Net Asset Value (NAV) Prediction using Dense Residual Models

Document Type : Research Paper

Authors

1 Department of Management Studies, Faculty of Management studies, Jamia Millia Islamia, New Delhi-110025, India.

2 Department of Computer Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia,New Delhi-110025, India.

3 Department of Computer Engineering, Jamia Millia Islamia University, Delhi, India.

10.22059/jitm.2023.95249

Abstract

Net Asset Value (NAV) has long been a key performance metric for mutual fund investors. Due to the considerable fluctuation in the NAV value, it is risky for investors to make investment decisions. As a result, accurate and reliable NAV forecasts can help investors make better decisions and profit. In this research, we have analysed and compared the NAV prediction performance of our proposed deep learning models, such as N-BEATS and NBSL, with the FLANN model in both univariate and multivariate settings for five Indian mutual funds for forecast periods of 15, 20, 45, 63, 126, and 252 days using RMSE, MAPE, and R2 as evaluation metrics. A large forecast horizon was chosen to assess the model's consistency, reliability, and accuracy. The result reveals that the N-BEATS model outperforms the FLANN and NBSL models in the univariate setting for all datasets and all prediction horizons. In a multivariate setting, the outcome demonstrates that the N-BEATS model outperforms the FLANN model across all datasets and prediction horizons. The result also shows that, as the number of forecast days grew, our suggested models, notably N-BEATS, maintained consistency and attained the highest R2 value throughout the longest forecast duration.

