A minimax model of portfolio optimization using data mining to predict interval return rate

Meng Yuan; Junzo Watada

doi:10.1109/FUZZ-IEEE.2014.6891693

A minimax model of portfolio optimization using data mining to predict interval return rate

Meng Yuan, Junzo Watada

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

In 1950s, Markowitzs first proposed portfolio theory based on a mean-variance (MV) model to balance the risk and profit of decentralized investment. The two main inputs of MV are expected return rate and the variance of expected return rate. The expected return rate is an estimated value which is often decided by experts. Various uncertainty of stock price brings difficulties to predict return rate even for experts. MV model has its tendency to maximize the influence of errors in the input assumptions. Some scholars used fuzzy intervals to describe the return rate. However, there were still some variables decided by experts. This paper proposes a classification method to find the latent relationship between the interval return rate and the trading data of a stock and predict the interval of return rate without consulting any expert. Then this paper constructs the portfolio model based on minimax rule with interval numbers. The evaluation results show that the proposed method is reliable.

Original language	English
Title of host publication	IEEE International Conference on Fuzzy Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2047-2054
Number of pages	8
ISBN (Print)	9781479920723
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2014.6891693
Publication status	Published - 2014 Sept 4
Event	2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing Duration: 2014 Jul 6 → 2014 Jul 11

Other

Other	2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014
City	Beijing
Period	14/7/6 → 14/7/11

Keywords

Classification
Interval number
Minimax
Portfolio

ASJC Scopus subject areas

Software
Artificial Intelligence
Applied Mathematics
Theoretical Computer Science

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10.1109/FUZZ-IEEE.2014.6891693

Cite this

Yuan, M & Watada, J 2014, A minimax model of portfolio optimization using data mining to predict interval return rate. in IEEE International Conference on Fuzzy Systems., 6891693, Institute of Electrical and Electronics Engineers Inc., pp. 2047-2054, 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014, Beijing, 14/7/6. https://doi.org/10.1109/FUZZ-IEEE.2014.6891693

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AB - In 1950s, Markowitzs first proposed portfolio theory based on a mean-variance (MV) model to balance the risk and profit of decentralized investment. The two main inputs of MV are expected return rate and the variance of expected return rate. The expected return rate is an estimated value which is often decided by experts. Various uncertainty of stock price brings difficulties to predict return rate even for experts. MV model has its tendency to maximize the influence of errors in the input assumptions. Some scholars used fuzzy intervals to describe the return rate. However, there were still some variables decided by experts. This paper proposes a classification method to find the latent relationship between the interval return rate and the trading data of a stock and predict the interval of return rate without consulting any expert. Then this paper constructs the portfolio model based on minimax rule with interval numbers. The evaluation results show that the proposed method is reliable.

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A minimax model of portfolio optimization using data mining to predict interval return rate

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Keywords

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