Application of Machine Learning: Automated Trading Informed by Event Driven Data
نویسندگان
چکیده
Models of stock price prediction have traditionally used technical indicators alone to generate trading signals. In this paper, we build trading strategies by applying machine-learning techniques to both technical analysis indicators and market sentiment data. The resulting prediction models can be employed as an artificial trader used to trade on any given stock exchange. The performance of the model is evaluated using the S&P500 index. Thesis Supervisor: Jacob K. White Title: Cecil H. Green Professor; Professor of Electrical Engineering and Computer Science
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