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Title: Stock Price Movement Prediction Using StockTwits and Topic-Sentiment Neural Network
Authors: Lee, William
Advisors: Fabozzi, Frank
Department: Operations Research and Financial Engineering
Class Year: 2016
Abstract: In this thesis, StockTwits - a social network of investors and traders that has similar structures to Twitter - is analyzed to extract topics and sentiments about a certain stock during a xed length of time using an Topic Sentiment Latent Dirichlet Allocation (TSLDA) and use the extracted topic-sentiment as features of a neural network to make a prediction of stock price movement. TSLDA is specifically studied in detail to develop a parallelization of the Collapsed Gibbs Sampling process.
Extent: 68 pages
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Operations Research and Financial Engineering, 2000-2017

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