiency and stock price “overreaction.” These explanations can be distinguished by examining returns over short time intervals since systematic changes in fundamental valuation over intervals like a week should not occur in efficient markets. The evidence suggests that the “winners” and “losers” one week experience sizeable return reversals the next week in a way that reflects apparent arbitrage profits which persist after corrections for bid-ask spreads and plausible transactions costs. This probably reflects inefficiency in the market for liquidity around large price changes.
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This article examines the contribution of stock price overreaction and delayed reaction to the profitability of contrarian strategies. The evidence indicates that stock prices overreact to firm-specific information, but react with a delay to common factors. Delayed reactions to common factors give rise to a size-related lead-lag effect in stock returns. In sharp contrast with the conclusions in the extant literature, however, this article finds that most of the contrarian profit is due to stock price overreaction and a very small fraction of the profit can be attributed to the lead-lag effect.
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If returns on some stocks systematically lead or lag those of others, a portfolio strategy that sells “winners” and buys “losers” can produce positive expected returns, even if no stock’s returns are negatively autocorrelated as virtually all models of overreaction imply. Using a particular contrarian strategy we show that, despite negative autocorrelation in individual stock returns, weekly portfolio returns are strongly positively autocorrelated and are the result of important cross-autocorrelations. We find that the returns of large stocks lead those of smaller stocks, and we present evidence against overreaction as the only source of contrarian profits.
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Portfolio strategies that buy stocks with high returns over the previous 3–12 months and sell stocks with low returns over this same time period perform well over the following 12 months. A recent article by Conrad and Kaul (1998) presents striking evidence suggesting that the momentum profits are attributable to cross-sectional differences in expected returns rather than to any time-series dependence in returns. This article shows that Conrad and Kaul reach this conclusion because they do not take into account the small sample biases in their tests and bootstrap experiments. Our unbiased empirical tests indicate that cross-sectional differences in expected returns explain very little, if any, of the momentum profits.
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I modify the uniform-price auction rules in allowing the seller to ration bidders. This allows me to provide a strategic foundation for underpricing when the seller has an interest in ownership dispersion. Moreover, many of the so-called "collusive-seeming" equilibria disappear.
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This paper presents new empirical evidence of predictability of individual stock returns. The negative first-order serial correlation in monthly stock returns is highly significant. Furthermore, significant positive serial correlation is found at longer lags, and the twelve-month serial correlation is particularly strong. Using the observed systematic behavior of stock return, one-step-ahead return forecasts are made and ten portfolios are formed from the forecasts. The difference between the abnormal returns on the extreme decile portfolios over the period 1934-87 is 2.49 percent per month. Copyright 1990 by American Finance Association.
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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 average returns on the last day of each quarter, which significantly affects size, idiosyncratic volatility, and illiquidity portfolios. The results have strong implications for the interpretation and analysis of many anomalies, such as asset growth and momentum.
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The literature documents heterogeneity in the delay of stock price reaction to systematic shocks, implying that asset risk depends on investment horizon. We study the pricing of risk factors across investment horizons. Value (liquidity) risk is priced over intermediate (short) horizons. Conditioning horizon-factor exposures on firm characteristics indicates that characteristics, with the exception of momentum, are not priced beyond their contribution to systematic risk. Long-horizon institutional investors overweight assets with high intermediate-horizon exposures to value risk and high short-horizon exposures to liquidity risk. The results highlight the importance of investment horizon in determining risk premia.
