Modelling Price Movements of Stock-Based Futures Contracts with Time Series: Tupraş Example

Year 2025, Volume: 10 Issue: 1, 655 - 671, 30.06.2025

Ahmet Akusta , Musa Gün

Abstract

This study investigates the modelling of price movements in stock-based futures contracts by applying advanced time series techniques, using the Tupraş futures as a case study. Recognizing the limitations of traditional ARIMA models in capturing the complexities of financial markets, the research develops an extended ARIMAX framework that incorporates key exogenous variables such as stock prices, market indices, exchange rates, interest rates, and inflation. The dataset spans from January 2017 to August 2023 with monthly observations. Data preprocessing, exploratory analysis, and stationarity test steps were applied to obtain robust and reliable estimates. Empirical results reveal that the ARIMAX model significantly outperforms the baseline and optimized ARIMA models, as indicated by improved accuracy metrics, including RMSE, MSE, R², AIC, and BIC. This research contributes to financial econometrics by demonstrating the explanatory power of exogenous-driven models in volatile and structurally complex markets such as energy derivatives.

Keywords

ARIMAX Model, Futures Contracts Forecasting, Time Series Analysis, Exogenous Variables, Economic Indicators

Ethical Statement

Çalışmada açık kaynak verisi kullanıldığından etik kurul onayı gerekmemektedir.

Supporting Institution

Destekleyen kuruluş bulunmamaktadır.

Hisse Senedine Dayalı Vadeli İşlem Sözleşmelerinin Fiyat Hareketlerinin Zaman Serileri ile Modellenmesi: Tüpraş Örneği

Abstract

Bu çalışma, hisse senedine dayalı vadeli işlem sözleşmelerinin fiyat hareketlerini modellemek amacıyla gelişmiş zaman serisi analiz tekniklerini kullanmakta ve Tüpraş vadeli işlemlerini örnek olay olarak ele almaktadır. Finansal piyasaların karmaşıklığını tam olarak yansıtamayan geleneksel ARIMA modellerinin sınırlılıkları dikkate alınarak, bu araştırmada hisse senedi fiyatları, piyasa endeksleri, döviz kurları, faiz oranları ve enflasyon gibi temel dışsal değişkenleri içeren genişletilmiş bir ARIMAX çerçevesi geliştirilmiştir. Çalışmada Ocak 2017 ile Ağustos 2023 arasındaki aylık veriler kullanılmıştır. Sağlam ve güvenilir tahminler elde edebilmek için veri ön işleme, keşifsel analiz, durağanlık testi adımları uygulanmıştır. Ampirik sonuçlar, RMSE, MSE, R², AIC ve BIC gibi doğruluk ölçütleri açısından değerlendirildiğinde, ARIMAX modelinin hem temel hem de optimize edilmiş ARIMA modellerinden belirgin şekilde daha iyi performans gösterdiğini ortaya koymaktadır. Bu araştırma, dışsal değişkenlere dayalı modellerin enerji türevleri gibi volatil ve yapısal olarak karmaşık piyasalarda sunduğu açıklayıcılığı ortaya koyarak finansal ekonometri alanına katkı sağlamaktadır.

Keywords

ARIMAX Modeli, Vadeli İşlem Sözleşmeleri Tahmini, Zaman Serisi Analizi, Dışsal Değişkenler, Ekonomik Göstergeler

