Investment Decision Based on Entropy Theory - Modern Economy

ME > Vol.10 No.4, April 2019

Modern Economy

Volume 10, Issue 4 (April 2019)

ISSN Print: 2152-7245 ISSN Online: 2152-7261

Google-based Impact Factor: 0.74 Citations h5-index & Ranking

Investment Decision Based on Entropy Theory ()

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Download as PDF (Size: 819KB) PP. 1211-1228

DOI: 10.4236/me.2019.104083 1,076 Downloads 2,796 Views Citations

Author(s)

Dechao Yin

Affiliation(s)

College of Economics, Jinan University, Jinan University, Guangzhou, China.

ABSTRACT

The study of investment theory plays an extremely important role in real-life investment activities and is the theoretical basis for determining how investors invest. The traditional investment theory is mainly the portfolio theory proposed by Markowitz. The core investment model is the expectation-variance model. However, the traditional expectation variance model does not completely measure the risk. This paper focuses on the concept of entropy in the original physics and information theory, and studies the rationality of the method of measuring the risk in the financial investment field with entropy and the advantages of other metrics such as the classical variance method. Based on Markowitz’s portfolio theory, the expectation-variance model is optimized, the entropy is used to measure the risk of income, and the entropy model is established to select a more effective portfolio than the expectation-variance model. This paper not only studies from the theoretical aspect, but also collects the actual securities data, through the calculation of the actual data, and uses MATLAB to simulate its effective boundary, test the theory and the effect of the obtained optimization model to verify the validity of the model, so that the entropy model can apply to actual investment activities.

KEYWORDS

Efficient Frontier, Entropy, Expectation-Variance Model, Portfolio Theory

Share and Cite:

Yin, D. (2019) Investment Decision Based on Entropy Theory. Modern Economy, 10, 1211-1228. doi: 10.4236/me.2019.104083.

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