Testing the Reliability of Technical Analysis Models for Short-Term Prediction of Prices on the Financial Markets

Robert Kuchar; Simona Haskova; Petr Suler

doi:10.23762/FSO_VOL12_NO2_3

Published : 2024-06-30

Vol. 12 No. 2 (2024)

Testing the Reliability of Technical Analysis Models for Short-Term Prediction of Prices on the Financial Markets

Robert Kuchar

https://orcid.org/0009-0001-2787-4344

Simona Haskova

https://orcid.org/0000-0002-4971-7972

Petr Suler

https://orcid.org/0000-0001-7562-0659

DOI: https://doi.org/10.23762/FSO_VOL12_NO2_3

Abstract

The issue of successful investment has been addressed since the beginning of the financial markets. For investors and the academic community, there is a strong motivation to test the reliability of models for predicting the development of prices on these markets. Our goal is to determine which models are optimal for short-term predictions during periods of high market volatility, and what their reliability is. A number of studies indicate that methods of technical analysis are a suitable approach for their ability to be as effective as modern methods, user-friendliness, and their ability to be applied by basic software. The price prediction reliability for the first two weeks of March 2022 is tested on the case of copper based on three conventional forecasting strategies: a) linear regression, b) naive forecasting, and c) exponential smoothing, with the frequencies of data being the daily prices of the preceding month, the daily prices of the preceding three months, and the annual average monthly prices of the preceding 13 months. The results show that linear regression is the most suitable model with the frequency distribution of daily historical prices for the period of three months (MAPE = 4.16%) and average monthly data from the preceding year (MAPE = 4.37%). Predictions based on the daily frequency of data from the period of one previous month achieved the worst predictive results for all the models. The outcomes of this study, reflecting conditions of high market uncertainty, can help investors minimise risks and maximise returns. They demonstrate which method of technical analysis is suitable in terms of predictive power and the choice of data frequency under high market volatility.

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financial market; copper price; volatility; prediction; technical analysis.

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Kuchar, R., Haskova, S., & Suler, P. (2024). Testing the Reliability of Technical Analysis Models for Short-Term Prediction of Prices on the Financial Markets. Forum Scientiae Oeconomia, 12(2), 42–64. https://doi.org/10.23762/FSO_VOL12_NO2_3

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References

Aliu, F., Hašková, S., & Šuleř, P. (2022). Sustainability of electricity prices and the consequences for the Prague Stock Exchange. Entrepreneurship and Sustainability Issues, 10(2), Article 473. https://doi.org/10.9770/jesi.2022.10.2(30)

Anghel, D. G. (2021). A reality check on trading rule performance in the cryptocurrency market: Machine learning vs. technical analysis. Finance Research Letters, 39. https://doi.org/10.1016/j.frl.2020.101655

Apanovych, Y., & Prágr, S. (2023). Determination of iron procurement strategy for manufacturing companies. Entrepreneurship and Sustainability Issues, 11(2), 331-348. https://doi.org/10.9770/jesi.2023.11.2(23)

Atkins, A., Niranjan, M., & Gerding, E. (2018). Financial news predicts stock market volatility better than close price. The Journal of Finance and Data Science, 4(2), 120-137. https://doi.org/10.1016/j.jfds.2018.02.002

Ayala, J., García-Torres, M., Noguera, J. L. V., Gómez-Vela, F., & Divina, F. (2021). Technical analysis strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems, 225. https://doi:10.1016/j.knosys.2021.107119

Balcilar, M., Ozdemir, Z. A., Ozdemir, H., & Wohar, M. E. (2020). Fed’s unconventional monetary policy and risk spillover in the US financial markets. The Quarterly Review of Economics and Finance, 78, 42-52. https://doi:10.1016/j.qref.2020.01.004

Barrow, D., Kourentzes, N., Sandberg, R., & Niklewski, J. (2020). Automatic robust estimation for exponential smoothing: Perspectives from statistics and machine learning. Expert Systems with Applications, 160. https://doi.org/10.1016/j.eswa.2020.113637

Biecek, P., & Burzykowski, T. (2021). Explanatory model analysis: explore, explain, and examine predictive models. Chapman and Hall/CRC.

