Open Access Open Access  Restricted Access Subscription Access

Deep Learning in Financial Analytics : Exchange Rate Modelling


Affiliations
1 University School of Humanities and Social Sciences, Guru Gobind Singh Indraprastha University, Sector-16-C, Dwarka, Delhi - 110 078, India

   Subscribe/Renew Journal


In finance, a major enthralling research question has been the accurate determination of future market and economic movements. A lot of researchers have tried to develop different models with different accuracies of prediction over the years. It appears that the full potential of deep learning has not been explored to study FX rates. The current study, therefore, explored the proficiency of deep neural networks in predictive modeling. I tested different models of artificial neural networks (using hyperparameters’ tuning like training algorithms, number of hidden layers, and hidden nodes) using neural network input-output fitting and tried to find the best fit model. The model was also validated by layered digital dynamic time series modeling using autoregression with two delays. The appraisal metrics used were regression R - value, MSE, time-series response plot, and error autocorrelation plot. It was concluded that the artificial neural network with a single hidden layer having 17 nodes and trained using the Levenberg– Marquardt algorithm gave the best performance in a minimum number of iterations. This study marks an extensive examination of ANN modeling. This model can be used by traders, investors, financiers, economists, bankers, speculators, hedgers, and governments to get insights into future forex rates and thus make profitable decisions. Various trading policies, import-export policies, and pricing of commodities in indigenous markets can be managed precisely. Future studies can use these models in simulated trading and help establish an alliance between statistical significance and economic significance.

Keywords

Forex Forecasting, Predictive Modeling, Deep Neural Network, Input-Output Fitting, Training Algorithm, Hidden Layer, Error Autocorrelation, Back-Propagation, Hyperparameter, Incremental Training.

JEL Classification Codes : C880, F470, G170

Paper Submission Date : June 25, 2021 ; Paper sent back for Revision : April 26, 2022 ; Paper Acceptance Date : May 20, 2022 ; Paper Published Online : September 15, 2022

