Prediction Of Stock Market Index Movements With Machine Learning

Author : Nazif AYYILDIZ
Publisher : Özgür Publications
Page : 121 pages
File Size : 54,9 Mb
Release : 2023-12-16
Category : Business & Economics
ISBN : 9789754478211

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Prediction of Stock Market Index Movements with Machine Learning by Nazif AYYILDIZ Pdf

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.

Author : Renuka Sharma,Kiran Mehta
Publisher : John Wiley & Sons
Page : 358 pages
File Size : 53,9 Mb
Release : 2024-04-10
Category : Computers
ISBN : 9781394214310

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Deep Learning Tools for Predicting Stock Market Movements by Renuka Sharma,Kiran Mehta Pdf

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.

Author : Joish Bosco,Fateh Khan
Publisher : GRIN Verlag
Page : 76 pages
File Size : 42,7 Mb
Release : 2018-09-18
Category : Computers
ISBN : 9783668800458

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Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network by Joish Bosco,Fateh Khan Pdf

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.

Author : Mahfuz Islam Khan Jabed
Publisher : Ocleno
Page : 172 pages
File Size : 41,5 Mb
Release : 2024-02-16
Category : Business & Economics
ISBN : 8210379456XXX

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Stock Market Price Prediction using Machine Learning Techniques by Mahfuz Islam Khan Jabed Pdf

Predicting stock market prices is a challenging task in the financial sector, where the Efficient Market Hypothesis (EMH) posits the impossibility of accurate prediction due to the inherent uncertainty and complexity of stock price behaviour. However, introducing Machine Learning algorithms has shown the feasibility of stock market price forecasting. This study employs advanced Machine Learning models that can predict stock price movements with the right level of accuracy if the correct parameter tuning and appropriate predictor models are developed. In this research work, the LSTM model, which is a type of Recurrent Neural Network (RNN), time series forecasting Facebook Prophet algorithm and Random Forest Regressor model have been implemented on 10 Dhaka Stock Market (DSEbd) listed companies and six international giants for predicting the stock and forecasting the future price. The dataset of domestic companies is extracted from the graphical representation of the DSEbd website, and the international companies' dataset is imported from Yahoo Finance. In this experiment, Facebook Prophet demonstrates a long period of forecasting with reasonable accuracy, capturing daily, weekly, and yearly seasonality, including holiday effects for market trend analysis. Remarkably, the LSTM model exhibits significant accuracy, yielding the best results with evaluation metrics, including RMSE (0.35), MAPE (0.50%), and MAE (0.30). The experimental results underscore the efficiency of LSTM for future stock forecasting, observed over 15 days of upcoming market prices. A comparison of the results shows that the LSTM model efficiently forecasts the next day's closing price.

Author : Vinaitheerthan Renganathan
Publisher : Vinaitheerthan Renganathan
Page : 107 pages
File Size : 50,9 Mb
Release : 2021-04-30
Category : Business & Economics
ISBN : 9789354579738

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Stock price analysis through Statistical and Data Science tools: An Overview by Vinaitheerthan Renganathan Pdf

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

Author : Dr.N.Srinivasan
Publisher : Clever Fox Publishing
Page : 56 pages
File Size : 43,5 Mb
Release : 2024-06-16
Category : Business & Economics
ISBN : 8210379456XXX

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Stock price Prediction a referential approach on how to predict the stock price using simple time series... by Dr.N.Srinivasan Pdf

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.

Author : Renuka Sharma,Kiran Mehta
Publisher : John Wiley & Sons
Page : 500 pages
File Size : 52,5 Mb
Release : 2024-05-14
Category : Computers
ISBN : 9781394214303

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Deep Learning Tools for Predicting Stock Market Movements by Renuka Sharma,Kiran Mehta Pdf

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.

Author : Joel Miller
Publisher : Independent Author
Page : 0 pages
File Size : 46,6 Mb
Release : 2023-04-04
Category : Business & Economics
ISBN : 1805254294

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Use of Machine Learning and Weak Supervision to Predict Stocks from Unlabeled Press Releases by Joel Miller Pdf

This thesis examines the effect of press releases on the Nordic stock market. A weak supervision approach is utilized to estimate the short-term effect on stock re-turns given press releases of different categories. By utilizing the data programming framework as implemented in the Snorkel library, approximately 24% of all press releases are categorized into a set of 10 distinct categories. Further, a collection of machine learning models for stock price prediction is developed, where simulation is conducted to determine how press releases may be used to forecast stock price movement. Stock price prediction is performed for large stock price movements and for stock price direction, where the result shows that the best performing model achieves a 53% F1-score and 54% accuracy respectively for the tasks. Finally, it appears that the labeled press releases can be used to increase the predictability of stock movements in the Nordic stock market.

