Download or read book The Use of Neural Networks GARCH Models and the Bollinger Bands Technical Indicator for Stock Trading Decision Making written by Yanqiong Dong and published by . This book was released on 2005 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bollinger Bands are a widely used technical indicator for measuring and displaying the volatility of securities. The bands accomplish this by showing whether prices are high with the use of an upper band, and whether they are low with the use of a lower band. The bands are based on the volatility (standard deviation) of the past price data. This indicator can aid in rigorous pattern recognition and is useful in comparing current price action to possible buy and sell signals, helping to arrive at a self-contained systematic trading decision. However, due to its inherent characteristics, the indicator can provide false signals during trading in some trending markets. The research in this thesis develops two modified models, one combining neural networks with the Bollinger Bands technical indicator, and another incorporating a GARCH-in-mean model with the Bollinger Bands technical indicator to predict and trade on the security trend. The assumption of the combined system is that the neural network or GARCH model will help to overcome the lagging aspects of the Bollinger Bands indicator by providing a next day forecast, allowing the trader to make the correct trading decisions. The profitability of the model is tested using 10 American stocks and indexes"--Abstract, leaf iii.

Download or read book Applied Computer Sciences in Engineering written by Juan Carlos Figueroa-García and published by Springer Nature. This book was released on 2019-10-09 with total page 779 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the 6th Workshop on Engineering Applications, WEA 2019, held in Santa Marta, Colombia, in October 2019. The 62 revised full papers and 2 short papers presented in this volume were carefully reviewed and selected from 178 submissions. The papers are organized in the following topical sections: computer science; computational intelligence; bioengineering; Internet of things; power applications; simulation systems; optimization.

Download or read book Bollinger on Bollinger Bands written by John Bollinger and published by McGraw Hill Professional. This book was released on 2001-08-21 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: John Bollinger is a giant in today’s trading community. His Bollinger Bands sharpen the sensitivity of fixed indicators, allowing them to more precisely reflect a market’s volatility. By more accurately indicating the existing market environment, they are seen by many as today’s standard—and most reliable—tool for plotting expected price action. Now, in Bollinger on Bollinger Bands, Bollinger himself explains how to use this extraordinary technique to compare price and indicator action and make sound, sensible, and profitable trading decisions. Concise, straightforward, and filled with instructive charts and graphs, this remarkable book will be essential reading for all serious traders, regardless of market. Bollinger includes his simple system for implementation, and techniques for combining bands and indicators.

Download or read book AETA 2019 Recent Advances in Electrical Engineering and Related Sciences Theory and Application written by Dario Fernando Cortes Tobar and published by Springer Nature. This book was released on 2020-08-10 with total page 750 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings book features selected papers on 12 themes, including telecommunication, power systems, digital signal processing, robotics, control systems, renewable energy, power electronics, soft computing and more. Covering topics such as optoelectronic oscillator at S-band and C-band for 5G telecommunications, neural networks identification of eleven types of faults in high voltage transmission lines, cyber-attack mitigation on smart low voltage distribution grids, optimum load of a piezoelectric-based energy harvester, the papers present interesting ideas and state-of-the-art overviews.

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.

Download or read book A Stock Trade Decision Making Method Based on Candlestick Technical Analysis Neural Network Model and Web Technologies written by and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book An easy approach to trading with bollinger bands written by Stefano Calicchio and published by Stefano Calicchio. This book was released on 2020-05-31 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: What are Bollinger bands and how does the application of this study tool to Online Trading work? For the first time a concrete and accessible guide shows you the mechanism of Bollinger bands applied to operational trading. Within this practical manual you will discover all the information you need to start studying the markets by following the principles of Bollinger Trading. From basic price analysis to the identification of market trends and pattern reversal, from setting moving averages to volume analysis and the use of the most famous oscillators. Forget the ineffective theoretical manuals from thousands of pages sold at crazy prices on the web and finally enjoy a reading able to give you the basic know how you have been looking for a long time at an unbeatable price ... because learning the basics of Bollinger trading has never been so simple!

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.

Download or read book Performance Evaluation of Neural Networks and GARCH Models for Forecasting Volatility and Option Strike Prices in a Bull Call Spread Strategy written by Ajitha Vejendla and published by . This book was released on 2007 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Investing in options has many advantages: they provide increased cost efficiency; they have the potential to deliver higher percentage returns due to increased leverage; and they offer a number of hedging and strategic alternatives. It is therefore worthwhile to investigate the option trading strategies that offer high payoffs. This thesis provides a performance evaluation of models used in the pricing of options for a bull spread options strategy. This strategy involves the purchase of a lower strike price option, along with the sale of a second higher strike price option. The strategy is highly profitable when the price of the underlying primitive reaches the second out-of-the-money strike price before the expiration date of the options, but no further. The challenge lies in choosing the optimal out-of-the-money option strike price. The option exercise price, past primitive price jumps, and primitive volatility shifts are the important factors that are to be analyzed. Since the understanding of the primitive volatility is important, this thesis applies performance measures to compare implied volatility and historical volatility using various neural network models. GARCH implied volatility values are provided as input to both the FNN and RNN models, generating a next day forecast for implied volatility. The performance of implied volatility as a volatility measurement is compared against the historical volatility. Based on these results, the neural network models, along with the GARCH models, are further evaluated for their forecasting ability of option strike prices in a bull call spread strategy. The purpose of the research is to see the performance of different neural network models for different stock options and volatility periods. The trading profitability of these models gives us an indication of the performance ability of the FNN, RNN and GARCH models"--Abstract, leaf iv.

