Nonlinear neural network forecasting model for stock index option price: Hybrid GJR–GARCH approach

Wang, Yi-Hsien

doi:10.1016/j.eswa.2007.09.056

Cited by 116 publications

(55 citation statements)

References 48 publications

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“…The biotechnology industry is committed to developing new products and producing economic rewards for the long term (Niosi 2003). Investors and shareholders are not myopic in their analysis of the true value of an emerging company (McMillan, Thomas 2005;Namara, Baden-Fuller 2007;Wang 2009;Ramchander et al 2012). The outbreak of an infectious disease may be long or short term, but overall stock performance within the biotechnology industry is as yet unknown.…”

Section: S113mentioning

confidence: 99%

An Investor's Perspective on Infectious Diseases and Their Influence on Market Behavior

Wang

Yang

Chen

2013

Journal of Business Economics and Management

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Abstract.Recently, increasing number of infectious diseases has swept the world. The outbreak of a contagious disease not only affects the health and lives of people but also causes economic growth to stagnate. Business in the biotechnology industry is closely related to infectious diseases but what exactly is the information value of the outbreak of infectious disease on biotechnology? This study investigates how such outbreaks can affect the performance of biotechnology stocks. In the past 10 years, major statutory infectious diseases in Taiwan have included ENTEROVIRUS 71, DENGUE FEVER, SARS and H1N1. The empirical results indicate that there is a significant abnormal return on company shares in Taiwan's biotechnology industry because of statutory infectious epidemics. The relationship between the financial ratios of biotechnology companies and abnormal returns was analyzed as part of this research. The results show that the influence on R&D ratios, current ratios and assets are significant. Empirical findings reveal that the investors rationally measure operating conditions of the biotechnology companies during outbreaks of major infectious disease and adjust portfolio allocation accordingly.

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Section: S113mentioning

confidence: 99%

An Investor's Perspective on Infectious Diseases and Their Influence on Market Behavior

Wang

Yang

Chen

2013

Journal of Business Economics and Management

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“…6(a) to 6(h) show the weekly forecasts for the first weeks of February, May, August, and November, respectively using both the FF and IFFHS algorithm, and the MAPE values are also given in Table-2. From the results it is clearly seen that the actual values of electricity prices in the considered week of November, 2014 are captured more accurately in comparison to the weekly forecast during Feb. [1][2][3][4][5][6][7]2014. This discrepancy indicates the presence of low spikes in electricity prices in November as compared to those in the month of February 2014.…”

Section: Fig 2 Flow Chart For Lrnfis Implementationmentioning

confidence: 62%

“…Significant research efforts have been undertaken to mine these highly chaotic time series databases in the way of either forecasting or classifying patterns in the database. Many techniques have been employed over the years for time series forecasting purpose, including statistical methods like Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models [1][2][3][4][5], intelligence techniques like artificial neural networks (ANNs) [6][7][8][9][10][11], fuzzy inference system (FIS) [12][13][14][15], and support vector machines (SVMs) [16][17][18][19][20], etc. Moreover, time series models like ARIMA, and GARCH, have also been proven to be effective in the stock and electricity price forecasting/modeling.…”

Section: Introductionmentioning

confidence: 99%

Times Series Forecasting using Chebyshev Functions based Locally Recurrent neuro-Fuzzy Information System

Parida¹,

Bisoi²,

Dash³

et al. 2017

IJCIS

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The model proposed in this paper, is a hybridization of fuzzy neural network (FNN) and a functional link neural system for time series data prediction. The TSK-type feedforward fuzzy neural network does not take the full advantage of the use of the fuzzy rule base in accurate input-output mapping and hence a hybrid model is developed using the Chebyshev polynomial functions to construct the consequent part of the fuzzy rules. The model to be known as locally recurrent neuro fuzzy information system (LRNFIS) is used to provide an expanded nonlinear transformation to the input space thereby increasing its dimension which will be adequate to capture the nonlinearities and chaotic variations in the time series. The locally recurrent nodes will provide feedback connections between outputs and inputs allowing signal flow in both forward and backward directions, giving the network a dynamic memory useful to mimic dynamic systems. For training the proposed LRNFIS, an improved firefly-harmony search (IFFHS) learning algorithm is used to estimate the parameters of the consequent part and feedback loop parameters. Three real world time series databases like the electricity price of PJM electricity market, the widely studied currency exchange rates between US Dollar (USD) and other four currencies i.e. Australian Dollar (AUD), Swiss Franc (CHF), Mexican Peso (MXN), Brazilian Real (BRL), along with S&P 500 and Nikkei 225 stock market data are used for performance validation of the newly proposed LRNFIS.

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“…The FTS model, which is based on the fuzzy logic, is a model for forecasting problems [9,13,18]. Song and Chissom first applied it for forecasting enrollments at the University of Alabama [19,20].…”

Section: Fuzzy Time Series Modelmentioning

confidence: 99%

The strategy of investment in the stock market using modified support vector regression model

2017

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Abstract. Stock indices forecasting has become a popular research issue in recent years.Although many statistical time series models have been applied in stock indices forecasting, they are limited to certain assumptions. Accordingly, the traditional statistical time series models might not be suitable for forecasting real-life stock indices data. Hence, this paper proposes a novel forecasting model to assist investors in determining a strategy for investments in the stock market. The proposed model is called the modified support vector regression model, which is composed of the correlation coefficient method, the sliding window algorithm and the support vector regression model. The results show that the forecasting accuracy of the proposed model is more stable than the existing models in terms of average and standard deviation of the root mean square error (RMSE) and the mean absolute percentage error (MAPE). Accordingly, the proposed model would be used to assist investors in determining a strategy for investing in stocks.

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Nonlinear neural network forecasting model for stock index option price: Hybrid GJR–GARCH approach

Cited by 116 publications

References 48 publications

An Investor's Perspective on Infectious Diseases and Their Influence on Market Behavior

An Investor's Perspective on Infectious Diseases and Their Influence on Market Behavior

Times Series Forecasting using Chebyshev Functions based Locally Recurrent neuro-Fuzzy Information System

The strategy of investment in the stock market using modified support vector regression model

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