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Predicting Stock Price Movements from Annual ReportsMicrosoft PowerPoint 2007


Title:Predicting Stock Price Movements from Annual Reports
Author(s):Zou, Daniel W.
Contributor(s):Brunner, Robert J.
Subject(s):Computer Science Engineering
machine learning
text analysis
stock market
natural language processing
algorithmic trading
Abstract:We propose a supervised machine learning system to learn from text and financial data and predict whether an asset will have positive, neutral, or negative excess returns one day after the release of a text document. Our system utilizes TF.IDF and non-negative matrix factorization to build document embeddings and an ensemble of gradient-boosted regression trees for classification. Our aim is to improve the performance of a benchmark classifier that uses past stock price history by digesting and extracting useful features from text data. We use a corpus containing over 100,000 Form 10-Ks and 10-Qs, which are annual and quarterly shareholder reports to the U.S. Securities and Exchange Commission (SEC) respectively. By incorporating textual features, we beat our baseline model significantly and present an asset-agnostic model for stock price movement predictions. Our work has implications for text analysis, corporate fraud detection, and algorithmic trading.
Issue Date:2018-04
Genre:Conference Paper / Presentation
Rights Information:Copyright 2018 Daniel W. Zou
Date Available in IDEALS:2018-05-03

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