By Aki-Hiro Sato (auth.), Shu-Heng Chen, Takao Terano, Ryuichi Yamamoto (eds.)
Agent-based modeling/simulation is an emergent method of the research of social and financial structures. It presents a bottom-up experimental way to be utilized to social sciences similar to economics, administration, sociology, and politics in addition to a few engineering fields facing social actions. This e-book contains chosen papers awarded on the 6th overseas Workshop on Agent-Based methods in financial and Social complicated platforms held in Taipei in 2009. now we have 39 displays within the convention, and 14 papers are chosen to be integrated during this quantity. those 14 papers are then grouped into six components: Agent-based monetary markets; monetary forecasting and funding; Cognitive modeling of brokers; Complexity and coverage research; Agent-based modeling of excellent societies; and Miscellany. The study provided right here indicates the cutting-edge during this swiftly starting to be field.
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Additional info for Agent-Based Approaches in Economic and Social Complex Systems VI: Post-Proceedings of The AESCS International Workshop 2009
0 0 20 40 Lag Fig. 1 Time series properties of Nasdaq the insignificant autocorrelation features of the raw returns for most lag periods and the significant autocorrelation features of the absolute returns are exhibited in the last two panels. -H. -Y. Yang To calibrate the model for mimicking the stylized facts of the daily data in the financial markets observed in Table 1 and Fig. 1, we adopt the same setup as . e. 02 %. 004. The other parameters used in this model are shown in Table 2. Under full information and homogeneous expectations, the homogeneous REE price is given below Pf = 1 (D − λ σD2 h) R−1 (13) where h is the average of the shares of the stock for each trader.
As a result, the cumulative return is largest when we use the appropriate short term correction length. From this analysis of the duration of short term correction, we obtain an optimal short term correction to the modified Sharpe ratio. This argument also demonstrates the way we can introduce short term correction. For our present data analysis for the Hang Seng stocks in Table 1, the short term correction is optimally set to 2 days, a consequence of the highly fluctuating property of the stock price.
The modified version of PSO algorithms has been applied by an inertia weight W t multiplying the velocity term in numerous other applications. This version can be expressed as: Vit+1 = W t ·Vit + c1 · r1 · Pit − Bti + c2 · r2 · Gt − Bti r1 , r2 ∼ U (0, 1) (11) Bt+1 = Bti + Vit+1 , i = 0, 1, . . , N − 1 i (12) where the updating velocity is bounded, and r1 and r2 are random variables with uniform distributions. Here, c1 and c2 are factors used to control any influences from local and global velocity terms.
Agent-Based Approaches in Economic and Social Complex Systems VI: Post-Proceedings of The AESCS International Workshop 2009 by Aki-Hiro Sato (auth.), Shu-Heng Chen, Takao Terano, Ryuichi Yamamoto (eds.)