Prediction of Gold and Silver Prices in an Emerging Economy: Comparative Analysis of Linear, Nonlinear, Hybrid, and Ensemble Models

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Saurabh Kumar

Abstract

This study compares the accuracy of different forecasting techniques for gold and silver returns in a leading emerging economy. The study employs four forecasting models: autoregressive integrated moving average (ARIMA), artificial neural network (ANN), hybrid, and ensemble models. The study takes data of more than 7 years and forecasting is carried out for different forecast horizons varying from 1- to 20-steps ahead. The results reveal that ARIMA model is the best model to predict the gold returns, whereas, the ANN model along with the ensemble model are the best to predict the silver returns. The results also indicate that there exists nonlinear patterns in the time-series data of gold and silver returns. The study has significant implications for investors, academia, and policymakers.

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References

Adhikari, R., and Agrawal, R. K. (2014), 'A combination of artificial neural network and random walk models for financial time series forecasting', Neural Computing and Applications, 24(6), 1441–1449. https://doi.org/10.1007/s00521-013-1386-y

Aye, G., Gupta, R., Hammoudeh, S., and Kim, W. J. (2015), 'Forecasting the price of gold using dynamic model averaging', International Review of Financial Analysis, 41, 257–266. https://doi.org/10.1016/j.irfa.2015.03.010

Balkin, S. D., and Ord, J. K. (2000), 'Automatic neural network modeling for univariate time series', International Journal of Forecasting, 16(4), 509–515. https://doi.org/10.1016/S0169-2070(00)00072-8

Bampinas, G., and Panagiotidis, T. (2015), 'Are gold and silver a hedge against inflation? A two century perspective', International Review of Financial Analysis, 41, 267–276. https://doi.org/10.1016/j.irfa.2015.02.007

Bauer, E., and Kohavi, R. (1999), 'An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants', Machine Learning, 36(1–2), 105–139. https://doi.org/10.1023/A:1007515423169

Baur, D. G., and Lucey, B. M. (2010), 'Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold', Financial Review, 45(2), 217–229. https://doi.org/10.1111/j.1540-6288.2010.00244.x

Baur, D. G., and McDermott, T. K. (2010), 'Is gold a safe haven? International evidence', Journal of Banking & Finance, 34(8), 1886–1898. https://doi.org/10.1016/j.jbankfin.2009.12.008

Box, G. E., and Jenkins, G. M. (1976), 'Time series analysis, control, and forecasting', in Holden-Day Inc., San Francisco, CA.

Chen, A.-S., and Leung, M. T. (2005), 'Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations', Journal of Forecasting, 24(6), 403–420. https://doi.org/10.1002/for.967

Chen, A.-S., Leung, M. T., and Daouk, H. (2003), 'Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index', Computers & Operations Research, 30(6), 901–923. https://doi.org/10.1016/S0305-0548(02)00037-0

Dunis, C. L., Laws, J., and Schilling, U. (2012), 'Currency trading in volatile markets: Did neural networks outperform for the EUR/USD during the financial crisis 2007–2009?', Journal of Derivatives & Hedge Funds, 18(1), 2–41. https://doi.org/10.1057/jdhf.2011.31

Dunis, C. L., and Nathani, A. (2007), 'Quantitative trading of gold and silver using nonlinear models', Neural Network World, 17(2), 93.

Ghazali, R., Jaafar Hussain, A., Mohd Nawi, N., and Mohamad, B. (2009), 'Non-stationary and stationary prediction of financial time series using dynamic ridge polynomial neural network', Neurocomputing, 72(10–12), 2359–2367. https://doi.org/10.1016/j.neucom.2008.12.005

Gupta, S., Kumar, S., and Kumar, P. (2016), 'EVALUATING THE PREDICTIVE POWER OF AN ENSEMBLE MODEL FOR ECONOMIC SUCCESS OF INDIAN MOVIES', The Journal of Prediction Markets, 10(1), 30–52. https://doi.org/10.5750/jpm.v10i1.1182

Hassani, H., Silva, E. S., Gupta, R., and Segnon, M. K. (2015), 'Forecasting the price of gold', Applied Economics, 47(39), 4141–4152. https://doi.org/10.1080/00036846.2015.1026580

Huang, W., Lai, K. K., Nakamori, Y., Wang, S., and Yu, L. (2007), 'Neural networks in finance and economics forecasting', International Journal of Information Technology & Decision Making, 6(1), 113–140. https://doi.org/10.1142/S021962200700237X

Karathanasopoulos, A. (2016), 'Modelling and trading the English stock market with novelty optimization techniques', Economics and Business Letters, 5(2), 50–57.

