Quantitative Neural Network Models - page 3

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The Foreign Exchange Market is the biggest and one of the most liquid markets in the world. This market has always been one of the most challenging markets as far as short term prediction is concerned. Due to the chaotic, noisy, and non-stationary nature of the data, the majority of the research has been focused on daily, weekly, or even monthly prediction. The literature review revealed that there is a gap for intra-day market prediction. Identifying this gap, this paper introduces a prediction and decision making model based on Artificial Neural Networks (ANN) and Genetic Algorithms. The dataset utilized for this research comprises of 70 weeks of past currency rates of the 3 most traded currency pairs: GBP\USD, EUR\GBP, and EUR\USD. The initial statistical tests confirmed with a significance of more than 95% that the daily FOREX currency rates time series are not randomly distributed. Another important result is that the proposed model achieved 72.5% prediction accuracy. Furthermore, implementing the optimal trading strategy, this model produced 23.3% Annualized Net Return. (c) 2013 Elsevier Ltd. All rights reserved.
 

This paper shows how the performance of the basic Local Linear Wavelet Neural Network model (LLWNN) can be improved with hybridizing it with fuzzy model. The new improved LLWNN based Neurofuzzy hybrid model is used to predict two currency exchange rates i.e. the U.S. Dollar to the Indian Rupee and the U.S. Dollar to the Japanese Yen. The forecasting of foreign exchange rates is done on different time horizons for 1 day, 1 week and 1 month ahead. The LLWNN and Neurofuzzy hybrid models are trained with the backpropagation training algorithm. The two performance measurers i.e. the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) show the superiority of the Neurofuzzy hybrid model over the LLWNN model.



 
Though machine learning has been applied to the foreign exchange market for algorithmic trading for quiet some time now, and neural networks(NN) have been shown to yield positive results, in most modern approaches the NN systems are optimized through traditional methods like the backpropagation algorithm for example, and their input signals are price lists, and lists composed of other technical indicator elements. The aim of this paper is twofold: the presentation and testing of the application of topology and weight evolving artificial neural network (TWEANN) systems to automated currency trading, and to demonstrate the performance when using Forex chart images as input to geometrical regularity aware indirectly encoded neural network systems, enabling them to use the patterns & trends within, when trading. This paper presents the benchmark results of NN based automated currency trading systems evolved using TWEANNs, and compares the performance and generalization capabilities of these direct encoded NNs which use the standard sliding-window based price vector inputs, and the indirect (substrate) encoded NNs which use charts as input. The TWEANN algorithm I will use in this paper to evolve these currency trading agents is the memetic algorithm based TWEANN system called Deus Ex Neural Network (DXNN) platform.
 
Predicting trends in the stock market is a subject of major interest for both scholars and financial analysts. The main difficulties of this problem are related to the dynamic, complex, evolutive and chaotic nature of the markets. In order to tackle these problems, this work proposes a day-trading system that ldquotranslatesrdquo the outputs of an artificial neural network into business decisions, pointing out to the investors the best times to trade and make profits. The ANN forecasts the lowest and highest stock prices of the current trading day. The system was tested with the two main stocks of the BM&FBOVESPA, an important and understudied market. A series of experiments were performed using different data input configurations, and compared with four benchmarks. The results were evaluated using both classical evaluation metrics, such as the ANN generalization error, and more general metrics, such as the annualized return. The ANN showed to be more accurate and give more return to the investor than the four benchmarks. The best results obtained by the ANN had an mean absolute percentage error around 50% smaller than the best benchmark, and doubled the capital of the investor.
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