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The predictive power of earnings conference calls : predicting stock price movement with earnings call transcripts

Solberg, Lars Erik; Karlsen, Jørgen
Master thesis
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http://hdl.handle.net/11250/2560960
Issue date
2018
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  • Master Thesis [2971]
Abstract
Earnings conference calls are considered a valuable text based information source for

investors. This paper investigates the possibility to predict the direction of stock prices

by analyzing the transcripts of earnings conference calls. The paper investigates 29 339

different earnings call transcript from 2014 to 2017 and classify the individual documents

to either be part of class up or down. Four different machine learning algorithms

are used to classify and predict based on the bag of words method. These machine learning

algorithms are Naive Bayes, Logistic regression with lasso regularization, Stochastic

Gradient Boosting, and Support Vector Machine. All models are compared to a benchmarks

based on S&P 500. The model with best performance is logistic regression with

a classification error of 43,8%. In total, 2 of 4 models beats the benchmark significantly,

namely logistic regression and gradient boosting. With these results, the paper

concludes that earnings calls contain predicting power for next day’s stock price direction.

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