Stock Movement Prediction with Deep Learning, Finance Tweets Sentiment, Technical Indicators, and Candlestick Charting

Stock Movement Prediction with Deep Learning, Finance Tweets Sentiment, Technical Indicators, and Candlestick Charting
Author: Yichuan Xu
Publisher:
Total Pages: 0
Release: 2020
Genre:
ISBN:
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Stock prediction has been a popular research topic. Due to its stochastic nature, predicting the future stock market remains a difficult problem. This thesis studies the application of Deep Neural Networks (DNNS) in investment from following perspectives: sentiment, stock technical indicators and candlestick charting. In our first experiment, we use DNN to process collective sentiment on the news dataset from Kaggle, and then compare the performance between DNN and traditional machine learning approach. In our second experiment, we build our own dataset that covers 80 stocks from the US stock market. Our attention-based LSTM model shows overall accuracy of 54.6% and MCC of 0.0478 on the aggregate dataset and the best individual stock achieve 64.7% of accuracy. Our third experiment studies the Japanese candlestick charting. In this experiment, harami patterns shows predictive power in our dataset and CNN model on candlestick charting shows great potential in stock market prediction.

Deep Learning Tools for Predicting Stock Market Movements

Deep Learning Tools for Predicting Stock Market Movements
Author: Renuka Sharma
Publisher: John Wiley & Sons
Total Pages: 358
Release: 2024-04-10
Genre: Computers
ISBN: 1394214316
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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.

A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing

A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing
Author: Sidra Mehtab
Publisher:
Total Pages: 6
Release: 2020
Genre:
ISBN:
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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.

Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network

Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network
Author: Joish Bosco
Publisher: GRIN Verlag
Total Pages: 82
Release: 2018-09-18
Genre: Computers
ISBN: 3668800456
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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.

Prediction of Stock Market Index Movements with Machine Learning

Prediction of Stock Market Index Movements with Machine Learning
Author: Nazif AYYILDIZ
Publisher: Özgür Publications
Total Pages: 121
Release: 2023-12-16
Genre: Business & Economics
ISBN: 975447821X
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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.

Investment Strategies

Investment Strategies
Author:
Publisher: BoD – Books on Demand
Total Pages: 158
Release: 2024-07-17
Genre: Business & Economics
ISBN: 1837681988
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Investment strategies relate to an extensive range of aspects and have attracted the attention of investors and students, academics, researchers, financial executives, portfolio managers, security analysts, financial engineers, practitioners, including at the level of Nobel prizes (Tobin, 1981, for the analysis of financial markets; Markowitz, Sharpe, 1990, for modern portfolio models; Black, Scholes, Merton, 1997, for option pricing; Akerlof, Stiglitz, Spence, 2001, for markets with asymmetric information). Even common people talk daily about investments, investment tactics and strategies, and how to obtain success. In the absence of an investment philosophy, they try to copy celebrities or professional advisors without understanding the mechanics of markets, their core beliefs, strengths, or weaknesses. Beyond the traditional stocks and bonds, there are many other types of assets and alternative investments, and investors are overwhelmed by the huge number of portfolio architecture and management options. Regardless of the types of investors, portfolios are no longer a simple list of assets, and their management requires impressive skills. Decision models have significantly evolved from the Markowitz portfolio model toward capital market paradigms in the context of managing unrealistic assumptions or adding the treatment of market imperfections, multiperiod objectives, and transaction costs. The index of portfolio risk provides an intuitive image of diversification. There is an interest in the integration of new visions in investment strategies: determinism, complexity, nonlinearity, self-organization and chaos, trading rules, evolutionary games, real-options games and artificial markets, bounded rationality, heterogeneous agents, and behavioral investments. From the evolutionary perspective, investors interpret information by encoding and categorization, trying to simplify the strategies by using rules of thumb and heuristics. The present work contributes to the understanding of current investment processes by offering the tactical and strategic elements specific to global markets as well as emerging ones in a multilayer approach useful to decision-makers, investors, students, and researchers in the field.

How can I get started Investing in the Stock Market

How can I get started Investing in the Stock Market
Author: Lokesh Badolia
Publisher: Educreation Publishing
Total Pages: 63
Release: 2016-10-27
Genre: Self-Help
ISBN:
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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.

Successful Stock Signals for Traders and Portfolio Managers, + Website

Successful Stock Signals for Traders and Portfolio Managers, + Website
Author: Tom K. Lloyd, Sr.
Publisher: John Wiley & Sons
Total Pages: 368
Release: 2013-07-22
Genre: Business & Economics
ISBN: 1118544528
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A comprehensive guide to technical analysis for both the novice and the professional Technical analysis is a vital tool for any trader, asset manager, or investor who wants to earn top returns. Successful Stock Signals for Traders and Portfolio Managers lets you combine technical analysis and fundamental analysis using existing technical signals to improve your investing performance. Author Tom Lloyd Sr. explains all the technical indicators you need to know, including moving averages, relative strength, support and resistance, sell and buy signals, candlesticks, point and figure charts, Fibonacci levels, Bollinger Bands, and both classic and new indicators. Merging these technical indicators with fundamental analysis will keep you in a portfolio of outperforming stocks, sharpen your fundamental buy discipline, and put your sell discipline on autopilot. Includes case studies applying technical analysis to current trending and hotly debated stocks like Facebook, LinkedIn, and Netflix Offers thorough and straightforward guidance on technical analysis for both professional and individual investors Covers the vital indicators in the public domain that investors need to know Whether you're an individual investor who wants to beat the indexes, a trader looking for high-risk, high-return positions, or a portfolio manager who wants to take a fundamental approach, this an ideal guide to technical analysis and indicators.

Deep Learning for Finance

Deep Learning for Finance
Author: Sofien Kaabar
Publisher: "O'Reilly Media, Inc."
Total Pages: 369
Release: 2024-01-08
Genre: Computers
ISBN: 1098148355
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Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar—financial author, trading consultant, and institutional market strategist—introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Understand and create machine learning and deep learning models Explore the details behind reinforcement learning and see how it's used in time series Understand how to interpret performance evaluation metrics Examine technical analysis and learn how it works in financial markets Create technical indicators in Python and combine them with ML models for optimization Evaluate the models' profitability and predictability to understand their limitations and potential