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A strategy that selects stocks based on their historical same-calendar-month returns earns an average return of 13% per year. We document similar return seasonalities in anomalies, commodities, and international stock market indices, as well as at the daily frequency. The seasonalities overwhelm unconditional differences in expected returns. The correlations between different seasonality strategies are modest, suggesting that they emanate from different systematic factors. Our results suggest that seasonalities are not a distinct class of anomalies that requires an explanation of its own, but rather that they are intertwined with other return anomalies through shared systematic factors. This article is protected by copyright. All rights reserved
Article
The ways financial analysts, traders, and other specialists use information and learn from each other are of fundamental importance to understanding how markets work and prices are set. This graduate-level textbook analyzes how markets aggregate information and examines the impacts of specific market arrangements--or microstructure--on the aggregation process and overall performance of financial markets. Xavier Vives bridges the gap between the two primary views of markets--informational efficiency and herding--and uses a coherent game-theoretic framework to bring together the latest results from the rational expectations and herding literatures. Vives emphasizes the consequences of market interaction and social learning for informational and economic efficiency. He looks closely at information aggregation mechanisms, progressing from simple to complex environments: from static to dynamic models; from competitive to strategic agents; and from simple market strategies such as noncontingent orders or quantities to complex ones like price contingent orders or demand schedules. Vives finds that contending theories like informational efficiency and herding build on the same principles of Bayesian decision making and that "irrational" agents are not needed to explain herding behavior, booms, and crashes. As this book shows, the microstructure of a market is the crucial factor in the informational efficiency of prices.
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This article develops a theory in which concentrated-trading patterns arise endogenously as a result of the strategic behavior of liquidity traders and informed traders. Our results provide a partial explanation for some of the recent empirical findings concerning the patterns of volume and price variability in intraday transaction data.
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In this paper, using the intraday data of the S&P 500 ETF from February 1, 1993 to December 31, 2013, we document an intraday momentum pattern that the first half-hour return on the market predicts the last half-hour return on the market. The predictability is both statistically and economically significant, and is stronger on more volatile days, higher volume days, recession days and some macroeconomic news release days. Moreover, the intraday momentum is also strong for ten other most actively traded ETFs. Economically, the trading behavior of daytraders and informed traders seems to be the driving forces behind the intraday momentum.
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In a classical one-period asset-pricing model, high expected returns are achieved only by accepting high levels of systematic risk. Allowing for heterogenous investment horizons across investors, some risks that require a premium over a particular horizon, may seem less consequential to investors facing a different investment horizon. This paper studies the pricing of commonly used systematic risk factors across investment horizons. We find that liquidity risk exhibits a premium that may constitute abnormal return (alpha) for patient investors because liquidity fluctuations are less apparent for long horizons. In contrast, market, value, and return-on-equity factors are predominantly priced when systematic risk is measured using long horizon returns. While value and return-on-equity appear to be characteristics at short horizons they behave like systematic risk factors at long horizons. Size, momentum, and investment, behave like characteristics at all horizons. The results highlight the importance of considering investment horizon in determining whether a cross-sectional return spread is alpha or a premium for systematic risk.
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Does limited attention among investors affect stock returns? We compare the response to earnings announcements on Friday, when investor inattention is more likely, to the response on other weekdays. If inattention influences stock prices, we should observe less immediate response and more drift for Friday announcements. Indeed, Friday announcements have a 15% lower immediate response and a 70% higher delayed response. A portfolio investing in differential Friday drift earns substantial abnormal returns. In addition, trading volume is 8% lower around Friday announcements. These findings support explanations of post-earnings announcement drift based on underreaction to information caused by limited attention. Copyright (c) 2009 the American Finance Association.
Article
We study the properties of rational expectation equilibria (REE) in dynamic asset pricing models with heterogeneously informed agents. We show that under mild conditions the state space of such models in REE can be infinite dimensional. This result indicates that the domain of analytically tractable dynamic models with asymmetric information is severely restricted. We also demonstrate that even though the serial correlation of returns is predominantly determined by the dynamics of stochastic equity supply, under certain circumstances asymmetric information can generate positive autocorrelation of returns.
