Style investing and portfolio composition based on fundamental ratios and technical indicators

Subject Areas : Journal of Investment Knowledge

Kamran  Pakize ¹ (Department of Financial Science, Kharazmi University)
Milad  Rahmani ² (Master of Financial Engineering, Kharazmi University)
Fatemeh  Azizzade ³ (Department of Financial Science, Kharazmi University)

Received: 2016-09-07 Accepted : 2016-10-20 Published : 2017-03-21

Keywords: Style Investing, Value Factor, Size Factor, Quality Factor, PSO, Technical Analysis, Fundamental analysis, Transaction Cost Constraint,

Abstract :

Portfolio management and optimization is a crutial issue in investment management. Style investing is a way to earn more returne rather than a benchmark index. A style can be thought of as any characteristic relating a group of securities that is important in explaining their return and risk. These factors are grounded in academic research and have solid explanations as to why they historically have provided a premium. Empirical studies show that these factors have exhibited excess returns above the market. This study describes a new approach to portfolio management using stocks. The investment models tested incorporate a fundamental and technical approach using financial ratios and technical indicators. Also the effect of using six equity risk premia factors(styles) are examined in the study as follows: Value,Growth, Low Cap size, Big Cap size ,High Quality and Low Quality on portfolio performance. For doing this, a Particle Swarm Optimization (PSO) is used to optimize the model. As a case study, Performance of proposed method is tested by historical data of Tehran Stock Exchange for years 2012 to 2015. Results show that the proposed method has obtained higher Sortino ratio rather than the Tehran Stock Exchange market index. Also, Small cap stocks portfolio captured higher returne rather than Big caps for all three years, and Growth stocks portfolio had bether performance in contrast to Value stock for 2012 and 2013. For the Quality factor, High quality stocs portfolio had a better performance in contrast to Low quality stocks portfolio for 2012 and 2013.

References:

*       اسلامی‌بیدگلی، غلامرضا، فلاح پور، سعید، سبزواری، بهادر(1391)،" مقایسه بازدهی روش‌های مختلف انتخاب سهام ارزشی و رشدی بر اساس مدل شش عاملی‌هاگن در بورس اوراق بهادار تهران"، فصل نامه دانش سرمایه گذاری، شماره1.

*       راعی، رضا، شواخی زواره، علیرضا.(1385)."بررسی عملکرد استراتژی های سرمایه گذاری در بورس اوراق بهادار تهران" ، نشریه علمی-پژوهشی تحقیقات مالی سال8، شماره 21.

*       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.

*       Briza, A.C.Naval, P.C. (2011). Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data. s.l.: Applied Soft Computing,. 11, 1191-1201.

*       Carhart, M.M. (1997). on Persistence in Mutual Fund Performance. s.l.: Journal of Finance,. 66(4), 457-472.

*       Casanova, I. J. Trading-LCS, (2010). Dynamic stock portfolio decision-making assistance model with based machine learning. . s.l.: congress on evolutionary computation.

*       Crama, Y. and Schyns, M. (2003). Simulated annealing for complex portfolio selection problems. s.l.: European Journal of Operational Research. 50: 546-571.

*       Dorener, K. et al. Pareto. (2004). Ant Colony Optimization: A Meta heuristic Approach to Multi objective Portfolio Selection. s.l.: Annals of Operations Research,. 2004. 131: 79-99.

*       Fama, E.F, French, K.R. (1993).  Common Risk Factors in the Returns on Stocks and Bonds. s.l.: Journal of Financial Economics,. 33, 3-56.

*       Golmakani, H.R., Fazel, M.  (2011). Constrained Portfolio Selection using Particle Swarm Optimization. s.l: Expert Systems with Applications. 38: 8327-8335.

*       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 systems based on technical analysis. s.l.: Genetic algorithms in applications. Intec,. Chapter 15.

*       Lin, C.C. and Liu.Y.T. iu.Y.T. (2008) Genetic algorithms for portfolio selection problems with minimum transaction lots. . s.l.: European Journal of Operational Research, 185: 393-404.

*       Markowitz.H. (1952). Portfolio selection. s.l.: Journal of Finance,. Vol. 7.

*       Mousavi, S.Esfahanipour, M. Fazel, M.H. (2014). A Novel Approach to Dynamic Portfolio Trading System Using Multitree Genetic Programming. s.l.: Knowledge Based Systems.

*       Pätäri, E. (2000). Essays on portfolio performance measurement. Lappeenranta.

*       Piotroski, J.D So, E.C. (2012) Identifying expectation errors in value/glamour strategies: a fundamental analysis approach. . s.l.: Finance stud. 25(9), 2841-2875

*       Sharp, W.F. (1964). Capital Asset Pricing: A Theory of Market Equilibrium under Conditions of Risk. s.l.: Journal of Finance,. 19, 425-441.

*       Silva, A.,Neves, R.,Horta, N. (2015). A hybrid approach to portfolio composition based on fundamental and technical indicators. s.l.: Expert systems with application. 42, 2036-2048.

*       Soleimani, H., Golmakani, H.R. and Salimi, M.H. (2009).  Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm. s.l. : Expert Systems with Applications,. 36: 5058-5063.

*       Stambaugh, Pastor L. (2001). Liquidity Risk and Expected Stock Returns. s.l.: Journal of Political Economy,. 111(3), 642-685.

*       Woodside-Oriakhi, M., Lucas, C. and Beasley, J.E. (2011).Heuristic algorithms for the cardinality constrained efficient frontier. s.l.: European Journal of Operational Research, 213: 538-550.

*      Zhu, H., Wang, Y., Wang, K. and Chen, Y. (2011) .Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem. s.l.: Expert Systems with Applications,. 38: 10161-10169