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A Performance Analysis Of Machine Learning Techniques In Stock Price Prediction
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Book Synopsis A Performance Analysis of Machine Learning Techniques in Stock Price Prediction by : Hasan Al-Quaid
Download or read book A Performance Analysis of Machine Learning Techniques in Stock Price Prediction written by Hasan Al-Quaid and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock market trends are of great interest to investors and corporations worldwide. The global financial system is intricately interconnected with the stock market, playing a central role in driving economic activity. In today's interconnected world, trading stocks has become a popular and accessible means for individuals and entities to generate income. Numerous academic researchers have explored the use of Artificial Intelligence (AI) for stock prediction and have claimed that their models can accurately forecast stock performance. The issue is that many of these studies rely on a single data source, namely, daily stock data and cannot predict future stock prices, more than 1 or 2 days, with a large degree of success. Additionally, the single data source may be influenced by a multitude of economic factors as well as public sentiment, which is the most significant. In this research paper, several of these AI models are tested to evaluate their claims regarding stock prediction capabilities. Based on our experiments utilizing AI models and the results gathered, it was concluded that it was not possible to predict future stock prices using one method alone. Therefore, in order to provide a greater accuracy in predicting future stocks, the use of an ensemble approach was proposed. While many researchers build their ensemble models by combining various Artificial Neural Network models with sentiment analysis. We have suggested a different approach using other kinds of AI models, along with enhancements to traditional sentiment analysis techniques.
Book Synopsis Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network by : Joish Bosco
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.
Book Synopsis Prediction of Stock Performance with Machine Learning Techniques by : David Loeliger
Download or read book Prediction of Stock Performance with Machine Learning Techniques written by David Loeliger and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Selecting high-performing stocks among a vast number of available securities is still one of the investor's prime concerns. While the number of different approaches to find these thriving stocks is enormous, many methods are based on fundamental financial indicators predicting future firm performance. With the continuing advances in computational sciences, machine learning methods are used to analyse the fundamental financial ratios for stock performance prediction. Given the large number of financial performance measures, it is evident that not all of them are equally useful to predict stock performances. Large differences in business models between different industries have the effect that financial ratios cannot be used to the same extent for performance predictions in every industry. Research in the field of performance prediction with machine learning methods on financial indicators currently focuses solely on entire markets, neglecting the different fundamental ratio characteristics between the industry sectors. Current research focuses only on prediction performance and therefore neglects the interpretation of the significance of the underlying financial indicators. This study therefore aims to employ a machine learning method for stock performance prediction not only on the overall market, but specifically for every major industrial sector. Additionally, the importance of the financial ratios used for the analysis is discussed with respect to concepts of classical financial analysis. This research shows the possibility to beat the stock market performance for specific years under analysis, applying a machine learning method that includes fundamental financial ratios. The industry breakdown shows that there are large differences in prediction ability between the different industries ranging from a rather predictable materials sector to an unpredictable information technology sector. Focusing on the importance of the financ.
Book Synopsis How can I get started Investing in the Stock Market by : Lokesh Badolia
Download or read book How can I get started Investing in the Stock Market written by Lokesh Badolia and published by Educreation Publishing. This book was released on 2016-10-27 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is well-researched by the author, in which he has shared the experience and knowledge of some very much experienced and renowned entities from stock market. We want that everybody should have the knowledge regarding the different aspects of stock market, which would encourage people to invest and earn without any fear. This book is just a step forward toward the knowledge of market.
Book Synopsis Stock price Prediction a referential approach on how to predict the stock price using simple time series... by : Dr.N.Srinivasan
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.
Book Synopsis Deep Learning Tools for Predicting Stock Market Movements by : Renuka Sharma
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 2024-04-10 with total page 358 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.
Book Synopsis Stock price analysis through Statistical and Data Science tools: An Overview by : Vinaitheerthan Renganathan
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 ICDSMLA 2019 written by Amit Kumar and published by Springer Nature. This book was released on 2020-05-19 with total page 2010 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected high-impact articles from the 1st International Conference on Data Science, Machine Learning & Applications 2019. It highlights the latest developments in the areas of Artificial Intelligence, Machine Learning, Soft Computing, Human–Computer Interaction and various data science & machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise.