References

• Ab Rahman, N. M. N., Nawi, A. S., & Naziman, Y. H. N. M. (2012). The price discovery of the Malaysian crude palm oil futures markets. Journal of Applied Finance and Banking, 2(4), 25.
• Abraham, R., & Harrington, C. (2016). Determinants of oil futures prices. Theoretical Economics Letters, 6, 742–749. http://dx.doi.org/10.4236/tel.[phone]
• Adaramola, A., Abere, M. A., & Ogiamien, O. F. (2023). Effect of exchange rate on stock price movement in Nigeria. Financial Markets, Institutions and Risks, 7(2), 18–27. https://doi.org/10.21272/fmir.7(2).18-27.2023
• Adineh, A. H., Narimani, Z., & Satapathy, S. C. (2020). Importance of data preprocessing in time series prediction using SARIMA: A case study. International Journal of Knowledge-Based and Intelligent Engineering Systems, 24(4), 331–342. https://doi.org/10.3233/KES-200065
• Ak, R., Türk, A., & İslatince, H. (2019). Estimation of energy prices in Turkey in the Nash-Cournot framework. International Journal of Business and Applied Social Science, 5(7), 1–15.
• Allen, D. E., Chang, C., McAleer, M., & Singh, A. K. (2018). A cointegration analysis of agricultural, energy and bio-fuel spot, and futures prices. Applied Economics, 50(7), 804–823. https://doi.org/10.1080/00036846.2017.1340581
• Alzoubi, M. (2022). Stock market performance: Reaction to interest rates and inflation rates. Banks and Bank Systems, 17(2), 189–198. https://doi.org/10.21511/bbs.17(2).2022.16
• Ayankoya, K., Calitz, A. P., & Greyling, J. H. (2016). Using neural networks for predicting futures contract prices of white maize in South Africa. In Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists (p. 3). Association for Computing Machinery. https://doi.org/10.1145/2987491.2987508
• Azevedo, T. C., Aiube, F. L., Samanez, C. P., Bisso, C. S., & Costa, L. A. (2015). The behavior of West Texas Intermediate crude-oil and refined products prices volatility before and after the 2008 financial crisis: An approach through analysis of futures contracts. Ingeniare. Revista chilena de ingeniería, 23(3), 395–405. https://doi.org/10.4067/S0718-33052015000300008
• Cabrera, G., Coronado, S., Rojas, O., & Romero-Meza, R. (2018). A Bayesian approach to model changes in volatility in the Mexican stock exchange index. Applied Economics, 50(15), 1716–1724. https://doi.org/10.1080/00036846.2017.1374536
• Cagaptay, S., & Evans, T. (2013). Turkey’s energy policy and the future of natural gas. Harvard Kennedy School, James A. Baker III Institute for Public Policy of Rice University.
• Caporin, M., & Fontini, F. (2017). The long-run oil–natural gas price relationship and the shale gas revolution. Energy Economics, 64, 511–519. https://doi.org/10.1016/j.eneco.2016.07.024
• Çatak, Ç. (2022). Energy derivatives—An analysis of the Turkish electricity market. Uluslararası İktisadi ve İdari İncelemeler Dergisi. https://doi.org/10.18092/ulikidince.930399
• Chan, K. F., & Gray, P. (2017). Do scheduled macroeconomic announcements influence energy price jumps? Journal of Futures Markets, 37(1), 71–89. https://doi.org/10.1002/fut.21796
• Chen, X., & Hu, Y. (2022). Volatility forecasts of stock index futures in China and the US – A hybrid LSTM approach. PLOS ONE, 17(7), e0271595. https://doi.org/10.1371/journal.pone.0271595
• Chi, S. C., Chen, H. P., & Cheng, C. H. (1999, July). A forecasting approach for stock index future using grey theory and neural networks. In IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Vol. 6, pp. 3850–3855). IEEE.
• Chuffart, T., & Hooper, E. (2019). An investigation of oil prices impact on sovereign credit default swaps in Russia and Venezuela. Energy Economics, 80, 904–916. https://doi.org/10.1016/j.eneco.2019.02.003
• Demirbas, A. (2006). Turkey's renewable energy facilities in the near future. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 28(6), 527–536. https://doi.org/10.1080/009083190927985
• Dickey, D. A., Bell, W. R., & Miller, R. B. (1986). Unit roots in time series models: Tests and implications. The American Statistician, 40(1), 12–26. https://doi.org/10.1080/00031305.1986.10475349
• Dincer, S. K., Kamal, K., & Gultekin, A. N. (2017). Energy policies for sustainable energy future in Turkey. Journal of Engineering Research and Applied Science, 6(1), 537–542.
