Machine Learning for Market Microstructure and （对市场微观结构和机器学习）.pdfVIP

Machine Learning for Market Microstructure and High Frequency Trading Michael Kearns Yuriy Nevmyvaka 1 Introduction In this chapter, we overview the uses of machine learning for high frequency trading and market microstructure data and problems. Machine learning is a vibrant subfield of computer science that draws on models and methods from statistics, algorithms, computational complexity, artificial intelli- gence, control theory, and a variety of other disciplines. Its primary focus is on computationally and informationally efficient algorithms for inferring good predictive models from large data sets, and thus is a natural candidate for application to problems arising in HFT, both for trade execution and the generation of alpha. The inference of predictive models from historical data is obviously not new in quantitative fi- nance; ubiquitous examples include coefficient estimation for the CAPM, Fama and French fac- tors [5], and related approaches. The special challenges for machine learning presented by HFT generally arise from the very fine granularity of the data — often microstructure data at the resolution of individual orders, (partial) executions, hidden liquidity, and cancellations — and a lack of under- standing of how such low-level data relates to actionable circumstances (such as profitably buying or selling shares, optimally executing a large order, etc.). In the language of machine learning, whereas models such as CAPM and its variants already prescribe what the relevant variables or “features” are for prediction or modeling (excess returns, book-to-market ratios, etc.), in many HFT problems one may have no prior intuitions about how (say) the distribution of liquidity in the order book relates to future price movements, if at all. Thusfeature selection orfeature engineering becomes an import