Research: Evaluating Transaction Costs and Market Impact in Algorithmic Trading Strateg...

Research question: How do explicit and implicit transaction costs influence the performance and risk-adjusted returns of common algorithmic trading strategies in liquid equity markets?

Background and Motivation: In the domain of quantitative finance, algorithmic trading strategies are widely used to exploit market inefficiencies and automate order execution. However, real-world implementation faces significant frictions in the form of transaction costs, including both explicit costs (commissions, fees) and implicit costs (slippage, market impact).

Research Gap: While the theoretical profitability of many algorithmic strategies is well established, there is limited comprehensive empirical analysis quantifying the effect of transaction costs and their decomposition on strategy performance across various market regimes.

Approach and Expected Contribution: This study will systematically design and backtest representative algorithmic trading strategies (e.g., VWAP, momentum, mean-reversion) using high-frequency equity data, incorporating detailed models for transaction costs. The research will use state-of-the-art cost estimation techniques and sensitivity analysis to evaluate the impact on returns, Sharpe ratios, and drawdowns, providing robust recommendations for strategy optimization.

Why It Matters: Understanding the real impact of transaction costs is critical for both practitioners and academics to bridge the gap between theoretical backtests and live trading performance, informing the development of more robust and practical trading algorithms.