NDLI: Multi-asset allocation based on financial market microstructure model

Content Provider	IEEE Xplore Digital Library
Author	Yemei Qin Hui Peng Yanhui Xi Xiaohong Chen
Copyright Year	2014
Description	Author affiliation: Coll. of Electr. & Inf. Eng., Changsha Univ. of Sci. & Technol., Changsha, China (Yanhui Xi) \|\| Sch. of Bus., Central South Univ., Changsha, China (Xiaohong Chen) \|\| Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China (Yemei Qin; Hui Peng)
Abstract	Financial market microstructure model is a phenomenon model which describes financial markets based the microstructure theory. The initial values of the unknown parameters and/or states of the model have a great impact on the model identification, so an estimation method which combines genetic algorithm, Kalman filter and maximum likelihood method is presented to estimate the unknown parameters and/or states of the microstructure model. Based on the identified model and the indirectly obtained market excess demand instead of the prediction for price, a dynamic multi-asset allocation strategy is proposed. Case analysis for a combination asset of two stocks and currency shows that the total assets under the control of dynamic allocation strategy are much more than those without allocation control, which proves that the proposed parameter estimate and asset allocation method are feasible and effective.
Starting Page	4276
Ending Page	4281
File Size	250872
Page Count	6
File Format	PDF
ISBN	9781479937073
e-ISBN	9781479937080
DOI	10.1109/CCDC.2014.6852931
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Technological innovation Histograms multi-asset allocation excess demand Biological system modeling Market Microstructure Model Genetic Algorithm Resource management Mathematical model Microstructure market liquidity Portfolios
Content Type	Text
Resource Type	Article

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