Algorithmic Trading and its Effect on Market Efficiency
TL;DR
It is concluded that investing in algorithmic trading platforms is highly viable, enhancing price discovery, market liquidity, and trading cost efficiency, far exceeding the 10% discount hurdle rate.
Abstract
This study, titled "Algorithmic Trading and Its Effect on Market Efficiency," evaluates algorithmic strategy shares, order execution slippage, exchange liquidity compression, and financial feasibility of automated execution systems in modern securities markets. Financial exchanges experience rapid electronification, where high-frequency trading (HFT) and statistical arbitrage account for 70% of total order flow. A five-year project lifecycle (2021- 2025) of an institutional algorithmic trading platform is evaluated using capital budgeting parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). Quantitative analysis indicates that statistical arbitrage represents 40% and HFT market making accounts for 30% of algorithmic trading volume. Deploying AI algorithmic co-location reduces order execution slippage to 1.1 basis points (bps) compared to 24.5 bps under manual floor trading. Higher execution speed drives daily exchange trading volume to 185,000 Crores while compressing average bid-ask spreads to 1.2 bps, raising algorithmic market share to 84.5% and driving the market variance ratio to 1.01 (indicating strong random-walk efficiency) by 2025. The financial model yields a positive NPV of 284.5 Crores and an IRR of 38.6%, far exceeding the 10% discount hurdle rate. The study concludes that investing in algorithmic trading platforms is highly viable, enhancing price discovery, market liquidity, and trading cost efficiency.