Emtia Piyasalarının Birlikte Hareketlerinin Veri Madenciliği ile İncelenmesi

Abstract

Emtialar, yatırımları çeşitlendirmek ve enflasyona karşı korunmak için alternatif bir yol olarak görülmüştür. Bu nedenle yatırımcıların bir piyasanın düşüşü veya yükselişi sonrasında diğer piyasaların veya finansal varlıkların hangi yöne doğru hareket edeceğini öngörmesi, hızlı ve etkili kararlar almasında kritik öneme sahiptir. Bu çalışmada, emtia piyasalarının birlikte hareketi veri madenciliğinde yer alan birliktelik kuralı ile analiz edilmiştir. Bu doğrultuda çalışmada 20 adet emtianın 01.01.2010-01.08.2023 tarihleri arasındaki 3216 işlem günündeki birlikte hareketleri analiz edilmiştir. Çalışmada birliktelik kuralı analizleri, Apriori ve FP-Growth algoritmaları kullanılarak gerçekleştirilmiştir. Hem Apriori hem de FP-Growth algoritmaları ile üretilen birliktelik kurallarının tümünde Brent petrolün diğer emtialara eşlik ettiği gözlemlenmiştir. Bu sonuç, Brent petrol fiyatlarının yukarı veya aşağı yönde hareketinin, Brent petrol fiyatlarını yakından takip eden yatırımcılara, karar vericilere ve politika yapıcılara, diğer emtiaların hareketi ile ilgili yol gösterici olabileceğini göstermektedir. Petrolün ekonomik sistemi etkileyen stratejik bir enerji kaynağı olduğu gerçeği göz önüne alındığında, bu sonucun şaşırtıcı olmadığı ifade edilebilir.

Keywords

• Emtia
• Emtia Piyasaları
• Veri Madenciliği
• Birliktelik Kuralı

An Investigation of Co-movements of Commodity Markets by Data Mining

Abstract

Commodities have been seen as an alternative way to diversify investments and protect against inflation. As a result, it is critically important for investors to predict the direction in which other stock exchanges or financial assets will move after a stock market’s rise or fall in order to make quick and effective decisions. In this study, the co-movement of commodity markets are analyzed with the association rule in data mining. In this direction, the movements of 20 commodities in 3216 trading days between 01.01.2010 and 01.08.2023 are analyzed in the study. Association rule analyses in the study are conducted using the Apriori and FP-Growth algorithms. It is observed that Brent crude oil accompanied other commodities in all association rules generated by both the Apriori and FP-Growth algorithms. This result suggests that the upward or downward movement of Brent oil prices may provide guidance to investors, decision makers and policymakers who closely follow Brent oil prices regarding the movement of other commodities. Considering the fact that oil is a strategic energy source that affects the economic system, this result is not surprising.

Keywords

• Commodity
• Commodity Markets
• Data mining
• Association Rule

References

1. Abid, I., Dhaoui, A., Goutte, S. and Guesmi, K. (2020). Hedging and diversification across commodity assets. Applied Economics, 52(23), 2472-2492. https://doi.org/10.1080/00036846.2019.1693016
2. Agrawal, R., Imieliński, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. In P. Buneman, S. Jajodia and W. Kim (Eds.), SIGMOD ’93: Proceedings of the 1993 ACM SIGMOD international conference on management of data (pp. 207-216). Papers presented at the SIGMOD International Conference on Management of Data, New York: Association for Computing Machinery.
3. Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules. In J.B. Bocca, M. Jarke and C. Zaniolo (Eds.), Proceedings of 20th international conference on very large data bases (pp. 487–499). Papers presented at the International Joint Conference on Very Large Data Bases, Santiago Chile, San Fransisco: Morgan Kaufmann Publishers.
4. Algieri, B. and Leccadito, A. (2017). Assessing contagion risk from energy and non-energy commodity markets. Energy Economics, 62, 312-322. https://doi.org/10.1016/j.eneco.2017.01.006
5. Arafah, A.A. and Mukhlash, I. (2015). The application of fuzzy association rule on co-movement analyze of Indonesian stock price. Procedia Computer Science, 59, 235-243. https://doi.org/10.1016/j.eneco.2017.01.006
6. Argiddi, R.V. and Apte, S.S. (2012). Future trend prediction of Indian IT stock market using association rule mining of transaction data. International Journal of Computer Applications, 39(10), 30-34. https://doi.org/10.5120/4858-7132
7. Arouri, M.E.H., Hammoudeh, S., Lahiani, A. and Nguyen, D.K. (2013). On the short-and long-run efficiency of energy and precious metal markets. Energy Economics, 40, 832-844. https://doi.org/10.1016/j.eneco.2013.10.004
8. Azeez, N.A., Ayemobola, T.J., Misra, S., Maskeliūnas, R. and Damaševičius, R. (2019). Network intrusion detection with a hashing based Apriori algorithm using Hadoop MapReduce. Computers, 8(4), 86. https://doi.org/10.3390/computers8040086

