Modeling the Momentum Effect in Stock Markets to Propose a New Portfolio Algorithm
Kazunori Umino*, Takamasa Kikuchi**, Masaaki Kunigami*, Takashi Yamada***, and Takao Terano*
*Tokyo Institute of Technology
4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
4-1-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8521, Japan
1677-1 Yoshida, Yamaguchi City, Yamaguchi 753-8511, Japan
This research has two objectives: (1) to model and analyze the momentum effect and (2) to propose a portfolio-reconstruction algorithm that uses the momentum effect to obtain excess return. The momentum effect tends to be present in the stock market and describes the phenomenon whereby rising (declining) stocks tend to continue to rise (decline). However, because existing research does not separate momentum effects from stock price fluctuations, it is not always possible to obtain an excess return when working with an unknown dataset that contains a momentum effect. In this research, we define a new external-force momentum-effect (EFME) model based on bias in stock price rises (declines). We prepared an artificial stock dataset that contained this momentum effect and constructed a portfolio with the proposed algorithm. Then, we analyzed the relationship between the EFME model and excess return and verify that excess return is obtained. Additionally, we confirmed that the proposed method yields higher excess return than the existing method when applied to artificial and real stock datasets.
-  E. F. Fama and K. R. French, “The cross-section of expected stock returns,” The J. of Finance, Vol.47, No.2, pp. 427-465, 1992.
-  N. Jegadeesh and S. Titman, “Returns to buying winners and selling losers: Implications for stock market efficiency,” The J. of Finance, Vol.48, No.1, pp. 65-91, 1993.
-  E. F. Fama and K. R. French, “Size, value, and momentum in international stock returns,” J. of Financial Economics, Vol.105, No.3, pp. 457-472, 2012.
-  H. Hong and J. C. Stein, “A unified theory of underreaction, momentum trading, and overreaction in asset markets,” The J. of Finance, Vol.54, No.6, pp. 2143-2184, 1999.
-  R. Novy-Marx, “Is momentum really momentum?,” J. of Financial Economics, Vol.103, No.3, pp. 429-453, 2012.
-  K. Hou, C. Xue, and L. Zhang, “Replicating Anomalies,” NBER Working Paper No.23394, National Bureau of Economic Research, 2017.
-  F. A. S. Postali and P. Picchetti, “Geometric Brownian motion and structural breaks in oil prices: a quantitative analysis,” Energy Economics, Vol.28, No.4, pp. 506-522, 2006.
-  R. R. Marathe and S. M. Ryan, “On the validity of the geometric Brownian motion assumption,” The Engineering Economist, Vol.50, No.2, pp. 159-192, 2005.
-  M. Bier and R. D. Astumian, “Biasing Brownian motion in different directions in a 3-state fluctuating potential and an application for the separation of small particles,” Physical Review Letters, Vol.76, No.22, pp. 4277-4280, 1996.
-  R. K. Y. Low and E. Tan, “The role of analyst forecasts in the momentum effect,” Int. Review of Financial Analysis, Vol.48, pp. 67-84, 2016.
-  W. F. Sharpe, “Capital asset prices: A theory of market equilibrium under conditions of risk,” The J. of Finance, Vol.19, No.3, pp. 425-442, 1964.
-  M. Eling and F. Schuhmacher, “Does the choice of performance measure influence the evaluation of hedge funds?,” J. of Banking & Finance, Vol.31, No.9, pp. 2632-2647, 2007.
-  R. Engle, R. Ferstenberg, and J. Russell, “Measuring and modeling execution cost and risk,” The J. of Portfolio Management, Vol.38, No.2, pp. 14-28, 2012.
-  E. Chong, C. Han, and F. C. Park, “Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies,” Expert Systems with Applications, Vol.83, pp. 187-205, 2017.
-  D. M. Q. Nelson, A. C. M. Pereira, and R. A. de Oliveira, “Stock market’s price movement prediction with LSTM neural networks,” 2017 Int. Joint Conf. on Neural Networks (IJCNN), pp. 1419-1426, 2017.
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