Market closings and concentration of stock trading: an empirical analysis

Balakrishnan, P.V.; Holland, A. Steven; Miller, James M.; Shankar, Siddharth

doi:10.1080/09603107.2013.826873

Cited by 2 publications

(1 citation statement)

References 19 publications

(22 reference statements)

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“…BMS [1] model the distribution of daily trading volumes in the NYSE, AMEX and Nasdaq markets from 1962 to 2005 and find that power law exponents have steadily increased indicating that daily trading has become more concentrated over time. In a subsequent paper, Balakrishnan, Holland, Miller and Shankar [17] also employ the power law exponent as a measure of trading concentration and find it is impacted by market closures and seasonality. Specifically, they report that trading concentrations are lower on the first trading day of the week; on the last two days of the quarter; and on the last few days of the calendar year.…”

Section: Power Law Exponent As a Measure Of Trading Concentrationmentioning

confidence: 99%

Evolutionary disruption of S&P 500 trading concentration: An intriguing tale of a financial innovation

2020

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The novel finding of Balakrishnan, Miller & Shankar (2008) that investors, overwhelmed by the plethora of stock investment offerings, limit their analysis and daily choices to only a small subset of stocks (i.e., herding behavior) now seems to be common wisdom (Iosebashvili, 2019). We investigate whether the introduction of an innovation in financial products designed to allow investors to trade the entire product bundle of S&P 500 stocks, namely S&P 500 index funds, altered "herding behavior" by creating a new class of index investors. We model the distribution of daily trading concentration as a power law function and examine changes over the last six decades. Intriguingly, we discover a unique pattern in the trading concentration distribution that exhibits two distinct trends. For the period 1960-75, the trading concentration of the S&P 500 stocks tracks the increasing trend for the entire market, i.e., the unevenness in trading has steadily increased. However, after the introduction of S&P 500 index funds in 1975, concentration of trading in the S&P 500 stocks has steadily decreased, i.e., trading distribution has become more even across all 500 stocks, contrary to the current belief of equity analysts. This is also in sharp contrast to the case of U.S. stocks that are not in the S&P 500 index where trading concentration has steadily increased. We further corroborate the uniqueness of the inverted V-shape by a counterfactual investigation of the trading concentration patterns for other sets of 500 stock portfolios. This uniquely distinctive trading concentration pattern for S&P 500 stocks appears to be driven by the increasing dominance of bundle trading by index investors.

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Section: Power Law Exponent As a Measure Of Trading Concentrationmentioning

confidence: 99%

Evolutionary disruption of S&P 500 trading concentration: An intriguing tale of a financial innovation

2020

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Trading Volume Concentration across S&P 500 Index Constituents—A Gini-Based Analysis and Concentration-Driven (Daily Rebalanced) Portfolio Performance Evaluation: Is Chasing Concentration Profitable?

Metelski,

Sobieraj

2024

JRFM

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The period from January 2020 to December 2022 was marked by a confluence of major events, including the COVID-19 pandemic, the Russia–Ukraine war, the energy crisis, surging inflation, Federal Reserve policy shifts, and banking turmoil, which collectively fueled heightened market volatility, risk management needs, and speculative trading opportunities, leading to volatile swings in trading volume concentration across financial markets, with periods of significant increases followed by rapid declines. This paper examines the variation in the concentration of trading volume across the full spectrum of S&P 500 companies, with a focus on explaining the reasons behind the stochastic changes in trading volume concentration. We analyze different concentration measurement methods, including the power law exponent, the Herfindahl–Hirschman Index, and the Gini-based Trading Concentration Index (TCI). The research employs a novel experimental design, comparing a concentration-driven portfolio, rebalanced daily based on the top 30 stocks by trading volume, against the S&P 500 benchmark. Our findings reveal that the Gini-based TCI fluctuated between 55.98% and 77.35% during the study period, with significant variations coinciding with major market events. The concentration-driven portfolio outperformed the S&P 500, achieving an annualized return of 10.66% compared to 5.89% for the index, with a superior Sharpe ratio of 0.325 versus 0.19. This performance suggests that following trading volume concentration can yield above-average results. However, this study also highlights the importance of understanding and managing the risks associated with concentrated portfolios. This study contributes to the literature on market dynamics and offers practical insights for investors and fund managers on optimizing portfolio strategies in response to evolving concentration patterns in financial markets.

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Market closings and concentration of stock trading: an empirical analysis

Cited by 2 publications

References 19 publications

Evolutionary disruption of S&P 500 trading concentration: An intriguing tale of a financial innovation

Evolutionary disruption of S&P 500 trading concentration: An intriguing tale of a financial innovation

Trading Volume Concentration across S&P 500 Index Constituents—A Gini-Based Analysis and Concentration-Driven (Daily Rebalanced) Portfolio Performance Evaluation: Is Chasing Concentration Profitable?

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