Download or read book Application of Machine Learning Tools in Predictive Modeling of Pairs Trade in Indian Stock Market written by Indranil Ghosh and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper applies machine learning tools in pairs trading. Three different algorithms, namely, Support Vector Machine (SVM), Random Forest (RF) and Adaptive Neuro Fuzzy Inference System (ANFIS), have been used for predictive modeling of the value of the ratio of share prices of pairs of companies. The study considers nine different independent variables/features for forecasting. The analytical framework combines the mean reverting property of the movement of a pair of prices along with technical indicators. We also use feature selection algorithms for justification of the nine independent variables. The results support our methodology and also selection of the features for prediction.
Download or read book A Machine Learning based Pairs Trading Investment Strategy written by Simão Moraes Sarmento and published by Springer Nature. This book was released on 2020-07-13 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm, OPTICS, to handle problem (i), and demonstrates that the suggested technique can outperform the common pairs search methods, achieving an average portfolio Sharpe ratio of 3.79, in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii), the authors introduce a forecasting-based trading model capable of reducing the periods of portfolio decline by 75%. However, this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model, an LSTM and an LSTM encoder-decoder.
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 Application of Ensemble Machine Learning in the Predictive Data Analytics of Indian Stock Market written by Marxia Oli Sigo and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: [Enter The world of today is high frequency data driven and characterized by the application and use of information technology for better business development and decision making. The price movements of stock markets are mainly influenced by micro and macro economic variables, legal framework and taxation policies of the respective economies. The crux of the issue lies in exactly forecasting the future stock price movements of individual firms, based on historical or past prices. Achieving the accuracy for forecasting the market trend has become difficult due to the prevalence of stochastic behavior in the stock market and volatility in the stock prices. This paper analyses the stochasticity of movement pattern of the most volatile, fifty company stocks (in terms of market capitalization) of NSE-Nifty, using ensemble machine learning method. The findings of the study would help the investors, to make rational and well informed investment decisions, to optimize the stock returns by investing in the most valuable stocks.
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 The Predictive Edge written by Alejandro Lopez-Lira and published by John Wiley & Sons. This book was released on 2024-07-02 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use ChatGPT to improve your analysis of stock markets and securities In The Predictive Edge: Outsmart the Market Using Generative AI and ChatGPT in Financial Forecasting, renowned AI and finance researcher Dr. Alejandro Lopez-Lira delivers an engaging and insightful new take on how to use large language models (LLMs) like ChatGPT to find new investment opportunities and make better trading decisions. In the book, you’ll learn how to interpret the outputs of LLMs to craft sounder trading strategies and incorporate market sentiment into your analyses of individual securities. In addition to a complete and accessible explanation of how ChatGPT and other LLMs work, you’ll find: Discussions of future trends in artificial intelligence and finance Strategies for implementing new and soon-to-come AI tools into your investing strategies and processes Techniques for analyzing market sentiment using ChatGPT and other AI tools A can’t-miss playbook for taking advantage of the full potential of the latest AI advancements, The Predictive Edge is a fully to-date and exciting exploration of the intersection of tech and finance. It will earn a place on the bookshelves of individual and professional investors everywhere.
Download or read book Stock Market Predicting Using Machine Learning and Data Analytics Techniques written by Bezan Lilauwala and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock market prediction is a popular and complex topic with a wide variety of influencing factors. There are hypotheses suggesting its impossibility and others arguing the opposite. In the past decade machine learning has become a widely adopted tool for financial analysis. When machine learning is applied to the stock market problem there are some interesting results. This paper identifies and builds some popular machine learning algorithms that are being applied to this problem to test their predictive capabilities. Linear Multivariate Regression, Random Forest and K-Nearest Neighbor Classification and Long Short-Term Memory are the 4 algorithms that have been built and tested. The data used for the models is taken from the Yahoo Finance and Finta APIs. The data is the historical price data that includes the daily opening, closing, high and low price and trade volume and financial technical indicators. All 4 algorithms showed some interesting results and produced different predictive capabilities. The conclusion states that the LSTM model applied with the historical data and financial technical indicators can produce some predictive benefit to stock market price prediction.
Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
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.