Brogaard, J., Ringgenberg, M. C., & Sovich, D. (2019). The economic impact of index investing. The Review of Financial Studies, 32(9), 3461-3499. https://doi:10.1093/rfs/hhy129

Burghauserová, M., Rousek, P., & Petrách, F. (2023). Demand function of coffee. Ad Alta: Journal of Interdisciplinary Research, 13(2).

Cella, C. (2020). Institutional investors and corporate investment. Finance Research Letters, 32. https://doi:10.1016/j.frl.2019.04.026

Chernii, V., Yanovska, V., Kravchenko, O., & Tvoronovych, V. (2022, March). The impact of modern macroeconomic factors on the development of the economic strategies of companies. In Forum Scientiae Oeconomia, 10(1), 50-66. https://doi.org/10.1007/s10668-020-00988-4

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj computer science, 7, Article e623. https://doi.org/10.7717/peerj-cs.623

Coakley, J., Marzano, M., & Nankervis, J. (2016). How profitable are FX technical trading rules?. International Review of Financial Analysis, 45, 273-282. https://doi:10.1016/j.irfa.2016.03.010

Cohen, G. (2021). Optimizing algorithmic strategies for trading bitcoin. Computational Economics, 57(2), 639-654. https://doi:10.1007/s10614-020-09972-6

Dai, Z., Dong, X., Kang, J., & Hong, L. (2020). Forecasting stock market returns: New technical indicators and two-step economic constraint method. The North American Journal of Economics and Finance, 53, 101216. https://doi:10.1016/j.najef.2020.101216

Dai, Z., Zhu, H., & Kang, J. (2021). New technical indicators and stock returns predictability. International Review of Economics & Finance, 71, 127-142. https://doi:10.1016/j.iref.2020.09.006

Devriendt, F., Berrevoets, J., & Verbeke, W. (2021). Why you should stop predicting customer churn and start using uplift models. Information Sciences, 548, 497-515. https://doi.org/10.1016/j.ins.2019.12.075

Díaz, J. D., Hansen, E., & Cabrera, G. (2020). A random walk through the trees: Forecasting copper prices using decision learning methods. Resources Policy, 69. https://doi.org/10.1016/j.resourpol.2020.101859

Driver, C., Grosman, A., & Scaramozzino, P. (2020). Dividend policy and investor pressure. Economic Modelling, 89, 559-576. https://doi:10.1016/j.econmod.2019.11.016

Funde, Y., & Damani, A. (2023). Comparison of ARIMA and exponential smoothing models in prediction of stock prices. The Journal of Prediction Markets, 17(1), 21-38. https://doi.org/10.5750/jpm.v17i1.2017

Gill, S. S., Wu, H., Patros, P., Ottaviani, C., Arora, P., Pujol, V. C., ... & Buyya, R. (2024). Modern computing: Vision and challenges. Telematics and Informatics Reports, Article 100116. https://doi.org/10.1016/j.teler.2024.100116

Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., ... & Uhlig, S. (2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, 19, Article 100514. https://doi.org/10.1016/j.iot.2022.100514

Gaytan, J. C. T., Ateeq, K., Rafiuddin, A., Alzoubi, H. M., Ghazal, T. M., Ahanger, T. A., ... & Viju, G. K. (2022). AI-based prediction of capital structure: Performance comparison of ANN SVM and LR models. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/8334927

Gavurova, B., Belas, J., Kocisova, K., & Kliestik, T. (2017). Comparison of selected methods for performance evaluation of Czech and Slovak commercial banks. Journal of Business Economics and Management, 18(5), 852-876. https://doi.org/10.3846/16111699.2017.1371637

Goutte, S., Le, H. V., Liu, F., & Von Mettenheim, H. J. (2023). Deep learning and technical analysis in cryptocurrency market. Finance Research Letters, 54, Article 103809. https://doi.org/10.1016/j.frl.2023.103809