Khashei, M., and Bijari, M. (2010), 'An artificial neural network (p, d, q) model for timeseries forecasting', Expert Systems with Applications, 37(1), 479–489. https://doi.org/10.1016/j.eswa.2009.05.044

Khashei, M., and Bijari, M. (2011), 'A novel hybridization of artificial neural networks and ARIMA models for time series forecasting', Applied Soft Computing, 11(2), 2664–2675. https://doi.org/10.1016/j.asoc.2010.10.015

Majhi, R., Panda, G., and Sahoo, G. (2009), 'Efficient prediction of exchange rates with low complexity artificial neural network models', Expert Systems with Applications, 36(1), 181–189. https://doi.org/10.1016/j.eswa.2007.09.005

Makridou, G., Atsalakis, G. S., Zopounidis, C., and Andriosopoulos, K. (2013), 'Gold price forecasting with a neuro-fuzzy-based inference system', International Journal of Financial Engineering and Risk Management, 1(1), 35–54. https://doi.org/10.1504/IJFERM.2013.053707

Morales, L., and Andreosso-O’Callaghan, B. (2011), 'Comparative analysis on the effects of the Asian and global financial crises on precious metal markets', Research in International Business and Finance, 25(2), 203–227. https://doi.org/10.1016/j.ribaf.2011.01.004

Özkan, F. (2013), 'Comparing the forecasting performance of neural network and purchasing power parity: The case of Turkey', Economic Modelling, 31, 752–758. https://doi.org/10.1016/j.econmod.2013.01.010

Paliwal, M., and Kumar, U. A. (2009), 'Neural networks and statistical techniques: A review of applications', Expert Systems with Applications, 36(1), 2–17. https://doi.org/10.1016/j.eswa.2007.10.005

Parisi, A., Parisi, F., and Díaz, D. (2008), 'Forecasting gold price changes: Rolling and recursive neural network models', Journal of Multinational Financial Management, 18(5), 477–487. https://doi.org/10.1016/j.mulfin.2007.12.002

Pierdzioch, C., Risse, M., and Rohloff, S. (2014), 'On the efficiency of the gold market: Results of a real-time forecasting approach', International Review of Financial Analysis, 32, 95–108. https://doi.org/10.1016/j.irfa.2014.01.012

Pierdzioch, C., Risse, M., and Rohloff, S. (2016), 'A boosting approach to forecasting gold and silver returns: economic and statistical forecast evaluation', Applied Economics Letters, 23(5), 347–352. https://doi.org/10.1080/13504851.2015.1073835

Selvanathan, E. A. (1991), 'A Note on the Accuracy of Business Economists’ Gold Price Forecasts', Australian Journal of Management, 16(1), 91–94. https://doi.org/10.1177/031289629101600106

Sermpinis, G., Dunis, C., Laws, J., and Stasinakis, C. (2012), 'Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time-varying leverage', Decision Support Systems, 54(1), 316–329. https://doi.org/10.1016/j.dss.2012.05.039

Sharma, S. S. (2016), 'Can consumer price index predict gold price returns?', Economic Modelling, 55, 269–278. https://doi.org/10.1016/j.econmod.2016.02.014

Stock, J. H., and Watson, M. W. (2003), 'Forecasting Output and Inflation: The Role of Asset Prices', Journal of Economic Literature, 41(3), 788–829. https://doi.org/10.1257/002205103322436197

Taskaya-Temizel, T., and Casey, M. C. (2005), 'A comparative study of autoregressive neural network hybrids', Neural Networks, 18(5–6), 781–789. https://doi.org/10.1016/j.neunet.2005.06.003

Tkáč, M., and Verner, R. (2016), 'Artificial neural networks in business: Two decades of research', Applied Soft Computing, 38, 788–804. https://doi.org/10.1016/j.asoc.2015.09.040

van Wezel, M., and Potharst, R. (2007), 'Improved customer choice predictions using ensemble methods', European Journal of Operational Research, 181(1), 436–452. https://doi.org/10.1016/j.ejor.2006.05.029

Zhang, G., Eddy Patuwo, B., and Y. Hu, M. (1998), 'Forecasting with artificial neural networks:: The state of the art', International Journal of Forecasting, 14(1), 35–62. https://doi.org/10.1016/S0169-2070(97)00044-7

Zhang, G. P. (2003), 'Time series forecasting using a hybrid ARIMA and neural network model', Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0

Zhang, G. P., and Qi, M. (2005), 'Neural network forecasting for seasonal and trend time series', European Journal of Operational Research, 160(2), 501–514. https://doi.org/10.1016/j.ejor.2003.08.037