Regularized least squares fuzzy support vector regression for financial time series forecasting

Khemchandani, Reshma; Jayadeva,; Chandra, Suresh

doi:10.1016/j.eswa.2007.09.035

Cited by 98 publications

(19 citation statements)

References 11 publications

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“…The result of their study shows that the proposed model could be a useful and effective assessment tool. In the same domain, [213] proposed a regularized least squares fuzzy support vector regression (RLFSVR) for financial forecasting which is using knowledge of the noisy and nonstationary financial time series data samples, to ameliorate generalization. RLFSVR needs only a single matrix inversion to find the regressor, regardless of the kernel used.…”

Section: Financial Prediction and Planningmentioning

confidence: 99%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

413

192

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Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

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Section: Financial Prediction and Planningmentioning

confidence: 99%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

413

192

View full text Add to dashboard Cite

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“…Polynomial function and radial basis function kernels are commonly used in trading systems. Due to superior performance recorded by polynomial function kernels in related work (Khemchandani and Jayadeva Chandra 2009;Patel et al 2015), we use a polynomial function kernel in our system. Degree of polynomial function (d), and regularization parameter (c) are the parameters of the SVM.…”

Section: Overview Of the Systemmentioning

confidence: 99%

A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms

2016

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This paper addresses problem of predicting direction and magnitude of movement of currency pairs in the foreign exchange market. The study uses Support Vector Machine with a novel approach for input data and trading strategy. The input data contain technical indicators generated from currency price data (i.e., open, high, low and close prices) and representation of these technical indicators as trend deterministic signals. The input data are also dynamically adapted to each trading day with genetic algorithm. The study incorporates a currency strength-biased trading strategy which selects the best pair to trade from the available set of currencies and is an improvement over the previous work. The accuracy of the prediction models are tested across several different sets of technical indicators and currency pair sets, spanning 5 years of historical data from 2010 to 2015. The experimental results suggest that using trend deterministic technical indicator signals mixed with raw data improves overall performance and dynamically adapting the input data to each trading period results in increased profits. Results also show that using a strength-biased trading strategy among a set of currency pair increases the overall prediction accuracy and profits of the models.

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“…The SVR is a non-parametric, regularized, and non-linear regression tool which has been applied to optimal control, time-series prediction, interval regression analysis, determination of initial structures for fuzzy neural networks, radial basis function networks, etc. (Hong and Hwang 2002;Chiang and Hao 2004;Juang and Hsieh 2009;Khemchandani et al 2009;Yang et al 2009). …”

Section: Introductionmentioning

confidence: 97%

A fuzzy regression model based on distances and random variables with crisp input and fuzzy output data: a case study in biomass production

Roldán

Martínez-Moreno

2011

Soft Comput

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Least-squares technique is well-known and widely used to determine the coefficients of a explanatory model from observations based on a concept of distance. Traditionally, the observations consist of pairs of numeric values. However, in many real-life problems, the independent or explanatory variable can be observed precisely (for instance, the time) and the dependent or response variable is usually described by approximate values, such as ''about £300'' or ''approximately $500'', instead of exact values, due to sources of uncertainty that may affect the response. In this paper, we present a new technique to obtain fuzzy regression models that consider triangular fuzzy numbers in the response variable. The procedure solves linear and non-linear problems and is easy to compute in practice and may be applied in different contexts. The usefulness of the proposed method is illustrated using simulated and real-life examples.

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Regularized least squares fuzzy support vector regression for financial time series forecasting

Cited by 98 publications

References 11 publications

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms

A fuzzy regression model based on distances and random variables with crisp input and fuzzy output data: a case study in biomass production

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