Gradojevic, N., Kukolj, D., Adcock, R., & Djakovic, V. (2023). Forecasting Bitcoin with technical analysis: A not-so-random forest?. International Journal of Forecasting, 39(1), 1-17. https://doi.org/10.1016/j.ijforecast.2021.08.001

Guo, B., & Lin, H. (2020). Volatility and jump risk in option returns. Journal of Futures Markets, 40(11), 1767-1792. https://doi:10.1002/fut.22107

Gurrib, I., Nourani, M., & Bhaskaran, R. K. (2022). Energy crypto currencies and leading US energy stock prices: are Fibonacci retracements profitable?. Financial Innovation, 8(1), 1-27. https://doi:10.1186/s40854-021-00311-8

Harris, R. D., & Mazibas, M. (2022). Portfolio optimization with behavioural preferences and investor memory. European Journal of Operational Research, 296(1), 368-387. https://doi:10.1016/j.ejor.2021.04.044

Hassanniakalager, A., Sermpinis, G., & Stasinakis, C. (2021). Trading the foreign exchange market with technical analysis and Bayesian Statistics. Journal of Empirical Finance, 63, 230-251. https://doi.org/10.1016/j.jempfin.2021.07.006

Hašková, S., Šuleř, P., & Kuchár, R. (2023). A Fuzzy Multi-Criteria Evaluation System for Share Price Prediction: A Tesla Case Study. Mathematics, 11(13), 3033. https://doi.org/10.3390/math11133033

Hu, G., & Jacobs, K. (2020). Volatility and expected option returns. Journal of Financial and Quantitative Analysis, 55(3), 1025-1060. https://doi:10.1017/S0022109019000310

Huang, C., Wen, S., Li, M., Wen, F., & Yang, X. (2021). An empirical evaluation of the influential nodes for stock market network: Chinese A-shares case. Finance Research Letters, 38. https://doi:10.1016/j.frl.2020.101517

Huang, J. Z., & Huang, Z. J. (2020). Testing moving average trading strategies on ETFs. Journal of Empirical Finance, 57, 16-32. https://doi:10.1016/j.jempfin.2019.10.002

Huang, M., Shao, W., & Wang, J. (2023). Correlations between the crude oil market and capital markets under the Russia–Ukraine conflict: A perspective of crude oil importing and exporting countries. Resources Policy, 80, Article 103233. https://doi.org/10.1016/j.resourpol.2022.103233

Huang, Z., & Martin, F. (2019). Pairs trading strategies in a cointegration framework: back-tested on CFD and optimized by profit factor. Applied Economics, 51(22), 2436-2452. https://doi:10.1080/00036846.2018.1545080

Hudson, R., & Urquhart, A. (2019). Technical analysis and cryptocurrencies. Annals of Operations Research, Forthcoming. http://dx.doi.org/10.2139/ssrn.3387950

Hung, C., & Lai, H. N. (2022). Information asymmetry and the profitability of technical analysis. Journal of Banking & Finance, 134, Article 106347. http://doi:10.1016/j.jbankfin.2021.106347

Ilic, I., Görgülü, B., Cevik, M., & Baydoğan, M. G. (2021). Explainable boosted linear regression for time series forecasting. Pattern Recognition, 120, Article 108144. https://doi.org/10.1016/j.patcog.2021.108144

Inaba, K. I. (2020). A global look into stock market comovements. Review of World Economics, 156(3), 517-555. http://doi:10.1007/s10290-019-00370-1

Islam, M. R., & Nguyen, N. (2020). Comparison of financial models for stock price prediction. Journal of Risk and Financial Management, 13(8), Article 181. https://doi.org/10.3390/jrfm13080181

Jiang, W. (2021). Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications, 184, Article 115537. https://doi.org/10.1016/j.eswa.2021.115537

