Version 1
: Received: 17 February 2021 / Approved: 18 February 2021 / Online: 18 February 2021 (17:22:50 CET)
How to cite:
Roozen, D.; Lelli, F. Stock Values and Earnings Call Transcripts: a Dataset Suitable for Sentiment Analysis. Preprints2021, 2021020424. https://doi.org/10.20944/preprints202102.0424.v1
Roozen, D.; Lelli, F. Stock Values and Earnings Call Transcripts: a Dataset Suitable for Sentiment Analysis. Preprints 2021, 2021020424. https://doi.org/10.20944/preprints202102.0424.v1
Roozen, D.; Lelli, F. Stock Values and Earnings Call Transcripts: a Dataset Suitable for Sentiment Analysis. Preprints2021, 2021020424. https://doi.org/10.20944/preprints202102.0424.v1
APA Style
Roozen, D., & Lelli, F. (2021). Stock Values and Earnings Call Transcripts: a Dataset Suitable for Sentiment Analysis. Preprints. https://doi.org/10.20944/preprints202102.0424.v1
Chicago/Turabian Style
Roozen, D. and Francesco Lelli. 2021 "Stock Values and Earnings Call Transcripts: a Dataset Suitable for Sentiment Analysis" Preprints. https://doi.org/10.20944/preprints202102.0424.v1
Abstract
The dataset reports a collection of earnings call transcripts, the related stock prices, and the related sector index. It contains a total of 188 transcripts, 11970 stock prices, and 1196 sector index values. Furthermore, all of these data originated in the period 2016-2020 and are related to the NASDAQ stock market. The data have been collected using Yahoo Finance and Thomson Reuters Eikon. Specifically, Yahoo Finance offered daily stock prices and traded volume. At the same time, Thomson Reuters Eikon has been used as source for the earnings call transcripts. The dataset can be used as a benchmark for the evaluation of several NLP techniques as well as machine learning algorithms for understanding their potential for financial applications. Moreover, it is also possible to expand the dataset by extending the period in which the data originated following a similar procedure.
dataset; stock; sentiment analysis; nlp; Nasdaq; stock prices; ML
Subject
Business, Economics and Management, Accounting and Taxation
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