• Ekinci, M. F., & Saygılı, H. (2023). Oil price pass-through on sector-level prices: Evidence from Turkey. Business and Economics Research Journal, 14(3), 321–335. https://doi.org/10.20409/berj.2023.418
• Ervina, N., Azwar, K., Susanti, E., & Dewi Nainggolan, C. (2022). Effect of inflation, exchange rates, and trading volume activity on stock price indices during the COVID-19 pandemic on companies in the basic industry and chemical sectors. International Journal of Science, Technology & Management, 3(6), 1650–1658. https://doi.org/10.46729/ijstm.v3i6.693
• Fernandez, V. (2016). Spot and futures markets linkages: Does contango differ from backwardation? Journal of Futures Markets, 36(4), 375–396. https://doi.org/10.1002/fut.21736
• Figlewski, S. (1984). Margins and market integrity: Margin setting for stock index futures and options. Journal of Futures Markets, 4(3), 385–416. https://doi.org/10.1002/fut.3990040307
• Fowler, C., Cai, X., Baker, J. T., Onnela, J.-P., & Valeri, L. (2024). Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies. Journal of the Royal Statistical Society: Series C, 73(3), 755–773. https://doi.org/10.1093/jrsssc/qlae010
• Go, Y. H., & Lau, W. Y. (2019). Palm oil spot-futures relation: Evidence from unrefined and refined products. Agricultural Economics (Zemědělská ekonomika), 65(3), 133–142. https://doi.org/10.17221/31/2018-AGRICECON
• Gomaa, M. (2012). An empirical investigation of the relationship between executive risk sharing and stock performance in new and old economy firms. Accounting and Finance Research, 1(1), 106. https://doi.org/10.5430/afr.v1n1p106
• Hanitha, V., Yoyo, T., & Silaswara, D. (2022). Analysis effect of BI rates, inflation and exchange rates on the composite stock price index on the Indonesia stock exchange 2016–2021. Akuntoteknologi, 14(1), 1–10. https://doi.org/10.31253/aktek.v14i1.1423
• Hitzemann, S. (2016). Macroeconomic fluctuations, oil supply shocks, and equilibrium oil futures prices. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2513624
• Holmes, M. J., & Otero, J. (2019). Re-examining the movements of crude oil spot and futures prices over time. Energy Economics, 82, 224–236. https://doi.org/10.1016/j.eneco.2017.08.034
• Hosseini, S. H., Shakouri G, H., & Peighami, A. (2016). A conceptual framework for the oil market dynamics: A systems approach. Energy Exploration & Exploitation, 34(2), 171–198. https://doi.org/10.1177/0144598716631656
• Hu, Y. (2016). Study on effects of CSI 300 stock index futures on Chinese stock market volatility. In Proceedings of the 2nd International Conference on Financial Management, Education and Information Technology Applications (pp. 344–350). Atlantis Press. https://doi.org/10.2991/IFMEITA-16.2016.65
• İsik, F., Ozden, G., & Kuntalp, M. (2012). Importance of data preprocessing for neural networks modeling: The case of estimating the compaction parameters of soils. Energy Education Science and Technology Part A: Energy Science Research, 29(2), 871–882.
• Joarder, S. (2018). The commodity futures volatility and macroeconomic fundamentals: The case of oil and oilseed commodities in India. International Econometric Review, 10(2), 33–50. https://doi.org/10.33818/ier.491326
• Jongadsayakul, W. (2022). Determinants of the successful single stock futures market in Thailand. Investment Management and Financial Innovations, 19(2), 274–284. http://dx.doi.org/10.21511/imfi.19(2).2022.24
• Kalra, V., & Aggarwal, R. (2017). Importance of text data preprocessing & implementation in RapidMiner. In Proceedings of the 2017 International Conference on Information Technology and Knowledge Management, 14, 71–75. https://doi.org/10.15439/2017KM46
• Kaplan, Y. A., & Aladağ, C. (2016). The importance of the different kinds of energy sources for energy future of Turkey. In EPJ Web of Conferences (Vol. 128, p. 05002). EDP Sciences. https://doi.org/10.1051/epjconf/201612805002
• Kara, B., Emir, Z., Seker, T., Bahadir, A., & Kaygusuz, K. (2017). Current state and future prospects of biomass energy in Turkey. Journal of Engineering Research and Applied Science, 6(1), 522–529.
