Automated trading systems statistical and machine learning methods and hardware implementation: a survey

Boming Huang · Yuxiang Huan · Li Da Xu · Li‐Rong Zheng · Zhuo Zou

140 citations0/0 files storedEnterprise Information Systems

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Stock Market Forecasting Methods Financial Markets and Investment Strategies FinTech, Crowdfunding, Digital Finance

Abstract

Automated trading, which is also known as algorithmic trading, is a method of using a predesigned computer program to submit a large number of trading orders to an exchange. It is substantially a real-time decision-making system which is under the scope of Enterprise Information System (EIS). With the rapid development of telecommunication and computer technology, the mechanisms underlying automated trading systems have become increasingly diversified. Considerable effort has been exerted by both academia and trading firms towards mining potential factors that may generate significantly higher profits. In this paper, we review studies on trading systems built using various methods and empirically evaluate the methods by grouping them into three types: technical analyses, textual analyses and high-frequency trading. Then, we evaluate the advantages and disadvantages of each method and assess their future prospects.

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References

2011Does Algorithmic Trading Improve Liquidity?2003Chapter 18 A survey of behavioral finance 2010The Information Content of Forward‐Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach 2015A Survey of Behavioral Finance 2000Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation 1993Fundamental Information Analysis 1995Herd Behaviour, Bubbles and Crashes 2013Quantifying Trading Behavior in Financial Markets Using Google Trends

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