Download or read book Neural Network Solutions for Trading in Financial Markets written by Dirk Emma Baestaens and published by Pitman Publishing. This book was released on 1994 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offers an alternative technique in forecasting to the traditional techniques used in trading and dealing. The book explains the shortcomings of traditional techniques and shows how neural networks overcome many of the disadvantages of these traditional systems.

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 2012-12-06 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume looks at financial prediction from a broad range of perspectives. It covers: - the economic arguments - the practicalities of the markets - how predictions are used - how predictions are made - how predictions are turned into something usable (asset locations) It combines a discussion of standard theory with state-of-the-art material on a wide range of information processing techniques as applied to cutting-edge financial problems. All the techniques are demonstrated with real examples using actual market data, and show that it is possible to extract information from very noisy, sparse data sets. Aimed primarily at researchers in financial prediction, time series analysis and information processing, this book will also be of interest to quantitative fund managers and other professionals involved in financial prediction.

Download or read book Artificial Intelligence in Financial Markets written by Christian L. Dunis and published by Springer. This book was released on 2016-11-21 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.

Download or read book Trading on the Edge written by Guido J. Deboeck and published by John Wiley & Sons. This book was released on 1994-04-18 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experts from the world's major financial institutions contributed to this work and have already used the newest technologies. Gives proven strategies for using neural networks, algorithms, fuzzy logic and nonlinear data analysis techniques to enhance profitability. The latest analytical breakthroughs, the impact on modern finance theory and practice, including the best ways for profitably applying them to any trading and portfolio management system, are all covered.

Download or read book Neural Networks in Finance written by Paul D. McNelis and published by Elsevier. This book was released on 2005-01-20 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website

Download or read book Application of Neural Networks to an Emerging Financial Market written by Mark T. Leung and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Although there exists some studies which deal with the issues of forecasting stock market index and development of trading strategies, most of the empirical findings are associated with the developed financial markets (e.g., U.S., U.K., and Japan). Currently, many international investment bankers and brokerage firms have major stakes in overseas markets. Given the economic success of Taiwan in the last two decades, the financial markets in this Asian country have attracted considerable global investments. Our study models and predicts the TSE Index using neural networks. Their performance is compared with that of parametric forecasting approaches, namely the Generalized Methods of Moments (GMM) and random walk. These rapidly growing financial markets are usually characterized by high volatility, relatively smaller capitalization, and less price efficiency, features which may hinder the effectiveness of those forecasting models developed for established markets. The good performance of the PNN suggests that the neural network models are useful in predicting the direction of index returns. Furthermore, PNN has demonstrated a stronger predictive power than both the GMM-Kalman filter and the random walk forecasting models. This superiority is partially attributed to PNN's ability to identify outliers and erroneous data. Compared to the other two parametric techniques examined in this study, PNN does not require any assumption of the underlying probability density functions of the class populations. The trading experiment shows that the PNN-guided trading strategies obtain higher profits than the other investment strategies utilizing the market direction generated by the parametric forecasting methods. In addition, the PNN-guided trading with multiple triggering thresholds is generally better than the one with single triggering thresholds. The multiple threshold version is able to consider the degree of certainty of a particular PNN classification and thereby reduce potential loss in the market.

Download or read book Virtual Trading written by Robert Arnold Klein and published by Irwin Professional Publishing. This book was released on 1995 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: In plain language, Virtual Trading, shows you how to proceed from data collection to system development to actual trading. For traders who want to stay on the cutting edge of market technology, Virtual Trading is a must read. Featuring contributions from the leading experts in the field, Virtual Trading provides in-depth information on every important aspect of artificial intelligence in trading. Highlights include: Synergistic market analysis using neural networks by Lou Mendelsohn; Developing a market-timing system using genetic algorithms by Casimir Klimasauskas; Neural networkds and stock market valuation by John Keal; Applying chaos theory to a neural network by Joseph Shepard; Developing a trading system that uses Al by Mark Jurik; Neural network techniques for time series analysis by Peter Davies.

