A stock market risk forecasting model through integration of switching regime, ANFIS and GARCH techniques

Kristjanpoller, Werner; Michell, Kevin

doi:10.1016/j.asoc.2018.02.055

Cited by 49 publications

(21 citation statements)

References 57 publications

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“…MS and MS-GARCH models have been used for several purposes, such as the characterization of stock indexes, currencies or commodities' time series [57][58][59][60][61][62][63][64][65][66][67][68][69][70]. The use of these models for the aforementioned has had also interesting extensions in Fuzzy logic and control applications [71,72].…”

Section: Literature Reviewmentioning

confidence: 99%

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Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Torre-Torres

Venegas-Martı́nez

Martínez-Torre-Enciso

2021

Mathematics

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In the present paper, we test the use of Markov-Switching (MS) models with time-fixed or Generalized Autoregressive Conditional Heteroskedasticity (GARCH) variances. This, to enhance the performance of a U.S. dollar-based portfolio that invest in the S&P 500 (SP500) stock index, the 3-month U.S. Treasury-bill (T-BILL) or the 1-month volatility index (VIX) futures. For the investment algorithm, we propose the use of two and three-regime, Gaussian and t-Student, MS and MS-GARCH models. This is done to forecast the probability of high volatility episodes in the SP500 and to determine the investment level in each asset. To test the algorithm, we simulated 8 portfolios that invested in these three assets, in a weekly basis from 23 December 2005 to 14 August 2020. Our results suggest that the use of MS and MS-GARCH models and VIX futures leads the simulated portfolio to outperform a buy and hold strategy in the SP500. Also, we found that this result holds only in high and extreme volatility periods. As a recommendation for practitioners, we found that our investment algorithm must be used only by institutional investors, given the impact of stock trading fees.

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Section: Literature Reviewmentioning

confidence: 99%

“…Finally, the use of MS-GARCH models in a fuzzy logic context could be also of interest as a potential extension. This by following [71,72], in order to reduce the impact of uncertainty effect from financial markets.…”

mentioning

confidence: 99%

Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Torre-Torres

Venegas-Martı́nez

Martínez-Torre-Enciso

2021

Mathematics

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“…Finally, they showed forecasting the success of a new tourism service by ANFIS has been forecasted with less than 8.43 per cent error. Kristjanpoller and Michell (2018) forecasted risk stock market volatility with the integration of switching regime. They used two techniques including ANFIS and generalized autoregressive conditional heteroscedastic (GARCH) for this forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrating neuro-fuzzy system and evolutionary optimization algorithms for short-term power generation forecasting

Rezaee

Dadkhah

Falahinia³

2019

IJESM

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Purpose This study aims to short-therm forecasting of power generation output for this purpose, an adaptive neuro-fuzzy inference system (ANFIS) is designed to forecast the output power of power plant based on climate factors considering wind speed and wind direction simultaneously. Design/methodology/approach Several methods and algorithms have been proposed for systems forecasting in various fields. One of the strongest methods for modeling complex systems is neuro-fuzzy that refers to combinations of artificial neural network and fuzzy logic. When the system becomes more complex, the conventional algorithms may fail for network training. In this paper, an integrated approach, including ANFIS and metaheuristic algorithms, is used for increasing forecast accuracy. Findings Power generation in power plants is dependent on various factors, especially climate factors. Operating power plant in Iran is very much influenced because of climate variation, including from tropical to subpolar, and severely varying temperature, humidity and air pressure for each region and each season. On the other hands, when wind speed and wind direction are used simultaneously, the training process does not converge, and the forecasting process is unreliable. The real case study is mentioned to show the ability of the proposed approach to remove the limitations. Originality/value First, ANFIS is applied for forecasting based on climate factors, including wind speed and wind direction, that have rarely been used simultaneously in previous studies. Second, the well-known and more widely used metaheuristic algorithms are applied to improve the learning process for forecasting output power and compare the results.

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“…A hybrid stock selection method that included stock prediction using extreme learning machine and stock scoring was applied on the A-share market of China (Yang et al, 2019). Switching regime, ANFIS and GARCH techniques have been employed in order to design a forecasting model for the stock market risk (Kristjanpoller & Mitchell, 2018). A combined fuzzy system and GARCH model was utilized to forecast stock market volatility (Hung, 2011).…”

Section: Introductionmentioning

confidence: 99%

Competitive / Collaborative Statistical Learning Framework for Forecasting Intraday Stock Market Prices: A Case Study

Belciug¹,

Săndiță²,

Costin³

et al. 2021

STUD INFORM CONTROL

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This paper presents an intelligent decision system based on statistical learning that regards the tactics of an investor in predicting the next intraday stock price. Significant percentages can be won or lost depending on the tactics applied for buying/selling shares. This paper includes a case study regarding the efficiency of a group of machine learning techniques that work together in a competitive/collaborative manner with a view to achieving an overall price forecast for the next intraday transaction. In order to illustrate the advantages of this intelligent decision system this work provides a concrete example concerning the price forecast for the next intraday transaction for Transilvania Bank (TLV), the stock market at the Bucharest Stock Exchange (BVB), Romania. An important part of the decision system lies in the competitive stage, because only the best competitors are chosen for the ultimate decision-making process. In the collaborative stage of the statistical learning framework one uses a weighted voting system that outputs the final intraday stock price. The results obtained show that this intelligent system outperforms each stand-alone method.

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A stock market risk forecasting model through integration of switching regime, ANFIS and GARCH techniques

Cited by 49 publications

References 57 publications

Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Integrating neuro-fuzzy system and evolutionary optimization algorithms for short-term power generation forecasting

Competitive / Collaborative Statistical Learning Framework for Forecasting Intraday Stock Market Prices: A Case Study

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