Extracting Knowledge from Technical Reports for the Valuation of West Texas Intermediate Crude Oil Futures
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This paper proposes and demonstrates an approach for the often-attempted problem of market prediction, framed as classification task. We restrict our study to a widely purchased and well recognized commodity, West Texas Intermediate crude oil, which experiences significant volatility. For this purpose, nine learners using features extracted from monthly International Energy Agency (IEA) reports to predict undervalued, overvalued, and accurate valuation of the oil futures between 2003 and 2015. The often touted “Efficient Market Hypothesis” (EMH) suggests that it is impossible for individual investors to “beat the market” as market and external forces, such as geopolitical crises and natural disasters, are nearly impossible to predict. However, four algorithms were statistically better at the 95% confidence interval than “Zero-Rule” and “Random-Guess” strategies which are expected to pseudo-reflect the EMH. Furthermore, the addition of text features can significantly improve performance compared to only using price history from the oil futures data, challenging the validity of the semi-strong versions of the EMH in the crude oil market.
KeywordsMachine learning Text mining Crude oil market
We acknowledge partial support by the NSF (CNS-1427536). Opinions, findings, conclusions, or recommendations in this material are the authors’ and do not reflect the views of the NSF.
- Mittermayer, a.M., & Knolmayer, G.F. (2006). Newscats: A news categorization and trading systems. In Sixth international conference on data mining (icdm’06) (pp. 1002-1007), (to appear in print), https://doi.org/10.1109/ICDM.2006.115.
- Berenson, M.L., Goldstein, M., Levine, D. (1983). Intermediate statistical methods and applications: a computer package approach, 2nd edn. Upper Saddle River: Prentice Hall.Google Scholar
- Bong-Chan, K. (1996). Time-varying risk premia, volatility, and technical trading rule profits: Evidence from foreign currency futures markets. Journal of Financial Economics, 41(2), 249–290. Retrieved from https://EconPapers.repec.org/RePEc:eee:jfinec:v:41:y:1996:i:2:p:249-290.CrossRefGoogle Scholar
- Choi, K., & Hammoudeh, S. (2010). Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment. Energy Policy, 38(8), 4388–4399. https://doi.org/10.1016/j.enpol.2010.03.067. Retrieved from http://www.sciencedirect.com/science/article/pii/S0301421510002570.CrossRefGoogle Scholar
- Crawford, M., Khoshgoftaar, T.M., Prusa, J.D. (2016). Reducing feature set explosion to facilitate real-world review spam detection. In The twenty-ninth international flairs conference.Google Scholar
- Graham, J.R., & Harvey, C.R. (1996). Market timing ability and volatility implied in investment newletters’ asset allocation recommendations (Tech. Rep.). National Bureau of Economic Research.Google Scholar
- Grossman, S.J., & Stiglitz, J.E. (1980). On the impossibility of informationally efficient markets. The American economic review, 70(3), 393–408.Google Scholar
- International Energy Agency. (n.d.). Monthly oil data service (mods). Retrieved from https://www.iea.org/statistics/mods/.
- Kaufmann, R.K., & Ullman, B. (2009). Oil prices, speculation, and fundamentals: Interpreting causal relations among spot and futures prices. Energy Economics, 31(4), 550–558. https://doi.org/10.1016/j.eneco.2009.01.013. Retrieved from http://www.sciencedirect.com/science/article/pii/S0140988309000243.
- Lai, K., & et al. (2005). Journal of Systems Science and Complexity, 18(2), 145–166.Google Scholar
- Lawrence, R. (1997). Using neural networks to forecast stock market prices. University of Manitoba, 333.Google Scholar
- Rachlin, G., Last, M., Alberg, D., Kandel, A. (2007). Admiral: A data mining based financial trading system . In 2007 ieee symposium on computational intelligence and data mining (pp. 720-0-725). https://doi.org/10.1109/CIDM.2007.368947.
- Seker, S.E., Mert, C., Al-Naami, K., Ozalp, N., Ayan, U. (2014). Time series analysis on stock market for text mining correlation of economy news. Retrieved from CoRR arXiv:1403.2002.
- Seliya, N., Khoshgoftaar, T.M., Van Hulse, J. (2009). A study on the relationships of classifier performance metrics. In 21st international conference on Tools with artificial intelligence, 2009. ictai’09 (pp. 59–66).Google Scholar
- Sun, A., Lachanski, M., Fabozzi, F.J. (2016). Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction. International Review of Financial Analysis, 48, 272–281. https://doi.org/10.1016/j.irfa.2016.10.009. Retrieved from http://www.sciencedirect.com/science/article/pii/S1057521916301600.CrossRefGoogle Scholar
- Witten, I.H., Frank, E., Hall, M.A., Pal, C.J. (2016). Data mining: practical machine learning tools and techniques. Morgan Kaufmann.Google Scholar
- Xie, W., Yu, L., Xu, S., Wang, S. (2006). A new method for crude oil price forecasting based on support vector machines. In Computational Science—ICCS 2006 (pp. 444–451).Google Scholar
- Yu, L., Wang, S., Lai, K. (2005). A rough-set-refined text mining approach for crude oil market tendency forecasting. International Journal of Knowledge and Systems Sciences, 2(1), 33– 46.Google Scholar