Book Synopsis Empirical Asset Pricing by : Wayne Ferson
Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Prediction of Stock Market Index Movements with Machine Learning by : Nazif AYYILDIZ
Download or read book Prediction of Stock Market Index Movements with Machine Learning written by Nazif AYYILDIZ and published by Özgür Publications. This book was released on 2023-12-16 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book titled "Prediction of Stock Market Index Movements with Machine Learning" focuses on the performance of machine learning methods in forecasting the future movements of stock market indexes and identifying the most advantageous methods that can be used across different stock exchanges. In this context, applications have been conducted on both developed and emerging market stock exchanges. The stock market indexes of developed countries such as NYSE 100, NIKKEI 225, FTSE 100, CAC 40, DAX 30, FTSE MIB, TSX; and the stock market indexes of emerging countries such as SSE, BOVESPA, RTS, NIFTY 50, IDX, IPC, and BIST 100 were selected. The movement directions of these stock market indexes were predicted using decision trees, random forests, k-nearest neighbors, naive Bayes, logistic regression, support vector machines, and artificial neural networks methods. Daily dataset from 01.01.2012 to 31.12.2021, along with technical indicators, were used as input data for analysis. According to the results obtained, it was determined that artificial neural networks were the most effective method during the examined period. Alongside artificial neural networks, logistic regression and support vector machines methods were found to predict the movement direction of all indexes with an accuracy of over 70%. Additionally, it was noted that while artificial neural networks were identified as the best method, they did not necessarily achieve the highest accuracy for all indexes. In this context, it was established that the performance of the examined methods varied among countries and indexes but did not differ based on the development levels of the countries. As a conclusion, artificial neural networks, logistic regression, and support vector machines methods are recommended as the most advantageous approaches for predicting stock market index movements.
Book Synopsis TIME-SERIES ANALYSIS: FORECASTING STOCK PRICE USING MACHINE LEARNING WITH PYTHON GUI by : Vivian Siahaan
Download or read book TIME-SERIES ANALYSIS: FORECASTING STOCK PRICE USING MACHINE LEARNING WITH PYTHON GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-02 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock trading and financial instrument markets offer significant opportunities for wealth creation. The ability to predict stock price movements has long intrigued researchers and investors alike. While some theories, like the Efficient Market Hypothesis, suggest that consistently beating the market is nearly impossible, others contest this viewpoint. Stock price prediction involves forecasting the future value of a given stock. In this project, we focus on the S&P 500 Index, which consists of 500 stocks from various sectors of the US economy and serves as a key indicator of US equities. To tackle this task, we utilize the Yahoo stock price history dataset, which contains 1825 rows and 7 columns including Date, High, Low, Open, Close, Volume, and Adj Close. To enhance our predictions, we incorporate technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation. In this book, for the forecasting task, we employ various regression algorithms including Linear Regression, Random Forest Regression, Decision Tree Regression, Support Vector Regression, Naïve Bayes Regression, K-Nearest Neighbor Regression, Adaboost Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, Catboost Regression, MLP Regression, Lasso Regression, and Ridge Regression. These models aim to predict the future Adj Close price of the stock based on historical data. In addition to stock price prediction, we also delve into predicting stock daily returns using machine learning models. We utilize K-Nearest Neighbor Classifier, Random Forest Classifier, Naive Bayes Classifier, Logistic Regression Classifier, Decision Tree Classifier, Support Vector Machine Classifier, LGBM Classifier, Gradient Boosting Classifier, XGB Classifier, MLP Classifier, and Extra Trees Classifier. These models are trained to predict the direction of daily stock returns (positive or negative) based on various features and technical indicators. To assess the performance of these machine learning models, we evaluate several important metrics. Accuracy measures the overall correctness of the predictions, while recall quantifies the ability to correctly identify positive cases (upward daily returns). Precision evaluates the precision of positive predictions, and the F1 score provides a balanced measure of precision and recall. Additionally, we consider macro average, which calculates the average metric value across all classes, and weighted average, which provides a balanced representation considering class imbalances. To enhance the user experience and facilitate data exploration, we develop a graphical user interface (GUI). The GUI is built using PyQt and offers an interactive platform for users to visualize and interact with the data. It provides features such as plotting boundary decisions, visualizing feature distributions and importance, comparing predicted values with true values, displaying confusion matrices, learning curves, model performance, and scalability analysis. The GUI allows users to customize the analysis by selecting different models, time periods, or variables of interest, making it accessible and user-friendly for individuals without extensive programming knowledge. The combination of exploring the dataset, forecasting stock prices, predicting daily returns, and developing a GUI creates a comprehensive framework for analyzing and understanding stock market trends. By leveraging machine learning algorithms and evaluating performance metrics, we gain valuable insights into the accuracy and effectiveness of our predictions. The GUI further enhances the accessibility and usability of the analysis, enabling users to make data-driven decisions and explore the stock market with ease.