• Kaygusuz, K. (2004). The role of renewables in future energy directions of Turkey. Energy Sources, 26(12), 1131–1140. https://doi.org/10.1080/00908310490441449
• Kilian, L., & Vega, C. (2011). Do energy prices respond to US macroeconomic news? A test of the hypothesis of predetermined energy prices. Review of Economics and Statistics, 93(2), 660–671. https://doi.org/10.1162/REST_a_00086
• Kiliç, A. M. (2006). Turkey's main energy sources and importance of usage in energy sector. Energy Exploration & Exploitation, 24(1–2), 1–17. https://doi.org/10.1260/014459806779388001
• Krishnan, R. S., & Mani, D. (2019). Uncertainty and compensation design in strategic interfirm contracts. Contemporary Accounting Research, 37(1), 542–574. https://doi.org/10.1111/1911-3846.12510
• Lanne, M., Lütkepohl, H., & Saikkonen, P. (2002). Comparison of unit root tests for time series with level shifts. Journal of Time Series Analysis, 23(6), 667–685. https://doi.org/10.1111/1467-9892.00285
• Lanne, M., Lütkepohl, H., & Saikkonen, P. (2003). Test procedures for unit roots in time series with level shifts at unknown time. Oxford Bulletin of Economics and Statistics, 65(1), 91–115. https://doi.org/10.1111/1468-0084.00036
• Li, X., & Zeyuan, W. (2022). The application of neural network in stock and futures forecasting. In Proceedings of the 4th International Conference on Economic Management and Model Engineering (ICEMME 2022), November 18–20, Nanjing, China. EAI. https://doi.org/10.4108/eai.18-11-2022.2326945
• Lu, Q., Li, Y., Chai, J., & Wang, S. (2020). Crude oil price analysis and forecasting: A perspective of “new triangle”. Energy Economics, 87, 104721. https://doi.org/10.1016/j.eneco.2020.104721
• Malhotra, M. (2015). Evaluating the hedging performance of oil and oilseeds futures in India. Paradigm, 19(2), 184–196. https://doi.org/10.1177/0971890715609990
• Miljkovic, D., & Goetz, C. (2020). The effects of futures markets on oil spot price volatility in regional US markets. Applied Energy, 273, 115288. https://doi.org/10.1016/j.apenergy.2020.115288
• Motengwe, C., & Alagidede, P. (2016). Maturity effects in futures contracts on the SAFEX market. Agrekon, 55(4), 331–355. https://doi.org/10.1080/03031853.2016.1243064
• Müftüler-Baç, M., & Başkan, D. (2011). The future of energy security for Europe: Turkey's role as an energy corridor. Middle Eastern Studies, 47(2), 361–378. https://doi.org/10.1080/00263206.2010.481176
• Ningshen, N., Jindal, V., & Bedi, P. (2024). CGM: A hybrid model for forecasting future stock price. In Symposium on Computing & Intelligent Systems (SCI) (pp. 126–137). New Delhi, India.
• Novandi, A. K., & Falah, M. F. (2023). The influence of inflation, interest rates, and exchange rates on the composite stock price index (IHSG) in Indonesia: English. Asian Economic and Business Development, 6(1), 43–48. https://doi.org/10.54204/AEBD/Vol6No1August2023006
• Okay, E. (2015). Creative energy alternatives: Cheap and clean future energy for Turkey. In Handbook of Research on Developing Sustainable Value in Economics, Finance, and Marketing (pp. 157–178). IGI Global Scientific Publishing.
• Öz, S., & Alyürük, M. (2020). Energy sector overview and future prediction for Turkey. Journal of Industrial Policy and Technology Management, 3(1), 59–69.
• Ozdamar, L., Yaşa, E., Kavas, N., & Vardar, G. (2020). Renewable energy investment prospects in Turkey’s power generation sector. International Journal of Renewable Energy Technology, 11(1), 1–12. https://doi.org/10.1504/IJRET.2020.106513
• Özdurak, C. (2021). Conditional correlations and volatility spillovers between crude oil price, Tüpraş and Enerjisa stock returns: A proposal for constructing an ultimate BIST energy index. Journal of Finance Letters (Special Issue 2), 15–32. https://doi.org/10.33203/MFY.844802
• Ozturk, H. K. (2004). Present status and future prospects of hydroelectric energy in Turkey. Energy Sources, 26(9), 829–840. https://doi.org/10.1080/00908310490451385
• Pradhan, K. C. (2011). The hedging effectiveness of stock index futures: Evidence for the S&P CNX Nifty index traded in India. South East European Journal of Economics and Business, 6(1), 111–123. https://doi.org/10.2478/v10033-011-0010-2