Preprint
We develop an asset pricing model with stochastic transaction costs and investors with heterogeneous horizons. Depending on their horizon, investors hold different sets of assets in equilibrium. This generates segmentation and spillover effects for expected returns, where the liquidity (risk) premium of illiquid assets is determined by investor horizons and the correlation between liquid and illiquid asset returns. We estimate our model for the cross-section of U.S. stock returns and find that it generates a good fit, mainly due to a combination of a substantial expected liquidity premium and segmentation effects, while the liquidity risk premium is small.
Article
We present a structural model of the stock market where a subset of the investors is infrequently present at the market. In our model the stocks’ return reversal pattern is exponential and the amount of return reversal, the speed of return reversal and stock’s transitory volatility are all related to liquidity. In contrast to common perception, fast return reversal is typically a sign of inefficient, illiquid markets, thus not a sign of efficiency. Other results are that the stock’s return liquidity premium and the cost of immediacy to transitory investors are non-monotonic in several structural parameters of the model, such as the number of market makers. Based on the entire available return history for NYSE and Amex traded stocks, we find that, on average, 29% of NYSE and Amex traded stocks’ excess returns revert within a month, that the pattern of return reversal is exponential, and that nearly 20% of daily volatility is transitory. Both the speed of return reversal and the amount of transitory volatility depend on the stock’s liquidity: For illiquid stocks, return reversals are faster and a greater amount of the volatility, 27%, is transitory. Our estimates of the total costs of immediacy suffered by investors, as a percentage of stock’s market capitalization, are non-monotonic in stock’s liquidity.
Article
We present a structural model of the stock market where a subset of the investors is infrequently present at the market. In our model the stocks’ return reversal pattern is exponential and the amount of return reversal, the speed of return reversal and stock’s transitory volatility are all related to liquidity. In contrast to common perception, fast return reversal is typically a sign of inefficient, illiquid markets, thus not a sign of efficiency. Other results are that the stock’s return liquidity premium and the cost of immediacy to transitory investors are non-monotonic in several structural parameters of the model, such as the number of market makers. Based on the entire available return history for NYSE and Amex traded stocks, we find that, on average, 24% of NYSE and Amex traded stocks’ excess returns revert within a week, that the pattern of return reversal is exponential, and that nearly 20% of daily volatility is transitory. Both the speed of return reversal and the amount of transitory volatility depend on the stock’s liquidity: For illiquid stocks, return reversals are faster and a greater amount of the volatility, 27%, is transitory. Our estimates of the total costs of immediacy suffered by investors, as a percentage of stock’s market capitalization, are non-monotonic in stock’s liquidity.
Article
This study provides evidence on joint characteristics of hourly common stock trading volume and returns on the New York Stock Exchange. Average volume traded shows significant differences across trading hours of the day and across days of the week. Average returns differ across hours of the day, and, to some extent, across days of the week. There is a strong contemporaneous relation between trading volume and returns and also a relation between trading volume and returns lagged up to four hours. Furthermore, the trading volume-returns relation is steeper for positive returns than for nonpositive returns.
Article
I describe asset price dynamics caused by the slow movement of investment capital to trading opportunities. The pattern of price responses to supply or demand shocks typically involves a sharp reaction to the shock and a subsequent and more extended reversal. The amplitude of the immediate price impact and the pattern of the subsequent recovery can reflect institutional impediments to immediate trade, such as search costs for trading counterparties or time to raise capital by intermediaries. I discuss special impediments to capital formation during the recent financial crisis that caused asset price distortions, which subsided afterward. After presenting examples of price reactions to supply shocks in normal market settings, I offer a simple illustrative model of price dynamics associated with slow-moving capital due to the presence of inattentive investors. Copyright (c) 2010 the American Finance Association.
Article
Motivated by psychological evidence that attention is a scarce cognitive resource, we model investors’ attention allocation in learning and study the effects of this on asset-price dynamics. We show that limited investor attention leads to category-learning behavior, i.e., investors tend to process more market and sector-wide information than firm-specific information. This endogenous structure of information, when combined with investor overconfidence, generates important features observed in return comovement that are otherwise difficult to explain with standard rational expectations models. Our model also demonstrates new cross-sectional implications for return predictability.
