Efficient portfolio construction by means of CVaR and <i>k</i>‐means++ clustering analysis: Evidence from the NYSE

Soleymani, Fazlollah; Vasighi, Mahdi

doi:10.1002/ijfe.2344

Cited by 12 publications

(6 citation statements)

References 36 publications

(40 reference statements)

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“…Khedmati and Azin ( 2020 ) include K -means and K -medoids but also spectral and hierarchical clustering considering transaction costs for different data sets. Soleymani and Vasighi ( 2020 ) addresses a large portfolio dataset to find the most and least riskiest K -means clusters of stocks based on VaR and CVaR measures and working only on financial returns. In unsupervised learning, specifically within partitional clustering and using diverse time-series representations, a significant research direction involves applying fuzzy clustering to economic time series.…”

Section: Artificial Intelligence Approaches For Signal Generationmentioning

confidence: 99%

Enhancing portfolio management using artificial intelligence: literature review

Sutiene,

Schwendner,

Sipos

et al. 2024

Front. Artif. Intell.

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Building an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process.

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Section: Artificial Intelligence Approaches For Signal Generationmentioning

confidence: 99%

Enhancing portfolio management using artificial intelligence: literature review

Sutiene,

Schwendner,

Sipos

et al. 2024

Front. Artif. Intell.

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show abstract

“…D'Urso et al ( 2013 2020) proposed a fuzzy clustering method based on cepstral representation, using the daily Sharpe ratio as a variable of clustering. Soleymani and Vasighi (2020) adapted a K-means to cluster NYSE stocks based on Value-at-Risk (VaR) and Conditioned Value-at-Risk (CVar) measures. Gubu et al (2020) presents a robust portfolio selection using the KAMILA algorithm on a combination of continuous and categorical variables with a robust covariance estimation.…”

Section: Clustering Techniques In Portfolio Selectionmentioning

confidence: 99%

Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm

Lorenzo

Arroyo

2023

Financ Innov

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Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates, which may result in poor out-of-sample performance. In particular, the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long. This is precisely the case in the cryptocurrency market, where there are hundreds of crypto assets that have been traded for a few years. We propose enhancing the mean-variance (MV) model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period. In the pre-selection stage, we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality. The prototypes of the clustering partition are automatically examined and the one that best suits our risk-aversion preference is selected. We then run the MV portfolio optimization with the crypto assets of the selected cluster. The proposed approach is tested for a period of 17 months in the whole cryptocurrency market and two selections of the cryptocurrencies with the higher market capitalization (175 and 250 cryptos). We compare the results against three methods applied to the whole market: classic MV, risk parity, and hierarchical risk parity methods. We also compare our results with those from investing in the market index . The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators. This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.

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“…However, we consider here that we have already chosen these two appropriate pairs. In fact, pairs trading can be implemented after constructing an efficient portfolio via the clustering analysis procedure in unsupervised machine learning when the stocks of a portfolio have been clustered based on their associated risks, see the recent work [17] for further information.…”

Section: A Review Of Pairs Tradementioning

confidence: 99%

Revisiting the Copula-Based Trading Method Using the Laplace Marginal Distribution Function

2022

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Pairs trading under the copula approach is revisited in this paper. It is well known that financial returns arising from indices in markets may not follow the features of normal distribution and may exhibit asymmetry or fatter tails, in particular. Due to this, the Laplace distribution is employed in this work to fit the marginal distribution function, which will then be employed in a copula function. In fact, a multivariate copula function is constructed on two indices (based on the Laplace marginal distribution), enabling us to obtain the associated probabilities required for the process of pairs trade and creating an efficient tool for trading.

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Efficient portfolio construction by means of CVaR and k‐means++ clustering analysis: Evidence from the NYSE

Cited by 12 publications

References 36 publications

Enhancing portfolio management using artificial intelligence: literature review

Enhancing portfolio management using artificial intelligence: literature review

Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm

Revisiting the Copula-Based Trading Method Using the Laplace Marginal Distribution Function

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