• Raut, S., & Shrivas, A. (2024). Stock market price prediction and forecasting using stacked LSTM. International Journal of Scientific Research in Engineering and Management, 8(3), 1–6. https://doi.org/10.55041/IJSREM29832
• Rodrigues, N., Losekann, L., & Silveira Filho, G. (2018). Demand of automotive fuels in Brazil: Underlying energy demand trend and asymmetric price response. Energy Economics, 74, 644–655. https://doi.org/10.1016/j.eneco.2018.07.005
• Scheitrum, D. P., Carter, C. A., & Revoredo-Giha, C. (2018). WTI and Brent futures pricing structure. Energy Economics, 72, 462–469. https://doi.org/10.1016/j.eneco.2018.04.039
• Shirodkar, S., & Raju, G. A. (2021). Futures trading, spot price volatility and structural breaks: Evidence from energy sector. International Journal of Energy Economics and Policy, 11(4), 230–239. https://doi.org/10.32479/ijeep.11086
• Sözen, A., & Arcaklıoğlu, E. (2007). Prospects for future projections of the basic energy sources in Turkey. Energy Sources, Part B: Economics, Planning, and Policy, 2(2), 183–201. https://doi.org/10.1080/15567240600813930
• Sun, B. Q., Guo, H., Karimi, H. R., Ge, Y., & Xiong, S. (2015). Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series. Neurocomputing, 151(Part 3), 1528–1536. https://doi.org/10.1016/j.neucom.2014.09.018
• Tevare, V., & Revankar, P. S. (2023, August). Forecasting stock prices with stack transformer. In 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT), Kollam, India (pp. 1262–1269). IEEE. https://doi.org/10.1109/ICCPCT58313.2023.10245652
• Tiwari, A. K., Trabelsi, N., Alqahtani, F., & Hammoudeh, S. (2019). Analysing systemic risk and time-frequency quantile dependence between crude oil prices and BRICS equity markets indices: A new look. Energy Economics, 83, 445–466. https://doi.org/10.1016/j.eneco.2019.07.014
• Toklu, E., & Kaygusuz, K. (2012). Present situation and future prospect of energy utilization in Turkey. Journal of Engineering Research and Applied Science, 1(2), 73–86.
• TradingView. (2023). BIST-TUPRS. https://www.tradingview.com/symbols/BIST-TUPRS/
• Truong, L. D., & Friday, H. S. (2021). The impact of the introduction of index futures on the daily returns anomaly in the Ho Chi Minh Stock Exchange. International Journal of Financial Studies, 9(3), 43. https://doi.org/10.3390/IJFS9030043
• Wahab, M. A. A., Mohamad, A., & Sifat, I. (2019). On contango, backwardation, and seasonality in index futures. The Journal of Private Equity, 22(2), 69–82. https://doi.org/10.3905/jpe.2019.1.076
• Wang, J., Cheng, Q., & Dong, Y. (2022). An XGBoost-based multivariate deep learning framework for stock index futures price forecasting. Kybernetes, 52(10), 4158–4177. https://doi.org/10.1108/K-12-2021-1289
• Wang, W., & Nie, H. (2018). Comparative study on forecasting model for stock index future price. Modern Economy, 9(4), 750–757. https://doi.org/10.4236/me.2018.94049
• West, M. (1997). Time series decomposition. Biometrika, 84(2), 489–494. https://doi.org/10.1093/BIOMET/84.2.489
• Xiong, X., Liang, J., Cui, Y., Zhang, W., & Zhang, Y. (2017). Analysis of the spot market’s T+1 trading system effects on the stock index futures market. Eurasia Journal of Mathematics, Science and Technology Education, 13(12), 7679–7693. https://doi.org/10.12973/EJMSTE/77941
• Xu, L. (2023). Comparison of future price prediction in context of machine learning approach. BCP Business & Management, 38, 850–855. https://doi.org/10.54691/bcpbm.v38i.3786
• Yildiz, T. (2010). Turkey’s energy policy, regional role and future energy vision. Insight Turkey, 12(3), 33–38. https://www.jstor.org/stable/26334102
• Yuksel, I., Arman, H., & Demirel, I. H. (2018, February). Clean energy for future energy policy in Turkey. In 2018 5th International Conference on Renewable Energy: Generation and Applications (ICREGA), Al Ain, United Arab Emirates (pp. 260–263). IEEE. http://dx.doi.org/10.1109/icrega.2018.8337590
• Zarnowitz, V., & Ozyildirim, A. (2006). Time series decomposition and measurement of business cycles, trends and growth cycles. Journal of Monetary Economics, 53(7), 1717–1739. https://doi.org/10.1016/J.JMONECO.2005.03.015
• Zhang, X., Wang, J., & Gao, Y. (2019). A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM. Energy Economics, 81, 899–913. https://doi.org/10.1016/j.eneco.2019.05.026