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 76 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 TECHNICAL ANALYSIS SUPER SIGNALS MANUAL GUIDE FOR PROFITABLE INVESTMENTS IN THE STOCK EXCHANGE written by Marcel Souza and published by GAVEA LAB . This book was released on with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the secrets of Technical Analysis and supercharge your investments with the "Technical Analysis Super Signals Manual." This comprehensive guide is your key to navigating the stock exchange with confidence, making profitable decisions, and achieving financial success. Imagine a world where you have the expertise to interpret stock market trends, identify potential entry and exit points, and make informed investment decisions that consistently yield impressive returns. "Technical Analysis Super Signals Manual" reveals the proven methods used by seasoned investors to stay ahead of the game and capitalize on lucrative opportunities. This book goes beyond the basics of stock trading, diving deep into advanced technical analysis techniques that empower you to read stock charts like a seasoned pro. Learn how to identify key chart patterns, trendlines, and support and resistance levels, giving you a strategic edge in the market. Discover the art of using various technical indicators to confirm trends and spot potential reversals, ensuring you never miss out on profitable trades. You'll gain access to invaluable tips on setting up and customizing your trading tools for maximum efficiency. Whether you're a novice investor or a seasoned trader, "Technical Analysis Super Signals Manual" provides you with step-by-step guidance on how to analyze market data, interpret price movements, and develop winning trading strategies. Learn the secrets of risk management and position sizing to protect your capital and minimize potential losses. Master the art of timing your trades for optimal results, maximizing your profits while minimizing risk. With "Technical Analysis Super Signals Manual," you'll gain the confidence to navigate the complexities of the stock market and make well-informed decisions based on real-time data and market trends. Unleash the potential of technical analysis to your advantage, giving you the ability to spot profit opportunities that others might overlook. This comprehensive guide will empower you to approach the stock market with a strategic mindset and achieve consistent success. Whether you're a day trader, swing trader, or long-term investor, "Technical Analysis Super Signals Manual" is your ultimate roadmap to mastering the art of technical analysis and unlocking the doors to financial prosperity. Don't miss this chance to elevate your investment game and unlock the boundless potential of technical analysis. "Technical Analysis Super Signals Manual" is your ticket to profitable investments and a brighter financial future. It's time to take control of your investments and set yourself up for a future filled with financial abundance and success. Embrace the strategies and techniques outlined in "Technical Analysis Super Signals Manual" and embark on a journey of profitable trading and investment excellence. Are you ready to become a skilled technical analyst and maximize your profits in the stock exchange? Don't wait any longer. Dive into "Technical Analysis Super Signals Manual" and start your journey to profitable investments today. Your path to financial freedom begins now.

Download or read book Intelligent Technical Analysis Using Neural Networks and Fuzzy Logic written by Vamsi Krishna Bogullu and published by . This book was released on 2002 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The objective of this study is to evaluate the use of fuzzy logic and neural networks for increasing the efficiency of using technical analysis for predicting stock trading signals. The goal is to develop a Fuzzy-Neuro model which combines the contradicting decisions of individual technical indicators into a single buy/sell decision and by doing so effectively predict the movement of stock price trends."--Page 3.

Download or read book A Quantitative Neural Network Model Qnnm for Stock Trading Decisions written by Faizul F. Noor and published by . This book was released on 2007 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Trading activities are based on technical analysis, market sentiment (asymmetric information, rumours, noise trading) and imitative behavoiur. This leads to unjustified biasness in decision making. To remove such subjectivity, this paper suggests a neural network model for the investors to decide whether buy or sell the shares. The model consists two wings - one, based on technical analysis and the other, on fundamental analysis. The integral part of this model is the existence of a hidden layer between the input layer and output layer. To remain away from the subjectivity, this model does not consider the behavioural factors in modeling.

