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"Valuation and Returns on Stock Return Volatility"

This paper provides deep insights into how fundamental analysis can be used to develop investment strategies in both stock and derivatives markets. Specifically, the study focuses on the complex relationships between individual firm characteristics, stock returns, volatility, and volatility risk premium (VRP).

1. Research Background and Motivation

• Limitations of Existing Studies: Traditional stock valuation models focus on the relationship between stock prices and company fundamentals, treating derivative pricing separately. Meanwhile, options market research takes prices as given and primarily explains derivatives pricing through volatility.

• New Connection Attempt: This study develops an accounting-based valuation model that bridges these two research streams, demonstrating how fundamental analysis influences both stock returns and stock return volatility.

• Investor Uncertainty: Investors face two types of uncertainty when investing in stocks: return uncertainty (variance of returns) and uncertainty about return variance (variance of variance).

• Volatility Risk Premium (VRP): Investors dislike uncertainty in volatility itself, so they demand a premium to bear this risk. While VRP plays a crucial role in options markets, it has been underexplored in traditional valuation literature.

2. Research Model

Core Assumption: The study presents a stock valuation model that accounts for firm earnings volatility, explaining the relationship between stock prices and price variance. It shows that stock risk premiums are related to volatility risk premiums.

Fundamental-Based Approach: The model establishes that corporate fundamentals (earnings, earnings growth, etc.) are linked to stock risk premiums and, consequently, to VRP.

Key Predictions:

• Expected stock returns have a positive quadratic relationship with stock return variance.

• Expected stock returns have a negative quadratic relationship with VRP.

3. Data and Methodology

Data Sources: The study utilizes multiple datasets, including CRSP (stock prices, dividends), I/B/E/S (earnings and earnings forecasts), and OptionMetrics (options data).

Analytical Methods:

• Fama-MacBeth regressions

• GARCH modeling

• Portfolio construction

4. Key Findings

Fundamentals and Return/Volatility Relationship:

• Expected returns, estimated using fundamental ratios (earnings-to-price ratio, expected earnings growth), exhibit a positive quadratic relationship with stock return variance and a negative quadratic relationship with VRP.

• Firms with higher expected returns tend to have higher return volatility and lower VRP.

Risk Aversion Effect:

• VRP is generally negative, indicating that investors demand compensation for exposure to high-volatility assets.

Options Trading Strategy:

• The study introduces an options selection strategy leveraging expected returns and volatility derived from the model.

• This strategy outperforms traditional volatility-based strategies.

5. Practical Implications

• Utilization in Options Markets: The study suggests the possibility of generating profits in options markets using fundamental analysis.

• Volatility Trading Strategies: Investors can enhance their volatility trading strategies by incorporating fundamental insights.

• Mitigating Information Asymmetry: Identifying overlooked fundamental information can uncover investment opportunities.

6. Conclusion

This study explains the relationships between stock returns, volatility, and VRP using a fundamental-based model and introduces an options trading strategy leveraging these insights. The findings provide valuable guidance for both investors and researchers in utilizing fundamental information in derivatives markets.

Summary:

• This paper provides evidence that corporate fundamentals influence not only stock returns and volatility but also volatility risk premiums.

• Fundamental analysis can be used to create innovative investment strategies in options markets.

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