Stock Return Predictability and Investor Sentiment: A High-Frequency Perspective

Sun, Licheng; Najand, Mohammad; Shen, Jiancheng

doi:10.2139/ssrn.2692328

Cited by 22 publications

(42 citation statements)

References 37 publications

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“…Meanwhile, predictability is an increasing function of trading volume using both metals futures contracts, copper, and steel. The above phenomenon is consistent with the results of both Gao et al () and Sun et al (). The results produced using the sign of the first and second‐to‐last returns are shown in Panel B of Table .…”

Section: Empirical Findingssupporting

confidence: 93%

“…The R 2 figures show a situation whereby higher levels of volatility increase predictability for both copper, and steel futures contracts. Again, this result is consistent with Gao et al () and Sun et al (). When we use the direction change we uncover the results given in Panel B of Table , with the predictability of the first trading session being an increasing function of volatility in soybean, copper and steel futures contracts.…”

Section: Empirical Findingssupporting

confidence: 93%

“…The use of half-hour returns is in line with earlier studies on intraday time-series momentum in financial markets (Gao et al, 2018;Gao, Xing, Youwei, & Xiong, 2019, Komarov, 2017Sun et al, 2016). However, can intraday time-series momentum also be observed at other time frequencies?…”

Section: Robustness Checkssupporting

confidence: 76%

“…Venter (2009) also follows the winner/loser ranking method to study intraday cross-sectional momentum on Johannesburg Stock Exchange stocks, arguing that a cross-sectional momentum effect can be uncovered within a day. Furthermore, Gao, Han, Zhengzi Li, and Zhou (2018) and Sun, Najand, and Shen (2016) utilize the time-series momentum approach of Moskowitz et al (2012) to study the S&P 500, they document an intraday time-series momentum pattern that the first half-hour return is able to predict the last half-hour return. Similarly, Elaut, Kevin, and Michael (2018) find that the first half-hour return can be used to predict the last half-hour return in the RUB-USD FX market.…”

mentioning

confidence: 99%

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Intraday time‐series momentum: Evidence from China

Jin

Kearney

et al. 2019

Journal of Futures Markets

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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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Section: Empirical Findingssupporting

confidence: 93%

Section: Empirical Findingssupporting

confidence: 93%

Section: Robustness Checkssupporting

confidence: 76%

mentioning

confidence: 99%

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Intraday time‐series momentum: Evidence from China

Jin

Kearney

et al. 2019

Journal of Futures Markets

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“…(Algaba et al, 2016) refer to this new strand of literature as Sentometrics. For instance, (Groß-Klußman and Hautsch, 2011) study the impact of unexpected news on the displayed quotes in a limit order book, (Sun et al, 2016) show that intraday S&P 500 index returns are predictable using lagged half-hour investor sentiment, (Antweiler and Frank, 2004;Borovkova and Mahakena, 2015;Allen et al, 2015;Smales, 2015) study the impact of sentiment on volatilities, (Peterson, 2016) investigates the trading strategies based on sentiment, (Tetlock, 2007;Garcia, 2013) consider the Dow Jones Industrial Average (DJIA) index predictability using sentiment, (Calomiris and Mamaysky, 2019) show how the predictability can be exploited in different markets around the world, (Ranco et al, 2015) analyse the impact of social media attention on market dynamics, (Borovkova, 2015) develops risk measures based on sentiment index, and (Lillo et al, 2015) 2 show that different types of investors react differently to news sentiment.…”

Section: Introductionmentioning

confidence: 99%

A Tale of Two Sentiment Scales: Disentangling Short-Run and Long-Run Components in Multivariate Sentiment Dynamics

2019

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We propose a novel approach to sentiment data filtering for a portfolio of assets. In our framework, a dynamic factor model drives the evolution of the observed sentiment and allows to identify two distinct components: a long-term component, modeled as a random walk, and a short-term component driven by a stationary VAR(1) process. Our model encompasses alternative approaches available in literature and can be readily estimated by means of Kalman filtering and expectation maximization. This feature makes it convenient when the cross-sectional dimension of the sentiment increases. By applying the model to a portfolio of Dow Jones stocks, we find that the long term component co-integrates with the market principal factor, while the short term one captures transient swings of the market associated with the idiosyncratic components and captures the correlation structure of returns. Finally, using quantile *

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Do the Shanghai–Hong Kong & Shenzhen–Hong Kong Stock Connect programs enhance co‐movement between the Mainland Chinese, Hong Kong, and U.S. stock markets?

Chen

2020

Int. J Fin Econ

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The Shanghai–Hong Kong Stock Connect (SH–HK–SC) and Shenzhen–Hong Kong Stock Connect (SZ–HK–SC) programs aim to strengthen the openness of Mainland Chinese stock market. They are considered milestones in the development process of the Mainland Chinese capital market. This paper studies the effects of the SH–HK–SC and SZ–HK–SC programs on the co‐movement between the Mainland Chinese, Hong Kong and U.S. stock markets in terms of dynamic conditional correlation by the t‐copula–DCC–GARCH model. We find that both programs do not substantively enhance the daily price co‐movements among the examined markets for all pairs except the Hong Kong–U.S. pair. The combination effect of the programs significantly enhances the weekly price co‐movements between the Mainland Chinese and Hong Kong, or U.S. stock market after the SZ–HK–SC program, with an insignificant effect on the Hong Kong–U.S. pair. Although the multiple breakpoint tests for the daily data and weekly data exhibit somewhat different results, the structural breakpoints of the dynamic correlation coefficients among the stock markets easily appear during the subprime crisis. Some possible explanations are provided for the results, and correspondent suggestions are given for investors and policymakers.

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Stock Return Predictability and Investor Sentiment: A High-Frequency Perspective

Cited by 22 publications

References 37 publications

Intraday time‐series momentum: Evidence from China

Intraday time‐series momentum: Evidence from China

A Tale of Two Sentiment Scales: Disentangling Short-Run and Long-Run Components in Multivariate Sentiment Dynamics

Do the Shanghai–Hong Kong & Shenzhen–Hong Kong Stock Connect programs enhance co‐movement between the Mainland Chinese, Hong Kong, and U.S. stock markets?

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