Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/58550
Title: The Optimizing Algorithm for Economic Cycles in ASEAN Stock Indexes
Authors: Satawat Wannapan
Pattaravadee Rakpuang
Chukiat Chaiboonsri
Keywords: Computer Science
Mathematics
Issue Date: 1-Jan-2018
Abstract: © 2018, Springer International Publishing AG, part of Springer Nature. This paper is aimed to employ econometric tools for clarifying switching regimes inside time-series trends of ASEAN’s stock indexes as well as suggesting the proportional target to invest in an optimal portfolio The tools are the Markov-Switching model (MS-model) and Markovian optimization for portfolio model selecting, respectively. Daily sampled stock variables in five sectors such as banking system, energy, financial agriculture, telecommunication, and real estates were collected during 2010 to 2017. Technically, the condition of stationarity in collected data is verified by using the ADF unit-root test. Empirically, the findings of the switching states estimation show that financial markets in ASEAN countries have been continuously growing since 2010. In other words, there are 1,358 times standing for bull periods and they are more than bear periods which are 322 times during total collected 1,680 days. The second crucial process is the portfolio optimization. The empirical results indicate that the most efficient choice to minimize the risk values in portfolios following optimal solutions is to focus on long-term investments rather than speculative investments. For bull periods, the stock exchange regarding real estate (LogProb) in Singapore potentially provides the best opportunity to invest. Conversely, the banking stock index (Maybank) in Indonesia is the stock that provides the most safety choice and lowest risk value when the ASEAN stock markets are in bear periods.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85043988048&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/58550
ISSN: 16113349
03029743
Appears in Collections:CMUL: Journal Articles

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