Exploiting investor sentiment for portfolio optimization

Banholzer, Nicolas; Heiden, Sebastian; Schneller, Dominik

doi:10.1007/s40685-018-0062-6

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…Similarly, Stambaugh et al (2012) find that exploiting under-pricing should be more difficult due to short-sale impediments and suggests that sentiment-induced overpricing is exploitable. In Banholzer et al (2019), the authors used copula opinion pooling of Meucci (2006) to study whether, in international stock markets, sentiment polarity can be directly incorporated into a portfolio optimization framework. The study implemented a strategy to exploit the medium-term reversal effect of sentiment on returns.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Accounting for unadjusted news sentiment for asset pricing

Eachempati

Srivastava

2021

QRFM

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“…We identified several approaches to understanding whether sentiment is a significant characteristic: 1) applying econometric cross-sectional and time series models to explain stock returns [19,23], 2) constructing portfolios [22,31,43,44], 3) forecasting stock returns [13], and 4) measuring comovement between investor sentiment and stock returns [2]. Some popular metrics of investor attention and sentiment, based on the content of various sources of information transmission, are given in Table 1.…”

Section: Literature Review and The Hypotheses Of The Studymentioning

confidence: 99%

A retail investor in a cobweb of social networks

Теплова¹,

Tomtosov

Соколова³

2022

PLoS ONE

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In this study, using AI, we empirically examine the irrational behaviour, specifically attention-driven trading and emotion-driven trading such as consensus trading, of retail investors in an emerging stock market. We used a neural network to assess the tone of messages on social media platforms and proposed a novel Hype indicator that integrates metrics of investor attention and sentiment. The sample of messages, which are written in Russian with slang expressions, was retrieved from a unique dataset of social network communication of investors in the Russian stock market. Applying different portfolio designs, we evaluated the effectiveness of the new Hype indicator against the factors of momentum, volatility, and trading volume. We found the possibility of building a profitable trading strategy based on the Hype indicator over a 6-month time horizon. Over short periods, the Hype indicator allows investors to earn more by buying stocks of large companies, and over «longer» periods, this indicator tends to perform better for illiquid stocks of small companies. As consensus trading tends to produce negative returns, the investment strategy of ‘Go against the crowd’ proves rewarding in the medium term of 3 months.

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“…For the problem of cognitive bias measurement, a review of the related literature can be roughly classified into several classes. The first class chooses to utilize the available indicators as the agent variables of cognitive bias [17][18][19][20], and investigate the regular changes of cognitive bias via these variables. The second class tends to measure by extracting the principal components from the influencing factors [21,22], with the hope that it could better eliminate the interference of other characteristics to the measurement.…”

Section: Introductionmentioning

confidence: 99%

An Improved MV Method for Stock Allocation Based on the State-Space Measure of Cognitive Bias with a Hybrid Algorithm

Wang

et al. 2020

Symmetry

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In classical finance theory, cognitive bias does not play any role in predicting returns. With the development of the economy, the classical theory gradually finds it difficult to offset the irrational demand through arbitrage. Due to the rise of behavioral economics, how to allocate stock portfolios in the highly subjective environment is an unavoidable problem. Considering the decision heterogeneity between the rational market and the irrational one, the mean-variance (MV) method was improved in the construction of a market bias index for stock portfolio allocation, which we called EMACB (exponential moving average of cognitive bias)-variance method. Besides, due to the lack of related research, we introduced a measure of aggregate investor cognitive bias by adopting the state-space model. Finally, the proposed method was applied in an investment allocation example to prove its feasibility, and its advantages were emphasized by a comparison with another relevant approach.

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Exploiting investor sentiment for portfolio optimization

Cited by 7 publications

References 43 publications

Accounting for unadjusted news sentiment for asset pricing

Accounting for unadjusted news sentiment for asset pricing

A retail investor in a cobweb of social networks

An Improved MV Method for Stock Allocation Based on the State-Space Measure of Cognitive Bias with a Hybrid Algorithm

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