Download or read book Stock Direction Forecasting Techniques written by Dr. Manminder Singh Saluja and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock price movement prediction has been one of the most challenging issues in finance since the time immemorial. Many researchers in past have carried out extensive studies with the intention of investigating the approaches that uncover the hidden information in stock market data. As a result of which, Artificial Intelligence and data mining techniques have come to the forefront because of their ability to map non-linear data. The study encapsulates market indicators with AI techniques to generate useful extracts to improve decisions under conditions of uncertainty. Three approaches (fundamental model, technical indicators model and hybrid model) have been tested using the standalone and integrated machine learning algorithms viz. SVM, ANN, GA-SVM, and GA-ANN and the results of all the three approaches have been compared in the four above mentioned methods. The core objective of this paper is to identify an approach from the above mentioned algorithms that best predicts the Indian stocks price movement. It is observed from the results that the use of GA significantly increases the accuracy of ANN and that the use of technical analysis with SVM and ANN is well suited for Indian stocks and can help investors and traders maximize their quarterly profits.
Download or read book Prediction of Stock Market Returns and Direction written by Roselyn Dimingo and published by . This book was released on 2019 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book ICT and Critical Infrastructure Proceedings of the 48th Annual Convention of Computer Society of India Vol II written by Suresh Chandra Satapathy and published by Springer Science & Business Media. This book was released on 2013-10-19 with total page 780 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains 85 papers presented at CSI 2013: 48th Annual Convention of Computer Society of India with the theme “ICT and Critical Infrastructure”. The convention was held during 13th –15th December 2013 at Hotel Novotel Varun Beach, Visakhapatnam and hosted by Computer Society of India, Vishakhapatnam Chapter in association with Vishakhapatnam Steel Plant, the flagship company of RINL, India. This volume contains papers mainly focused on Data Mining, Data Engineering and Image Processing, Software Engineering and Bio-Informatics, Network Security, Digital Forensics and Cyber Crime, Internet and Multimedia Applications and E-Governance Applications.
Download or read book AI for Sharetrading written by Rakesh Kumar and published by Independently Published. This book was released on 2024-04-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to the world of "AI for Share Trading," where cutting-edge technology meets the dynamic realm of financial markets. In this book, we embark on a journey to explore the intersection of artificial intelligence (AI) and share trading, unveiling the transformative potential of AI in revolutionizing how we approach investment strategies, risk management, and decision-making processes. As technology continues to advance at an unprecedented pace, AI emerges as a game-changer in the world of finance, offering innovative solutions to complex challenges and unlocking new opportunities for investors and traders alike. From predictive modeling techniques and machine learning algorithms to natural language processing and reinforcement learning, AI equips us with powerful tools to analyze market trends, identify patterns, and make informed decisions in real-time. Through a comprehensive exploration of AI methodologies, trading strategies, and case studies, this book serves as a definitive guide for investors, traders, and finance professionals seeking to leverage the transformative potential of AI in share trading. Whether you're a seasoned investor looking to enhance your portfolio management strategies or a novice trader seeking to navigate the complexities of financial markets, "AI for Share Trading" provides invaluable insights, practical techniques, and real-world examples to empower you on your journey to success. Join us as we embark on an exhilarating exploration of AI-driven share trading, where innovation meets opportunity, and the future of finance unfolds before our eyes. Let's harness the power of AI to unlock new frontiers in share trading and shape the future of finance together.
Download or read book Application of Machine Learning written by Jason W. Leung and published by . This book was released on 2016 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: Models of stock price prediction have traditionally used technical indicators alone to generate trading signals. In this paper, we build trading strategies by applying machine-learning techniques to both technical analysis indicators and market sentiment data. The resulting prediction models can be employed as an artificial trader used to trade on any given stock exchange. The performance of the model is evaluated using the S&P 500 index.
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 Pairs Trading written by Steffen Thalmann and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study applies two machine learning algorithms to pairs trading. Stocks are selected based on the minimum distance measure and a modified selection procedure incorporating minimum distance, reversal frequency, and industry classification. Artificial neural networks and random forests are then used to derive trading signals for the selected pairs treating the future stock price direction as classification problem. To benchmark their performance, they are compared to a conventional strategy that derives trading signals through a simple threshold rule. The different strategies are applied on all liquid stocks in the CRSP universe from January 2003 to December 2017. To test the strategies in a realistic setting, commissions, market impact, and shorting fees are accounted for. The obtained results show that the machine learning algorithms performed worse than the benchmark approach. After transaction costs all strategies are unprofitable. Furthermore, neural networks tend to trade excessively while random forests hardly trade at all. An analysis of classification performance measure reveal that the overall prediction performance is insufficient for the complex system introduced here to generate reliable trading signals.