Jiménez-Navarro, M. J., Martínez-Ballesteros, M., Martínez-Álvarez, F., & Asencio-Cortés, G. (2023). PHILNet: A novel efficient approach for time series forecasting using deep learning. Information Sciences, 632, 815-832. https://doi.org/10.1016/j.ins.2023.03.021

Jin, X. (2021). What do we know about the popularity of technical analysis in foreign exchange markets? A skewness preference perspective. Journal of International Financial Markets, Institutions and Money, 71. https://doi.org/10.1016/j.intfin.2020.101281

Jin, X. (2022). Performance of intraday technical trading in China’s gold market. Journal of International Financial Markets, Institutions and Money, 76. https://doi.org/10.1016/j.intfin.2021.101481

Karanasos, M., Yfanti, S., & Hunter, J. (2021). Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises. Annals of operations research, 1-40. https://doi.org/10.1007/s10479-021-04042-y

Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2020). Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing, 1-24. https://doi.org/10.1007/s12652-020-01839-w

Khurshid, A., Chen, Y., Rauf, A., & Khan, K. (2023). Critical metals in uncertainty: How Russia-Ukraine conflict drives their prices?. Resources Policy, 85, Article 104000. https://doi.org/10.1016/j.resourpol.2023.104000

Koutsandreas, D., Spiliotis, E., Petropoulos, F., & Assimakopoulos, V. (2022). On the selection of forecasting accuracy measures. Journal of the Operational Research Society, 73(5), 937-954. https://doi.org/10.1080/01605682.2021.1892464

Kumar, R., Kumar, P., & Kumar, Y. (2020). Time series data prediction using IoT and machine learning technique. Procedia computer science, 167, 373-381. https://doi.org/10.1016/j.procs.2020.03.240

Kwilinski, A., Rebilas, R., Lazarenko, D., Stezhko, N., & Dzwigol, H. (2023, December). The Impact of the COVID-19 Pandemic on the Evolution of Investment Markets in Central and Eastern Europe. Forum Scientiae Oeconomia, 11(4), 157-186. https://doi.org/10.23762/FSO_VOL11_NO4_8

Lai, H. C., Tseng, T. C., & Huang, S. C. (2016). Combining value averaging and Bollinger Band for an ETF trading strategy. Applied economics, 48(37), 3550-3557. https://doi.org/10.1080/00036846.2016.1142653

Lee, T. K., Cho, J. H., Kwon, D. S., & Sohn, S. Y. (2019). Global stock market investment strategies based on financial network indicators using machine learning techniques. Expert Systems with Applications, 117, 228-242. https://doi.org/10.1016/j.eswa.2018.09.005

León-Castro, E., Espinoza-Audelo, L. F., Aviles-Ochoa, E., Merigó, J. M., & Kacprzyk, J. (2019). A new measure of volatility using induced heavy moving averages. Technological and Economic Development of Economy, 25(4), 576-599. https://doi.org/10.3846/tede.2019.9374

Li, Y., Goodell, J. W., & Shen, D. (2021). Comparing search-engine and social-media attentions in finance research: Evidence from cryptocurrencies. International Review of Economics & Finance, 75, 723-746. https://doi.org/10.1016/j.iref.2021.05.003

Lin, C., Yang, N. T., Chou, R. K., & Ko, K. C. (2021). A timing momentum strategy. Accounting & Finance, 62(S1), 1339-1379. https://doi.org/10.1111/acfi.12825

Lin, X., Palazzo, B., & Yang, F. (2020). The risks of old capital age: Asset pricing implications of technology adoption. Journal of monetary economics, 115, 145-161. https://doi.org/10.1016/j.jmoneco.2019.06.001

Lin, Y., & Wang, Y. (2024). The impact of the Russia–Ukraine war on volatility spillovers. International Review of Financial Analysis, 93, Article 103194.