Book Synopsis Information Technology and Systems by : Álvaro Rocha
Download or read book Information Technology and Systems written by Álvaro Rocha and published by Springer. This book was released on 2019-01-28 with total page 976 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features a selection of articles from The 2019 International Conference on Information Technology & Systems (ICITS’19), held at the Universidad de Las Fuerzas Armadas, in Quito, Ecuador, on 6th to 8th February 2019. ICIST is a global forum for researchers and practitioners to present and discuss recent findings and innovations, current trends, professional experiences and challenges of modern information technology and systems research, together with their technological development and applications. The main topics covered are: information and knowledge management; organizational models and information systems; software and systems modeling; software systems, architectures, applications and tools; multimedia systems and applications; computer networks, mobility and pervasive systems; intelligent and decision support systems; big data analytics and applications; human–computer interaction; ethics, computers & security; health informatics; information technologies in education; cybersecurity and cyber-defense; electromagnetics, sensors and antennas for security.
Book Synopsis Introduction to Artificial Neural Systems by : Jacek M. Zurada
Download or read book Introduction to Artificial Neural Systems written by Jacek M. Zurada and published by Brooks/Cole. This book was released on 1995 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Nature of Statistical Learning Theory by : Vladimir Vapnik
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.
Book Synopsis Implementing Machine Learning for Finance by : Tshepo Chris Nokeri
Download or read book Implementing Machine Learning for Finance written by Tshepo Chris Nokeri and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bring together machine learning ()ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures. The book introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios. By the end of this book, you should be able to explain how algorithmic trading works and its practical application in the real world, and know how to apply supervised and unsupervised ML and DL models to bolster investment decision making and implement and optimize investment strategies and systems. You will: Understand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio management Know the concepts of feature engineering, data visualization, and hyperparameter optimization Design, build, and test supervised and unsupervised ML and DL models Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices Structure and optimize an investment portfolio with preeminent asset classes and measure the underlying risk.
Book Synopsis 2020 IEEE Asia Pacific Conference on Computer Science and Data Engineering (CSDE) by : IEEE Staff
Download or read book 2020 IEEE Asia Pacific Conference on Computer Science and Data Engineering (CSDE) written by IEEE Staff and published by . This book was released on 2020-12-16 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The conference title is belonging 100 in the area of IEEE Computer Society This event would be a wonderful gathering between IEEE members in the area of South Pacific, Australia and the rest of the world to share the latest development in the area of computer science and data engineering The conference will be a forum for participants to discuss state of the art innovations in technologies which have been made available by the researchers & IT professionals and will feature plenary and panel sessions as well as technical paper presentations and poster sessions Workshop by international experts on ICT and Data Engineering applications will also be available The conference theme for 2020 is Visualise the Future through Data CSDE 2020 is also aimed to promote discussion about the pedagogical potential of new sustainable technologies for the developing countries
Book Synopsis Machine Learning in Stock Market Prediction by : Shubham Argade
Download or read book Machine Learning in Stock Market Prediction written by Shubham Argade and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predicting the stock price has always been a topic of great interest to both investors and researchers. Machine learning algorithms combined with massive volumes of financial data have proven to be useful tools for stock prediction. However, as the efficient market hypothesis says, market cannot be entirely predicted so it is extremely difficult to apply the findings of these studies to realworld investment trading techniques and make price predictions. This paper represents a brief overview of machine learning techniques for prediction of the stock closing price as well as the direction of stock's future price movement. In this study, machine learning techniques including the Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Finally, the study discusses the limitations of each technique and their application in real-world problems.