Download or read book Computational Intelligence Techniques for Trading and Investment written by Christian Dunis and published by Routledge. This book was released on 2014-03-26 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational intelligence, a sub-branch of artificial intelligence, is a field which draws on the natural world and adaptive mechanisms in order to study behaviour in changing complex environments. This book provides an interdisciplinary view of current technological advances and challenges concerning the application of computational intelligence techniques to financial time-series forecasting, trading and investment. The book is divided into five parts. The first part introduces the most important computational intelligence and financial trading concepts, while also presenting the most important methodologies from these different domains. The second part is devoted to the application of traditional computational intelligence techniques to the fields of financial forecasting and trading, and the third part explores the applications of artificial neural networks in these domains. The fourth part delves into novel evolutionary-based hybrid methodologies for trading and portfolio management, while the fifth part presents the applications of advanced computational intelligence modelling techniques in financial forecasting and trading. This volume will be useful for graduate and postgraduate students of finance, computational finance, financial engineering and computer science. Practitioners, traders and financial analysts will also benefit from this book.

Download or read book An Investigation of Artificial Neural Networks Applied to Pairs Trading in South Africa written by Richard Otto Buhr and published by . This book was released on 2014 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of artificial intelligence models could enhance the process by which decisions are made for trading in the financial markets. In this project the aim was to define, create, apply and analyse the results of an artificial neural network based solution for so-called pairs trading. A literature study is presented where a brief summary is given of the current body of knowledge with regards to artificial neural networks and their application in finance. Additionally an introduction of the software packages used in this project is given. In a separate chapter a short introduction on trading strategies and pairs trading, specifically, is included. The design of the artificial neural network is discussed in detail with both training and execution results from experiments critically examined. Finally the question ‘Are artificial neural networks a viable tool applied to pairs trading in the South African market?’ is answered based on the empirical evidence.

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 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 Developing an Effective Model for Detecting Trade Based Market Manipulation written by Jose Joy Thoppan and published by Emerald Group Publishing. This book was released on 2021-05-05 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developing an Effective Model for Detecting Trade-Based Market Manipulation determines an appropriate model to help identify stocks witnessing activities that are indicative of potential manipulation through three separate but related studies.

Download or read book Neural Networks in Finance and Investing written by Robert R. Trippi and published by Irwin Professional Publishing. This book was released on 1993 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many believe that neural networks will eventually out-perform even the best traders and investors, yet this extraordinary technology remained largely inaccessible to practitioners--prior to this landmark text. Nowhere else will you find such a thorough and relevant examination of the applications and potential of this cutting-edge technology. This book not only contains many examples of neural networks for prediction and risk assessment, but provides promising systems for forecasting and explaining price movements of stocks and securities. Sections include neural network overview; analysis of financial condition; business failure prediction; debt risk assessment; security market applications; and neural network approaches to financial forecasting.

Download or read book Machine Learning and AI in Finance written by German Creamer and published by Routledge. This book was released on 2021-04-05 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm. Machine Learning and AI in Finance explores the most recent advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options. The chapters in this book were originally published as a special issue of the Quantitative Finance journal.

Download or read book The Advertised Mind written by Erik Du Plessis and published by Kogan Page Publishers. This book was released on 2005 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Du Plessis draws on information about the working of the human brain from psychologists, neurologists, and artificial intelligence specialists to suggest why "ad-liking" is such an important factor in advertisement and how it predisposes consumers to buy the brand that is being advertised.

Download or read book Fractal Approaches for Modeling Financial Assets and Predicting Crises written by Nekrasova, Inna and published by IGI Global. This book was released on 2018-02-09 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an ever-changing economy, market specialists strive to find new ways to evaluate the risks and potential reward of economic ventures. They start by assessing the importance of human reaction during the economic planning process and put together systems to measure financial markets and their longevity. Fractal Approaches for Modeling Financial Assets and Predicting Crises is a critical scholarly resource that examines the fractal structure and long-term memory of the financial markets in order to predict prices of financial assets and financial crises. Featuring coverage on a broad range of topics, such as computational process models, chaos theory, and game theory, this book is geared towards academicians, researchers, and students seeking current research on pricing and predicting financial crises.

Download or read book Artificial Neural Networks written by Ali Roghani and published by Createspace Independent Publishing Platform. This book was released on 2016-08-09 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal. Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style. Neural networks have been touted as all-powerful tools in stock-market prediction. Companies such as MJ Futures claim amazing 199.2% returns over a 2-year period using their neural network prediction methods. They also claim great ease of use; as technical editor John Sweeney said in a 1995 issue of "Technical Analysis of Stocks and Commodities," "you can skip developing complex rules (and redeveloping them as their effectiveness fades) . . . just define the price series and indicators you want to use, and the neural network does the rest."