Analyzing the Performance of Algorithmic Trading Strategies in Emerging Markets

Algorithmic trading has become a key driver of efficiency and innovation in global financial markets, yet its adoption and effectiveness in emerging economies remain less explored. This study examines the performance of selected algorithmic trading strategies within emerging stock markets, focusing on factors such as market volatility, liquidity constraints, and technological infrastructure. Using historical price data and transaction records, multiple strategies—including trend-following, mean reversion, and momentum-based models—are implemented and evaluated through rigorous backtesting. Performance is measured in terms of profitability, risk-adjusted returns, and execution efficiency. The research also considers how market-specific characteristics, such as regulatory frameworks and trading volume patterns, influence outcomes. Findings aim to highlight both the potential and the limitations of algorithmic approaches in environments where market dynamics differ from developed economies. The results provide valuable insights for traders, policymakers, and financial institutions seeking to optimize algorithmic systems in rapidly evolving, high-growth markets.