Development of Trading Strategies Using Time Series Based on Robust Interval Forecasts

Development of Trading Strategies Using Time Series Based on Robust Interval Forecasts Evgeny Nikulchev Department of Digital Data Processing Technologies, MIREA—Russian Technological University, Moscow 119454, Russia http://orcid.org/0000-0003-1254-9132 Alexander Chervyakov Federal Treasury, Ministry of Finance of the Russian Federation, Moscow 101000, Russia http://orcid.org/0000-0002-5638-8361

The task of time series forecasting is to estimate future values based on available observational data. Prediction Intervals methods are aimed at finding not the next point, but the interval that the future value or several values on the forecast horizon can fall into given current and historical data. This article proposes an approach for modeling a robust interval forecast for a stock portfolio. Here, a trading strategy was developed to profit from trading stocks in the market. The study used real trading data of real stocks. Forty securities were used to calculate the IMOEX. The securities with the highest weight were the following: GAZP, LKOH, SBER. This definition of the strategy allows operating with large portfolios. Increasing the accuracy of the forecast was carried out by estimating the interval of the forecast. Here, a range of values was considered to be a result of forecasting without considering specific moments, which guarantees the reliability of the forecast. The use of a predictive interval approach for the price of shares allows increasing their profitability.
05 15 2023 99 computation11050099 https://creativecommons.org/licenses/by/4.0/ 10.3390/computation11050099 https://www.mdpi.com/2079-3197/11/5/99 https://www.mdpi.com/2079-3197/11/5/99/pdf 10.3390/pr7030164 Uslu, B., Eren, T., Gür, Ş., and Özcan, E. (2019). Evaluation of the difficulties in the internet of things (IoT) with multi-criteria decision-making. Processes, 7. Dolgui A review on robust assembly line balancing approaches IFAC-Pap. 2019 52 987 Aubakirov Development of system architecture for e-government cloud platforms Int. J. Adv. Comput. Sci. Appl. 2016 7 253 Nikulchev Programming Technologies for the Development of Web-Based Platform for Digital Psychological Tools Int. J. Adv. Comput. Sci. Appl. 2018 9 34 Nikou Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms Intell. Syst. Account. Financ. Manag. 2019 10.1002/isaf.1459 26 164 Chen Bitcoin price prediction using machine learning: An approach to sample dimension engineering J. Comput. Appl. Math. 2020 10.1016/j.cam.2019.112395 365 112395 Long An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in Chinese stock exchange market Appl. Soft Comput. 2020 10.1016/j.asoc.2020.106205 91 106205 Fang Cryptocurrency trading: A comprehensive survey Financ. Innov. 2022 10.1186/s40854-021-00321-6 8 1 Nikulchev Control synthesis for tasks with a given output function decentralized SISO-systems IOP Conf. Ser. Mater. Sci. Eng. 2021 10.1088/1757-899X/1027/1/012023 1027 012023 Yamashita SISO approaches for linear programming based methods for tuning decentralized PID controllers J. Process Control 2020 10.1016/j.jprocont.2020.08.004 94 75 10.3390/sym15040781 Nikulchev, E., and Chervyakov, A. (2023). Prediction Intervals: A Geometric View. Symmetry, 15. Samokhin Methods and effective algorithms for solving multidimensional integral equations Russ. Technol. J. 2022 10.32362/2500-316X-2022-10-6-70-77 10 70 Liu A robust time series prediction method based on empirical mode decomposition and high-order fuzzy cognitive maps Knowl. Based Syst. 2020 10.1016/j.knosys.2020.106105 203 106105 Hyndman A brief history of forecasting competitions Int. J. Forecast. 2020 10.1016/j.ijforecast.2019.03.015 36 7 Kandakoglu A robust multicriteria clustering methodology for portfolio decision analysis Comput. Ind. Eng. 2022 10.1016/j.cie.2022.108803 174 108803 Shary, S.P. (2020). Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy etc. Methods and Their Applications, Springer. Zhou Multi-objective prediction intervals for wind power forecast based on deep neural networks Inf. Sci. 2021 10.1016/j.ins.2020.10.034 550 207 Zhang Wind speed prediction method using shared weight long short-term memory network and Gaussian process regression Appl. Energy 2019 10.1016/j.apenergy.2019.04.047 247 270 Khosravi Lower upper bound estimation method for construction of neural network-based prediction intervals IEEE Trans. Neural Netw. Learn. Syst. 2011 10.1109/TNN.2010.2096824 22 337 Quan Short-term load and wind power forecasting using neural network-based prediction intervals IEEE Trans. Neural Netw. Learn. Syst. 2014 10.1109/TNNLS.2013.2276053 25 303 Taormina ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS Eng. Appl. Artif. Intell. 2015 10.1016/j.engappai.2015.07.019 45 429 Zhang Direct interval forecasting of wind speed using radial basis function neural networks in a multi-objective optimization framework Neurocomputing 2016 10.1016/j.neucom.2016.03.061 205 53 Tang, A., and Lahtinen, J.H. (2023, May 11). Applying Software Engineering Principles to Develop Parcel Delay Forecasting Models Using Tracking Data: A Study of the Models ARIMA, BSTS, and GAM. Available online: https://hdl.handle.net/20.500.12380/305139. Bousqaoui Comparative analysis of short-term demand predicting models using ARIMA and deep learning Int. J. Electr. Comput. Eng. 2021 11 3319