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Author: Luis Alberto Valencia Vega Publisher: ISBN: Category : Finance Languages : en Pages :
Book Description
The financial market is a very complex nonlinear series of time. There have been a lot of opinions in the topic of the predictability of it. The need to predict a next day, week, or month has always existed for the final purpose of making money. The most common way of forecasting this time series is with statistic methods and linear regression models. However, the use of artificial intelligence algorithms may have a better outcome, due to the capability of them to handle nonlinear data. The present thesis will be focused on evaluating the use of artificial intelligence algorithms as forecasters for financial markets stock prices. Two algorithms will be used, Feed-Forward Neural networks and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). All forecasts are made with the purpose of a short term trading strategy. Three stocks will be used as an example of the consistency of the method; Google, Apple and the Mexican stock ALFA. These three stocks have different distributed data and different behavior from the neural networks and ANFIS ¡s expected.
Author: Luis Alberto Valencia Vega Publisher: ISBN: Category : Finance Languages : en Pages :
Book Description
The financial market is a very complex nonlinear series of time. There have been a lot of opinions in the topic of the predictability of it. The need to predict a next day, week, or month has always existed for the final purpose of making money. The most common way of forecasting this time series is with statistic methods and linear regression models. However, the use of artificial intelligence algorithms may have a better outcome, due to the capability of them to handle nonlinear data. The present thesis will be focused on evaluating the use of artificial intelligence algorithms as forecasters for financial markets stock prices. Two algorithms will be used, Feed-Forward Neural networks and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). All forecasts are made with the purpose of a short term trading strategy. Three stocks will be used as an example of the consistency of the method; Google, Apple and the Mexican stock ALFA. These three stocks have different distributed data and different behavior from the neural networks and ANFIS ¡s expected.
Author: Joish Bosco Publisher: GRIN Verlag ISBN: 3668800456 Category : Computers Languages : en Pages : 82
Book Description
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: Jason E. Kutsurelis Publisher: ISBN: Category : Languages : en Pages : 99
Book Description
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.
Author: Jason Kutsurelis Publisher: ISBN: 9781423557302 Category : Languages : en Pages : 112
Book Description
This research examines and analyzes 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 using neural networks as a forecasting tool for the individual investor. This study builds upon the work done by Edward 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.
Author: Dirk Emma Baestaens Publisher: Pitman Publishing ISBN: Category : Business & Economics Languages : en Pages : 274
Book Description
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 ISBN: 9781852335311 Category : Business & Economics Languages : en Pages : 292
Book Description
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.
Author: Francis E. H. Tay Publisher: World Scientific ISBN: 9789812791375 Category : Business & Economics Languages : en Pages : 204
Book Description
Exotic methods refer to specific functions within general soft computing methods such as genetic algorithms, neural networks and rough sets theory. They are applied to ordinary shares for a variety of financial purposes, such as portfolio selection and optimization, classification of market states, forecasting of market states and data mining. This is in contrast to the wide spectrum of work done on exotic financial instruments, wherein advanced mathematics is used to construct financial instruments for hedging risks and for investment.In this book, particular aspects of the general method are used to create interesting applications. For instance, genetic niching produces a family of portfolios for the trader to choose from. Support vector machines, a special form of neural networks, forecast the financial markets; such a forecast is on market states, of which there are three OCo uptrending, mean reverting and downtrending. A self-organizing map displays in a vivid manner the states of the market. Rough sets with a new discretization method extract information from stock prices."
Author: Jaromír Vrbka Publisher: Springer Nature ISBN: 3030756491 Category : Technology & Engineering Languages : en Pages : 197
Book Description
The aim of this publication is to identify and apply suitable methods for analysing and predicting the time series of gold prices, together with acquainting the reader with the history and characteristics of the methods and with the time series issues in general. Both statistical and econometric methods, and especially artificial intelligence methods, are used in the case studies. The publication presents both traditional and innovative methods on the theoretical level, always accompanied by a case study, i.e. their specific use in practice. Furthermore, a comprehensive comparative analysis of the individual methods is provided. The book is intended for readers from the ranks of academic staff, students of universities of economics, but also the scientists and practitioners dealing with the time series prediction. From the point of view of practical application, it could provide useful information for speculators and traders on financial markets, especially the commodity markets.
Author: Ali Roghani Publisher: Createspace Independent Publishing Platform ISBN: 9781536976830 Category : Languages : en Pages : 108
Book Description
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."
Author: Luis Alberto Valencia Vega
Author: Luis Alberto Valencia Vega
Author: Joish Bosco
Author: Jason E. Kutsurelis
Author: Jason Kutsurelis
Author: Dirk Emma Baestaens
Author: Jimmy Shadbolt
Author: Zhuowen Wang
Author: Francis E. H. Tay
Author: Jaromír Vrbka
Author: Ali Roghani