Author : Ethan Shaotran
Publisher : Unknown
Page : 111 pages
File Size : 45,5 Mb
Release : 2018-06-10
Category : Electronic
ISBN : 1092671102

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Stock Prediction with Deep Learning by Ethan Shaotran Pdf

For centuries, human beings have tried to predict the future, whether it be NBA playoffs, weather, or elections. In this book, we tackle the common misconception that the stock market cannot be predicted, and build a stock prediction algorithm to beat the stock market, using Deep Learning, Data Analysis, and Natural Language Processing techniques.If you're new to Artificial Intelligence and Python, and are curious to learn more, this is a great book for you! Industry experts also have plenty to learn from the variety of methods and techniques used in data collection and manipulation.ABOUT THE AUTHOREthan Shaotran is an AI developer, researcher, and author of "Stock Prediction with Deep Learning". He is the founder of Energize.AI, where he built a financial stock prediction algorithm that outperformed the stock market in 2017. He is currently working on a thought experiment series to raise awareness on AI-related societal challenges within the AI community, regarding regulation and potential moral hazards, as well as autonomous vehicle driving software. Ethan has studied Economics and AI courses from Harvard, Stanford, and USF, is an affiliate with the Harvard Kennedy School's AI Initiative and is a member of the Association for Computing Machinery.

Author : Jehan Shareef
Publisher : Unknown
Page : 112 pages
File Size : 46,7 Mb
Release : 2015-07-24
Category : Electronic
ISBN : 0692498109

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Prediction of International Stock Market Movements Using a Statistical Time Series Analysis Method by Jehan Shareef Pdf

Author : Hasan Al-Quaid
Publisher : Unknown
Page : 0 pages
File Size : 43,8 Mb
Release : 2023
Category : Electronic
ISBN : OCLC:1424641557

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A Performance Analysis of Machine Learning Techniques in Stock Price Prediction by Hasan Al-Quaid Pdf

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.

Author : Ajith Abraham,Sabri Pllana,Gabriella Casalino,Kun Ma,Anu Bajaj
Publisher : Springer Nature
Page : 592 pages
File Size : 44,5 Mb
Release : 2023-05-30
Category : Technology & Engineering
ISBN : 9783031274404

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Intelligent Systems Design and Applications by Ajith Abraham,Sabri Pllana,Gabriella Casalino,Kun Ma,Anu Bajaj Pdf

This book highlights recent research on intelligent systems and nature-inspired computing. It presents 223 selected papers from the 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022), which was held online. The ISDA is a premier conference in the field of computational intelligence, and the latest installment brought together researchers, engineers, and practitioners whose work involves intelligent systems and their applications in industry. Including contributions by authors from 65 countries, the book offers a valuable reference guide for all researchers, students, and practitioners in the fields of computer science and engineering.

Author : Amit Konar,Diptendu Bhattacharya
Publisher : Springer
Page : 242 pages
File Size : 55,8 Mb
Release : 2017-03-25
Category : Technology & Engineering
ISBN : 9783319545974

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Time-Series Prediction and Applications by Amit Konar,Diptendu Bhattacharya Pdf

This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at the end of each chapter to the readers’ ability and understanding of the topics covered.

Author : Moonis Ali,Young Sig Kwon,Chang-Hwan Lee,Juntae Kim,Yongdai Kim
Publisher : Springer
Page : 755 pages
File Size : 52,8 Mb
Release : 2015-04-30
Category : Computers
ISBN : 9783319190662

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Current Approaches in Applied Artificial Intelligence by Moonis Ali,Young Sig Kwon,Chang-Hwan Lee,Juntae Kim,Yongdai Kim Pdf

This book constitutes the refereed conference proceedings of the 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, held in Seoul, South Korea, in June 2015. The 73 revised full papers presented were carefully reviewed and selected from 105 submissions. The papers cover a wide range of topics in applied artificial intelligence including reasoning, robotics, cognitive modeling, machine learning, pattern recognition, optimization, text mining, social network analysis, and evolutionary algorithms. They are organized in the following topical sections: theoretical AI, knowledge-based systems, optimization, Web and social networks, machine learning, classification, unsupervised learning, vision, image and text processing, and intelligent systems applications.

Author : Management Association, Information Resources
Publisher : IGI Global
Page : 1516 pages
File Size : 48,7 Mb
Release : 2022-05-13
Category : Computers
ISBN : 9781668462928

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Research Anthology on Machine Learning Techniques, Methods, and Applications by Management Association, Information Resources Pdf

Machine learning continues to have myriad applications across industries and fields. To ensure this technology is utilized appropriately and to its full potential, organizations must better understand exactly how and where it can be adapted. Further study on the applications of machine learning is required to discover its best practices, challenges, and strategies. The Research Anthology on Machine Learning Techniques, Methods, and Applications provides a thorough consideration of the innovative and emerging research within the area of machine learning. The book discusses how the technology has been used in the past as well as potential ways it can be used in the future to ensure industries continue to develop and grow. Covering a range of topics such as artificial intelligence, deep learning, cybersecurity, and robotics, this major reference work is ideal for computer scientists, managers, researchers, scholars, practitioners, academicians, instructors, and students.