Market-Neutral Strategies in Mid-Cap Portfolio Management: A Data-Driven Approach to Long-Short Equity

ArXiv ID: 2412.12576 “View on arXiv”

Authors: Unknown

Abstract

Mid-cap companies, generally valued between $2 billion and $10 billion, provide investors with a well-rounded opportunity between the fluctuation of small-cap stocks and the stability of large-cap stocks. This research builds upon the long-short equity approach (e.g., Michaud, 2018; Dimitriu, Alexander, 2002) customized for mid-cap equities, providing steady risk-adjusted returns yielding a significant Sharpe ratio of 2.132 in test data. Using data from 2013 to 2023, obtained from WRDS and following point-in-time (PIT) compliance, the approach guarantees clarity and reproducibility. Elements of essential financial indicators, such as profitability, valuation, and liquidity, were designed to improve portfolio optimization. Testing historical data across various markets conditions illustrates the stability and resilience of the tactic. This study highlights mid-cap stocks as an attractive investment route, overlooked by most analysts, which combine transparency with superior performance in managing portfolios.

Keywords: Mid-cap Equities, Long-Short Equity, Portfolio Optimization, Factor Investing, Risk-Adjusted Returns

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Street Traders
• Why: The paper relies on standard statistical methods like Sharpe ratio and regression without advanced mathematical derivations, but demonstrates strong empirical rigor through detailed data sourcing (WRDS/CRSP/Compustat), strict point-in-time compliance, and reported backtest results from 2013-2023.

  flowchart TD
    A["Research Goal:<br>Develop & Test a Market-Neutral<br>Long-Short Strategy for Mid-Caps"] --> B["Data Input<br>2013-2023 WRDS Data<br>PIT Compliant"]
    B --> C["Methodology:<br>Factor Selection<br>Profitability, Valuation, Liquidity"]
    C --> D["Computational Process:<br>Portfolio Optimization & Backtesting"]
    D --> E{"Key Outcomes"}
    E --> F["Significant Sharpe Ratio<br>2.132 (Test Data)"]
    E --> G["Stable Returns Across<br>Various Market Conditions"]
    E --> H["Confirmed Mid-Caps as<br>Attractive Risk-Adjusted Asset Class"]