Keywords

References

 
Alam, S et al. (2021). A lossless digital watermarking scheme based on a new 3D-hyper chaotic map. Int J Electron Bus, 16 (1), 88–102. https://doi.org/10.1504/IJEB.2021.112769
Alam, S et al. (2021). Dual secure robust watermarking scheme based on hybrid optimization algorithm for image security. Pers Ubiquit Comput 1–13. https://doi.org/10.1007/s00779-021-01597-2
Anish, C.M., & Majhi, B. (2015). An ensemble model for net asset value prediction. IEEE Power, Communication and Information Technology Conference, 392–396.  https://doi.org/10.1109/PCITC.2015.7438197.
Anish, C.M., & Majhi, B. (2015). Net asset value prediction using FLANN model. International Journal of Science and Research, 4(2), 2222–2227.
Anish, C.M., & Majhi, B. (2016). Prediction of mutual fund net asset value using low complexity feedback neural network. IEEE International Conference on Current Trends in Advanced Computing, 1–5. https://doi.org/10.1109/ICCTAC.2016.7567345.
Bressan, G. M., de Azevedo, B. C. F., & de Souza, R. M. (2020). A fuzzy approach for diabetes mellitus type 2 classification. Brazilian Archives of Biology and Technology, 63. https://doi.org/10.1590/1678-4324-2020180742
Chiang, W.-C., Urban, T. L., & Baldridge, G.W. (1996). A neural network approach to mutual fund net asset value forecasting. Omega, 24(2), 205-215. https://doi.org/10.1016/0305-0483(95)00059-3.
Devi, U. R., & Uma, K. (2019). A Study on Fuzzy Expert System for Diagnosis of Diabetes Mellitus. International Journal of Applied Engineering Research (IJAER), 14(4), 129–139. https://www.ripublication.com/ijaerspl2019/ijaerv14n4spl_16.pdf
Galicia, A., Talavera-Llames, R., Troncoso, A., Koprinska, I., & Martinez-Alvarez, F. (2019). Multi- step forecasting for big data time series based on ensemble learning. Knowledge-Based Systems, 16, 830-841. https://doi.org/10.1016/j.knosys.2018.10.009.
Geman, O., Chiuchisan, I., & Toderean, R. (2017). Application of Adaptive Neuro-Fuzzy Inference System for diabetes classification and prediction. 2017 E-Health and Bioengineering Conference, EHB 2017, Dm, 639–642. https://doi.org/10.1109/EHB.2017.7995505
Henrique, B.M., Sobreiro, V.A., & Kimura, H. (2019). Literature review: machine learning   techniques applied to financial market prediction. Expert Syst. Appl, 124, 226–251.    https://doi.org/10.1016/J.ESWA.2019.01.012.
Hota, S., Jena, S.K., Gupta, B.K., & Mishra, D. (2021). An Empirical Comparative Analysis of NAV   Forecasting Using Machine Learning Techniques. In: Mishra, D. (eds.), Intelligent and Cloud   Computing. Smart Innovation, Systems and Technologies, 153, 565-572.  https://doi.org/10.1007/978-981-15-6202-0_58.
Hota, S., Pati, S.P., & Satapathy, P. (2021). Forecasting of Net Asset Value of Indian Mutual Funds Using Firefly Algorithm-Based Neural Network Model. In: Sharma, R. et al. (eds.), Green Technology for Smart City and Society. Lecture Notes in Networks and Systems, 151.                      https://doi.org/10.1007/978-981-15-8218-9_18.
Hota, S., Satapathy, P., Pati, S. P., & Mishra, D. (2018). Net Asset Value Prediction Using Extreme Learning Machine with Dolphin Swarm Algorithm. 2nd International Conference on Data Science and Business Analytics (ICDSBA), 13-18. https://doi.org/10.1109/ICDSBA.2018.00010.
Howsalya Devi, R. D., Bai, A., & Nagarajan, N. (2020). A novel hybrid approach for diagnosing diabetes mellitus using farthest first and support vector machine algorithms. Obesity Medicine, 17, 100152. https://doi.org/10.1016/j.obmed.2019.100152
Indro, D. C., Jiang, C. X., Patuwo, B. E., & Zhang, G. P. (1999). Predicting mutual fund performance using Artificial Neural Networks, Omega, 27(3), 373-380. https://doi.org/10.1016/S0305-0483(98)00048-6.
Khalil, R. M., & Al-Jumaily, A. (2017). Machine learning based prediction of depression among type 2 diabetic patients. Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017, 2018-Janua, 1–5. https://doi.org/10.1109/ISKE.2017.8258766
Khan, T., Singh, K., Manjul, M., Ahmad, M. N., Zain, A. M., & Ahmadian, A. (2022). A Temperature-Aware Trusted Routing Scheme for Sensor Networks: Security Approach. Computers & Electrical Engineering, 98, 107735.
Koudjonou, K.M., & Rout, M. (2020). A stateless deep learning framework to predict net assetvalue. Neural Computing & Appliations, 32, 1–19. https://doi.org/10.1007/s00521-019-04525-x.
Kumar, A., Singh, K., & Khan, T. (2021). L-RTAM: Logarithm based reliable trust assessment model for WBSNs. Journal of Discrete Mathematical Sciences and Cryptography, 24(6), 1701-1716.
Kumar, A., Singh, K., Khan, T., Ahmadian, A., Saad, M. H. M., & Manjul, M. (2021). ETAS: an efficient trust assessment scheme for BANs. IEEE Access, 9, 83214-83233.
Law, A., Evani, B., & Ghosh, A. (2019). Optimized Functional Link Artificial Neural Network for Multi-label Classification. ICONIP Proceedings. Australian Journal of Intelligent Information Processing Systems, 16, 56-63
Lin, H.-S., Chen, M.-L., Tong, C.-C., & Dai, J.-W. (2007). Using grey and RBFNN to predict the Net Asset Value of single nation equity funds-a case study of Taiwan, US, and Japan. IEEE International Conference on Grey Systems and Intelligent Services, 892-897. http://dx.doi.org/10.1109/GSIS.2007.4443402.
Lukmanto, R. B., Suharjito, Nugroho, A., & Akbar, H. (2019). Early detection of diabetes mellitus using feature selection and fuzzy support vector machine. Procedia Computer Science, 157, 46–54. https://doi.org/10.1016/j.procs.2019.08.140
Majhi, B., Anish, C.M., & Majhi, R. (2021). On development of novel hybrid and robust adaptive models for net asset value prediction. Journal of King Saud University- Computer and Information Sciences, 33(6), 647-657.
Mili, F., & Hamdi, M. (2013). A comparative study of expansion functions for evolutionary hybrid functional link artificial neural networks for data mining and classification. International Conference on Computer Applications Technology (ICCAT), 1-8. https://doi.org/10.1109/ICCAT.2013.6521977.
