Social Media and News Sentiment Analysis for Advanced Investment Strategies

Yang, Steve Y.; Mo, Sheung Yin Kevin

doi:10.1007/978-3-319-30319-2_11

Cited by 4 publications

(4 citation statements)

References 41 publications

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“…Thus, the negative psychological perceptions of investors have a significant positive effect on investor sentiment, and this result is robust. In line with Yang and Mo [ 27 ], who demonstrate the influence of news and social media on investors’ sentiments, this paper reveals that negative psychological perceptions induced by negative news significantly amplify investor sentiment. The results of this paper provide evidence that investor sentiment serves as a crucial mechanism through which negative psychological perceptions of investors impact platform liquidity and verify Hypothesis 3.…”

Section: Channel Analysissupporting

confidence: 87%

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Will the negative psychological perceptions of investors reduce platform liquidity? Evidence from China’s online loans

Qin,

Liu,

et al. 2023

PLoS ONE

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Market liquidity can reflect whether financial market conditions are favorable and is the primary concern for investors when making investment decisions. Therefore, investors’ psychological perception and confidence in the quality of products (assets) are particularly important. Using 264 of China’s online loan platforms from August 2017 to November 2018, we investigate the impact of the negative psychological perceptions of investors on platform liquidity. The empirical results suggest that the negative psychological perceptions of investors reduce platform liquidity and increase platform liquidity risk. Using the Baidu Search Index to measure investor sentiment, we find that the negative psychological perceptions of investors affect platform liquidity by affecting investor sentiment, which provides a good channel for explaining the main conclusions. Heterogeneity analysis shows that the impact of the negative psychological perceptions of investors on platform liquidity is smaller in high-quality platforms with higher market share and higher registered capital. Meanwhile, we also find that the impact of negative psychological perceptions of investors is greater in private platforms, after the rectification policy, with positive net inflow, and in first- and second-tier cities and coastal cities. Precautionary financial regulatory policies are necessary, not punishment ex post. The research findings of this article can assist investors, platform managers, and regulatory agencies in identifying the liquidity characteristics of platforms, which can contribute to strengthening market liquidity management and financial risk control and provide some reference and support for formulating sustainable development policies in the financial industry.

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Section: Channel Analysissupporting

confidence: 87%

“…In addition, Yang and Mo [ 27 ] discover that news and social media affect investors’ sentiments, which drives the upward or downward movements of financial markets. Investor sentiment refers to systematic biases in investors’ future expectations [ 28 ].…”

Section: Research Hypothesesmentioning

confidence: 99%

Will the negative psychological perceptions of investors reduce platform liquidity? Evidence from China’s online loans

Qin,

Liu,

et al. 2023

PLoS ONE

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“…In Yang and Mo (2016), the authors discuss various approaches to formulate investment strategies using sentiment variation data, surprises and market feedback strength (Hirshleifer, 2006). The study shows that the strategies yield superior risk-adjusted returns over other benchmark strategies.…”

Section: Theoretical Background and Literature Surveymentioning

confidence: 99%

Accounting for unadjusted news sentiment for asset pricing

Eachempati

Srivastava

2021

QRFM

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Purpose A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment. Design/methodology/approach Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results. Findings Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6. Originality/value The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.

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“…While the types of news posted in Twitter vary from serious news events to movie and celebrities' news, in this research we focus on finance, disaster and politics news. Researchers have long investigated the relationship between social media activity and market prices to improve investment strategies [12]. [13] used Twitter data to monitor stock price change and trade volume.…”

Section: News Sharing In Twittermentioning

confidence: 99%

A Study of the Relationship Between the Geographic Locations of the User and Participation in Twitter During Different Types of News Events

Ahmed

Watters

2018

TMLAI

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Twitter is one of the most active social networks in news sharing. People report local events sometimes faster than news agencies. During major events, such as earthquakes and presidential elections, people share tweets, retweets, images and links related to the event, creating an overwhelming number of posts. The amount of data generated by social media provides a shared resource for the discovery of information for individuals, organizations and governments, but the large stream of tweets makes tracking interesting posts a challenging task. Understanding user behavior during different contexts provides substantial insight to get the most out of the social media. The objective of this work is to investigate the relationship between user participation, news type and geographic locations of Twitter's users. The results show that financial news tweets have distinct user behavior compared to tweets about political events and tweets about disasters. Tweets about financial events tend to have more original tweets and more links, than tweets about political events, which had larger numbers of retweets. The investigation of the relationship between the style of user's participation and the type of news provides insight for social media management, specifically, for using information diffusion proactively, real time filtering, and locationaware news recommendation systems. We found that there are relationships between the country of the user, the type of news and user tweeting behavior for news related tweets. News type is strongly correlated to users' behavior and was found to have a stronger relationship to users' behavior than geographic location.

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Social Media and News Sentiment Analysis for Advanced Investment Strategies

Cited by 4 publications

References 41 publications

Will the negative psychological perceptions of investors reduce platform liquidity? Evidence from China’s online loans

Will the negative psychological perceptions of investors reduce platform liquidity? Evidence from China’s online loans

Accounting for unadjusted news sentiment for asset pricing

A Study of the Relationship Between the Geographic Locations of the User and Participation in Twitter During Different Types of News Events

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