Sharpe-Driven Stock Selection and Liquidiy-Constrained Portfolio Optimization: Evidence from the Chinese Equity Market

TL;DR

This paper presents a novel framework for stock selection and portfolio optimization in the Chinese equity market that combines Sharpe ratio screening with liquidity constraints, demonstrating superior empirical performance over traditional buy-and-hold strategies.

Contribution

It introduces a new integrated methodology that combines Sharpe-driven stock selection with liquidity-aware mean-variance optimization, tailored for emerging markets.

Findings

Achieves 25% annualized return and 1.71 Sharpe ratio in backtests.

Outperforms the buy-and-hold benchmark in return, Sharpe ratio, and drawdown.

Provides a scalable, adaptive framework for liquidity-sensitive asset allocation.

Abstract

This paper develops and empirically evaluates a Sharpe-driven stock selection and liquidity-constrained portfolio optimization framework designed for the Chinese equity market. The proposed methodology integrates three sequential stages: Sharpe-ratio-based universe selection, liquidity-adjusted mean-variance optimization, and multi-layered risk management implemented within an automated trading bot. Using daily price and volume data from 2023 to 2025 across the A-share universe, the framework dynamically identifies stocks exhibiting strong risk-adjusted performance while accounting for trading frictions and liquidity asymmetries that are common in emerging markets. Empirical backtests reveal that the proposed strategy achieves an annualized return of 25 percent, a Sharpe ratio of 1.71, and a maximum drawdown of 8.2 percent. These results significantly outperform the Buy-and-Hold benchmark, which records an annualized return of 21 percent, a Sharpe ratio of 1.62, and a drawdown of 7.6 percent over the same period. The superior performance demonstrates that incorporating risk-adjusted selection and liquidity-aware constraints enhances both profitability and stability, enabling the portfolio to capture upside potential while maintaining drawdown resilience. Beyond its empirical success, this study contributes methodologically by bridging classical mean-variance theory with practical liquidity adjustments and dynamic Sharpe-based screening. The resulting system not only improves the tradability of optimized portfolios but also provides a scalable and adaptive framework for quantitative asset allocation in liquidity-sensitive markets, offering new evidence that disciplined risk-return optimization can outperform passive investment strategies in the post-2023 Chinese equity landscape.