Modeling Overlapped Mutual Funds’ Portfolios: A Bipartite Network Approach

Lavín, Jaime F.; Valle, Mauricio A.; Magner, Nicolás S.

doi:10.1155/2019/1565698

Cited by 6 publications

(10 citation statements)

References 52 publications

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“…ese market indices are part of the benchmarks published by Bloomberg for each stock market at the country and region level. We included the VOX CBOE in our robustness out-of-sample test following [20] that incorporated with a control for the monthly volatility of each region and monthly variation of VIX index.…”

Section: Realized Variancementioning

confidence: 99%

“…ese indices belong to regional stock indices published by Complexity Bloomberg for each stock market at the country and region level. As mentioned above, we included the CBOE VIX index in our robustness section as part of an out-of-sample test following Lavin et al [20], who incorporated the monthly variation of the VIX index as a control for the monthly volatility of each regional market.…”

Section: E Datamentioning

confidence: 99%

“…e literature also explored how the distribution of the links affects the systemic reaction to shocks and how the connectivity of critical nodes or hub nodes could destabilize and even cause the entire network to collapse [12][13][14][15]. Likewise, other relevant topics are related to transaction networks of financial assets, portfolio selection, risk management, overlapped portfolios, integration of financial markets, and financial crises [16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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The Volatility Forecasting Power of Financial Network Analysis

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This investigation connects two crucial economic and financial fields, financial networks, and forecasting. From the financial network’s perspective, it is possible to enhance forecasting tools, since econometrics does not incorporate into standard economic models, second-order effects, nonlinearities, and systemic structural factors. Using daily returns from July 2001 to September 2019, we used minimum spanning tree and planar maximally filtered graph techniques to forecast the stock market realized volatility of 26 countries. We test the predictive power of our core models versus forecasting benchmarks models in and out of the sample. Our results show that the length of the minimum spanning tree is relevant to forecast volatility in European and Asian stock markets, improving forecasting models’ performance. As a new contribution, the evidence from this work establishes a road map to deepening the understanding of how financial networks can improve the quality of prediction of financial variables, being the latter, a crucial factor during financial shocks, where uncertainty and volatility skyrocket.

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Section: Realized Variancementioning

confidence: 99%

Section: E Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Volatility Forecasting Power of Financial Network Analysis

et al. 2020

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“…When two mutual funds invest in the same stock, the stock acts as an event node that can link the two funds. e bipartite network method helps us capture the interdependencies between the mutual funds and the stocks [59] and can reflect the information advantages of mutual funds obtained from the two sources of listed companies and mutual funds. Specifically, we define two types of nodes: mutual funds and stocks.…”

Section: Fund Network Constructionmentioning

confidence: 99%

Fund Network Centrality, Hard-to-Value Portfolio, and Investment Performance

Cang

Ren

et al. 2020

Complexity

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Based on the quarterly data of mutual funds in China from the fourth quarter of 2004 to the fourth quarter of 2019, this paper constructs a series of complex bipartite networks based on the overlapped portfolios of mutual funds and then explores the influences of fund network position on mutual fund’s investment behavior and performance. This paper finds that a mutual fund with shorter information transmission path to other entities in the fund network (i.e., having higher closeness centrality) or with stronger ties with those entities in important information positions (i.e., having higher eigenvector centrality) will achieve better investment performance. However, a stronger mediating role over the potential information flow of the fund network (i.e., having higher betweenness centrality) cannot help a mutual fund increase performance. The empirical results also indicate that a mutual fund holding stock portfolios with high valuation difficulties caused by the market or fundamental information uncertainty will achieve better investment performance, while holding hard-to-value portfolios caused by limited public information will reduce the performance of the fund. Furthermore, high closeness centrality or eigenvector centrality can help mutual funds deal with the disclose problems of public information, thus reducing the likelihood of a mutual fund holding hard-to-value portfolios caused by limited public information to achieve worse performance. Eigenvector centrality brings information advantages about company fundamentals, so it is easier for a mutual fund with high eigenvector centrality to profit from holding hard-to-value portfolios caused by the fundamental information uncertainty. The conclusions of this paper can enhance our understanding of the fund network and its information mechanism and shed new light on mutual fund’s information advantages and related asset allocation strategies.

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“…Recently, portfolio selection has received extensive attentions [1][2][3][4]. e portfolio selection model studies how to allocate investment funds among different assets to guarantee profits and disperse investment risk.…”

Section: Introductionmentioning

confidence: 99%

A Possibilistic Portfolio Model with Fuzzy Liquidity Constraint

Sui

2020

Complexity

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Investors are concerned about the reliability and safety of their capital, especially its liquidity, when investing. This paper sets up a possibilistic portfolio selection model with liquidity constraint. In this model, the asset return and liquidity are fuzzy variables which follow the normal possibility distributions. Liquidity is measured as the turnover rate of the asset. On the basis of possibility theory, we transform the model into a quadratic programming problem to obtain its solution. We illustrate that, in the process of investment, investors can make better use of capital by choosing their investment portfolios according to their expected return and asset liquidity.

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Modeling Overlapped Mutual Funds’ Portfolios: A Bipartite Network Approach

Cited by 6 publications

References 52 publications

The Volatility Forecasting Power of Financial Network Analysis

The Volatility Forecasting Power of Financial Network Analysis

Fund Network Centrality, Hard-to-Value Portfolio, and Investment Performance

A Possibilistic Portfolio Model with Fuzzy Liquidity Constraint

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