A hybrid ANFIS model based on AR and volatility for TAIEX forecasting

Chang, Jing-Rong; Wei, Liang-Ying; Cheng, Ching‐Hsue

doi:10.1016/j.asoc.2010.04.010

Cited by 83 publications

(55 citation statements)

References 22 publications

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“…Considering that the results are based on a 100 day out-of-sample period, the model shows moderate returns (5% to 10%) as in the A C C E P T E D M A N U S C R I P T case of BLT, AV, DGE, HSBA, RIO and XTA, and excellent returns on all other stocks. These results validate the popularity of ANFIS in finance (Boyacioglu and Avci, 2010;Chang et al, 2011;Tan et al, 2011;Kablan and Ng, 2011;Chen, 2013;Vella and Ng, 2014b;Wei et al, 2014) and the active research in improving the model and application techniques. This also validates our proposal to use ANFIS as our main benchmark model and use it as a basis to seek further improvements.…”

Section: Experiments 1: Comparison Against Benchmark Modelsmentioning

confidence: 51%

“…Successful application and active continuous research in improving ANFIS based techniques in trading applications is demonstrated by numerous publications (Gradojevic, 2007;Boyacioglu and Avci, 2010;Chang et al, 2011;Tan et al, 2011;Kablan and Ng, 2011;Chen, 2013;Vella and Ng, 2014b;Wei et al, 2014). Moreover Vella and Ng (2014b) showed the increased stability of ANFIS in terms of risk-adjusted performance when compared to ANN alone.…”

Section: A C C E P T E D Mmentioning

confidence: 95%

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Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Vella

2016

Expert Systems with Applications

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Highlights• We investigate the viability of Type-2 fuzzy systems in high frequency trading.• We propose Type-2 models based on a generalisation of the popular ANFIS model.• Type-2 models score significant risk adjusted performance improvements over Type-1.• Benefits of Type-2 models increase with higher trading frequencies. AbstractIn this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT.

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Section: Experiments 1: Comparison Against Benchmark Modelsmentioning

confidence: 51%

Section: A C C E P T E D Mmentioning

confidence: 95%

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Vella

2016

Expert Systems with Applications

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“…Hence, this algorithm is employed in the present study to estimate Fr with the ANFIS network. In addition to these algorithms, the more recent use of hybrid ANFIS has led to improved ANFIS prediction results (Cus et al 2009;Shoorehdeli et al 2009;Chang et al 2011;Chen 2013;Bui et al 2016aBui et al , 2017b. Therefore, Differential Evolution (DE) is employed in this study and the results are compared with the hybrid algorithm results.…”

Section: Fourth Layermentioning

confidence: 99%

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Ebtehaj

Bonakdari

2017

Appl Water Sci

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Since the flow entering a sewer contains solid matter, deposition at the bottom of the channel is inevitable. It is difficult to understand the complex, three-dimensional mechanism of sediment transport in sewer pipelines. Therefore, a method to estimate the limiting velocity is necessary for optimal designs. Due to the inability of gradient-based algorithms to train Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for non-deposition sediment transport prediction, a new hybrid ANFIS method based on a differential evolutionary algorithm (ANFIS-DE) is developed. The training and testing performance of ANFIS-DE is evaluated using a wide range of dimensionless parameters gathered from the literature. The input combination used to estimate the densimetric Froude number (Fr) parameters includes the volumetric sediment concentration (C V ), ratio of median particle diameter to hydraulic radius (d/R), ratio of median particle diameter to pipe diameter (d/D) and overall friction factor of sediment (k s ). The testing results are compared with the ANFIS model and regression-based equation results. The ANFIS-DE technique predicted sediment transport at limit of deposition with lower root mean square error (RMSE = 0.323) and mean absolute percentage of error (MAPE = 0.065) and higher accuracy (R 2 = 0.965) than the ANFIS model and regression-based equations.

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“…In order to compare the forecasting results with other researchers' work [9,17,23,25,[44][45][46][47][48], the authentic TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) is employed to illustrate the forecasting process. The data from January 1999 to October 1999 are used as the training time series and the data from November 1999 to December 1999 are used as the testing dataset.…”

Section: A Novel Forecasting Model Based On Neutrosophic Logical Relamentioning

confidence: 99%

“…Many studies use TAIEX1999 as an example to illustrate their proposed forecasting methods [9,17,25,34,[44][45][46][47][48]. In order to compare the accuracy with their models, we also use TAIEX1999 to Step 1.…”

Section: Forecasting Taiwan Stock Exchange Capitalization Weighted Stmentioning

confidence: 99%

Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity

Guan

Zhao

2017

Symmetry

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Abstract:The daily fluctuation trends of a stock market are illustrated by three statuses: up, equal, and down. These can be represented by a neutrosophic set which consists of three functions-truth-membership, indeterminacy-membership, and falsity-membership. In this paper, we propose a novel forecasting model based on neutrosophic set theory and the fuzzy logical relationships between the status of historical and current values. Firstly, the original time series of the stock market is converted to a fluctuation time series by comparing each piece of data with that of the previous day. The fluctuation time series is then fuzzified into a fuzzy-fluctuation time series in terms of the pre-defined up, equal, and down intervals. Next, the fuzzy logical relationships can be expressed by two neutrosophic sets according to the probabilities of different statuses for each current value and a certain range of corresponding histories. Finally, based on the neutrosophic logical relationships and the status of history, a Jaccard similarity measure is employed to find the most proper logical rule to forecast its future. The authentic Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets are used as an example to illustrate the forecasting procedure and performance comparisons. The experimental results show that the proposed method can successfully forecast the stock market and other similar kinds of time series. We also apply the proposed method to forecast the Shanghai Stock Exchange Composite Index (SHSECI) to verify its effectiveness and universality.

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A hybrid ANFIS model based on AR and volatility for TAIEX forecasting

Cited by 83 publications

References 22 publications

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity

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