Implementation of ANN and GARCH for Stock Price Forecasting

Hendra Mayatopani

Abstract


For simulating intricate goalfunctions, neural networks are a technology that is employed in artificial intelligence. The usage of artificial neural networks is becoming more popular.(ANNs) to certain sorts of tasks, for example learning to comprehend complicated sensor data collected in the real world, is one of the most effective methods of learning approaches available. The usage of time series models in financial time series prediction has grown significantly over the past decade, and their relevance in this area continues to expand. To be more specific, the goal of this research is to determine whether neural networks have the ability to predict financial time series in general, or, more specifically, whether they have the ability to predict future patterns i The stock market in the United States is characterized by the European Union, and Brazil, among other things. They are compared to a well-known forecasting approach, generalized autoregressive conditional heteroskedasticity, in this research, and their accuracy is shown to be superior (GARCH). Aside from that, the optimal network design for each data sample is developed for each data sample. According to this article, ANNs are capable of forecasting the stock markets under examination, and their resilience may be increased by varying the network topology utilized to construct them. Aside from that, the results of this research demonstrate that ANNs outperform GARCH models in terms of efficiency of statistical performance.


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Keywords


ANN; GARCH; Stock Price; Data Mining

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References


Y. Sun, Q. Jin, Q. Cheng, and K. Guo, “New tool for stock investment risk management,” Ind. Manag. Data Syst., vol. 120, no. 2, pp. 388–405, Jan. 2020, doi: 10.1108/IMDS-03-2019-0125.

Y. Huang, C. Deng, X. Zhang, and Y. Bao, “Forecasting of stock price index using support vector regression with multivariate empirical mode decomposition,” J. Syst. Inf. Technol., vol. 2, no. 11, pp. 55–76, Jan. 2020, doi: 10.1108/JSIT-12-2019-0262.

B. Liu and M. Tan, “Overconfidence and forecast accuracy,” Stud. Econ. Financ., vol. 38, no. 3, pp. 601–618, Jan. 2021, doi: 10.1108/SEF-12-2017-0345.

Y. Zhang, J. Gao, and H. Zhou, “Breeds Classification with Deep Convolutional Neural Network,” ACM Int. Conf. Proceeding Ser., pp. 145–151, 2020, doi: 10.1145/3383972.3383975.

F. Aslam, K. S. Mughal, A. Ali, and Y. T. Mohmand, “Forecasting Islamic securities index using artificial neural networks: performance evaluation of technical indicators,” J. Econ. Adm. Sci., vol. 37, no. 2, pp. 253–271, Jan. 2021, doi: 10.1108/JEAS-04-2020-0038.

S. Rath, B. K. Sahu, and M. R. Nayak, “Application of quasi-oppositional symbiotic organisms search based extreme learning machine for stock market prediction,” Int. J. Intell. Comput. Cybern., vol. 12, no. 2, pp. 175–193, Jan. 2019, doi: 10.1108/IJICC-10-2018-0145.

R. Östermark, “The forecasting performance of Cartesian ARIMA search and a vector‐valued state space model,” Kybernetes, vol. 29, no. 1, pp. 83–104, Jan. 2000, doi: 10.1108/03684920010308862.

R. M. K. T. Rathnayaka, D. M. K. . Seneviratna, and W. Jianguo, “Grey system based novel approach for stock market forecasting,” Grey Syst. Theory Appl., vol. 5, no. 2, pp. 178–193, Jan. 2015, doi: 10.1108/GS-04-2015-0014.

S. B. Liau and S. B. Reid, “Combined deep learning classifiers for stock market prediction: integrating stock price and news sentiments,” Kybernetes, vol. 4, no. 6, Jan. 2021, doi: 10.1108/K-06-2021-0457.

Y. Seetharam and J. Britten, “Non-linear modelling of market cycles in South Africa,” Int. J. Emerg. Mark., vol. 10, no. 4, pp. 670–683, Jan. 2015, doi: 10.1108/IJoEM-05-2013-0079.

M.-H. Shou, Z.-X. Wang, D.-D. Li, and Y.-T. Zhou, “Forecasting the price trends of digital currency: a hybrid model integrating the stochastic index and grey Markov chain methods,” Grey Syst. Theory Appl., vol. 11, no. 1, pp. 22–45, Jan. 2021, doi: 10.1108/GS-12-2019-0068.

W. K. Loo, “Predictability of HK-REITs returns using artificial neural network,” J. Prop. Invest. Financ., vol. 38, no. 4, pp. 291–307, Jan. 2020, doi: 10.1108/JPIF-07-2019-0090.

J. E. Jarrett, “Random walk, capital market efficiency and predicting stock returns for Hong Kong Exchanges and Clearing Limited,” Manag. Res. News, vol. 31, no. 2, pp. 142–148, Jan. 2008, doi: 10.1108/01409170810846858.

J. E. Jarrett, “Efficient markets hypothesis and daily variation in small Pacific‐basin stock markets,” Manag. Res. Rev., vol. 33, no. 12, pp. 1128–1139, Jan. 2010, doi: 10.1108/01409171011092185.

R. Östermark, “Multiple input transfer function noise modelling in the time domain, Empirical evidence on Scandinavian stock data,” Kybernetes, vol. 29, no. 3, pp. 355–380, Jan. 2000, doi: 10.1108/03684920010795312.

R. Östermark, “Modelling dynamic systems with biased regression and spectral methods,” Kybernetes, vol. 24, no. 6, pp. 38–43, Jan. 1995, doi: 10.1108/03684929510094271.

P. Yin, G. Dou, X. Lin, and L. Liu, “A hybrid method for forecasting new product sales based on fuzzy clustering and deep learning,” Kybernetes, vol. 49, no. 12, pp. 3099–3118, Jan. 2020, doi: 10.1108/K-10-2019-0688.

S. P. Mohanty, S. Gopalkrishnan, and A. Mahendra, “The intertwined relationship of shadow banking and commercial banks’ deposit growth: evidence from India,” Int. J. Innov. Sci., vol. 3, no. 6, pp. 33–57, Jan. 2021, doi: 10.1108/IJIS-01-2021-0022.

W. Dbouk and L. Kryzanowski, “Determinants of credit spread changes for the financial sector,” Stud. Econ. Financ., vol. 27, no. 1, pp. 67–82, Jan. 2010, doi: 10.1108/10867371011022984.

A. Dinc and A. Mamedov, “Optimization of surface quality and machining time in micro-milling of glass,” Aircr. Eng. Aerosp. Technol., vol. 3, no. 6, Jan. 2021, doi: 10.1108/AEAT-06-2021-0187.

A. J. Black, D. G. McMillan, and F. J. McMillan, “Cointegration between stock prices, dividends, output and consumption,” Rev. Account. Financ., vol. 14, no. 1, pp. 81–103, Jan. 2015, doi: 10.1108/RAF-09-2013-0103.

L. T. He, “Forecasting of housing stock returns and housing prices,” J. Financ. Econ. Policy, vol. 7, no. 2, pp. 90–103, Jan. 2015, doi: 10.1108/JFEP-01-2014-0004.

J. Manickavasagam, “An investigational analysis on forecasting intraday values,” Benchmarking An Int. J., vol. 27, no. 2, pp. 592–605, Jan. 2020, doi: 10.1108/BIJ-11-2018-0361.


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