A perceptual computer for hierarchical portfolio selection based on interval type-2 fuzzy sets

Karimi, Mohsen; Tahayori, Hooman; Tirdad, Kayvan; Sadeghian, Alireza

doi:10.1007/s41066-021-00311-0

Cited by 11 publications

(5 citation statements)

References 61 publications

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“…Noteworthy among these methods are (i) multiobjective optimization (Wang et al, 2022;, (ii) eigen portfolios using principal component analysis (Sen & Dutta, 2022b;Sen & Mehtab, 2022a), (iii) risk parity-based methods (Sen & Dutta, 2022a;Sen & Dutta, 2022c;Sen et al, 2021c;Sen et al, 2021f), and (iv) swarm intelligence-based approaches (Corazza et al, 2021;Thakkar & Chaudhuri, 2021). The use of genetic algorithms (Kaucic et al, 2019), fuzzy sets (Karimi et al, 2022), prospect theory (Li et al, 2021), and quantum evolutionary algorithms (Chou et al, 2021) are also proposed in the literature.…”

Section: Related Workmentioning

confidence: 99%

A Performance Analysis of Technical Indicators on the Indian Stock Market

Sen,

Waghela,

Rakshit

2024

Preprint

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In the dynamic world of financial markets, investors and traders alike seek tools and methodologies that can provide insights into potential price movements and help them make informed decisions. Technical analysis stands at the forefront of such methodologies, offering a systematic approach to interpreting historical price data and identifying trends and patterns that may guide future market behavior.

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Section: Related Workmentioning

confidence: 99%

A Performance Analysis of Technical Indicators on the Indian Stock Market

Sen,

Waghela,

Rakshit

2024

Preprint

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“…The multiobjective optimization techniques [15], principal component analysis [16], deep learning LSTM models [17][18][19], future risk estimation methods [20], and swarm intelligence-based approaches [21][22] are some of the very popular portfolio optimization methods. Various other approaches such as the use of genetic algorithms [23], fuzzy sets [24], prospect theory [25], quantum evolutionary algorithms [26], and time series decomposition [27] for robust portfolio design are also proposed in the literature.…”

Section: Related Workmentioning

confidence: 99%

Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market

Sen¹,

Dutta²

2022

2022 International Conference on Decision Aid Sciences and Applications (DASA)

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Portfolio optimization is a challenging problem that has attracted considerable attention and effort from researchers. The optimization of stock portfolios is a particularly hard problem since the stock prices are volatile and estimation of their future volatilities and values, in most cases, is very difficult, if not impossible. This work uses three ratios, the Sharpe ratio, the Sortino ratio, and the Calmar ratio, for designing the mean-variance optimized portfolios for six important sectors listed in the National Stock Exchange (NSE) of India. Three portfolios are designed for each sector maximizing the ratios based on the historical prices of the ten most important stocks of each sector from Jan 1. 2017 to Dec 31. 2020. The evaluation of the portfolios is done based on their cumulative returns over the test period from Jan 1, 2021, to Dec 31, 2021. The ratio that yields the maximum cumulative returns for both the training and the test periods for the majority of the sectors is identified. Additionally, the sectors which exhibit the maximum cumulative returns for the same ratio are also identified. The results provide useful insights for the investors in the stock market in making their investment decisions based on the current return and risks associated with the six sectors and their stocks.

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“…Notable among these methods are multiobjective optimization (Wang et al, 2022;Zheng & Zheng, 2022), eigen portfolios using principal component analysis (Sen & Dutta, 2022b;Sen & Mehtab, 2022a), risk parity-based methods (Sen & Dutta, 2022a;Sen & Dutta, 2022c;Sen et al, 2021c;Sen et al, 2021f), and swarm intelligence-based approaches (Corazza et al, 2021;Thakkar & Chaudhuri, 2021). The use of genetic algorithms (Kaucic et al, 2019), fuzzy sets (Karimi et al, 2022), prospect theory (Li et al, 2021), and quantum evolutionary algorithms (Chou et al, 2021) are also proposed in the literature.…”

Section: Related Workmentioning

confidence: 99%

Portfolio Optimization for the Indian Stock Market

Sen¹,

Dutta²

2022

Encyclopedia of Data Science and Machine Learning

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Portfolio optimization has been an area of research that has attracted a lot of attention from researchers and financial analysts. Designing an optimum portfolio is a complex task since it not only involves accurate forecasting of future stock returns and risks but also needs to optimize them. This article presents a systematic approach to portfolio design using two risk optimization approaches, the hierarchical risk parity algorithm and the hierarchical equal risk contribution algorithm on seven critical sectors and the NIFTY 50 stocks listed in the National Stock Exchange of India of the Indian stock market. The portfolios are built following the two approaches on historical stock prices from Jan 1, 2016 to Dec 31, 2020. The portfolio performances are evaluated on the test data from Jan 1, 2021 to Nov 1, 2021. The backtesting results of the portfolios indicate that the performance of the HRP portfolio is superior to that of the HERC portfolio on both training and the test data for the majority of the sectors studied in this work.

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A perceptual computer for hierarchical portfolio selection based on interval type-2 fuzzy sets

Cited by 11 publications

References 61 publications

A Performance Analysis of Technical Indicators on the Indian Stock Market

A Performance Analysis of Technical Indicators on the Indian Stock Market

Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market

Portfolio Optimization for the Indian Stock Market

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