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 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 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 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 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 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 A Neural Network Model for Predicting Stock Market Prices written by Barack Wanjawa and published by LAP Lambert Academic Publishing. This book was released on 2014-08-11 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock exchanges are considered major players in the financial sector of many countries. In such exchanges, it is Stockbrokers who execute stock trade deals and advise clients on where to invest. Most of these Stockbrokers use technical, fundamental or time series analysis in trying to predict future stock prices, so as to advise clients on appropriate investments. However, these strategies do not usually guarantee good returns because they guide on trends and not the most likely trade price of a future date. It is therefore necessary to explore improved methods of prediction. The research uses Artificial Neural Network (ANN) that is feedforward multi-layer perceptron (MLP) with error backpropagation to develop a model ANN of configuration 5:21:21:1 using 80% data for training in 130,000 cycles. The research then develops a prototype and tests it using 2008-2012 data from various stock markets, such as the Nairobi Securities Exchange (NSE) and New York Stock Exchange (NYSE). Results showed that the model predicted prices with MAPE of 0.71% to 2.77%. Validation done using Neuroph & Encog showed close RMSE. The model can therefore be used in any typical stock market predict.

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 A Neural network model for stock market prediction written by Davide Chinetti and published by . This book was released on 1993 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Forecasting Financial Markets Using Neural Networks written by Jason E. Kutsurelis and published by . This book was released on 1998 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research examines andanalyzes the use of neural networks as a forecasting tool. Specifically a neural network's ability to predict future trends of Stock Market Indices is tested. Accuracy is compared against a traditional forecasting method, multiple linear regression analysis. Finally, the probability of the model's forecast being correct is calculated using conditional probabilities. While only briefly discussing neural network theory, this research determines the feasibility and practicality of usingneural networks as a forecasting tool for the individual investor. This study builds upon the work done byEdward Gately in his book Neural Networks for Financial Forecasting. This research validates the work of Gately and describes the development of a neural network that achieved a 93.3 percent probability of predicting a market rise, and an 88.07 percent probability of predicting a market drop in the S&P500. It was concluded that neural networks do have the capability to forecast financial markets and, if properly trained, the individual investor could benefit from the use of this forecasting tool.

Download or read book Machine Learning in Quantitative Finance History Theory and Applications written by Mcghee and published by . This book was released on 2019-06-07 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Approximation of Periodic Functions written by S. B. Stechkin and published by American Mathematical Soc.. This book was released on 1974 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Papers and articles about periodic functions approximation.