9. Basak, S. and Pavlova, A. (2016). A model of financialization of commodities. The Journal of Finance, 71(4), 1511-1556. https://doi.org/10.1111/jofi.12408
10. Basu, D. and Miffre, J. (2013). Capturing the risk premium of commodity futures: The role of hedging pressure. Journal of Banking and Finance, 37(7), 2652-2664. https://doi.org/10.1016/j.jbankfin.2013.02.031
11. Bramer, M. (2016). Principles of data mining. London: Springer.
12. CFTC. (2023). Commodity futures trading commission. Retrieved from https://www.cftc.gov/
13. Chalid, D.A. and Handika, R. (2022). Comovement and contagion in commodity markets. Cogent Economics & Finance, 10(1), 2064079. https://doi.org/10.1080/23322039.2022.2064079
14. Chao, X., Kou, G., Peng, Y. and Viedma, E.H. (2021). Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: An application in financial inclusion. European Journal of Operational Research, 288(1), 271-293. https://doi.org/10.1016/j.ejor.2020.05.047
15. Chen, M.S., Han, J. and Yu, P.S. (1996). Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866-883. doi:10.1109/69.553155
16. Cheng, I.H. and Xiong, W. (2014). Financialization of commodity markets. Annual Review of Financial Economics, 6(1), 419-441. https://doi.org/10.1146/annurev-financial-110613-034432
17. Daskalaki, C. and Skiadopoulos, G. (2011). Should investors include commodities in their portfolios after all? New evidence. Journal of Banking & Finance, 35(10), 2606-2626. https://doi.org/10.1016/j.jbankfin.2011.02.022
18. Diamandis, P.F. (2009). International stock market linkages: Evidence from Latin America. Global Finance Journal, 20(1), 13-30. https://doi.org/10.1016/j.gfj.2009.03.005
19. Dunham, M.H. (2006). Data mining: Introductory and advanced topics. London: Pearson.
20. Erpolat, S. (2012). Otomobil yetkili servislerinde birliktelik kurallarının belirlenmesinde Apriori ve FP-Growth algoritmalarının karşılaştırılması. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 12(1), 151-166. Erişim adresi: https://earsiv.anadolu.edu.tr/
21. Frankel, J.A. and Rose, A.K. (2010) Determinants of agricultural and mineral commodity prices (HKS Faculty Research Working Paper No. RWP10-038). Retrieved from https://dash.harvard.edu/handle/1/4450126
22. Gilbert, C.L. (2010). Commodity speculation and commodity investment. Market Review, 28, 26-46. doi:10.5555/20103231905
23. Han, J., Kamber, M. and Pei, J. (2012). Data mining concepts and techniques. New York: Elsevier.
24. Han, J., Pei, J. and Kamber, M. (2011). Data mining: Concepts and techniques. New York: Elsevier.
25. Han, J., Pei, J. and Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM Sigmod Record, 29(2), 1-12. https://doi.org/10.1145/335191.335372
26. Heil, T.L., Peter, F.J. and Prange, P. (2022). Measuring 25 years of global equity market co-movement using a time-varying spatial model. Journal of International Money and Finance, 128, 102708. https://doi.org/10.1016/j.jimonfin.2022.102708
27. Hernández, C.J.B., García-Medina, A. and Porro, V.M.A. (2021). Study of the behavior of cryptocurrencies in turbulent times using association rules. Mathematics, 9(14), 1620. https://doi.org/10.3390/math9141620
28. Ho, G.T., Ip, W.H., Wu, C.H. and Tse, Y.K. (2012). Using a fuzzy association rule mining approach to identify the financial data association. Expert Systems with Applications, 39(10), 9054-9063. https://doi.org/10.1016/j.eswa.2012.02.047
