Forecasting the future prices of stock by analyzing the past and current price movements in determining the trend are always areas of interest of Chartists who believe in studying the action of the market itself rather than the past and current performances of the company. Stock price prediction has ignited the interest of researchers who strive to develop better predictive models with a fair degree of accuracy. The autoregressive integrated moving average (ARIMA)model introduced by Box and Jenkins in 1970has been in the limelight in econometrics literature for time series prediction, which has been at the core of explaining many economic and finance phenomena. ARIMA models in the research domain of finance and economics, especially stock markets, have shown an efficient capability to generate short-term forecasts and have hence beenable to outperform complex structural models in short-term prediction.This paper presents a stock price predictive model using the ARIMA model to analyze the sensitivity of such models to different time horizons used in the estimation of trends and verifies the validity of such forecasts in terms of their degree of precision. Published historical stock data, on an actively traded public sector bank's share and historical movements in the banking sector index in which the selected bank is a constituent, obtained from National Stock Exchange(NSE), India andwebsites of Yahoo finance are used to build and develop stock price forecasts and index movement predictive models. The experiments with dynamic as well as static forecasting methods used revealed that the ARIMA model has a strong potential for short-term prediction and can offer better precision than from long term trend estimates. As a stock price prediction or index movement forecast tool, it can be relied extensively in deciding entry and exit to and from the volatile markets,notwithstanding the fact the risk the investor faces on account of noise or shocks still can be erroneous making the entire prediction irrespective of its degree of precision irrelevant.

This study presents a Business Intelligence (BI) approach to forecast daily changes in 27 stocks’ prices from 8 industries. The BI approach uses a financial data mining technique specifically Neural Network to assess the feasibility of financial forecasting compared to regression model using ordinary least squares estimation method. We used eight indicators such as macroeconomic indicators, microeconomic indicators, political indicators, market indicators, market sentiment indicators, institutional investor, business cycles, and calendar anomaly to predict changes in stocks’ prices. The results shows NN model better predicts stock prices with up to 92% of forecasting accuracy.

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

Master's Thesis from the year 2010 in the subject Economics - Other, grade: 1,0, London School of Economics, course: Management & Strategy, language: English, abstract: Prediction markets are online trading platforms where contracts on future events are traded with payoffs being exclusively linked to event occurrence. Scientific research has shown that market prices of such contracts imply high forecasting accuracy through effective information aggregation of dispersed knowledge. This phenomenon is related to incentives for truthful aggregation in the form of real-money or play-money rewards. The question whether real- or play-money incentives enhance higher relative forecast accuracy has been addressed by previous works with diverse findings. The current state of empirical research in his field is subject to two inherent deficiencies. First, inter-market studies suffer from market disparities and differences in the definition of underlying events. Comparisons between two different platforms (one for play-money contracts, one for real-money contracts) are potentially biased by different trading behaviour. Second, the majority of studies are based upon identical datasets of market platforms (IOWA stock exchange, Tradesports/Intrade, NewsFutures). This thesis contributes new insights by analysing 44,169 trading observations on ipredict, where real-money and play-money contracts are traded on a variety of events. Forecasting accuracy is analysed on overall trading activity as well as comparison of equal contracts under different monetary incentive schemes. Statistical models are built to analyse the influence of order volumes and days to expiry under both incentive schemes. Ignoring different events in underlying trading activity, play-money contracts imply statistically insignificant excess accuracy. In direct comparison of equal events, real-money contracts, however, real-money contracts predict at significantly higher accuracy. This thesis finds a relationship between order volumes and forecasting accuracy whereas the influence of days to expiry and aggregated volumes showed lower R2 than was expected by formed hypotheses.

An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.

Stock price analysis involves different methods such as fundamental analysis and technical analysis which is based on data related to price movement of the stock in the past. Price of the stock is affected by various factors such as company’s performance, current status of economy and political factor. These factors play an important role in supply and demand of the stock which makes the price to be volatile in the short term. Investors and stock traders aim to book profit through buying and selling the stocks. There are different statistical and data science tools are being used to predict the stock price. 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 www.vinaitheerthan.com/book.php