Algorithmic Trading

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Overview

Algorithmic trading studies how to turn market data and other information into rules for selecting assets, managing portfolios, and placing orders. Common approaches include statistical and machine-learning factors, deep reinforcement learning for portfolio choice or execution, market-making models, and newer language-model agents for research and strategy generation. Researchers test these systems with historical walk-forward backtests, limit-order-book simulations, and increasingly prospective or live evaluations, measuring returns alongside risk and trading costs. A central challenge is showing that apparent gains survive data leakage controls, changing market regimes, execution frictions, and the feedback effects of deploying similar strategies.

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151 papers27 papers in the last four weeks, up 59% on the four weeks before. 0.2% of all new papers.

May 13, 2024

Data-driven measures of high-frequency trading

Public data do not identify high-frequency trading (HFT), and standard proxies do not separate liquidity-supplying from liquidity-demanding strategies. We overcome this measurement challenge by training machine learning models on proprietary Nasdaq data to map observed HFT activity to public intraday variables. Applying this mapping, we generate daily measures of liquidity-supplying and liquidity-demanding HFT for all U.S. stocks from 2010 to 2023. The measures largely subsume standard proxies and capture time-series variation that those proxies miss. Using proprietary Euronext Paris data, we provide evidence that the approach generalizes across markets and remains predictive years after training. The 14-year panel lets us study HFT and market quality over time. Supply-side HFT is consistently associated with greater pre-announcement information acquisition, more informed trading, and lower bid-ask spreads, while demand-side HFT is associated with the opposite patterns. During COVID-19, HFT-supplied liquidity remained resilient and its association with lower spreads strengthened.

G. Ibikunle, B. Moews, D. Muravyev +1

Edinburgh Centre for Financial Innovations, The University of Edinburgh · RoZetta Institute, Sydney · Centre for Statistics, The University of Edinburgh +4

Quantitative FinanceAlgorithmic Trading
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