Data Science and Statistical tools assume only the stock price's historical data in predicting the future stock price. Statistical tools include measures such as Graph and Charts which depicts the general trend and time series tools such as Auto Regressive Integrated Moving Averages (ARIMA) and regression analysis.
Data Science tools include models like Decision Tree, Support Vector Machine (SVM), Artificial Neural Network (ANN) and Long Term and Short Term Memory (LSTM) Models.
Current methods include carrying out sentiment analysis of tweets, comments and other social media discussion to extract the hidden sentiment expressed by the users which indicate the positive or negative sentiment towards the stock price and the company.
The book provides an overview of the analyzing and predicting stock price movements using statistical and data science tools using R open source software with hypothetical stock data sets. It provides a short introduction to R software to enable the user to understand analysis part in the later part.
The book will not go into details of suggesting when to purchase a stock or what at price. The tools presented in the book can be used as a guiding tool in decision making while buying or selling the stock.
Vinaitheerthan Renganathan
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