29. Inaba, K.I. (2020). A global look into stock market comovements. Review of World Economics, 156(3), 517-555. https://doi.org/10.1007/s10290-019-00370-1
30. Jalpa, P.P. and Rustom, M.D. (2017). A novel hybrid method for generating association rules for stock market data. International Journal of Latest Technology in Engineering, Management & Applied Science 3rd Special Issue on Engineering and Technology 6(7), 6-15. Retrieved from https://www.ijltemas.in/
31. Ji, Q. and Fan, Y. (2012). How does oil price volatility affect non-energy commodity markets? Applied Energy, 89(1), 273-280. https://doi.org/10.1016/j.apenergy.2011.07.038
32. Kang, W., Tang, K. and Wang, N. (2023). Financialization of commodity markets ten years later. Journal of Commodity Markets, 30, 100313. https://doi.org/10.1016/j.jcomm.2023.100313
33. Kantardzic, M. (2020). Data mining: Concepts, models, methods, and algorithms. John Wiley & Sons.
34. Karaatlı, M., Kocabıyık, T., Yalçıner Çal, D. ve Çolak, M. (2021). BIST-30 Endeksinde yer alan payların ortak hareketlerinin veri madenciliği kapsamında birliktelik kuralı ile incelenmesi. Finansal Araştırmalar ve Çalışmalar Dergisi, 13(25), 548-571. https://doi.org/10.14784/marufacd.976609
35. Kartal, B., Sert, M.F. and Kutlu, M. (2022). Determination of the world stock indices' co-movements by association rule mining. Journal of Economics, Finance and Administrative Science, 27(54), 231- 246. https://doi.org/10.1108/JEFAS-04-2020-0150
36. Kaur, J. and Dharni, K. (2022). Assessing efficacy of association rules for predicting global stock indices. Decision, 49(3), 329-339. https://doi.org/10.1007/s40622-022-00327-8
37. Kavitha, M. and Selvi, S.T. (2016). Comparative study on Apriori algorithm and FP growth algorithm with pros and cons. International Journal of Computer Science Trends and Technology, 4(4), 161-164. Retrieved from https://api.semanticscholar.org
38. Kirikkaleli, D. and Güngör, H. (2021). Co-movement of commodity price indexes and energy price index: A wavelet coherence approach. Financial Innovation, 7(1), 15. https://doi.org/10.1186/s40854-021-00230-8
39. Kocabıyık, T., Dağ, O. ve Karaatlı, M. (2021). Borsa İstanbul endekslerinin birlikte hareketi: FP Growth algoritması ile bir uygulama. Uluslararası İşletme, Ekonomi ve Yönetim Perspektifleri Dergisi (IJBEMP), 5(2), 659-672. doi:10.29228/ijbemp.52518
40. Kotu, V. and Deshpande, B. (2018). Data science: Concepts and practice. Massachusetts: Morgan Kaufmann.
41. Kou, G., Xu, Y., Peng, Y., Shen, F., Chen, Y., Chang, K. and Kou, S. (2021). Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decision Support Systems, 140, 113429. https://doi.org/10.1016/j.dss.2020.113429
42. Lee, C.C. and Lee, C.C. (2023). International spillovers of US monetary uncertainty and equity market volatility to China’s stock markets. Journal of Asian Economics, 84, 101575. https://doi.org/10.1016/j.asieco.2022.101575
43. Liao, S.H. and Chou, S.Y. (2013). Data mining investigation of co-movements on the Taiwan and China stock markets for future investment portfolio. Expert Systems with Applications, 40(5), 1542-1554. https://doi.org/10.1016/j.eswa.2012.08.075
