Machine learning methods forfinancial forecasting and tradingprofitability: Evidence during theRussia–Ukraine war
DOI:
https://doi.org/10.1108/REGE-05-2022-0079Palabras clave:
Algorithmic trading, Support Vector Machines, Russia-Ukraine war, Efficient market hypothesis, Trading profitabilityResumen
Purpose – Evaluate the effectiveness of machine learning models to yield profitability over the market benchmark, notably in periods of systemic instability, such as the ongoing war between Russia and Ukraine.
Design/methodology/approach – Computational experiments using Support Vector Machine classifiers to predict stock price movements for three financial markets and construct profitable trading strategies to subsidize investors’ decision-making.
Findings – On average, machine learning models outperformed the market benchmarks during the more volatile period of the Russia-Ukraine war, but not during the period before the conflict. Moreover, the hyperparameter combinations for which the profitability is superior were found to be highly sensitive to small variations during the model training process.
Practical implications – Investors should proceed with caution when applying machine learning models for stock price forecasting and trading recommendation, as their superior performance for volatile periods – in terms of generating abnormal gains over the market – was not observed for a period of relative stability in the economy.
Originality/value – This paper’s approach to search for financial strategies that succeed in outperforming the market provides empirical evidence about the effectiveness of state-of-the-art machine learning techniques before and after the conflict deflagration, being of potential value for researchers on quantitative finance and market professionals who operate in the financial segment.
Descargas
Referencias
Albuquerque, P. H. M., de Moraes Souza, J. G., & Kimura, H. (2021). Artificial intelligence in portfolio formation and forecast: Using different variance-covariance matrices. Communications in Statistics-Theory and Methods, 1-18.
Alhashel, B. S., Almudhaf, F. W., and Hansz, J. A. (2018). Can technical analysis generate superior returns in securitized property markets? Evidence from East Asia markets. Pacific-Basin Finance Journal, 47:92–108.
Ayala, J., García-Torres, M., Noguera, J. L. V., Gómez-Vela, F., and Divina, F. (2021). Technical analysis strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems, 225:107119.
Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192-213.
Berkhout, P., Bergevoet, R., & van Berkum, S. (2022). A brief analysis of the impact of the war in Ukraine on food security (No. 2022-033). Wageningen Economic Research.
Biersack, J. and O’lear, S. (2014). The geopolitics of Russia’s annexation of Crimea: narratives, identity, silences, and energy. Eurasian geography and economics, 55(3):247– 269.
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992, July). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory (pp. 144-152).
Bustos, O. and Pomares-Quimbaya, A. (2020). Stock market movement forecast: A systematic review. Expert Systems with Applications, 156:113464.
Chen, H., Xiao, K., Sun, J., and Wu, S. (2017). A double-layer neural network framework for high-frequency forecasting. ACM Transactions on Management Information Systems (TMIS), 7(4):11.
Chen, Y. and Hao, Y. (2017). A feature weighted support vector machine and k-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications, 80:340–355.
Claesen, M. and De Moor, B. (2015). Hyperparameter search in machine learning. arXiv preprint arXiv:1502.02127.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3):273– 297.
Dastile, X., Celik, T., and Potsane, M. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing, 91:106263.
De Spiegeleer, J., Madan, D. B., Reyners, S., and Schoutens, W. (2018). Machine learning for quantitative finance: fast derivative pricing, hedging and fitting. Quantitative Finance, 18(10):1635–1643.
Dixon, M. F., Halperin, I., & Bilokon, P. (2020). Machine learning in Finance (Vol. 1170). Springer International Publishing.
Emerson, S., Kennedy, R., O'Shea, L., & O'Brien, J. (2019, May). Trends and applications of machine learning in quantitative finance. In 8th international conference on economics and finance research (ICEFR 2019).
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of finance, 25(2):383–417.
Gardner, H. (2016). The Russian annexation of Crimea: regional and global ramifications. European Politics and Society, 17(4):490–505.
Ghasemzadeha, M., Mohammad-Karimi, N., and Ansari-Samani, H. (2020). Machine learning algorithms for time series in financial markets. Advances in Mathematical Finance and Applications, 5(4):479–490.
Gogas, P., & Papadimitriou, T. (2021). Machine learning in economics and finance. Computational Economics, 57(1), 1-4.
Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577.
Gu, S., Kelly, B., and Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5):2223–2273.
Gunduz, H., Yaslan, Y., and Cataltepe, Z. (2017). Intraday prediction of borsa istanbul using convolutional neural networks and feature correlations. Knowledge-Based Systems, 137:138–148.
Haq, A. U., Zeb, A., Lei, Z., and Zhang, D. (2021). Forecasting daily stock trend using multifilter feature selection and deep learning. Expert Systems with Applications, 168:114444.
Hilbert, M. and Darmon, D. (2020). How complexity and uncertainty grew with algorithmic trading. Entropy, 22(5):499.
