Intelligent Systems and Financial Forecasting

Kingdon, Jason; Taylor, J.G.; Mannion, C. L. T.

doi:10.1007/978-1-4471-0949-5

Cited by 41 publications

(24 citation statements)

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“…The complexity of NNR models suggests that they are capable of superior forecasts, as shown in this chapter, however this is not always the case. They are essentially nonlinear techniques and may be less capable in linear applications than traditional forecasting techniques (Balkin and Ord, 2000;Campbell et al, 1997;Lisboa and Vellido, 2000;Refenes and Zaidi, 1993).…”

Section: Discussionmentioning

confidence: 99%

“…For a full discussion on neural networks, please refer to Haykin (1999), Kaastra and Boyd (1996), Kingdon (1997), or Zhang et al (1998). Notwithstanding, we provide below a brief description of NNR models and procedures.…”

Section: Neural Network Models: Theory and Methodologymentioning

confidence: 99%

“…The two objectives are, of course, contradictory. The solution is to find a model with the smallest possible complexity, and yet which can still describe the data set (Haykin, 1999;Kingdon, 1997).…”

Section: Issues In Neural Network Modellingmentioning

confidence: 99%

“…The principle is to include in the network variables that are strongly significant. In addition, a small bias weight is preferred (Diekmann and Gutjahr, 1998;Kingdon, 1997;Previa, 2001). The input to a hidden layer Hinton graph of the nnr1 model produced by Previa is presented in Figure 1.15.…”

Section: Neural Network Modelling Proceduresmentioning

confidence: 99%

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Applications of Advanced Regression Analysis for Trading and Investment

Dunis¹,

Williams²

2003

Applied Quantitative Methods for Trading and Investment

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This chapter examines and analyses the use of regression models in trading and investment with an application to foreign exchange (FX) forecasting and trading models. It is not intended as a general survey of all potential applications of regression methods to the field of quantitative trading and investment, as this would be well beyond the scope of a single chapter. For instance, time-varying parameter models are not covered here as they are the focus of another chapter in this book and Neural Network Regression (NNR) models are also covered in yet another chapter.In this chapter, NNR models are benchmarked against some other traditional regressionbased and alternative forecasting techniques to ascertain their potential added value as a forecasting and quantitative trading tool.In addition to evaluating the various models using traditional forecasting accuracy measures, such as root-mean-squared errors, they are also assessed using financial criteria, such as risk-adjusted measures of return.Having constructed a synthetic EUR/USD series for the period up to 4 January 1999, the models were developed using the same in-sample data, leaving the remainder for out-ofsample forecasting, October 1994 to May 2000, and May 2000 to July 2001, respectively. The out-of-sample period results were tested in terms of forecasting accuracy, and in terms of trading performance via a simulated trading strategy. Transaction costs are also taken into account.It is concluded that regression models, and in particular NNR models do have the ability to forecast EUR/USD returns for the period investigated, and add value as a forecasting and quantitative trading tool. * The views expressed herein are those of the authors, and not necessarily those of Girobank.

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Section: Discussionmentioning

confidence: 99%

Section: Neural Network Models: Theory and Methodologymentioning

confidence: 99%

Section: Issues In Neural Network Modellingmentioning

confidence: 99%

Section: Neural Network Modelling Proceduresmentioning

confidence: 99%

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Applications of Advanced Regression Analysis for Trading and Investment

Dunis¹,

Williams²

2003

Applied Quantitative Methods for Trading and Investment

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“…Genetic algorithms have been found to be capable of finding solutions for a wide variety of problems for which no acceptable algorithmic solutions exist. The GA methodology is particularly suited for optimization, a problem solving technique in which one or more very good solutions are searched for in a solution space consisting of a large number of possible solutions [5]. GA reduce the search space by continually evaluating the current generation of candidate solutions, discarding the ones ranked as poor, and producing a new generation through crossbreeding and mutating those ranked as good [7].…”

Section: Introductionmentioning

confidence: 99%

An overview of GA and PGA

Nayak¹,

Mishra²,

Mohanty³

2017

IJCA

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Genetic algorithms have been proven to be both an efficient and effective means of solving certain types of search and optimization problems. Genetic algorithms have been applied with positive results in many areas including scheduling problems, neural networking, face recognition and other NPcomplete problems. The idea behind GA´s is to extract optimization strategies nature uses successfully -known as Darwinian Evolution -and transform them for application in mathematical optimization theory to find the global optimum in a defined phase space. Another popular way to improve genetic algorithms is to run them in parallel, some parallel genetic algorithms have performed very well compared to the standard non-parallel genetic algorithm. Parallel genetic algorithms focus their efforts at simulating multiple species and include not only the standard operations for crossover and mutation but also operations for migration between different populations. Genetic algorithm (GA) which is a meta-heuristic algorithm has been successfully applied to solve the scheduling problem. The fitness evaluation is the most time consuming GA operation for the CPU time, which affects the GA performance. This paper proposes and implements a synchronous master-slave parallelization where the fitness evaluated in parallel. The rest of paper organized as follow: genetic algorithm, parallel genetic algorithm, proposed algorithm, theoretical analysis, practical analysis, and conclusion.

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Future profiling of time series behavior

Singh

2000

Int. J. Intell. Syst.

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Intelligent Systems and Financial Forecasting

Cited by 41 publications

References 0 publications

Applications of Advanced Regression Analysis for Trading and Investment

Applications of Advanced Regression Analysis for Trading and Investment

An overview of GA and PGA

Future profiling of time series behavior

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