scholarly journals Stock Values and Earnings Call Transcripts: a Dataset Suitable for Sentiment Analysis

2021 ◽  
Author(s):  
Dexter Roozen ◽  
Francesco Lelli
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Prices ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Financial Applications ◽  
Thomson Reuters ◽  
Stock Values

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.

scholarly journals Stock Price Prediction using Machine Learning Algorithms

2022 ◽  
Vol 10 (1) ◽  
pp. 667-673
Author(s):  
Shreya Pawaskar
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Polynomial Regression ◽  
Learning Algorithms ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Stock Market Returns ◽  
Finance Industry ◽  
Stock Price Prediction

Abstract: Machine learning has broad applications in the finance industry. Risk Analytics, Consumer Analytics, Fraud Detection, and Stock Market Predictions are some of the domains where machine learning methods can be implemented. Accurate prediction of stock market returns is extremely difficult due to volatility in the market. The main factor in predicting a stock market is a high level of accuracy and precision. With the introduction of artificial intelligence and high computational capacity, efficiency has increased. In the past few decades, the highly theoretical and speculative nature of the stock market has been examined by capturing and using repetitive patterns. Various machine learning algorithms like Multiple Linear Regression, Polynomial Regression, etc. are used here. The financial data contains factors like Date, Volume, Open, High, Low Close, and Adj Close prices. The models are evaluated using standard strategic indicators RMSE and R2 score. Lower values of these two indicators mean higher efficiency of the trained models. Various companies employ different types of analysis tools for forecasting and the primary aim is the accuracy to obtain the maximum profit. The successful prediction of the stock will be an invaluable asset for the stock market institutions and will provide real-life solutions to the problems of the investors. Keywords: Stock prices, Analysis, Accuracy, Prediction, Machine Learning, Regression, Finance

scholarly journals Stock Market Prediction using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3628-3632
Author(s):  
Prof. Gowrishankar B S
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stock Market Prediction ◽  
The Future ◽  
Complicated Model

Stock market is one of the most complicated and sophisticated ways to do business. Small ownerships, brokerage corporations, banking sectors, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithms to predict the future stock price for exchange by using pre-existing algorithms to help make this unpredictable format of business a little more predictable. The use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The data has to be cleansed before it can be used for predictions. This paper focuses on categorizing various methods used for predictive analytics in different domains to date, their shortcomings.

A Regression Analysis of Stock Market Prediction Using Machine Learning Algorithms

2021 ◽  
Author(s):  
Niraj Shukla ◽  
Subham Sanoriya ◽  
Narendra Yadav ◽  
Sudhakar Mourya ◽  
A S Mohammed Shariff
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Stock Market ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stock Market Prediction
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