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"Risk and Return Models for Equity Markets and Implied Equity Risk Premium"
This study analyzes methodologies for estimating the Implied Equity Risk Premium (ERP), a key factor in equity market risk-return models, and evaluates the limitations of traditional approaches.

1. Research Objectives

• Utilize Google Trends search volume data and other market indicators to empirically analyze ERP in the U.S. stock market.

• Examine financial analysts’ and investors’ approaches to equity return estimation and compare their effectiveness relative to benchmarks.

• Explore the relationship between capital investment, earnings expectations, and long-term bond yields.

2. Research Methodology

Collected data from 1988 to 2009:

• S&P 500 index, 6-month Treasury bills, and 10-year Treasury bonds.

Applied various ERP estimation methods, including:

• Historical returns approach

• Survey-based expectations

• DCF (Discounted Cash Flow) model-based ERP estimates

Conducted OLS (Ordinary Least Squares) regression analysis to evaluate the relationships between key variables.

3. Key Findings

1) ERP Estimation is Highly Unstable

• Using the DCF model, ERP estimates were inconsistent and highly volatile across different time periods.

• In some cases, the estimated ERP was negative, highlighting the sensitivity of ERP calculations to model assumptions and input data.

2) Model Limitations

• Challenges in long-term growth forecasting:
  The model struggles to predict long-term economic growth trends, making ERP estimates unreliable.

• Inability to capture short-term volatility:
  The approach fails to incorporate market shocks or investor sentiment fluctuations.

• Mismatch with investor expectations:
  The model may not accurately reflect how investors form risk-return expectations in real markets.

3) Limited Data Coverage

• The study relied on a relatively short sample period (1988-2009).

• Extending the dataset could improve the robustness of ERP estimates.

Conclusion

This study highlights the difficulties in estimating ERP using traditional models, emphasizing that:

• ERP estimation is complex and highly sensitive to model choice and data inputs.

• Existing methods fail to consistently capture investor expectations and market conditions.

• Future research should explore more advanced models and incorporate alternative data sources (e.g., Google Trends).

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