User
Subscription Login to verify subscription
Notifications
Font Size

  • Agarwal, S., & Khan, J. (2019). Neural nets for stock indices: Investigating effect of change in hyperparameters. Theoretical Economics Letters, 9(3), 511–529. https://doi.org/10.4236/tel.2019.93036
  • Agarwal, S. (2019). Mutual funds are subject to market risks: Empirical evidence from India. The Journal of Wealth Management, 22(2), 66–85. https://doi.org/10.3905/jwm.2019.22.2.066
  • Agarwal, S. (2022). Deep learning-based sentiment analysis: Establishing customer dimension as the lifeblood of business management. Global Business Review, 23(1), 119–136. https://doi.org/10.1177/0972150919845160
  • Chang, P.-C., Liu, C.-H., Lin, J.-L., Fan, C.-Y., & Ng, C. S. (2009). A neural network with a case based dynamic window for stock trading prediction. Expert Systems with Applications, 36(3), 6889–6898. https://doi.org/10.1016/j.eswa.2008.08.077
  • Cheng, J.-H., Chen, H-P., & Lin, Y-M. (2010). A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4.5. Expert Systems with Applications, 37(3), 1814–1820. https://doi.org/10.1016/j.eswa.2009.07.019
  • Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054
  • Fletcher, D., & Goss, E. (1993). Forecasting with neural networks : An application using bankruptcy data. Information & Management, 24(3), 159–167. https://doi.org/10.1016/0378-7206(93)90064-Z
  • Ghiassi, M., & Saidane, H. (2005). A dynamic architecture for artificial neural networks. Neurocomputing, 63, 397–413. https://doi.org/10.1016/j.neucom.2004.03.014
  • Ghiassi, M., Saidane, H., & Zimbra, D. K. (2005). A dynamic artificial neural network model for forecasting time series events. International Journal of Forecasting, 21(2), 341–362. https://doi.org/10.1016/j.ijforecast.2004.10.008
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. http://www.deeplearningbook.org
  • Gupta, N., & Dalal, Y. (2022). Reconnoitering price discovery and market efficiency process among Indian HRITHIK stocks using VAR causality and VECM tests. Indian Journal of Finance, 16(2), 37–50. https://doi.org/10.17010/ijf/2022/v16i2/162434
  • Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389–10397. https://doi.org/10.1016/j.eswa.2011.02.068
  • Hamzaçebi, C., Akay, D., & Kutay, F. (2009). Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting. Expert Systems with Applications, 36(2), 3839–3844. https://doi.org/10.1016/j.eswa.2008.02.042
  • Hong, Y., & Lee, T.-H. (2003). Inference on predictability of foreign exchange rates via generalized spectrum and nonlinear time series models. The Review of Economics and Statistics, 85(4), 1048–1062. https://doi.org/10.1162/003465303772815925
  • Hu, M. Y., Zhang, G., Jiang, C. X., & Patuwo, B. E. (1999). A cross-validation analysis of neural network out-of-sample performance in exchange rate forecasting. Decision Sciences, 30(1), 197–216. https://doi.org/10.1111/j.1540-5915.1999.tb01606.x
  • Huck, N. (2019). Large data sets and machine learning: Applications to statistical arbitrage. European Journal of Operational Research, 278(1), 330–342. https://doi.org/10.1016/j.ejor.2019.04.013
  • Joshi, N. (2021). Volatility, open interest, and trading volume in Indian futures markets. Indian Journal of Finance, 15(11), 41–54. https://doi.org/10.17010/ijf/2021/v15i11/166831
  • Kayacan, E., Ulutas, B., & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert Systems with Applications, 37(2), 1784–1789. https://doi.org/10.1016/j.eswa.2009.07.064
  • Kiani, K. M. (2005). Detecting business cycle asymmetries using artificial neural networks and time series models. Computational Economics, 26, 65–89. https://doi.org/10.1007/s10614-005-7366-2
  • Kim, A., Yang, Y., Lessmann, S., Ma, T., Sung M.-C., & Johnson, J. E. (2020). Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting. European Journal of Operational Research, 283(1), 217–234. https://doi.org/10.1016/j.ejor.2019.11.007
  • Kuan, C.-M., & White, H. (1994). Artificial neural networks: An econometric perspective. Econometric Reviews, 13(1), 1–91. https://doi.org/10.1080/07474939408800273
  • Lenard, M. J., Alam, P., & Madey, G. R. (1995). The application of neural networks and a qualitative response model to the auditor's going concern uncertainty decision. Decision Sciences, 26(2), 206–227. https://doi.org/10.1111/j.1540-5915.1995.tb01426.x
  • Liao, Z., & Wang, J. (2010). Forecasting model of global stock index by stochastic time effective neural network. Expert Systems with Applications, 37(1), 834–841. https://doi.org/10.1016/j.eswa.2009.05.086
  • Lyons, R. K. (2001). New perspective on FX markets: Order-flow analysis. International Finance, 4(2), 303–320. https://doi.org/10.1111/1468-2362.00075
  • Medeiros, M. C., Veiga, A., & Pedreira, C. E. (2001). Modeling exchange rates: Smooth transitions, neural networks, and linear models. IEEE Transactions on Neural Networks, 12(4), 755–764. https://doi.org/10.1109/72.935089
  • Moghaddam, A. H., Moghaddam, M. H., & Esfandyari, M. (2016). Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science, 21(41), 89–93. https://doi.org/10.1016/j.jefas.2016.07.002
  • Sager, M. J., & Taylor, M. P. (2006). Under the microscope: The structure of the foreign exchange market. International Journal of Finance & Economics, 11(1), 81–95. https://doi.org/10.1002/ijfe.277
  • Salchenberger, L. M., Cinar, E. M., & Lash, N. A. (1992). Neural networks: A new tool for predicting thrift failures. Decision Sciences, 23(4), 899–916. https://doi.org/10.1111/j.1540-5915.1992.tb00425.x
  • Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5, Article 16. https://doi.org/10.1186/s40854-019-0131-7
  • Seth, N., & Sidhu, A. (2021). Price discovery and volatility spillover for Indian energy futures market in the pre-and post-crisis periods. Indian Journal of Finance, 15(8), 24–39. https://doi.org/10.17010/ijf/2021/v15i8/165816
  • Shahvaroughi Farahani, M., & Razavi Hajiagha, S. H. (2021). Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models. Soft Computing, 25, 8483 – 8513. https://doi.org/10.1007/s00500-021-05775-5
  • Singh, A., Jain, M., Jain, S., & Gupta, B. (2021). A new modus operandi for determining post - IPO pricing : Analysis of Indian IPOs using artificial neural networks. Indian Journal of Finance, 15(1), 8–22. https://doi.org/10.17010/ijf/2021/v15i1/157011
  • Swanson, N. R., & White, H. (1995). A model-selection approach to assessing the information in the term structure using linear models and artificial neural networks. Journal of Business & Economic Statistics, 13(3), 265–275. https://doi.org/10.1080/07350015.1995.10524600
  • Swanson, N. R., & White, H. (1997a). A model selection approach to real-time macroeconomic forecasting using linear models and artificial neural networks. The Review of Economics and Statistics, 79(4), 540–550. https://doi.org/10.1162/003465397557123
  • Swanson, N. R., & White, H. (1997b). Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models. International Journal of Forecasting, 13(4), 439 – 461. https://doi.org/10.1016/S0169-2070(97)00030-7
  • Tyree, E. W., & Long, J. A. (1995). Forecasting currency exchange rates: Neural networks and the random walk model. In, Proceedings of the Third International Conference on Artificial Intelligence Applications, New York. http://citeseer.nj.nec.com/131893.html
  • Villada-Duque, F., López-Lezama, J. M., & Barrientos-Marín, J. (2020). Forecasting prices in financial markets using artificial neural networks. International Journal of Engineering Research and Technology, 13(11), 3247–3250. https://doi.org/10.37624/IJERT/13.11.2020.3247-3250
  • Zhang, G., & Hu, M. Y. (1998). Neural network forecasting of the British pound/US dollar exchange rate. Omega, 26(4), 495–506. https://doi.org/10.1016/S0305-0483(98)00003-6
  • Zurada, J. M., Foster, B. P., Ward, T. J., & Barker, R. M. (1999). Neural networks versus logit regression model for predicting financial distress response variables. Journal of Applied Business Research, 15(1), 21–30. https://doi.org/10.19030/jabr.v15i1.5685

