Guaranteed Rate of Return for Excess Investment in a Fuzzy Portfolio Analysis

Tsaur, Ruey-Chyn; Chiu, Chien‐Liang; Huang, Yin-Yin

doi:10.1007/s40815-020-00990-y

Cited by 21 publications

(9 citation statements)

References 26 publications

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“…Yang et al 22 solve a fuzzy multiperiod mean‐minimax risk portfolio model with investor attitude. Tsaur et al 23 propose a fuzzy portfolio model that considers the excess investment based on selected guaranteed rates of return for some assets. Liu et al 24 consider an international asset allocation problem in a fuzzy uncertain environment.…”

Section: Introductionmentioning

confidence: 99%

Dynamic portfolio optimization using technical analysis‐based clustering

Khan

Mehlawat

2022

Int J of Intelligent Sys

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An accurate prediction of asset prices is perhaps the biggest challenge of any study in portfolio optimization.Asset prices are affected by several random and nonrandom factors, which makes them difficult to forecast. This paper proposes a two-phase dynamic portfolio optimization approach. In the first phase, assets are clustered into buy, sell, and hold groups using technical indicators. We provide a methodology to integrate the investor attitude (optimistic, pessimistic, or neutral) during the clustering phase. In the second phase, we input the clustered groups into a portfolio optimization model to obtain the optimum asset allocations. We use coherent fuzzy numbers to model the asset returns to integrate the investor attitude in this phase. The optimization model is solved using a genetic algorithm. The portfolios are rebalanced at regular intervals as new data becomes available. We illustrate the proposed methodology on a 100-asset problem of the US stock market. We analyze the real-world performance of the obtained portfolios. We compare the performance of the proposed approach with the mean-variance model, and other portfolios, such as the naïve portfolio and the NASDAQ-100 index.

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Section: Introductionmentioning

confidence: 99%

Dynamic portfolio optimization using technical analysis‐based clustering

Khan

Mehlawat

2022

Int J of Intelligent Sys

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“…Investors use portfolio selection as a tool to predict investment returns accurately to manage future uncertainties, and most portfolio selection models are formulated based on the probability theory. [10] In this paper, the purpose of the research is to study the problem of restructuring investment portfolios to provide accurate and professional investment suggestions to our potential investors through Markowitz Model and Index Model under five different constraints. Several aspects are considered to analyze the results: minimum variance portfolio, maximal Sharpe Ratio, capital allocation line, minimal risk frontier, efficient frontier, and minimal return frontier.…”

Section: Introductionmentioning

confidence: 99%

Optimal Portfolio under Five Constraints in the Markowitz Model and the Index Model

Liu¹

2022

BCPBM

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Investing in stocks is an inseparable part of modern life. Besides, choosing a good investment portfolio with high return and respectively low risk is the demand and desire of most modern investors. This paper tries to obtain an optimal investment portfolio to provide accurate and professional investment suggestions to the potential investors through the Markowitz Model and the Index Model under five different constraints. It has been considered several aspects to analyse the results: minimum variance portfolio, maximal Sharpe Ratio, etc. Some findings are given, including that the options of investment portfolio available under no constraint are larger compared to portfolios without permission of short-selling and investing SPX. Besides, the influence of the former is more significant than that of the latter; and then, the corresponding impact of different constraints ---is discussed through efficient frontiers. Additionally, comparing the Sharpe Ratio under two different models, it can be found that the Markowitz Model is a better option.

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“…In 1952, American economics Markowitz first proposed the use of mathematical models, which is called mean-variance models, to analyse investment portfolios. As investment profits increase, investment portfolios inevitably become one of the hot spots for investors [13]. To maximizing returns and minimizing risks is the major target for every financial investor [1].…”

Section: Introductionmentioning

confidence: 99%

Application of Mean-Variance Model in the U.S. Capital Market

Lin¹

2022

Advances in Economics, Business and Management Research

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Portfolio optimization is a popular procedure that is widely used in the financial industry. This paper conducts asset allocation analysis for diversified assets, including iron and steel industry, technology, healthcare, information industry and energy areas. There are five assets selected from the different areas which perform well in recent years. This paper uses three methods, namely Meanvariance analysis, CAPM and FF3F model, to find the portfolio optimization. Also, this paper uses the weights to analyse the performance of portfolio in different methods. The result shows that, in the FF3F model, 'LMBEX' contains the largest weight in both maximum sharpe ratio portfolio and minimum variance portfolio, while in the CAPM, 'ADX' and 'LMBEX' account for the largest weight in maximum sharpe ratio portfolio and minimum variance portfolio, respectively. This research may be useful to the potential investors who interested in steel, technology, healthcare, information, and energy industries.

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Guaranteed Rate of Return for Excess Investment in a Fuzzy Portfolio Analysis

Cited by 21 publications

References 26 publications

Dynamic portfolio optimization using technical analysis‐based clustering

Dynamic portfolio optimization using technical analysis‐based clustering

Optimal Portfolio under Five Constraints in the Markowitz Model and the Index Model

Application of Mean-Variance Model in the U.S. Capital Market

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