An online portfolio selection algorithm using clustering approaches and considering transaction costs

Khedmati, Majid; Azin, Pejman

doi:10.1016/j.eswa.2020.113546

Cited by 30 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Clustering previously has been employed in online portfolio selection [6]- [10]. Similar approaches are given by Khedmati et al (2020) where portfolios are optimised using clustering techniques, market windows, Pattern-Matching and similar day samples [6], [8]. Nanda et al (2010) found that Kmeans clustering provided the best result for online portfolio selection based on cluster compactness using the Bombay Stock Exchange [10].…”

Section: Methodsmentioning

confidence: 99%

AMA-K: Aggressive Multi-Temporal Allocation An Algorithm for Aggressive Online Portfolio Selection

Kruger¹,

Zyl²,

Paskaramoorthy³

2021

Preprint

View full text Add to dashboard Cite

Online portfolio selection is an integral component of wealth management. The fundamental undertaking is to maximise returns while minimising risk given investor constraints. We aim to examine and improve modern strategies to generate higher returns in a variety of market conditions. By integrating simple data mining, optimisation techniques and machine learning procedures, we aim to generate aggressive and consistent high yield portfolios. This leads to a new methodology of Pattern-Matching that may yield further advances in dynamic and competitive portfolio construction. The resulting strategies outperform a variety of benchmarks, when compared using Maximum Drawdown, Annualised Percentage Yield and Annualised Sharpe Ratio, that make use of similar approaches. The proposed strategy returns showcase acceptable risk with high reward that performs well in a variety of market conditions. We conclude that our algorithm provides an improvement in searching for optimal portfolios compared to existing methods.

show abstract

Section: Methodsmentioning

confidence: 99%

AMA-K: Aggressive Multi-Temporal Allocation An Algorithm for Aggressive Online Portfolio Selection

Kruger¹,

Zyl²,

Paskaramoorthy³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Another relevant reference is Khedmati and Azin (2020), who presents an online selection algorithm based on the pattern matching principle where it uses K-means, k-medoids, spectral, and hierarchical clustering for the selection of the best investing time window.…”

Section: Online 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

View full text Add to dashboard Cite

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.

show abstract

“…After the initial pitfall of Markov-Model-based methods (Ramchand & Susmel., 1998;Ang & Bekaert, 2003;Hamilton, 1989), mainly due to the curse of dimensionality, literature has started to look for alternative methods to cluster similar temporal data points into a same group based on certain comparison criteria (Khedmati & Azin, 2020). Such temporal clustering methods can mostly be divided into two approaches: subsequent clustering and point clustering.…”

Section: Market States Clusteringmentioning

confidence: 99%

Dynamic Portfolio Optimization with Inverse Covariance Clustering

Wang

Aste

2022

SSRN Journal

View full text Add to dashboard Cite

An online portfolio selection algorithm using clustering approaches and considering transaction costs

Cited by 30 publications

References 15 publications

AMA-K: Aggressive Multi-Temporal Allocation An Algorithm for Aggressive Online Portfolio Selection

AMA-K: Aggressive Multi-Temporal Allocation An Algorithm for Aggressive Online Portfolio Selection

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

Dynamic Portfolio Optimization with Inverse Covariance Clustering

Contact Info

Product

Resources

About