Do We Price Happiness? Evidence from Korean Stock Market

ArXiv ID: 2308.10039 “View on arXiv”

Authors: Unknown

Abstract

This study explores the potential of internet search volume data, specifically Google Trends, as an indicator for cross-sectional stock returns. Unlike previous studies, our research specifically investigates the search volume of the topic ‘happiness’ and its impact on stock returns in the aspect of risk pricing rather than as sentiment measurement. Empirical results indicate that this ‘happiness’ search exposure (HSE) can explain future returns, particularly for big and value firms. This suggests that HSE might be a reflection of a firm’s ability to produce goods or services that meet societal utility needs. Our findings have significant implications for institutional investors seeking to leverage HSE-based strategies for outperformance. Additionally, our research suggests that, when selected judiciously, some search topics on Google Trends can be related to risks that impact stock prices.

Keywords: Sentiment Analysis, Google Trends, Cross-Sectional Returns, Risk Pricing, Equity

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper applies relatively simple linear regression and portfolio sorting methods (low math complexity) but uses real financial data (Korean stock market via DataGuide, Google Trends), performs rigorous backtesting with Fama-MacBeth regressions, double sorts, and provides detailed statistical results (t-values, significant returns), indicating strong empirical implementation.

  flowchart TD
    A["Research Goal<br>Impact of 'Happiness' Search Volume<br>on Stock Returns"] --> B["Data Collection"]
    B --> C["Data Preparation"]
    C --> D["Computational Modeling"]
    D --> E["Key Findings"]

    B --> B1["Google Trends Data<br>Happiness Search Exposure HSE"]
    B --> B2["Korean Stock Market Data<br>Returns & Firm Characteristics"]

    C --> C1["Align Temporal Data<br>Monthly Frequency"]
    C --> C2["Define Variables<br>Portfolios by Size & Value"]

    D --> D1["Time-Series Regressions<br>Fama-MacBeth Approach"]
    D --> D2["Factor Analysis<br>Risk Pricing Evaluation"]

    E --> E1["HSE Significantly Predicts Returns<br>Big & Value Firms"]
    E --> E2["HSE Reflects Firm Utility Production<br>Risk Pricing Mechanism"]
    E --> E3["Implications for Investors<br>HSE-based Strategies"]