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This paper examines two alternative models of the process generating stock returns. Under the calendar time hypothesis, the process operates continuously and the expected return for Monday is three times the expected return for other days of the week. Under the trading time hypothesis, returns are generated only during active trading and the expected return is the same for each day of the week. During most of the period studied, from 1953 through 1977, the daily returns to the Standard and Poor's composite portfolio are inconsistent with both models. Although the average return for the other four days of the week was positive, the average for Monday was significantly negative during each of five-year subperiods.
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This paper presents a new pattern in the cross-section of expected stock returns. Stocks tend to have relatively high (or low) returns every year in the same calendar month. We recognize the annual cross-sectional autocorrelation pattern documented in Jegadeesh [1990. Evidence of predictable behavior of security returns. Journal of Finance 45, 881–898] at lags of 12, 24, and 36 months as part of a general pattern that lasts up to 20 annual lags, superimposed on the general momentum/reversal patterns. This pattern explains an economically and statistically significant magnitude of the cross-sectional variation in average stock returns. Volume and volatility exhibit similar seasonal patterns but they do not explain the seasonality in returns. The pattern is independent of size, industry, earnings announcements, dividends, and fiscal year. The results are consistent with the existence of a persistent seasonal effect in stock returns.
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We analyze theoretically and empirically the implications of information asymmetry for equilibrium asset pricing and portfolio choice. In our partially revealing dynamic rational expectations equilibrium, portfolio separation fails, and indexing is not optimal. We show how uninformed investors should structure their portfolios, using the information contained in prices to cope with winner’s curse problems. We implement empirically this price- contingent portfolio strategy. Consistent with our theory, the strategy outperforms economically and statistically the index. While momentum can arise in the model, in the data, the momentum strategy does not outperform the price-contingent strategy, as predicted by the theory.
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This study analyzes the dynamics of daily mutual fund flows. A Vector Auto Regression (VAR) of flows and returns shows that the behavior of fund investors is more consistent with contrarian rather than momentum characteristics. Past fund flows have a positive impact on future fund returns, with the long-term information effect dominating the transient price-pressure effect. Seasonality in daily flows, such as day-of-week and day-of-month patterns are present, and daily flows are generally mean-reverting. Probit regressions indicate that fund investment objective, marketing policy and level of active management explain cross-sectional variation in the behavioral patterns displayed in daily flows. Our results are robust to the different methods of calculating daily flows based on whether or not the day-end TNA figures include the current-day's flow. Throughout the analysis, we contrast the dynamics of daily flows with established results for monthly fund flows and find important differences between the two.
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We use seasonality in stock trading activity associated with summer vacation as a source of exogenous variation to study the relationship between trading volume and expected return. Using data from 51 stock markets, we first confirm a widely held belief that stock turnover is significantly lower during the summer because market participants are on vacation. Interestingly, we find that mean stock return is also lower during the summer for countries with significant declines in trading activity. This relationship is not due to time-varying volatility. Moreover, both large and small investors trade less and the price of trading (bid-ask spread) is higher during the summer. These findings suggest that heterogeneous agent models are essential for a complete understanding of asset prices.
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A model of competitive stock trading is developed in which investors are heterogeneous in their information and private investment opportunities and rationally trade for both informational and noninformational motives. The author examines the link between the nature of heterogeneity among investors and the behavior of trading volume and its relation to price dynamics. It is found that volume is positively correlated with absolute changes in prices and dividends. The author shows that informational trading and noninformational trading lead to different dynamic relations between trading volume and stock returns. Copyright 1994 by University of Chicago Press.
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This article develops a multiperiod rational expectations model of stock trading in which investors have differential information concerning the underlying value of the stock. Investors trade competitively in the stock market based on their private information and the information revealed by the market-clearing prices, as well as other public news. We examine how trading volume is related to the information flow in the market and how investors’ trading reveals their private information.
