Download or read book Predictive Model for Short term Stock Market Trends written by David Glenmore Fordham and published by . This book was released on 1972 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning Tools for Predicting Stock Market Movements written by Renuka Sharma and published by John Wiley & Sons. This book was released on 2024-05-14 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis. The book: details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average; explains the rapid expansion of quantum computing technologies in financial systems; provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions; explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers. Audience The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.

Download or read book Technical Analysis written by Charles D. Kirkpatrick II and published by FT Press. This book was released on 2010-11-08 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: Already the field's most comprehensive, reliable, and objective guidebook, Technical Analysis: The Complete Resource for Financial Market Technicians, Second Edition has been thoroughly updated to reflect the field's latest advances. Selected by the Market Technicians Association as the official companion to its prestigious Chartered Market Technician (CMT) program, this book systematically explains the theory of technical analysis, presenting academic evidence both for and against it. Using hundreds of fully updated illustrations, the authors explain the analysis of both markets and individual issues, and present complete investment systems and portfolio management plans. They present authoritative, up-to-date coverage of tested sentiment, momentum indicators, seasonal affects, flow of funds, testing systems, risk mitigation strategies, and many other topics. This edition thoroughly covers the latest advances in pattern recognition, market analysis, and systems management. The authors introduce new confidence tests; cover increasingly popular methods such as Kagi, Renko, Kase, Ichimoku, Clouds, and DeMark indicators; present innovations in exit stops, portfolio selection, and testing; and discuss the implications of behavioral bias for technical analysis. They also reassess old formulas and methods, such as intermarket relationships, identifying pitfalls that emerged during the recent market decline. For traders, researchers, and serious investors alike, this is the definitive book on technical analysis.

Download or read book Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network written by Joish Bosco and published by GRIN Verlag. This book was released on 2018-09-18 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: Project Report from the year 2018 in the subject Computer Science - Technical Computer Science, , course: Computer Science, language: English, abstract: Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environment during the 2 sample period. In this project, we use stock data instead of daily data in order to reduce the probability of uncertain noise, and relatively increase the sample size within a certain period of time. By non-stationarity, one means that the distribution of stock data is various during time changing. Non-linearity implies that feature correlation of different individual stocks is various. Efficient Market Hypothesis was developed by Burton G. Malkiel in 1991.

Download or read book Stock price analysis through Statistical and Data Science tools An Overview written by Vinaitheerthan Renganathan and published by Vinaitheerthan Renganathan. This book was released on 2021-04-30 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing written by Sidra Mehtab and published by . This book was released on 2020 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, and collect its daily price movement over a period of three years (2015-2017). Based on the data of 2015-2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week. For predicting the price movement patterns, we use a number of classification techniques, while for predicting the actual closing price of the stock, various regression models have been used. We also build a Long and Short-Term Memory (LSTM)-based deep learning network for predicting the closing price of the stocks and compare the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by integrating a sentiment analysis module on Twitter data to correlate the public sentiment of stock prices with the market sentiment. This has been done using Twitter sentiment and previous week closing values to predict stock price movement for the next week. We tested our proposed scheme using a cross validation method based on Self Organizing Fuzzy Neural Networks (SOFNN) and found extremely interesting results.

Download or read book Trend Forecasting with Intermarket Analysis written by Louis B. Mendelsohn and published by John Wiley & Sons. This book was released on 2012-10-15 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this groundbreaking new edition, Mendelsohn gives you the weapon to conquer the limitations of traditional technical trading-intermarket analysis. To compete in today's rapidly changing economy, you need a method that can identify reoccurring patterns within individual financial markets and between related global markets. You need tools that lead, not lag. Step by step, Mendelsohn shows how combining technical, fundamental, and intermarket analysis into one powerful framework can give you an early edge to accurately forecasting trends. Inside, you'll discover: Precise trading strategies that can be used by both day traders and position traders. The limitations of traditional technical analysis methods-and how to overcome them. How neural network computational modeling can create leading, not lagging, moving averages for more accurate forecasting. Innovative, quantitative trend forecasting indicators at the cutting edge of market analysis. PLUS-an introduction to VantagePoint Software, which makes Mendelsohn's "new economy" trading methods work simply-and effectively. This software applies the pattern recognition capabilities of advanced neural networks to analyze intermarket data on literally hundreds of global financial markets each day.