Download or read book Algorithmic Trading Technical Indicators written by SQ2 SYSTEMS AB and published by SQ2 SYSTEMS AB. This book was released on 2023-09-20 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Algorithmic Trading: Technical Indicators" is your go-to guide for unraveling the power of technical indicators in algorithmic trading. If you're intrigued by data-driven signals that inform trading decisions, this book is your key to mastering the art of technical analysis. Designed for traders and investors seeking a practical introduction to technical indicators, this book simplifies the complex world of charts, patterns, and signals. It provides clear insights into how historical price and volume data can drive trading strategies. Explore the fundamental principles of technical analysis, where historical data becomes your ally in making informed trading decisions. Delve into the secrets of candlestick patterns, moving averages, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Bollinger Bands. These indicators will become your trusted tools for identifying trends, overbought or oversold conditions, and potential reversals. "Algorithmic Trading: Technical Indicators" offers practical guidance on incorporating these indicators into your trading strategy. Discover how to recognize entry and exit points, effectively manage risk with stop-loss and take-profit levels, and enhance your decision-making. This book provides accessible insights without delving into complex technical examples or deep understanding. It's perfect for beginners curious about the power of technical analysis or experienced traders looking to refine their algorithmic strategies. Whether you're new to technical indicators or seeking to enhance your trading skills, "Algorithmic Trading: Technical Indicators" equips you with the knowledge and tools to confidently navigate the world of algorithmic trading through the lens of technical analysis. Join us in harnessing the potential of data-driven trading signals in today's dynamic financial markets.

Download or read book Competitive Co evolution of Trend Reversal Indicators Using Particle Swarm Optimisation written by Evangelos Papacostantis and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Intelligence has found a challenging testbed for various paradigms in the financial sector. Extensive research has resulted in numerous financial applications using neural networks and evolutionary computation, mainly genetic algorithms and genetic programming. More recent advances in the field of computational intelligence have not yet been applied as extensively or have not become available in the public domain, due to the confidentiality requirements of financial institutions. This study investigates how co-evolution together with the combination of par- ticle swarm optimisation and neural networks could be used to discover competitive security trading agents that could enable the timing of buying and selling securities to maximise net profit and minimise risk over time. The investigated model attempts to identify security trend reversals with the help of technical analysis methodologies. Technical market indicators provide the necessary market data to the agents and reflect information such as supply, demand, momentum, volatility, trend, sentiment and retracement. All this is derived from the security price alone, which is one of the strengths of technical analysis and the reason for its use in this study. The model proposed in this thesis evolves trading strategies within a single pop- ulation of competing agents, where each agent is represented by a neural network. The population is governed by a competitive co-evolutionary particle swarm optimi- sation algorithm, with the objective of optimising the weights of the neural networks. A standard feed forward neural network architecture is used, which functions as a market trend reversal confidence. Ultimately, the neural network becomes an amal- gamation of the technical market indicators used as inputs, and hence is capable of detecting trend reversals. Timely trading actions are derived from the confidence output, by buying and short selling securities when the price is expected to rise or fall respectively. No expert trading knowledge is presented to the model, only the technical market indicator data. The co-evolutionary particle swarm optimisation model facilitates the discovery of favourable technical market indicator interpretations, starting with zero knowledge. A competitive fitness function is defined that allows the evaluation of each solution relative to other solutions, based on predefined performance metric objectives. The relative fitness function in this study considers net profit and the Sharpe ratio as a risk measure. For the purposes of this study, the stock prices of eight large market capitalisation companies were chosen. Two benchmarks were used to evaluate the discovered trading agents, consisting of a Bollinger Bands/Relative Strength Index rule-based strategy and the popular buy-and-hold strategy. The agents that were discovered from the proposed hybrid computational intelligence model outperformed both benchmarks by producing higher returns for in-sample and out-sample data at a low risk. This indicates that the introduced model is effective in finding favourable strategies, based on observed historical security price data. Transaction costs were considered in the evaluation of the computational intelligent agents, making this a feasible model for a real-world application. Copyright.

Download or read book Neural Networks and the Financial Markets written by Jimmy Shadbolt and published by Springer Science & Business Media. This book was released on 2002-08-06 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is abook about the methods developed byour research team, over a period of 10years, for predicting financial market returns. Thework began in late 1991, at a time when one ofus (Jimmy Shadbolt) had just completed a rewrite of the software used at Econostat by the economics team for medium-term trend prediction of economic indica- tors.Looking for anewproject, itwassuggestedthatwelook atnon-linear modelling of financial markets, and that a good place to start might be with neural networks. One small caveat should be added before we start: we use the terms "prediction" and "prediction model" throughout the book, although, with only such a small amount of information being extracted about future performance, can we really claim to be building predictors at all? Some might saythat the future ofmarkets, especially one month ahead, is too dim to perceive. We think we can claim to "predict" for two reasons. Firstlywedoindeedpredictafewper cent offuturevalues ofcertainassets in terms ofpast values ofcertainindicators, asshown by our trackrecord. Secondly, we use standard and in-house prediction methods that are purely quantitative. Weallow no subjective viewto alter what the models tell us. Thus weare doing prediction, even if the problem isvery hard. So while we could throughout the book talk about "getting a better view of the future" or some such euphemism, we would not be correctly describing what it isweare actually doing. Weare indeed getting abetter view of the future, by using prediction methods.

Download or read book Understanding Bollinger Bands written by Edward D. Dobson and published by Wasendorf & Associates Incorporated. This book was released on 1994 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bollinger Bands can be tremendously helpfill in market analysis and timing. They are contained in nearly every technical analysis software package. This booklet is the definitive guide to their proper use and interpretation.