Trading Value of the Stocks
作者: 董文堂 ：;
Abstract: Valuing of stocks price is a difficult topic for study. The high-frequency trading data such as the prices, the trading volumes, the market capitalization and the buying-selling dynamics at trading make up a dynamical system of variable market capitalization. Any factor that influences the stock prices will all be changed eventually into prices and volumes of buying in or selling out shares in the system, and the difference between the buying dynamics and the selling dynamics causes the fluctuation of the prices and results in changes of the market capitalization of the stocks. The momentum properties of the variable market capitalization system are described by a differential equation of the prices evolution. A new conception, trading value of the stocks that versus time and the trading data, is showed by the solution of the equation. The trading value reveals the properties that there are of both time-sharing randomness and stage tendency, and depicts a feature that there is fluctuation of the prices round the trading value at trading. So a new quantitative method for valuing of the stocks is advanced based on the trading value.
文章引用: 董文堂 (2012) 股票的交易价值。 金融， 2， 126-130. doi: 10.12677/fin.2012.22013
 R. F. Engle, C. W. J. Granger. Co-integration and error correction: representation, estimation, and testing. Econometric, 1987, 55(2): 251-276.
 J. Alvarez-Ramirez, C. Ibarra-Valdez. Modeling stock market dynamics based on conservation principles. Physica A, 2001, 301(1-4): 493-511.
 K. Ilinski. Physics of finance: Gauge modeling in non-equilibrium pricing. Chichester: John Wiley & Sons Ltd., 2001.
 Z. A. Ozdemir, E. Cakan. Non-linear dynamic linkages in the international stock markets. Physica A, 2007, 377(1): 173-180.
 B. H. Hong, K. E. Lee and J. K. Hwang, et al. Fluctuations of trading volume in a stock market. Physica A, 2009, 388(6): 863- 868.
 J. Y. Potvin, P. Soriano and M. Vallèe. Generating trading rule on the stock markets with genetic programming. Computer and Operations Research, 2004, 31(7): 1033-1047.
 Y. L Chuang, M. H. Hsu and Y. F. Wang, et al. Forecasting stock price index using grey system. The Journal of Grey System, 2004, 2: 179-186.
 D. Wakowiec, P. Gnaciński and W. Miklaszewski. Amplified imitation in percolation model of stock market. Physica A, 2004, 331(1-2): 269-278.
 E. Canessa. Stock market and motion of a variable mass spring. Physica A, 2009, 388(11): 2168-2172.
 R. Friedrich, J. Peinke and C. Renner. How to quantify deterministic and random influences on the statistics of the foreign exchange market. Physical Review Letters, 2000, 84(22): 5224-5227.
 J. Masoliver, M. Montero and J. M. Porra. A dynamics model describing stock market price distributions. Physica A, 2000, 283(3-4): 559-567.
 M. S. Baptista, I. L. Caldas. Stock market dynamics. Physica A, 2002, 312(3-4): 539-564.
 P. A. Shively. The nonlinear dynamics of stock prices. The Quarterly Review of Economics and Finance, 2003, 43(3): 505- 517.
 R. Bartiromo. Dynamics of stock prices. Physical Review E, 2004, 69: Article ID 067108.
 D. G. McMillan. Non-linear dynamics in international stock market returns. Review of Financial Economics, 2005, 14(1): 81-89.
 R. N. Mantegna, H. E. Stanley. Scaling behaviour in the dynamics of an economic index. Nature, 1995, 376: 46-49.
 J. Maskawa. Multivariate markov chain modeling for stock markets. Physica A, 2003, 324(1-2): 317-322.
 J. D. Sterman. Business dynamics, systems thinking and modeling for a complex world. New York: Irwin McGraw-Hill, 2000.