How effective are neural networks at forecasting and prediction? A review and evaluation

Adya, Monica; Collopy, Fred

doi:10.1002/(sici)1099-131x(1998090)17:5/6<481::aid-for709>3.0.co;2-q

Cited by 329 publications

(92 citation statements)

References 53 publications

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“…Mainly artificial neural networks have been applied to technical financial forecasting [23] as they have the ability to learn complex non-linear mapping and self-adaptation for different statistical distributions [24].…”

Section: Discussionmentioning

confidence: 99%

Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system

2007

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The increasing reliance on Computational Intelligence techniques like Artificial Neural Networks and Genetic Algorithms to formulate trading decisions have sparked off a chain of research into financial forecasting and trading trend identifications. Many research efforts focused on enhancing predictive capability and identifying turning points. Few actually presented empirical results using live data and actual technical trading rules. This paper proposed a novel RSPOP Intelligent Stock Trading System, that combines the superior predictive capability of RSPOP FNN and the use of widely accepted Moving Average and Relative Strength Indicator Trading Rules. The system is demonstrated empirically using real live stock data to achieve significantly higher Multiplicative Returns than a conventional technical rule trading system. It is able to outperform the buy-and-hold strategy and generate several folds of dollar returns over an investment horizon of four years. The Percentage of Winning Trades was increased significantly from an average of 70% to more than 92% using the system as compared to the conventional trading system; demonstrating the system's ability to filter out erroneous trading signals generated by technical rules and to preempt any losing trades. The system is designed based on the premise that it is possible to capitalize on the swings in a stock counter's price, without a need for predicting target prices.

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Section: Discussionmentioning

confidence: 99%

Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system

2007

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“…There is a well-established tradition in forecasting research of comparing techniques on the basis of empirical results (Adya and Collopy 1998). Comparison of the business forecasts should be based on out-of-sample performance, with the testing sample different from the training sample.…”

Section: Methodsmentioning

confidence: 99%

A hybrid neural network cybernetic system for quantifying cross-market dynamics and business forecasting

Ao¹

2010

Soft Comput

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The internal structure of a complex system can manifest itself with correlations among its components. In global business, the interactions between different markets cause collective lead-lag behavior having special statistical properties which reflect the underlying dynamics. In this work, a cybernetic system of combining the vector autoregression (VAR) and genetic algorithm (GA) with neural network (NN) is proposed to take advantage of the lead-lag dynamics, to make the NN forecasting process more transparent and to improve the NN's prediction capability. Two business case studies are carried out to demonstrate the advantages of our proposed system. The first one is the tourism demand forecasting for the Hong Kong market. Another business case study is the modeling and forecasting of Asian Pacific stock markets. The multivariable time series data is investigated with the VAR analysis, and then the NN is fed with the relevant variables determined by the VAR analysis for forecasting. Lastly, GA is used to cope with the time-dependent nature of the co-relationships among the variables. Experimental results show that our system is more robust and makes more accurate prediction than the benchmark NN. The contribution of this paper lies in the novel application of the forecasting modules and the high degree of transparency of the forecasting process.

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“…The hidden layer performs the inter-effect between neurons. Neurons in the hidden layers perform the computations of the network and add degrees of freedom to the network, sending the results to the neurons in the output layer and the output layer presents the output variable (Adya and Collopy 1998).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A comparison of neural networks and adaptive neuro-fuzzy inference systems for the prediction of water diffusion through carbon nanotubes

Kamali

Binesh

2012

Microfluid Nanofluid

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Given the fact that artificial intelligence tools such as neural network and fuzzy logic are capable of learning and inferencing from the past to capture the patterns that exist in the data, this study presents an intelligent method for the forecasting of water diffusion through carbon nanotubes where predictions are generated from neurofuzzy structures using molecular dynamics data. Therefore, this research was mainly focused on combining molecular dynamics with artificial intelligence methods in order to reduce the computational time of biomolecular and nanofluidic simulations. Two different artificial intelligence methods are applied for the time-dependent water diffusion forecasting: artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFISs). The effects of different sizes of training sample sets on forecasting performance of ANN and ANFIS are investigated as well. Four different evaluation methods are used to measure the performance and forecasting accuracy of these two methods. As a result, ANFIS presents the higher accuracy than neural network method based on the comparison of these different evaluation methods adopted in this research. The results reported in this research demonstrate that combining of molecular dynamics with artificial intelligence methods can be one of the most powerful and beneficial tools for prediction of important nanofluidic parameters.

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How effective are neural networks at forecasting and prediction? A review and evaluation

Cited by 329 publications

References 53 publications

Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system

Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system

A hybrid neural network cybernetic system for quantifying cross-market dynamics and business forecasting

A comparison of neural networks and adaptive neuro-fuzzy inference systems for the prediction of water diffusion through carbon nanotubes

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