Abstract Views: 16

PDF Views: 0




  • Deep Learning in Financial Analytics : Exchange Rate Modelling

Abstract Views: 16  |  PDF Views: 0

Authors

Sonali Agarwal
University School of Humanities and Social Sciences, Guru Gobind Singh Indraprastha University, Sector-16-C, Dwarka, Delhi - 110 078, India

Abstract


In finance, a major enthralling research question has been the accurate determination of future market and economic movements. A lot of researchers have tried to develop different models with different accuracies of prediction over the years. It appears that the full potential of deep learning has not been explored to study FX rates. The current study, therefore, explored the proficiency of deep neural networks in predictive modeling. I tested different models of artificial neural networks (using hyperparameters’ tuning like training algorithms, number of hidden layers, and hidden nodes) using neural network input-output fitting and tried to find the best fit model. The model was also validated by layered digital dynamic time series modeling using autoregression with two delays. The appraisal metrics used were regression R - value, MSE, time-series response plot, and error autocorrelation plot. It was concluded that the artificial neural network with a single hidden layer having 17 nodes and trained using the Levenberg– Marquardt algorithm gave the best performance in a minimum number of iterations. This study marks an extensive examination of ANN modeling. This model can be used by traders, investors, financiers, economists, bankers, speculators, hedgers, and governments to get insights into future forex rates and thus make profitable decisions. Various trading policies, import-export policies, and pricing of commodities in indigenous markets can be managed precisely. Future studies can use these models in simulated trading and help establish an alliance between statistical significance and economic significance.

Keywords


Forex Forecasting, Predictive Modeling, Deep Neural Network, Input-Output Fitting, Training Algorithm, Hidden Layer, Error Autocorrelation, Back-Propagation, Hyperparameter, Incremental Training.

JEL Classification Codes : C880, F470, G170

Paper Submission Date : June 25, 2021 ; Paper sent back for Revision : April 26, 2022 ; Paper Acceptance Date : May 20, 2022 ; Paper Published Online : September 15, 2022


References





DOI: https://doi.org/10.17010/ijf%2F2022%2Fv16i9%2F172157