Misra S., Li, H. (2020). Chapter 9 - Noninvasive fracture characterization based on the classification of sonic wave travel times. Machine Learning for Subsurface Characterization. Gulf Professional Publishing.  243-287. https://doi.org/10.1016/B978-0-12-817736-5.00009-0.
Mohanty, S., & Dash, R. (2021). A flower pollination algorithm based Chebyshev polynomial neural network for net asset value prediction. Evolutionary Intelligence, 1-17.  https://doi.org/10.1007/s12065-021-00645-3.
Narula, A., Jha, C.B., & Panda, G. (2015). Development and Performance Evaluation of Three Novel Prediction Models for Mutual Fund NAV Prediction. Annual Research Journal of SymbiosisCentre for Management Studies, 3, 227-238.
Niswati, Z., Mustika, F. A., & Paramita, A. (2018). Fuzzy logic implementation for diagnosis of Diabetes Mellitus disease at Puskesmas in East Jakarta. Journal of Physics: Conference Series, 1114 (1). https://doi.org/10.1088/1742-6596/1114/1/012107
Oreshkin, B.N., Carpov, D., Chapados, N., & Bengio, Y. (2020). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. ArXiv, abs/1905.10437.
Priyadarshini, E. (2015). A comparative analysis of prediction using Artifcial Neural network and auto regressive integrated moving average. ARPN Journal of Engineering and Applied Sciences, 10(7), 3078-3081.
Priyadarshini, E., & Babu, A.C. (2011). Prediction of the Net Asset Aalues of Indian mutual funds using Auto-Regressive Integrated Moving Average (ARIMA).Journal of Computer Application Research and development, 1(1). 43-48.
Priyadarshini, E., & Babu, A.C. (2012). A Comparative Analysis for forecasting the NAV’s of Indian Mutual Fund using Multiple Regression Analysis and Artificial Neural Networks. International Journal of Trade, Economics and Finance, 3(5), 347-350. https://doi.org/ 10.7763/IJTEF.2012.V3.225.
Raj, R. S., Sanjay, D. S., Kusuma, M., & Sampath, S. (2019). Comparison of Support Vector Machine and Naïve Bayes Classifiers for Predicting Diabetes. 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2019, 41–45. https://doi.org/10.1109/ICATIECE45860.2019.9063792
Rajeswari, A. M., Sidhika, M. S., Kalaivani, M., & Deisy, C. (2018). Prediction of Prediabetes using Fuzzy Logic based Association Classification. Proceedings of the International Conference on Inventive Communication and Computational Technologies, ICICCT 2018, Icicct, 782–787. https://doi.org/10.1109/ICICCT.2018.8473159
Ray, P., & Vina, V. (2004). Neural Network Models for Forecasting Mutual Fund Net Asset Value. 8th Capital Markets Conference, Indian Institute of Capital Markets Paper. http://dx.doi.org/10.2139/ssrn.872269.
Rout, M., Koudjonou, K.M., & Satapathy, S.C. (2021). Analysis of net asset value prediction using low complexity neural network with various expansion techniques. Evolutionary Intelligence, 14 (2). https://doi.org/10.1007/s12065-020-00365-0.
Santos Júnior, D.S.de O., De Oliveira, J.F.L., & De Mattos Neto, P.S.G. (2019). An intelligent hybridization of ARIMA with machine learning models for time series forecasting. Knowledge-Based Systems, 175, 72-86. https://doi.org/10.1016/j.knosys.2019.03.011.
Sarwar, M. A., Kamal, N., Hamid, W., & Shah, M. A. (2018). Prediction of diabetes using machine learning algorithms in healthcare. ICAC 2018 - 2018 24th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing, September, 1–6. https://doi.org/10.23919/IConAC.2018.8748992
Shakeel, M. R., Siddiqui, T. A., & Alam, S. (2023). Feature Selection in Corporate Bankruptcy Prediction Using ML Techniques: A Systematic Literature Review. Advances in Signal Processing, Embedded Systems and IoT. Lecture Notes in Electrical Engineering, 992. pringer, Singapore. https://doi.org/10.1007/978-981-19-8865-3_32
Siami-Namini, S., Tavakoli, N., & Saimi-Namini, A. (2019). The Performance of LSTM and BiLSTM in Forecasting Time Series. IEEE International Conference on Big Data. 3285-3292. https://doi.org/10.1109/BigData47090.2019.9005997
Siddiqui, T. A., Shakeel, M. R., & Alam, S. (2023). Mena Sukuk Price Prediction Modelling using Prophet Algorithm. Intelligent Data Analytics, IoT, and Blockchain, CRC press. Taylor & Francis Group.
Swain, A., Mohanty, S., & Das, A. (2013). Comparative Risk Analysis on Prediction of Diabetes Mellitus Using Machine Learning Approach. 1, 3312–3317. International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016
Thakkar, H., Shah, V., Yagnik, H., & Shah, M. (2021). Comparative anatomization of data mining and fuzzy logic techniques used in diabetes prognosis. Clinical EHealth, 4, 12–23. https://doi.org/10.1016/j.ceh.2020.11.001
Thakur, D. (2018, July 6). LSTM and its equations. https://medium.com/@divyanshu132/lstm-and-its-equations-5ee9246d04af
Undre, P., Kaur, H., & Patil, P. (2015). Improvement in prediction rate and accuracy of diabetic diagnosis system using fuzzy logic hybrid combination. 2015 International Conference on Pervasive Computing: Advance Communication Technology and Application for Society, ICPC 2015, 00(c). https://doi.org/10.1109/PERVASIVE.2015.7087029
Verma, D, M. N. (2017). Using Data mining classification Techniques. 2017 International Conference on Intelligent Sustainable Systems (ICISS), Iciss, 533–538.
Vijiyakumar, K., Lavanya, B., Nirmala, I., & Sofia Caroline, S. (2019). Random forest algorithm for the prediction of diabetes. 2019 IEEE International Conference on System, Computation, Automation and Networking, ICSCAN 2019, 1–5. https://doi.org/10.1109/ICSCAN.2019.8878802
Yan, H., Liu, W., Liu, X., Kong, H., & Lv, C. (2010). Predicting Net Asset Value of Investment FundBased on BP Neural Network. IEEE International Conference on Computer Application and System Modeling, 10, pp. 635-637.  https://doi.org/10.1109/ICCASM.2010.5622625

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