Lyócsa, Š., Molnár, P., & Výrost, T. (2021). Stock market volatility forecasting: Do we need high-frequency data?. International Journal of Forecasting, 37(3), 1092-1110. https://doi.org/10.1016/j.ijforecast.2020.12.001

Ma, Y., Yang, B., & Su, Y. (2021). Stock return predictability: Evidence from moving averages of trading volume. Pacific-Basin Finance Journal, 65. https://doi.org/10.1016/j.pacfin.2021.101494

Masini, R. P., Medeiros, M. C., & Mendes, E. F. (2023). Machine learning advances for time series forecasting. Journal of economic surveys, 37(1), 76-111. https://doi.org/10.1111/joes.12429

Megaritis, A., Vlastakis, N., & Triantafyllou, A. (2021). Stock market volatility and jumps in times of uncertainty. Journal of International Money and Finance, 113, Article 102355. https://doi.org/10.1016/j.jimonfin.2021.102355

McIver, R. P., & Kang, S. H. (2020). Financial crises and the dynamics of the spillovers between the US and BRICS stock markets. Research in International Business and Finance, 54. https://doi.org/10.1016/j.ribaf.2020.101276

Monahan, S. J. (2018). Financial statement analysis and earnings forecasting. Foundations and Trends® in Accounting, 12(2), 105-215. http://dx.doi.org/10.1561/1400000036

Mustafa, S., Bajwa, A. A., & Iqbal, S. (2022). A New Fuzzy Grach Model to forecast Stock Market Technical Analysis. Operational Research in Engineering Sciences: Theory and Applications, 5(1), 185-204. https://doi.org/10.31181/oresta040422196m

Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., & Salwana, E. (2020). Deep learning for stock market prediction. Entropy, 22(8), 840. https://doi.org/10.3390/e22080840

Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., & Mosavi, A. (2020). Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access, 8, 150199-150212. https://doi.org/10.1109/ACCESS.2020.3015966

Nasdaq (March, 2022) HG:CMX Historical Data. https://www.nasdaq.com/market-activity/commodities/hg:cmx/historical

Ni, Y., Cheng, Y., Liao, Y., & Huang, P. (2020). Does board structure affect stock price overshooting informativeness measured by stochastic oscillator indicators?. International Journal of Finance & Economics, 27(2), 2290-2302. https://doi.org/10.1002/ijfe.2273

Nonejad, N. (2021). An overview of Dynamic Model Averaging techniques in time‐series econometrics. Journal of Economic Surveys, 35(2), 566-614. https://doi.org/10.1111/joes.12410

Nonejad, N. (2021). Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results. Energy Economics, 104. https://doi.org/10.1016/j.eneco.2021.105635

Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 53(4), 3007-3057. https://doi.org/10.1007/s10462-019-09754-z

Paldino, G. M., De Stefani, J., De Caro, F., & Bontempi, G. (2021). Does automl outperform naive forecasting?. Engineering proceedings, 5(1), 36. https://doi.org/10.3390/engproc2021005036

Picasso, A., Merello, S., Ma, Y., Oneto, L., & Cambria, E. (2019). Technical analysis and sentiment embeddings for market trend prediction. Expert Systems with Applications, 135, 60-70. https://doi.org/10.1016/j.eswa.2019.06.014

Pietronudo, M. C., Croidieu, G., & Schiavone, F. (2022). A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management. Technological Forecasting and Social Change, 182, Article 121828. https://doi.org/10.1016/j.techfore.2022.121828

Poongodi, M., Sharma, A., Vijayakumar, V., Bhardwaj, V., Sharma, A. P., Iqbal, R., & Kumar, R. (2020). Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system. Computers & Electrical Engineering, 81, Article 106527. https://doi.org/10.1016/j.compeleceng.2019.106527

Rajnoha, R., Merková, M., Dobrovič, J., & Rózsa, Z. (2018). Business performance management and FDI: key differences between foreign and domestic-owned firms–a case of Slovakia. Journal of Business Economics and Management, 19(1), 42-62. https://doi.org/10.3846/jbem.2018.1538

Raza, S. A., Masood, A., Benkraiem, R., & Urom, C. (2023). Forecasting the volatility of precious metals prices with global economic policy uncertainty in pre and during the COVID-19 period: Novel evidence from the GARCH-MIDAS approach. Energy Economics, 120, Article 106591. https://doi.org/10.1016/j.eneco.2023.106591

