FEATURES OF FORECASTING STOCK QUOTE CHANGES USING MOVING AVERAGES AND OSCILLATORS: CASE STUDY OF AN OIL PRODUCTION COMPANY
DOI:
https://doi.org/10.15407/economyukr.2024.11.074Keywords:
quotes; technical analysis indicator; moving average; simple moving average; exponential moving average; linearly weighted moving average; oscillator; stock exchange; stock transaction; stock marketAbstract
Today, a wide range of factors impact a company's share price: from fundamental internal factors to political decisions of the authorities, industry, macroeconomic and world trends. Accordingly, the investor faces the problem of both choosing an approach to determining the industry and target asset and interpreting the analysis results, as well as the problem of the entry point to the market.
In this context, a number of relevant problems of forecasting share price changes for the oil production company Exxon Mobile Corp. on the stock exchange related to the specifics of using such technical analysis tools as moving averages and oscillators are revealed. The impact of various types of moving average settings, as well as their combinations, on the correctness of forecasting the direction of changes in the company's share price is analyzed. Based on this, tasks that are solved using indicators of such kind are formulated. An approach to the selection of technical analysis indicators and their settings when developing a forecasting system is proposed. Various options for the formation and interpretation of signals generated by indicators and their combinations regarding the further change in the asset price are considered, as well as several criteria for comparing the effectiveness of approaches at the testing stage. The results of using several variants of forecasting system are calculated and compared, the optimal ones are determined according to the selection criteria. Weekly quotes from 2000 through 2024 are used for calculations, based on them the optimal combination of indicators for use in the forecasting system is established. Areas of possible optimization and toolkit that can be used for these purposes are identified. Based on the findings of the study, a conclusion is formulated on the possibility of using the proposed approach to building a forecasting system to perform real stock transactions with company’s shares.
References
Khobta, V., Derevyanko, V. (2022). Justification of investment decisions on the stock market. Scientific Papers of Donetsk National Technical University. Series: Economic, 1(25), 13-19. http://doi.org/10.31474/1680-0044-2022-1(25)-13-19 [in Ukrainian].
Zakamulin, V., Giner, J. (2020). Trend following with momentum versus moving averages: A tale of differences. Quantitative Finance. Vol. 20. Iss. 6. P. 985-1007. https://doi.org/10.1080/14697688.2020.1716057
Garcia, M., Pereira, A., Acebal, J., de Magalhães, A. (2020). Forecast model for financial time series: An approach based on harmonic oscillators. Physica A: Statistical Mechanics and its Applications. Vol. 549, 124365. https://doi.org/10.1016/j.physa.2020.124365
Khand, S. Anand, V., Qureshi, M., Katper, N. (2019). The Performance of Exponential Moving Average, Moving Average Convergence-Divergence, Relative Strength Index and Momentum Trading Rules in the Pakistan Stock Market. Indian Journal of Science and Technology. Vol. 12. Iss. 26. P. 1-22. https://dx.doi.org/10.17485/ijst/2019/v12i26/145117
Pramudya, R., Ichsani, S. (2020). Efficiency of technical analysis for the stock trading. International Journal of Finance & Banking Studies. Vol. 9. No. 1. P. 58-67. https://doi.org/10.20525/ijfbs.v9i1.666
Katic, D, Richter, S. (2024). Financial market geometry: The tube oscillator. arXiv preprint arXiv:2407.08036. https://doi.org/10.48550/arXiv.2407.08036
Barroso, B., Cardoso, R., Melo, M. (2021). Performance analysis of the integration between Portfolio Optimization and Technical Analysis strategies in the Brazilian stock market. Expert Systems with Applications. Vol. 186, 115687. https://doi.org/10.1016/j.eswa.2021.115687
Ni, Y., Day, M., Cheng, Y., Huang, P. (2022). Can investors profit by utilizing technical trading strategies? Evidence from the Korean and Chinese stock markets. Financial Innovation, 8(1), 54. https://doi.org/10.1186/s40854-022-00358-1
Kang, B.-K. (2021). Improving MACD technical analysis by optimizing parameters and modifying trading rules: evidence from the Japanese Nikkei 225 futures market. Journal of Risk and Financial Management, 14(1), 37. https://doi.org/10.3390/jrfm14010037
Padilha, V., Magnani, V., Gatsios, R., Lima, F., Antonio, R. (2024). Trend and Momentum Technical Indicators for Investing in Market Indices. EkBis: Jurnal Ekonomi dan Bisnis, 8(1), 74-86. https://doi.org/10.14421/EkBis.2024.8.1.2163
Taleb, N. (2022). Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets. Kyiv. 320 p. [in Ukrainian].
Williams, B. (1998). New trading dimensions: How to profit from chaos in stocks, bonds, and commodities. John Wiley & Sons.
Murphy, J. (1999). Technical analysis of financial markets: A comprehensive guide to trading methods and applications. New York, New York Institute of Finance.
Aronson, D. (2011). Evidence-based technical analysis: applying the scientific method and statistical inference to trading signals. John Wiley & Sons.
Joseph, V., Vakayil, A. (2021). SPlit: An Optimal Method for Data Splitting. Technometrics, 64(2), 166-176. https://doi.org/10.1080/00401706.2021.1921037
Russell, S. (2024). Comparing the Awesome Oscillator to a Time-Based Trade: A Framework for Testing Stock Trading Algorithms. Information Systems Department, University of Maryland Baltimore County. URL: http://www.smrussell.net/papers/StockFramework.pdf
Savchenko, V. (2023). Comparison of systems of forecasting the direction of changes in the exchange rate of a financial instrument using simple, exponential and linear weighted moving averages. Scientific Notes of the University "KROK". No. 3(71). P. 19-30. https://doi.org/10.31732/2663-2209-2022-71-19-30 [in Ukrainian].
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