Re: Neural networks for trading

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A novel approach using modular neural networks to forecast exchange rates based on harmonic patterns in Forex market is introduced. The proposed approach employs three algorithms to predict price, validate its prediction and update the system. The model is trained by historical data using major currencies in Forex market. The proposed system's predictions were evaluated by comparing its results with a non-modular neural network. Results showed that the infrastructure market data consist of significant accurate relations that a single network cannot detect these relations and separate trained networks in specific tasks are needed. Comparison of modular and non-modular systems showed that modular neural network outperforms the other one.


Re: Neural networks for trading

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Financial forecasting is a difficult task due to the intrinsic complexity of the financial system. A simplified approach in forecasting is given by "black box" methods like neural networks that assume little about the structure of the economy. In the present paper we relate our experience using neural nets as financial time series forecast method. In particular we show that a neural net able to forecast the sign of the price increments with a success rate slightly above 50% can be found. Target series are the daily closing price of different assets and indexes during the period from about January 1990 to February 2000.

Re: Neural networks for trading

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he financial industry has been becoming more and more dependent on advanced computing technologies in order to maintain competitiveness in a global economy. Hence, the stock price prediction problem using data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. Fuzzy logic (FL) and Artificial Neural Network (ANN) present an exciting and promising technique with a wide scope for the applications of prediction. There is a growing interest in both fields of fuzzy logic computing and the financial world in the use of fuzzy logic to predict future changes in prices of stocks, exchange rates, commodities, and other financial time series. Fuzzy logic provides a way to draw definite conclusions from vague, ambiguous or imprecise information. Artificial Neural Network is one of data mining techniques being widely accepted in the business area due to its ability to learn and detect relationships among nonlinear variables. The ANN outperforms statistical regression models and also allows deeper analysis of large data sets, especially those that have the tendency to fluctuate within a short of time period. In this paper, we investigate the ability of Fuzzy logic and multilayer perceptron (MLP), which is a kind of the ANN, to tackle the financial time series stock forecasting problem. The proposed approaches were tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the comparison between those techniques is performed to examine their effectiveness.

Re: Neural networks for trading

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These diagrams explore the use of Sydney Lamb’s relational network notion for linguistics to represent the logical structure of complex collection of attractor landscapes (as in Walter Freeman’s account of neuro-dynamics). Given a sufficiently large system, such as a vertebrate nervous system, one might want to think of the attractor net as itself being a dynamical system, one at a higher order than that of the dynamical systems realized at the neuronal level. Constructions include: variety ('is-a' inheritance), simple movements, counting and place notation, orientation in time and space, language, learning.


Re: Neural networks for trading

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In the last decade, neural networks have drawn noticeable attention from many computer and operations researchers. While some previous studies have found encouraging results with using this artificial intelligence technique to predict the movements of established financial markets, it is interesting to verify the persistence of this performance in the emerging markets. These rapid 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. In this study, we attempt to model and predict the direction of return on the Taiwan Stock Exchange Index, one of the fastest growing financial exchanges in developing Asian countries. Our approach is based on the notion that trading strategies guided by forecasts of the direction of price movement may be more effective and lead to higher profits. The Probabilistic Neural Network (PNN) is used to forecast the direction of index return after it is trained by historical data. The forecasts are applied to various index trading strategies, of which the performances are compared with those generated by the buy and hold strategy, and the investment strategies guided by the forecasts estimated by the random walk model and the parametric Generalized Methods of Moments (GMM) with Kalman filter. Empirical results show that the PNN-based investment strategies obtain higher returns than other investment strategies examined in this study. The influences of the length of investment horizon and the commission rate are also considered.

Re: Neural networks for trading

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In this paper we present the Radial Basis Neural Network Function. We examine some simple numerical examples of time-series in economics and finance. The forecasting performance is significant superior, especially in financial time-series, to traditional econometric modeling indicating that artificial intelligence procedure are more appropriate. Some MATLAB routines are presented for further application research.

Re: Neural networks for trading

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This study uses Genetic Programming (GP) to discover new types of volatility forecasting models for financial time series. GP is a convenient tool to explore the space of potential forecasting models and to select the more robust solutions. The application to foreign exchange financial problems requires an exact symmetry induced by the interchange of currencies. In GP, this symmetry is enforced by using a strongly typed GP approach and syntactic restrictions on the node set. GP convergence is increased by a few orders of magnitude by optimizing the constants in the GP trees with a local optimization algorithm. The various algorithms are compared on the discovery of the symmetric transcendental function cosine. For the volatility forecast, the optimization is performed using return time series sampled hourly, possibly including aggregated returns at longer time horizons. The in-sample optimization and out-of-sample tests are performed on 13 years of high frequency data for two foreign exchange time series. The out-of-sample forecasting performance of these new models are compared with the corresponding performance of some popular ARCH-types models, and GP consistently outperform the benchmarks. In particular, GP discovered that cross products of returns at different time horizons improve substantially the forecasting performance.

Re: Neural networks for trading

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Wavelet networks (WNs) are a new class of networks which have been used with great success in a wide range of application. However a general accepted framework for applying WNs is missing from the literature. In this study, we present a complete statistical model identification framework in order to apply WNs in various applications. The following subjects were thorough examined: the structure of a WN, methods to train a WN, initialization algorithms, variable significance and variable selection algorithms, a model selection method and finally methods to construct confidence and prediction intervals. Our proposed framework was tested in two simulated cases and in one real dataset consisting of daily temperatures in Berlin. Our results have shown that the proposed algorithms produce stable and robust results indicating that our proposed framework can be applied in various applications.


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