Rouf, N., Malik, M. B., Arif, T., Sharma, S., Singh, S., Aich, S., & Kim, H. C. (2021). Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions. Electronics, 10(21), Article 2717. https://doi.org/10.3390/electronics10212717

Sagheer, A., & Kotb, M. (2019). Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing, 323, 203-213. https://doi.org/10.1016/j.neucom.2018.09.082

Sermpinis, G., Hassanniakalager, A., Stasinakis, C., & Psaradellis, I. (2021). Technical analysis profitability and Persistence: A discrete false discovery approach on MSCI indices. Journal of International Financial Markets, Institutions and Money, 73, Article 101353. https://doi.org/10.1016/j.intfin.2021.101353

Sharaf, M., Hemdan, E. E. D., El-Sayed, A., & El-Bahnasawy, N. A. (2021). StockPred: a framework for stock Price prediction. Multimedia Tools and Applications, 80(12), 17923-17954. https://doi.org/10.1007/s11042-021-10579-8

Siddiqi, H. (2019). Anchoring-adjusted option pricing models. Journal of Behavioral Finance, 20(2), 139-153. https://doi.org/10.1080/15427560.2018.1492922

Smyl, S. (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 36(1), 75-85. https://doi.org/10.1016/j.ijforecast.2019.03.017

Srivastava, P. R., Zhang, Z. J., & Eachempati, P. (2021). Deep neural network and time series approach for finance systems: predicting the movement of the Indian stock market. Journal of Organizational and End User Computing (JOEUC), 33(5), 204-226. https://doi.org/10.4018/JOEUC.20210901.oa10

Svetunkov, I., Chen, H., & Boylan, J. E. (2023). A new taxonomy for vector exponential smoothing and its application to seasonal time series. European Journal of Operational Research, 304(3), 964-980.

Svoboda, M., & Říhová, P. (2021). Stock price prediction using Markov chains analysis with varying state space on data from the Czech republic. Economics and Management, 24(4), 142-155. http://hdl.handle.net/11025/46493

Tan, S. H., Lai, M. M., Tey, E. X., & Chong, L. L. (2020). Testing the performance of technical analysis and sentiment-TAR trading rules in the Malaysian stock market. The North American Journal of Economics and Finance, 51. https://doi.org/10.1016/j.najef.2018.12.007

Thakkar, A., & Chaudhari, K. (2021). A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions. Expert Systems with Applications, 177, Article 114800. https://doi.org/10.1016/j.eswa.2021.114800

Thamarai, M., & Malarvizhi, S. P. (2020). House Price Prediction Modeling Using Machine Learning. International Journal of Information Engineering & Electronic Business, 12(2). https://doi.org/10.5815/ijieeb.2020.02.03

Tashiro, D., Matsushima, H., Izumi, K., & Sakaji, H. (2019). Encoding of high-frequency order information and prediction of short-term stock price by deep learning. Quantitative Finance, 19(9), 1499-1506. https://doi.org/10.1080/14697688.2019.1622314

Umar, Z., Polat, O., Choi, S. Y., & Teplova, T. (2022). The impact of the Russia-Ukraine conflict on the connectedness of financial markets. Finance Research Letters, 48, Article 102976. https://doi.org/10.1016/j.frl.2022.102976

Uras, N., Marchesi, L., Marchesi, M., & Tonelli, R. (2020). Forecasting Bitcoin closing price series using linear regression and neural networks models. PeerJ Computer Science, 6, e279. https://doi.org/10.7717/peerj-cs.279

Vachhani, H., Obiadat, M. S., Thakkar, A., Shah, V., Sojitra, R., Bhatia, J., & Tanwar, S. (2019, October). Machine learning based stock market analysis: A short survey. In International Conference on Innovative Data Communication Technologies and Application (pp. 12-26). Springer, Cham. https://doi.org/10.1007/978-3-030-38040-3_2