Hsu, M.-W., Lessmann, S., Sung, M.-C., Ma, T., and Johnson, J. E. (2016). Bridging the divide in financial market forecasting: machine learners vs. financial economists. Expert Systems with Applications, 61:215–234.
Karatnycky, A. (2005). Ukraine’s orange revolution. Foreign Affairs, 84:35.
Khodunov, A. et al. (2022). The orange revolution in Ukraine. Societies and Political Orders in Transition:501–515.
Kozak, S., Nagel, S., and Santosh, S. (2020). Shrinking the cross-section. Journal of Financial Economics, 135(2):271–292.
Li, A. W. and Bastos, G. S. (2020). Stock market forecasting using deep learning and technical analysis: a systematic review. IEEE access, 8:185232–185242.
Lodi, A. L., Machado, A., Santos, B., Lopes, J. P., Mattos, L., Bezzon, L., and Bonifacio, M. (2022). Ukraine and Russia Tensions: What are the possible consequences of a military conflict in the region? StoneX Special Report:1–12.
Merello, S., Ratto, A. P., Oneto, L., & Cambria, E. (2019, July). Ensemble application of transfer learning and sample weighting for stock market prediction. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Min, B. H. and Borch, C. (2021). Systemic failures and organizational risk management in algorithmic trading: Normal accidents and high reliability in financial markets. Social Studies of Science:03063127211048515.
Najafova, M. (2022). Impact of war between Russia and Ukraine on food security. Available at <https://policycommons.net/artifacts/2329915/impact-of-war-between-russia-and-ukraine-on-food-security/3090540>.
Nakano, M., Takahashi, A., and Takahashi, S. (2018). Bitcoin technical trading with artificial neural network. Physica A: Statistical Mechanics and its Applications, 510:587 – 609.
Nitoiu, C. (2016). Towards conflict or cooperation? the Ukraine crisis and EU-Russia relations. Southeast European and Black Sea Studies, 16(3):375–390.
Nti, I. K., Adekoya, A. F., and Weyori, B. A. (2020). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 53(4):3007– 3057.
Orhan, E. (2022). The Effects of the Russia-Ukraine War on Global Trade. Journal of International Trade, Logistics and Law, 8(1), 141-146.
Ozbayoglu, A. M., Gudelek, M. U., and Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 93:106384.
Peng, Y., Albuquerque, P. H. M., de Sá, J. M. C., Padula, A. J. A., and Montenegro, M. R. (2018). The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with support vector regression. Expert Systems with Applications, 97:177–192.
Peng, Y., Albuquerque, P. H. M., Kimura, H., and Saavedra, C. A. P. B. (2021). Feature selection and deep neural networks for stock price direction forecasting using technical analysis indicators. Machine Learning with Applications, 5:100060.
Peng, Y. and Nagata, M. H. (2020). An empirical overview of nonlinearity and overfitting in machine learning using covid-19 data. Chaos, Solitons & Fractals, 139:110055.
Probst, P., Boulesteix, A.-L., and Bischl, B. (2019). Tunability: importance of hyperparameters of machine learning algorithms. The Journal of Machine Learning Research, 20(1):1934–1965.
Renault, T. (2020). Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages. Digital Finance, 2(1):1–13.
Saâdaoui, F., Jabeur, S. B., & Goodell, J. W. (2022). Causality of geopolitical risk on food prices: Considering the Russo–Ukrainian conflict. Finance Research Letters, 49, 103103.
Saluschev, S. (2014). Annexation of Crimea: Causes, analysis and global implications. Global Societies Journal, 2: 37–46.
Sezer, O. B. and Ozbayoglu, A. M. (2018). Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing, 70:525–538.
Shynkevich, Y., McGinnity, T. M., Coleman, S. A., Belatreche, A., and Li, Y. (2017). Forecasting price movements using technical indicators: Investigating the impact of varying input window length. Neurocomputing, 264:71–88.
Taghian, M., Asadi, A., and Safabakhsh, R. (2022). Learning financial asset-specific trading rules via deep reinforcement learning. Expert Systems with Applications:116523.
Tao, R., Su, C.-W., Xiao, Y., Dai, K., and Khalid, F. (2021). Robo advisors, algorithmic trading and investment management: Wonders of fourth industrial revolution in financial markets. Technological Forecasting and Social Change, 163:120421.
Vijh, M., Chandola, D., Tikkiwal, V. A., and Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia Computer Science, 167:599–606.
Weerts, H. J., Mueller, A. C., and Vanschoren, J. (2020). Importance of tuning hyperparameters of machine learning algorithms. arXiv preprint arXiv:2007.07588.
Weng, B., Ahmed, M. A., and Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79:153–163.
White, S. and McAllister, I. (2009). Rethinking the ‘orange revolution’. Journal of Communist Studies and Transition Politics, 25(2-3):227–254.
