Smart portfolio using quantitative investment models
Subject Areas :
Financial engineering
reza mansourian
1
(Department of Accounting and Financial Management, Maragheh Branch, Islamic Azad University, Maragheh, Iran)
Nader Rezaei
2
(Department of Accounting and Financial Management, Bonab Branch, Islamic Azad University, Bonab, Iran)
sayyedAli Nabavichashmi
3
(Department of Financial Management, Babol Branch, Islamic Azad University, Mazandaran, iran.)
Ahmad Pouyanfar
4
(Department of Finance, Khatam University, Tehran, iran.)
Ali Abdollahi
5
(Department of Mathematics, Maragheh Branch, Islamic Azad University, Maragheh, iran)
Received: 2019-09-02
Accepted : 2019-10-06
Published : 2020-09-22
Keywords:
"Smart Portfolio",
"Momentum Algorithm",
"Kalman Filter",
Abstract :
In the past few decades, the identification of state variables and parameters of a model from measured data has increased dramatically. This widespread growth has created a growing need for integrated models. Achieving sustained and long-term economic growth requires optimal resource allocation, and this is not possible without the use of financial markets, especially efficient capital markets, so portfolio optimization and wealth allocation between different assets are among the most important issues in investing. In this research, in order to implement smart financial portfolio, it is tried to improve the existing optimization methods based on Sharp Ratio performance and to present an intelligent method for trading based on different algorithms. For this purpose, first, create a quantitative investment model using momentum algorithm and long-term investment model over a 6-year time horizon using monthly stock exchange data and then a set of smart models (general functions, general average and The general algorithm (developed by Kalman filter), which calculates the amount of capital using smart patterns to maximize return and avert negative return on equity investments and optimize capital investing to make the proposed structure perform better than other algorithms. Conventional and can fit and alternative approaches to achieve better results finally, the results indicate that the proposed model is effective and efficient.
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Asmerilda Hitaj, Giovanni Zambruno, (2016), Are Smart Beta strategies suitable for Hedge Funds portfolios?, Review of Financial Economics, doi:10.1016/j.rfe.2016.03.001
Achelis(2000). Journal of Accounting Research. s.l.: Vision Books,
Ang A., Chen J. and Xing Y. (2006). Downside Risk: Review of Financial Studies, 19(4), 1191-1239.
Black, F. (1972). Capital Market Equilibrium with Restricted Borrowing. s.l.: Journal of Finance,. 45(3), 444-455.
Chetran saran mehra, adam prugel , Bennett, (2016), Constructing Smart Portfolios From Data Driven Quantitative Investment Models, A thesis submitted in partial ful_llment for the degree of Doctor of Philosophy.
Raza , Muhammad Wajid & Ashraf, Dawood, (2018). "Does the Application of Smart Beta Strategies Enhance Portfolio Performance? The Case of Islamic Equity Investments," Working Papers 2018-1, The Islamic Research and Teaching Institute (IRTI).
Baba, T. Kawachi, T. Nomura, Y. Sakatani, (2004), “Utilization of NNs & Gas for improving the traditional technical analysis in the financial market”, SICE annual Conference, 2(2), 1409-1412.
Torrubiano, R.and Suarez ,A(2008).”A Hybrid Optimization Approach to Index Tracking”, Operation Research Journal, 166
Fabozzi, F. J. & Markowitz, H. M. (2011). Equity Valuation and Portfolio Management. Vol. 199. John Wiley & Sons.
Hirabayashi, A., Aranha, C., & Hitoshi, I. (2009). Optimization of the trading rule in foreign exchange using genetic algorithm. . s.l.: ACM Genetic and Evolutionary Computation,. 1529-1536.
Huang, C., Chang, C., Li, Kuo, Bo, Lin, Hsieh, T., & Chang, B. (2012). A genetic-search model for first-day returns using fundamentals. . s.l.: Machine Learning and Cybernetics, 5, 1662-1667.
Kaucic, M. (2012). Portfolio management using artificial trading
Huck, N. & Afawubo, K. (2015). Pairs trading and selection methods: is cointegration superior? Applied Economics. 47(6): pp: 599-613.
Harry Markowitz, 1991, Portfolio selection: efficient diversification of investments, Wiley-Blackwell.
Hon, M.T., I. tonks (2003). “Momentum in the UK stock markets”, Journal of Multinational Financial Management. 13 (1):43-70.
Jegadeesh, N. and Titman, S. (1993). Returns to buying winners and selling losers: implications for stock market efficiency, Journal of Finance, Vol. 48, pp. 65-91.
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of basic Engineering. 82(1): 35-45.
Simpson, P.W., & Osborn, D.R., & Sensier, M. (2001). Modelling businesscycle movements in the UK economy. Economica, 68: 243-267.
Usta, I., Y.M. Kantar (2011). Mean-Variance-Skewness-Entropy Measures: A Multi-Objective Approach for Portfolio Selection, Entropy. 13: 117-133.
F. Sharpe, “Capital asset prices: A theory of market equilibrium under conditions of risk,” The Journal of Finance, pp. 425–442, 1964.
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World Scientific Press, 2011.
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Gatev, W. N. Goetzmann, and K. G. Rouwenhorst, “Pairs trading: Performance of a relative-value arbitrage rule,” Review of Financial Studies, vol. 19, pp. 797–827, 2006.
Kelly, “A new interpretation of information rate,” Information Theory, vol. 2, pp. 185–189, 1956.
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