Download or read book Stock Market Prediction Using Machine Learning and Deep Learning written by Amir Ebrahimi and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last century, the stock market has had several notable growths and declines. Prediction and analysis of financial markets, such as Stock Market prediction, have always been challenging for investors worldwide due to the non-linear nature of financial markets. With the help of Data Science, Machine Learning, and Deep Learning, prediction in Stock Market has become feasible and more reliable. This research aims to find the most accurate models for Stock Market prediction by utilizing machine learning and deep learning algorithms, such as Support Vector Regression (SVR), Long Short-term Memory (LSTM), and Random Forest Regression. Several technical analysis indicators are utilized in the models as features to improve the accuracy of the models. In addition, several transactional signals are generated and used as input features into each prediction model. Our models' training and testing performance are evaluated using Root-Mean-Square Error (RMSE) to find the average error for each model. The evaluations indicate how the models are efficient for predicting the stock price.

Download or read book Applied Soft Computing and Communication Networks written by Sabu M. Thampi and published by Springer Nature. This book was released on 2021-07-01 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes thoroughly refereed post-conference proceedings of the International Applied Soft Computing and Communication Networks (ACN 2020) held in VIT, Chennai, India, during October 14–17, 2020. The research papers presented were carefully reviewed and selected from several initial submissions. The book is directed to the researchers and scientists engaged in various fields of intelligent systems.

Download or read book Stock Market Prediction Through Sentiment Analysis of Social Media and Financial Stock Data Using Machine Learning written by Mohammad Al Ridhawi and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Given the volatility of the stock market and the multitude of financial variables at play, forecasting the value of stocks can be a challenging task. Nonetheless, such prediction task presents a fascinating problem to solve using machine learning. The stock market can be affected by news events, social media posts, political changes, investor emotions, and the general economy among other factors. Predicting the stock value of a company by simply using financial stock data of its price may be insufficient to give an accurate prediction. Investors often openly express their attitudes towards various stocks on social medial platforms. Hence, combining sentiment analysis from social media and the financial stock value of a company may yield more accurate predictions. This thesis proposes a method to predict the stock market using sentiment analysis and financial stock data. To estimate the sentiment in social media posts, we use an ensemble-based model that leverages Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models. We use an LSTM model for the financial stock prediction. The models are trained on the AAPL, CSCO, IBM, and MSFT stocks, utilizing a combination of the financial stock data and sentiment extracted from social media posts on Twitter between the years 2015-2019. Our experimental results show that the combination of the financial and sentiment information can improve the stock market prediction performance. The proposed solution has achieved a prediction performance of 74.3%.

Download or read book Predict Market Swings With Technical Analysis written by Michael McDonald and published by John Wiley & Sons. This book was released on 2002-10-02 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fresh perspective on predicting the market The experience of Wall Street investment manager and analyst Michael McDonald offers a new perspective on how to navigate the turbulent ups and downs of the markets. His innovative approach to the stock market teaches investors how to use new investment strategies intended to replace the "buy and hold forever" strategies of yesterday. McDonald discusses what a "trading range" market is-a roller-coaster ride in which the market will neither gain nor lose much ground-and guides readers through this market with his proven investment strategies. This book provides an understandable way to make sense of the unpredictable stock market, taking into account more complex theories, including chaos and contrarian approaches. Along with his expert advice, McDonald presents four investing paradoxes that will help investors make smarter decisions now and predict where the market is heading, using his proven theories.

Download or read book Stock Market Prediction Using Time Series Analysis written by Kamalakannan J and published by . This book was released on 2018 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock market is a market that enables seamless exchange of buying and selling of company stocks. Every Stock Exchange has their own Stock Index value. Index is the average value that is calculated by combining several stocks. This helps in representing the entire stock market and predicting the market's movement over time. The Equity market can have a profound impact on people and the country's economy as a whole. Therefore, predicting the stock trends in an effective manner can minimize the risk of investing and maximize profit. In our paper, we are using the Time Series Forecasting methodology for predicting and visualizing the predictions. Our focus for prediction will be based on the technical analysis using historic data and ARIMA Model. Autoregressive Integrated Moving Average (ARIMA) model has been used extensively in the field of finance and economics as it is known to be robust, efficient and has a strong potential for short-term share market prediction.

Download or read book The Nature of Statistical Learning Theory written by Vladimir Vapnik and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book 2021 5th International Conference on Information Systems and Computer Networks ISCON written by Institute of Electrical and Electronics Engineers and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning for Time Series Forecasting written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2018-08-30 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.

Download or read book An R Companion to Applied Regression written by John Fox and published by SAGE Publications. This book was released on 2011 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to provide a broad introduction to the R statistical environment in the context of applied regression analysis, which is typically studied by social scientists and others in a second course in applied statistics.

Download or read book Stock price Prediction a referential approach on how to predict the stock price using simple time series written by Dr.N.Srinivasan and published by Clever Fox Publishing. This book was released on with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about the various techniques involved in the stock price prediction. Even the people who are new to this book, after completion they can do stock trading individually with more profit.