University of Illinois Urbana-Champaign

Predicting Stock Price Movements from Annual Reports

Zou, Daniel W.

Content Files
211594.pptx

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https://hdl.handle.net/2142/99862

Description

Title
Predicting Stock Price Movements from Annual Reports
Author(s)
Zou, Daniel W.
Contributor(s)
Brunner, Robert J.
Issue Date
2018-04
Keyword(s)
  • Computer Science Engineering
  • machine learning
  • text analysis
  • stock market
  • natural language processing
  • algorithmic trading
Date of Ingest
2018-05-03T19:09:15Z
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.
Type of Resource
  • text
  • image
Genre of Resource
Conference Paper / Presentation
Permalink
http://hdl.handle.net/2142/99862
Copyright and License Information
Copyright 2018 Daniel W. Zou

Owning Collections

Undergraduate Research Symposium 2018 PRIMARY