44. Liao, S.H., Chu, P.H. and You, Y.L. (2011). Mining the co-movement between foreign exchange rates and category stock indexes in the Taiwan financial capital market. Expert Systems with Applications, 38(4), 4608-4617. https://doi.org/10.1016/j.eswa.2010.09.134
45. Liao, S.H., Ho, H.H. and Lin, H.W. (2008). Mining stock category association and cluster on Taiwan stock market. Expert Systems with Applications, 35(1-2), 19-29. https://doi.org/10.1016/j.eswa.2007.06.001
46. Liu, C., Zhang, X. and Zhou, Z. (2023). Are commodity futures a hedge against inflation? A Markov-switching approach. International Review of Financial Analysis, 86, 102492. https://doi.org/10.1016/j.irfa.2023.102492
47. Liu, X., Zhao, Y. and Sun, M. (2017). An improved Apriori algorithm based on an evolution-communication tissue-like p system with promoters and inhibitors. Discrete Dynamics in Nature and Society, 2017, 6978146. https://doi.org/10.1155/2017/6978146
48. Masum, Z.H. (2019). Mining stock category association on Tehran stock market. Soft Computing, 23, 1165–1177. https://doi.org/10.1007/s00500-017-2835-9
49. Matesanz, D., Torgler, B., Dabat, G. and Ortega, G.J. (2014). Co‐movements in commodity prices: A note based on network analysis. Agricultural Economics, 45(S1), 13-21. doi:10.1111/agec.12126
50. Mbarki, I., Khan, M.A., Karim, S., Paltrinieri, A. and Lucey, B.M. (2023). Unveiling commodities-financial markets intersections from a bibliometric perspective. Resources Policy, 83, 103635. https://doi.org/10.1016/j.resourpol.2023.103635
51. Memis, E. and Kaya, H. (2019). Association rule mining on the BIST100 stock exchange. Paper presented at the 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). Ankara, Turkey. Retrieved from https://ieeexplore.ieee.org/document/8932923
52. Mensi, W., Tiwari, A., Bouri, E., Roubaud, D. and Al-Yahyaee, K.H. (2017). The dependence structure across oil, wheat, and corn: A wavelet-based copula approach using implied volatility indexes. Energy Economics, 66, 122-139. https://doi.org/10.1016/j.eneco.2017.06.007
53. Na, S.H. and Sohn, S.Y. (2011). Forecasting changes in Korea composite stock price index (KOSPI) using association rules. Expert Systems with Applications, 38(7), 9046-9049. https://doi.org/10.1016/j.eswa.2011.01.025
54. NASDAQ. (2024). Nasdaq website [Dataset]. Retrieved from https://www.nasdaq.com/
55. Nazlioglu, S. and Soytas, U. (2012). Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. Energy Economics, 34(4), 1098-1104. https://doi.org/10.1016/j.eneco.2011.09.008
56. Ohashi, K. and Okimoto, T. (2016). Increasing trends in the excess comovement of commodity prices. Journal of Commodity Markets, 1(1), 48-64. https://doi.org/10.1016/j.jcomm.2016.02.001 Paranjape-Voditel, P. and Deshpande, U. (2013). A stock market portfolio recommender system based on association rule mining. Applied Soft Computing, 13(2), 1055-1063. https://doi.org/10.1016/j.asoc.2012.09.012
57. Patel, R.J. (2019). BRICS emerging markets linkages. The Journal of Private Equity, 22(4), 42-59. Retrieved from https://www.jstor.org/
58. Prasanna, S. and Ezhilmaran, D. (2016). Association rule mining using enhanced Apriori with modified GA for stock prediction. International Journal of Data Mining, Modelling and Management, 8(2), 195-207. https://doi.org/10.1504/IJDMMM.2016.077162
