The effect of self-organizing map architecture based on the value migration network centrality measures on stock return. Evidence from the US market

Siudak, Dariusz

doi:10.1371/journal.pone.0276567

Cited by 6 publications

(2 citation statements)

References 103 publications

(126 reference statements)

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“…Portfolio optimization in finance is the optimal allocation of financial assets in different stocks [ 28 , 29 ], mutual funds, bonds, digital currencies [ 16 , 30 , 31 ], etc. to maximize the returns with risk tolerance [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

“…Among these methods, using the hierarchical clustering in the network topology has emerged as an efficient approach for selecting stocks to generate optimal portfolios [ 10 , 28 , 45 , 48 ]. Researchers utilize diverse linear correlation methodologies across various financial markets to evaluate the investment potential of assets possessing unique topological features [ 10 , 28 , 49 – 52 ], and implement minimum variance correlation strategies to optimize portfolio weights [ 31 , 46 ]. Nevertheless, the exploration of portfolio optimization through nonlinear network topologies remains limited.…”

Section: Introductionmentioning

confidence: 99%

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Global motion filtered nonlinear mutual information analysis: Enhancing dynamic portfolio strategies

Peng,

Wen,

Jiang

et al. 2024

PLoS ONE

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The complex financial networks, with their nonlinear nature, often exhibit considerable noises, inhibiting the analysis of the market dynamics and portfolio optimization. Existing studies mainly focus on the application of the global motion filtering on the linear matrix to reduce the noise interference. To minimize the noise in complex financial networks and enhance timing strategies, we introduce an advanced methodology employing global motion filtering on nonlinear dynamic networks derived from mutual information. Subsequently, we construct investment portfolios, focusing on peripheral stocks in both the Chinese and American markets. We utilize the growth and decline patterns of the eigenvalue associated with the global motion to identify trends in collective market movement, revealing the distinctive portfolio performance during periods of reinforced and weakened collective movements and further enhancing the strategy performance. Notably, this is the first instance of applying global motion filtering to mutual information networks to construct an investment portfolio focused on peripheral stocks. The comparative analysis demonstrates that portfolios comprising peripheral stocks within global-motion-filtered mutual information networks exhibit higher Sharpe and Sortino ratios compared to those derived from global-motion-filtered Pearson correlation networks, as well as from full mutual information and Pearson correlation matrices. Moreover, the performance of our strategies proves robust across bearish markets, bullish markets, and turbulent market conditions. Beyond enhancing the portfolio optimization, our results provide significant potential implications for diverse research fields such as biological, atmospheric, and neural sciences.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Global motion filtered nonlinear mutual information analysis: Enhancing dynamic portfolio strategies

Peng,

Wen,

Jiang

et al. 2024

PLoS ONE

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Cohesion and segregation in the value migration network: Evidence from network partitioning based on sector classification and clustering

Siudak

2023

Soc. Netw. Anal. Min.

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Cluster structure detection of the network is a basic problem of complex network analysis. This study investigates the structure of the value migration network using data from 499 stocks listed in the S&P500 as of the end of 2021. An examination is carried out whether the process of value migration creates a cluster structure in the network of companies according to economic activity. Specifically, the cohesion and segregation of the extracted modules in the network division according to (i) sector classification, (ii) community division, and (iii) network clustering decomposition are assessed. The results of this study show that the sector classification of the value migration network has a non-cohesive structure, which means that the flow of value in the financial market occurs between companies from various industries. Moreover, the divisions of the value migration network based on community detection and clustering algorithm are characterized by intra-cluster similarity between the vertices and have a strong community structure. The structure of the network division into modules corresponding to the classification of economic sectors differs significantly from the partition based on the algorithms applied.

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Assessing systemic risk and connectedness among dirty and clean energy markets from the quantile and expectile perspectives

Syuhada,

Hakim,

Suprijanto

2024

Energy Economics

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The effect of self-organizing map architecture based on the value migration network centrality measures on stock return. Evidence from the US market

Cited by 6 publications

References 103 publications

Global motion filtered nonlinear mutual information analysis: Enhancing dynamic portfolio strategies

Global motion filtered nonlinear mutual information analysis: Enhancing dynamic portfolio strategies

Cohesion and segregation in the value migration network: Evidence from network partitioning based on sector classification and clustering

Assessing systemic risk and connectedness among dirty and clean energy markets from the quantile and expectile perspectives

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