Neural Network Solutions For Trading In Financial Markets PDF Download
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| Author | : Dirk Emma Baestaens |
| Publisher | : Pitman Publishing |
| Total Pages | : 274 |
| Release | : 1994 |
| Genre | : Business & Economics |
| ISBN | : |
Download Neural Network Solutions for Trading in Financial Markets Book in PDF, ePub and Kindle
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.
| Author | : Jimmy Shadbolt |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 266 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1447101510 |
Download Neural Networks and the Financial Markets Book in PDF, ePub and Kindle
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.
| Author | : Christian L. Dunis |
| Publisher | : Springer |
| Total Pages | : 349 |
| Release | : 2016-11-21 |
| Genre | : Business & Economics |
| ISBN | : 1137488808 |
Download Artificial Intelligence in Financial Markets Book in PDF, ePub and Kindle
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.
| Author | : Paul D. McNelis |
| Publisher | : Elsevier |
| Total Pages | : 261 |
| Release | : 2005-01-20 |
| Genre | : Computers |
| ISBN | : 0080479650 |
Download Neural Networks in Finance Book in PDF, ePub and Kindle
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
| Author | : Mark T. Leung |
| Publisher | : |
| Total Pages | : |
| Release | : 2006 |
| Genre | : |
| ISBN | : |
Download Application of Neural Networks to an Emerging Financial Market Book in PDF, ePub and Kindle
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.
| Author | : Robert Arnold Klein |
| Publisher | : Irwin Professional Publishing |
| Total Pages | : 392 |
| Release | : 1995 |
| Genre | : Business & Economics |
| ISBN | : |
Download Virtual Trading Book in PDF, ePub and Kindle
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.
| Author | : Christian Dunis |
| Publisher | : Routledge |
| Total Pages | : 236 |
| Release | : 2014-03-26 |
| Genre | : Business & Economics |
| ISBN | : 1136195106 |
Download Computational Intelligence Techniques for Trading and Investment Book in PDF, ePub and Kindle
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.
| Author | : Joish Bosco |
| Publisher | : GRIN Verlag |
| Total Pages | : 76 |
| Release | : 2018-09-18 |
| Genre | : Computers |
| ISBN | : 3668800456 |
Download Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network Book in PDF, ePub and Kindle
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 | : Robert R. Trippi |
| Publisher | : Irwin Professional Publishing |
| Total Pages | : 513 |
| Release | : 1993 |
| Genre | : Artificial intelligence |
| ISBN | : 9781557384522 |
Download Neural Networks in Finance and Investing Book in PDF, ePub and Kindle
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.
| Author | : Jose Joy Thoppan |
| Publisher | : Emerald Group Publishing |
| Total Pages | : 120 |
| Release | : 2021-05-05 |
| Genre | : Business & Economics |
| ISBN | : 1801173966 |
Download Developing an Effective Model for Detecting Trade-Based Market Manipulation Book in PDF, ePub and Kindle
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.
