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"Market-Neutral Strategies in Mid-Cap Portfolio Management: A Data-Driven Approach to Long-Short Equity"
This paper examines the effectiveness of a market-neutral long-short strategy in the U.S. mid-cap stock market, leveraging market inefficiencies and quantitative optimization techniques.

1. Research Objectives

• Develop an effective long-short strategy that exploits inefficiencies in mid-cap stocks.

• Optimize portfolios using financial indicators such as profitability, valuation, and liquidity.

• Assess stability and resilience across different market conditions.

2. Research Methodology

• Used U.S. stock data (2013-2023) sourced from WRDS and Compustat.

• Ensured point-in-time (PIT) compliance to prevent data leakage.

• Applied data preprocessing techniques such as outlier removal, multicollinearity adjustments, and feature selection.

• Maintained market neutrality while optimizing portfolios based on profitability, valuation, and liquidity factors.

• Conducted backtesting across various market conditions to evaluate strategy performance.

3. Key Findings

1.High Sharpe Ratio Achieved:

• The backtested strategy (2013-2023) produced a Sharpe ratio of 2.132, demonstrating strong risk-adjusted returns.

2.Effectiveness of a Data-Driven Approach:

• Combining fundamental factors (profitability, valuation, and liquidity) proved to be highly effective.

• Reinforces the importance of fundamental analysis in investment decision-making.

3.Stability and Resilience of Market-Neutral Strategy:

• Backtesting across different market conditions confirmed the strategy’s robustness and consistency.

• Suggests that the strategy is not overly dependent on specific market trends.

4.Optimization Enhances Risk Management:

• Portfolio optimization effectively controlled risk while outperforming market benchmarks.

• The balanced long-short position reduced exposure to market volatility.

Conclusion

This study confirms that a market-neutral long-short strategy can generate stable returns in the mid-cap segment. By leveraging data-driven optimization techniques, the strategy effectively exploits market inefficiencies while maintaining stability across market conditions.
A high Sharpe ratio indicates superior risk-adjusted returns, reinforcing its advantage over traditional benchmarks.

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