Portfolio construction and crowding

Bruno, Salvatore; Chincarini, Ludwig B.; Ohara, Frank

doi:10.1016/j.jempfin.2018.02.003

Cited by 10 publications

(4 citation statements)

References 23 publications

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“…The application of random matrix theory to the study of financial correlation matrices dates back to Laloux et al (1999) and Plerou et al (1999). Since then, the Marchenko-Pastur theorem (Marchenko and Pastur 1967) has been used in several investment and portfolio optimisation studies, including: Sharifi et al (2004), Urama et al (2017) and Bruno et al (2018).…”

Section: The Role Of the Marchenko-pastur Theorem In Determining Port...mentioning

confidence: 99%

“…For instance, Bruno et al (2018) use a Marchenko-Pastur adjustment to reduce the effect of portfolio similarity between different fund managers, reducing what they call crowding by between 14% and 60%. Sharifi et al (2004) approach the problem of estimation with specific regard to portfolio stability.…”

Section: The Role Of the Marchenko-pastur Theorem In Determining Port...mentioning

confidence: 99%

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An eigenvalue distribution derived ‘Stability Measure’ for evaluating Minimum Variance portfolios

Smyth

Broby

2022

Quantitative Finance

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The Minimum Variance portfolio is subject to varying degrees of stability and robustness. We, therefore, propose a theoretical measure of its stability relative to a Marchenko-Pastur derived random correlation matrix. We demonstrate its practical use on the S&P 400, the S&P 500, the S&P 600 and the Russell 1000. Using historic market data from 2002 to 2021, we perform an optimisation on the empirical correlation matrix eigenvalue distribution to determine the implied variance ν(t) for the underlying data-generating process. Through monitoring its change over time ν(t), we provide a Stability Measure for the Minimum Variance portfolio and thereby help researchers measure changes to estimation risk and manage rebalancing regimes.

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Section: The Role Of the Marchenko-pastur Theorem In Determining Port...mentioning

confidence: 99%

Section: The Role Of the Marchenko-pastur Theorem In Determining Port...mentioning

confidence: 99%

An eigenvalue distribution derived ‘Stability Measure’ for evaluating Minimum Variance portfolios

Smyth

Broby

2022

Quantitative Finance

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“…Studying the empirical relation between market clustering and price instability is relevant from both an academic and a supervisory point of view. First, the existing empirical literature on the topic focuses on only indirect measures of group behavior: overlapping portfolios [ 1 , 2 ], similarities in performance dynamics [ 3 , 4 ], dynamics of the number of owners per stock [ 5 ], or buyer and seller volume imbalance [ 6 , 7 ]. The suggestion that price fluctuations originate from uncoordinated or inefficient interaction among investors seems obvious, but due to limited data and lack of suitable methods, such effects have not yet been investigated directly.…”

Section: Introductionmentioning

confidence: 99%

Crowded Trades, Market Clustering, and Price Instability

Kralingen

Garlaschelli

Scholtus

et al. 2021

Entropy

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“…Studying the empirical relation between market clustering and price instability is relevant from both an academic and a supervisory point of view. Firstly, the existing empirical literature on the topic focuses on only indirect measures of group behavior: overlapping portfolio's (Anton and Polk, 2014;Bruno et al, 2018), similarities in performance dynamics (Pojarliev and Levich, 2011;Kinlaw et al, 2018), dynamics of the number of owners per stock (Hong and Jiang, 2013), or buyer and seller volume imbalance (Yang and Zhou, 2016;Jia and Yang, 2017). The suggestion that price fluctuations originate from uncoordinated or inefficient interaction among investors, seems obvious, but due to limited data and lack of suitable methods such effects have not yet been investigated directly.…”

Section: Introductionmentioning

confidence: 99%

Crowded Trades, Market Clustering, and Price Instability

et al. 2020

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Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock the clustering measure captures the degree of trading overlap among any two investors in that stock. We investigate the effect of crowded trades on stock price stability and show that market clustering has a causal effect on the properties of the tails of the stock return distribution, particularly the positive tail, even after controlling for commonly considered risk drivers. Reduced investor pool diversity could thus negatively affect stock price stability. source of price instability, but also a channel of volatility spillovers, eventually resulting in correlated price jumps. In that case, market clustering would foster systemic risks. Market clustering might be an example of an existing market structure that can amplify seemingly unimportant events into wide-spread market volatility. In case market clusters coincide with otherwise interconnected institutions, for example banks, common asset devaluation can be a crucial default contagion channel, as suggested in recent interdisciplinary research (Tsatskis, 2012;Glasserman and Young, 2016;Levy-Carciente et al., 2015).Market clustering is expected to cause price shocks, because it amplifies the effect of existing sources of price fluctuations. More specifically, market clustering is expected to increase the chance of price shocks in two different situations: Firstly, when the order deluge due to the group behaviour overwhelms the supply (Stein, 2009;Braun-Munzinger et al., 2018) and, secondly, when the supply is thin due to the homogeneity of the investors' pool, i.e. a lack of liquidity at one side of the order book (Weber and Rosenow, 2006). In both situations market clustering increases the chance that the demand exceeds the supply, either in buy or sell orders.We start our investigation of the influence of trading patterns by studying the relation between market clustering and the price dynamics of individual stocks. Our market clustering measure is unique in the sense that it quantifies two aspects of group behavior: clustering and crowdedness. We define price instability as an increase of the number of sharp price fluctuations, such that the tails of the log return distribution are heavier. Specifically, we investigate whether there is a causal relation between market clustering and the skewness, kurtosis, tail indices, positive and negative outlier counts, changes in downside risk and upside gains.The analysis of trading patterns depends on the ability to distinguish to what extent the patterns are the result of higher order features like group behaviour instead of lower order properties. In this research, we represent stock trading by a complex bipartite network (a two-layer network with non-trivial topological features). The two layers are the investors and the stocks. The links between the layers are the trades during a particular time period.We use the maxi...

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Portfolio construction and crowding

Cited by 10 publications

References 23 publications

An eigenvalue distribution derived ‘Stability Measure’ for evaluating Minimum Variance portfolios

An eigenvalue distribution derived ‘Stability Measure’ for evaluating Minimum Variance portfolios

Crowded Trades, Market Clustering, and Price Instability

Crowded Trades, Market Clustering, and Price Instability

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