Abstract
Recent works have shown that besides fundamental factors like interest rate, inflation index and foreign exchange rates, behavioral factors like consumer sentiments and global economic stability play an important role in driving gold prices at shorter time resolutions. In this work we have done comprehensive modeling of price movements of gold, using three feature sets, namely- macroeconomic factors (using CPI index and foreign exchange rates), investor fear features (using US Economy Stress Index and gold ETF Volatility Index) and investor behavior features (using the sentiment of Twitter feeds and web search volume index from Google Search Volume Index). Our results bring insights like high correlation (upto 0.92 for CPI) between various features, which is a significant improvement over earlier works. Using Grangers causality analysis, we have validated that the movement in gold price is greatly affected in the short term by some features, consistently over a five week lag. Finally, we implemented forecasting techniques like expert model mining system (EMMS) and binary SVM classifier to demonstrate forecasting performance using different features.
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References
Huina, M., Xiao, Z., Bollen, J.: Twitter mood predicts the stock market. Computer 1010(3003v1), 1–8 (2010)
Antweiler, W., Frank, M.Z.: Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards. SSRN eLibrary (2001)
Karrie, K., Gilbert, E.: Widespread worry and the stock market. Artificial Intelligence, 58–65 (2010)
Bhayani, R., Huang, L., Go, A.: Twitter Sentiment Classification using Distant Supervision
Yagil, J., Qadan, M.: Fear sentiments and gold price: testing causality in-mean and in-variance. Applied Economics Letters 19(4), 363–366 (2012)
Laura, S.: Forecasting structural time series models and the kalman filter. Journal of Applied Econometrics 6(3), 329–331 (1991)
Welpe, I.M., Sprenger, T.O.: Tweets and Trades: The Information Content of Stock Microblogs. SSRN eLibrary (2010)
He, J., Yang, N.: Statistical investigation into the accelerating cyclical dynamics among gold, dollar and u.s. interest rate. In: Modeling Risk Management for Resources and Environment in China, pp. 67–76 (2011)
Fuehres, H., Gloor, P.A., Zhang, X.: Predicting stock market indicators through twitter i hope it is not as bad as i fear. Procedia - Social and Behavioral Sciences 26(0), 55–62 (2011)
Fuehres, H., Gloor, P.A., Zhang, X.: Predicting asset value through twitter buzz 113, 23–34 (2012)
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Kumar, J., Rao, T., Srivastava, S. (2012). Economics of Gold Price Movement-Forecasting Analysis Using Macro-economic, Investor Fear and Investor Behavior Features. In: Srinivasa, S., Bhatnagar, V. (eds) Big Data Analytics. BDA 2012. Lecture Notes in Computer Science, vol 7678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35542-4_10
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DOI: https://doi.org/10.1007/978-3-642-35542-4_10
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