Financial Asset Management Using Artificial Neural Networks
Authors: 
William A. Young II, Gary R. Weckman, Roohollah Younes Sinaki, Azadeh Sadeghi, Dustin S. LynchAuthors Info & Claims
William A. Young II, Gary R. Weckman, Roohollah Younes Sinaki, Azadeh Sadeghi, Dustin S. LynchAuthors Info & ClaimsPages 66 - 86
Abstract
Investors typically build portfolios for retirement. Investment portfolios are typically based on four asset classes that are commonly managed by large investment firms. The research presented in this article involves the development of an artificial neural network-based methodology that investors can use to support decisions related to determining how assets are allocated within an investment portfolio. The machine learning-based methodology was applied during a time period that included the stock market crash of 2008. Even though this time period was highly volatile, the methodology produced desirable results. Methodologies such as the one presented in this article should be considered by investors because they have produced promising results, especially within unstable markets.
References
[1]
Akinwale, A., Arogundade, O., & Adekoya, A. (2009). Translated Nigeria Stock Market Prices Using Artificial Neural Network for Effective Prediction. Journal of Theoretical and Applied Information Technology, 9, 36–43.
[2]
Burney, K. (1997). Introduction to Neural Networks. UCL Press.
[3]
Canner, N., Mankiw, N., & Weil, D. (1997). An Asset Allocation Puzzle. The American Economic Review, 87(1), 181–191.
[4]
Charles Schwab. (2015). Corporation. Retrieved from https://en.wikipedia.org/wiki/Charles_Schwab_Corporation
[5]
Chawla, N., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-Sampling Technique. Artificial Intelligence Research, 16, 321–357.
[6]
Chiang, W. (1996). A Neural Network Approach to Mutual Fund Net Asset Value Forecasting. Management Science, 24(2), 205–215.
[7]
Dictionary. (2015). Moving Average. Retrieved from http://dictionary.reference.com/browse/moving+average
[8]
Economic Research Federal Reserve Bank of St. Louis. (2014). Retrieved from https://research.stlouisfed.org/useraccount/datalists/126909
[9]
Encyclopedia Britannica. (2015). Bear market. Retrieved from http://www.britannica.com/EBchecked/topic/57332/bear-market
[10]
Encyclopedia Britannica. (2015). Bull market. Retrieved from http://www.britannica.com/EBchecked/topic/84336/bull-market
[11]
Hui, S., Yap, M., & Prakash, P. (2000). A Hybrid Time Lagged Network for Predicting Stock Prices. International Journal of the Computer, the Internet, and Management, 8(3).
[12]
Jiang, Z., Xu, D., & Liang, J. (2017). A deep reinforcement learning framework for the financial portfolio management problem. arXiv preprint.
[13]
Lakshminarayanan, S. (2005). An Integrated Stock Market Forecasting Model Using Neural Networks (M.S. Thesis). Ohio University.
[14]
Morningstar Principia. (2015). PNR General. Retrieved from http://advisor.morningstar.com/Principia/pdf/PRN_General_040212.pdf
[15]
Neural Networks. (1989). Multilayer feedforward networks are universal approximators. Retrieved from http://portal.acm.org/citation.cfm?id=70408
[16]
Principe, J., Euliano, N., & Lefebvre, W. (1999). Neural and adaptive systems: Fundamentals through simulations. John Wiley & Sons Inc.
[17]
Qian, B., & Rasheed, K. (2007). Stock market prediction with multiple classifiers. Applied Intelligence, 26(1), 25–33.
[18]
Skapura, D. (1996). Building Neural Networks. Addison-Wesley.
[19]
Suvarna, V. (2013). SMOTE Based Protein Fold Prediction Classification. Advances in Computing & Inform. Technology, 541–550.
[20]
Vaisla, K. B. (2010). An Analysis of the Performance of Artificial Neural Network Technique for Stock Market Forecasting. International Journal on Computer Science and Engineering, 2, 2104–2109.
[21]
Weckman, G., Dravenstott, R., Young, W.II, Ardjmand, E., Millie, D., & Snow, A. (2016). A Prescriptive Stock Market Investment Strategy for the Restaurant Industry using an Artificial Neural Network Methodology. International Journal of Business Analytics, 3(3), 1–21.
[22]
Weckman, G., Lakshminarayanan, S., Snow, A., & Marvel, J. (2008). An Integrated Stock Market Forecasting Model Using Neural Networks. International Journal of Business Forecasting and Marketing Intelligence, 1(1), 30.
[23]
Young, W., Holland, W., & Weckman, G. (2008). Determining Hall of Fame Status for Major League Baseball Using an Artificial Neural Network. Journal of Quantitative Analysis in Sports, 4(4).
[24]
Young, W.II, & Weckman, G. (2009). Using a heuristic approach to derive a grey-box model through an artificial neural network knowledge extraction technique. Neural Computing & Applications, 19(3), 353–366.
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IGI Global
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Published: 01 July 2020
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