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Investor emotions and asset prices
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Bin Hasan, Mohammad Shehub (2021) Investor emotions and asset prices. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b3781034~S15
Abstract
This thesis explores how variations in investor emotions influence their portfolio decisions and consequently affect asset prices. Broadly, emotions can be either ‘integral’ or ‘incidental’. Integral emotions such as excitement and anxiety enter into investor decision-making processes directly and are fundamental in nature. In contrast, incidental emotions such as weather-induced mood, sports sentiment, or music are indirect, more short-lived, and less powerful. The finance literature principally focuses on the relationship between incidental emotions and market returns. This creates a lacuna to explore the influence of integral emotions on asset prices. My thesis attempts to bridge this research gap and contributes by investigating the impact of integral emotions, such as their states of excitement and anxiety, on investor decision-making and asset prices in real world rather than experimental markets.
In my three empirical chapters, I show how investors’ emotional attachments to stocks are priced in the cross-section of stock returns, can predict local stock returns, and explain a broad range of asset pricing anomalies when included in a factor model.
In Chapter 2, I develop a novel market emotion index focusing on investors’ integral emotions, in particular excitement and anxiety. I measure stock-specific emotion sensitivity – emotion beta – to changes in the market emotion index, which measures the ‘emotional utility’ stocks have for investors. Drawing on the psychology literature, I demonstrate that investors derive high emotional utility from stocks that have ‘emotional glitter’ compared to stocks with low emotional utility. This, I show, contributes to short-term mispricing and creates return predictability in the broad cross-section of U.S. stocks. A Long-Short emotion-based trading strategy generates an alpha of 4.92%. This mispricing ameliorates in about four months. This return predictability mechanism is distinct and incremental to the effects of mood, sentiment, uncertainty, and narrative tone. Collectively, I demonstrate that integral investor emotions play a key role in investors’ portfolio decisions leading to return predictability.
If investors develop ‘love’/‘hate’ relationships with their stocks as I find in Chapter 2, then obviously these relationships will be stronger for stocks with which investors are familiar. The body of literature on geography and stocks returns shows that investors prefer domestic to foreign, and state to out-of-state stocks. In Chapter 3, I draw on this strand of literature along
with psychology and show that investors’ ‘emotional exuberance’ about the state of the stock market as reflected in the local media helps predict local stock returns. This local return predictability differs to the effects of local economic conditions, sentiment, local optimism, and local bias. I demonstrate local investors’ emotional exuberance, as measured by their level of excitement minus anxiety, creates mispricing in a geographic segment of the stock market. An emotion-driven geography-based Long-Short trading strategy earns an annualized alpha of 9.17%. Arbitrage forces of nonlocals take about six months to completely absorb the emotion-driven local mispricing I identify. Specifically, in chapter 3, I focus on local investor emotional dynamics and examine its ability to influence their portfolio decisions and future local stock returns.
In Chapters 2 and 3, I establish investor emotions are an important determinant of asset prices. Thus, asset pricing models should consider these as a tradable pricing factor helping to explain a broad range of asset pricing anomalies. Hence, finally, in Chapter 4, I introduce investors’ emotional relationships with the stocks they invest in, as measured by their emotional utility, directly as a priced factor and include this in an asset pricing model. This emotion factor generates an average excess return of 0.39% per month with a t-statistic of 3.34. Specifically, I propose a 4-factor ‘market-behavioral-emotional’ composite model and show this is able to explain most traditional and recently proposed asset pricing model factors. Conversely, none of the existing factor models can account for this investor emotion-based factor suggesting it is capturing something distinct. In parallel, my newly proposed emotion-imbued behavioral factor model explains most of the robust asset pricing anomalies reported in the literature.
Considering my three main chapters together, I believe my thesis makes an important and original contribution to the asset pricing and investor psychology literature by empirically demonstrating the impact of investors’ integral emotions on their decision-making in complex real-world settings as opposed to more narrowly-based laboratory studies.
Item Type: | Thesis (PhD) | ||||
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Subjects: | H Social Sciences > HF Commerce H Social Sciences > HG Finance |
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Library of Congress Subject Headings (LCSH): | Investments -- Psychological aspects, Assets (Accounting) -- Prices, Finance -- Psychological aspects | ||||
Official Date: | September 2021 | ||||
Dates: |
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Institution: | University of Warwick | ||||
Theses Department: | Warwick Business School | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Supervisor(s)/Advisor: | Taffler, Richard J. ; Kumar, Alok | ||||
Sponsors: | Commonwealth Scholarship Commission in the United Kingdom ; University Grants Commission (Bangladesh) | ||||
Extent: | x, 187 leaves : illustrations, charts | ||||
Language: | eng |
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