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We analyze an overlapping generations model with fixed costs of stock market participation. Participation in the stock market is determined endogenously and covaries positively with preceding innovations in dividends. The equilibrium share price is positively related to market participation of the same period and to information about future dividends. There is “rational trend chasing” in the sense that, although all agents are rational, market participation rises after an increase of the share price and falls after a decrease. Finally, we show that the endogenous fluctuations of market participation lead to increased volatility of the share price.
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In this article we use a single unifying framework to analyze the sources of profits to a wide spectrum of return-based trading strategies implemented in the literature. We show that less than 50% of the 120 strategies implemented in the article yield statistically significant profits and, unconditionally, momentum and contrarian strategies are equally likely to be successful. However, when we condition on the return horizon (short, medium, or long) of the strategy, or the time period during which it is implemented, two patterns emerge. A momentum strategy is usually profitable at the medium (3- to 12-months) horizon, while a contrarian strategy nets statistically significant profits at long horizons, but only during the 1926–1947 subperiod. More importantly, our results show that the cross-sectional variation in the mean returns of individual securities included in these strategies play an important role in their profitability. The cross-sectional variation can potentially account for the profitability of momentum strategies and it is also responsible for attenuating the profits from price reversals to long-horizon contrarian strategies.
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A number of empirical studies have reached the conclusion that stock price volatility cannot be fully explained within the standard dividend discount model. This article proposes a resolution based upon a model that contains both a random supply of risky assets and finitely lived agents who trade in a multiple security environment. As the analysis shows there exist $2^K$ equilibria when K securities trade. The low volatility equilibria have properties analogous to those found in the infinitely lived agent models of Campbell and Kyle (1991) and Wang (1993, 1994). In contrast, the high-volatility equilibria have very different characteristics. Within the high-volatility equilibria very large price variances can be generated with very small supply shocks. Adding securities to the economy further reduces the required supply shocks. Using previously established empirical results the model can reconcile the data with supply shocks that are less than 10% as large as observed return shocks. These results are shown to hold even when the dividend process is mean reverting.
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The average returns on low‐capitalization stocks are unusually high relative to those on large‐capitalization stocks in early January, a phenomenon known as the turn‐of‐the‐year effect. This paper finds that the ratio of stock purchases to sales by individual investors displays a seasonal pattern, with individuals having a below‐normal buy/sell ratio in late December and an above‐normal ratio in early January. Year‐to‐year variation in the early January buy/sell ratio explains forty‐six percent of the year‐to‐year variation in the turn‐of‐the‐year effect during 1971–1985.
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This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions.
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This paper studies an overlapping generations model with multiple securities and heterogeneously informed agents. The model produces multiple equilibria, including highly volatile equilibria that can exhibit strong or weak correlations between asset returns-even when asset supplies and future dividends are uncorrelated across assets. Less informed agents rationally behave like trend-followers, while better informed agents follow contrarian strategies. Trading volume has a hump-shaped relation with information precision and is positively correlated with absolute price changes. Finally, accurate information increases the volatility and correlation of stock returns in the highly volatile, strongly correlated equilibrium. Copyright 2008 by The American Finance Association.
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Market liquidity is modeled as being determined by the demand and supply of immediacy. Exogenous liquidity events coupled with the risk of delayed trade create a demand for immediacy. Market makers supply immediacy by their continuous presence. and willingness to bear risk during the time period between the arrival of final buyers and sellers. In the long run the number of market makers adjusts to equate the supply and demand for immediacy. This determine the equilibrium level of liquidity in the market. The lower is the autocorrelation in rates of return, the higher is the equilibrium level of liquidity.
Alessandro Joost Driessen Patrick Tuijp 2012 Pricing liquidity risk with heterogeneous investment horizons
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