Abstract
In modern finance theory, the Capital Asset Pricing Model (CAPM) is used to price an individual security or a portfolio. The model makes use of the relation between the systematic risk and the asset’s expected rate of return to show how the market must price individual securities according to their security risk categories. In this study, traditional multivariate time series analysis, Kalman filter and neural networks are utilized to estimate the pricing model of a stock (YunNanBaiYao, YNBY) in Shenzhen Stock Exchange Market in China. From the case, we can see that the CAPM is valid in its theory, but there is still a room to improve the accuracy of pricing achieved with traditional regression and econometrics methods. Among those alternatives, Kalman filter and Neural networks seem to be promising and to deserve a try. Besides, it is indicated that how to combine various technical methods together to pricing a security or a portfolio could be worthwhile to research.
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Zeng, A., Pan, D., Haidong, Y., Guangqiang, X. (2014). Applications of Multivariate Time Series Analysis, Kalman Filter and Neural Networks in Estimating Capital Asset Pricing Model. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8482. Springer, Cham. https://doi.org/10.1007/978-3-319-07467-2_53
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DOI: https://doi.org/10.1007/978-3-319-07467-2_53
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