Vezeris, D. T., Schinas, C. J., Kyrgos, T. S., Bizergianidou, V. A., & Karkanis, I. P. (2020). Optimization of backtesting techniques in automated high frequency trading systems using the d-Backtest PS method. Computational Economics, 56(4), 975-1054. https://doi.org/10.1007/s10614-019-09956-1

Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia computer science, 167, 599-606. https://doi.org/10.1016/j.procs.2020.03.326

Vo-Van, T., Che-Ngoc, H., Le-Dai, N., & Nguyen-Trang, T. (2021). A New Strategy for Short-Term Stock Investment Using Bayesian Approach. Computational Economics, 1-25. https://doi.org/10.1007/s10614-021-10115-8

Wang, M. H., Ke, M. C., Liao, T. L., Chiang, Y. C., & Hsu, C. H. (2020). Alternative estimation method of earnings growth rate for PEGR strategy. The North American Journal of Economics and Finance, 53, Article 101190. https://doi.org/10.1016/j.najef.2020.101190

Wang, W. J., Tang, Y., Xiong, J., & Zhang, Y. C. (2021). Stock market index prediction based on reservoir computing models. Expert Systems with Applications, 178, Article 115022. https://doi.org/10.1016/j.eswa.2021.115022

Wang, Y., Liu, L., & Wu, C. (2020). Forecasting commodity prices out-of-sample: Can technical indicators help?. International Journal of Forecasting, 36(2), 666-683. https://doi.org/10.1016/j.ijforecast.2019.08.004

Wang, Z., Hong, T., & Piette, M. A. (2020). Building thermal load prediction through shallow machine learning and deep learning. Applied Energy, 263, Article 114683. https://doi.org/10.1016/j.apenergy.2020.114683

Wen, F., Xiao, J., Xia, X., Chen, B., Xiao, Z., & Li, J. (2019). Oil prices and chinese stock market: Nonlinear causality and volatility persistence. Emerging Markets Finance and Trade, 55(6), 1247-1263. https://doi.org/10.1080/1540496X.2018.1496078

Xu, Q. (2024). Quantifying the Impact of the COVID-19 Pandemic on Copper Price Trends Using Time Series Analysis. Highlights in Science, Engineering and Technology, 88, 1251-1256. https://doi.org/10.54097/j5t0eg31

Yamamoto, R. (2022). Predictor choice, investor types, and the price impact of trades on the tokyo stock exchange. Computational Economics, 59(1), 325-356. https://doi.org/10.1007/s10614-020-10084-4

Yamani, E. (2021). Foreign exchange market efficiency and the global financial crisis: Fundamental versus technical information. The Quarterly Review of Economics and Finance, 79, 74-89. https://doi.org/10.1016/j.qref.2020.05.009

Ysmailov, B. (2021). Interest rates, cash and short-term investments. Journal of Banking & Finance, 132, Article 106225. https://doi.org/10.1016/j.jbankfin.2021.106225

Zhang, F. (2021). Extreme learning machine for stock price prediction. The International Journal of Electrical Engineering & Education, 60(1), 3972-3985. https://doi.org/10.1177/0020720920984675

Zheng, X., Liang, K., Xia, Q., & Zhang, D. (2021). Best Subset Selection for Double-Threshold-Variable Autoregressive Moving-Average Models: The Bayesian Approach. Computational Economics, 1-27. https://doi.org/10.1007/s10614-021-1124-7

Zhang, Y., Chu, G., & Shen, D. (2021). The role of investor attention in predicting stock prices: The long short-term memory networks perspective. Finance Research Letters, 38, Article 101484. https://doi.org/10.1016/j.frl.2020.101484

Zhang, Y., Li, S., & Kong, F. (2021). Survival probability of nonlinear oscillators endowed with fractional derivative element and subjected to evolutionary excitation: A stochastic averaging treatment with path integral concepts. Probabilistic Engineering Mechanics, 66, Article 103156. https://doi.org/10.1016/j.probengmech.2021.103156