59. Rehman, M.U., Bouri, E., Eraslan, V. and Kumar, S. (2019). Energy and non-energy commodities: An asymmetric approach towards portfolio diversification in the commodity market. Resources Policy, 63, 101456. https://doi.org/10.1016/j.resourpol.2019.101456
60. Sidhu, S., Meena, U. K., Nawani, A., Gupta, H. and Thakur, N. (2014). FP Growth algorithm implementation. International Journal of Computer Applications, 93(8), 6-10. doi:10.5120/16233-5613
61. Singhal, S., Choudhary, S. and Biswal, P.C. (2019). Return and volatility linkages among international crude oil price, gold price, exchange rate and stock markets: Evidence from Mexico. Resources Policy, 60, 255-261. https://doi.org/10.1016/j.resourpol.2019.01.004
62. Son, L.H., Chiclana, F., Kumar, R., Mittal, M., Khari, M., Chatterjee, J.M. and Baik, S.W. (2018). ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization. Knowledge-Based Systems, 154, 68–80. https://doi.org/10.1016/j.knosys.2018.04.038
63. Srisawat A. (2011). An application of association rule mining based on stock market. Paper presented at the 3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMIA), Macao, China: IEEE. Retrieved from https://ieeexplore.ieee.org/document/6108440
64. Stoll, H.R. and Whaley, R. (2010). Commodity index investing and commodity futures prices. Journal of Applied Finance (Formerly Financial Practice and Education), 20(1). Retrieved from https://papers.ssrn.com
65. Teker, T. ve Konuşkan, A (2022). Fan token fiyatlarında birliktelik etkisi. Uluslararası İşletme, Ekonomi ve Yönetim Perspektifleri Dergisi, 6(2), 359-376. http://dx.doi.org/10.29228/ijbemp.65914
66. Umar, Z., Jareño, F. and Escribano, A. (2022). Dynamic return and volatility connectedness for dominant agricultural commodity markets during the COVID-19 pandemic era. Applied Economics, 54(9), 1030-1054. https://doi.org/10.1080/00036846.2021.1973949
67. Ünsal, Ö. (2020). Veri madenciliği teknikleri ile hisse senetleri arasındaki fiyat etkileşimlerinin belirlenmesi. Mühendislik Bilimleri ve Tasarım Dergisi, 8(5), 106-112. https://doi.org/10.21923/jesd.834105
68. Vacha, L., Janda, K., Kristoufek, L. and Zilberman, D. (2013). Time–frequency dynamics of biofuel–fuel–food system. Energy Economics, 40, 233-241. https://doi.org/10.1016/j.eneco.2013.06.015
69. Vivian, A. and Wohar, M.E. (2012). Commodity volatility breaks. Journal of International Financial Markets, Institutions and Money, 22(2), 395-422. https://doi.org/10.1016/j.intfin.2011.12.003
70. Wang, Y., Wu, C. and Yang, L. (2014). Oil price shocks and agricultural commodity prices. Energy Economics, 44, 22-35. https://doi.org/10.1016/j.eneco.2014.03.016
71. Yang, D.L., Hsieh, Y.L. and Wu, J. (2006). Using data mining to study upstream and downstream causal relationship in stock market. In X. Luo, A.A. Almohammedi, C-H. Chen, S. Guan and D. Pamucar (Eds.), Advances in intelligent systems research (pp. 528-531). Papers presented at the 9th Joint International Conference on Information Sciences (JCIS-06). Atlantis Press. doi:10.2991/jcis.2006.191
72. Zhang, X., Liu, Q., Tang, Y., Liu, G., Ning, X. and Chen, J. (2021). A FP-Growth algorithm based fault analysis method for distribution terminal unit. In J. Shi (Ed.), 2021 IEEE/IAS industrial and commercial power system Asia (I&CPS Asia) (pp. 1463-1467). Papers presented at the IEEE/IAS Industrial and Commercial Power System Asia Conference, Chengdu, China: IEEE.