This is the introduction to Chapter 2 in the volume titled Analysis and Forecasting of Financial Time Series Using Statistical, Econometric, Machine Learning and Deep Learning Models, edited by Jaydip Sen and Sidra Mehtab to be published by Cambridge Scholars Publishing Limited, Welbeck Road, Newcastle Upon Tyne, NE6 2PA, United Kingdom. Copyright Protected Materials. CHAPTER 2 MACHINE LEARNING AND DEEP LEARNING IN STOCK PRICE PREDICTION JAYDIP SEN and SIDRA MEHTAB Introduction Prediction of future movement of stock prices has been the subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted. On the other hand, there are work that have shown that, if correctly modeled, stock prices can be predicted with a fairly reasonable degree of accuracy. The latter have focused on choice of variables, appropriate functional forms and techniques of forecasting. In this regard, Sen and Datta Chaudhauri propose a novel approach of stock price forecasting based on a time series decomposition approach of the stock prices time series (Sen & Datta Chaudhuri, 2016a; Sen & Datta Chaudhuri, 2016b; Sen & Datta Chaudhuri, 2016c; Sen & Datta Chaudhuri, 2017a; Sen & Datta Chaudhuri, 2017b; Sen & Datta Chaudhuri, 2018; Sen, 2017a; Sen, 2017b). There is also an extent of literature on technical analysis of stock prices where the objective is to identify patterns in stock movements and profit from it. The literature is geared towards making money from stock price movements, and various indicators like Bollinger Band, moving average convergence divergence (MACD), relative strength index (RSI), moving average, momentum stochastics, meta Sine wave etc., have been devised towards this end. There are also patterns like Head and Shoulders, Triangle, Flag, Fibonacci Fan, Andrew's Pitchfork etc., which are extensively used by traders for gain. These approaches provide the user with visual manifestations of the indicators which helps the ordinary investors to understand which way stock prices may move. In this chapter, we propose a granular approach to stock price prediction by combining statistical and machine learning methods of prediction on technical analysis of stock prices. We present several approaches for short-term stock price movement forecasting using various classification and regression techniques and compare their performance in prediction of stock price movement. We believe, this approach will provide several useful information to the investors in stock market who are particularly interested in short-term investments for profit. This work is an extended version of our previous work (Sen & Datta Chaudhuri, 2017c). In the present work, we have extended our predictive framework by including five more classification and five more regression models including an advanced deep learning model. The rest of the chapter is organized as follows. In the section titled “Problem Statement”, we present a clear statement of our problem at hand. The section titled “Related Work”, provides a brief review of the literature on stock price movement modelling and prediction. In section titled “Methodology”, we present a detailed discussion on the methodology that we have followed in this work. The section titled “Performance Results” describes the details of all the predictive models built in this work and the results they have produced. A comparative analysis has been presented on the performance of the models in this section. Finally, the section titled “Conclusion” concludes the chapter.