Institutional Overcrowding Everyday

Abstract

We operationalize Stein’s (2009) overcrowding concept in the context of investor response to daily stock return shocks and examine its underlying behavioral motivation. We define chasing at the overshooting point as evidence of overcrowding. Using trading data by investor type from Korea, we find that while institutional investors display informed trading, they fail to recognize the predictable overshooting point when responding to return shocks and/or accompanying information, resulting in an overcrowded response. The main consequence is suffering a cost of immediacy. Size of institutional crowding is positively related to the size of initial response and institutional investor population, but unrelated to future returns, with no evidence of adapting to past performance; collectively consistent with overcrowding driven by aversion to miss out.

Keywords:

• Institutional investors
• Overcrowding
• Investor response to return/information shocks
• Overshooting and short-term reversals in stock returns

JEL Classification:

• G11
• G12
• G41
• G14

Notes

1 They use either low-frequency 13F data or incomplete proxies such as short interest (or securities lending) in short-leg stocks or indirect proxies such as the degree of comovement among stocks in strategy portfolios.

2 Among the few stock markets in the world, which provide exact and complete investor trading data, KRX is the most developed and the most similar to the US stock market in terms of stock return behaviour (documentation of similarities is available from the authors). Thus, findings on KRX are likely to have external validity for the US stock market.

3 The term “aversion to miss out” symmetrically applies to avoiding being caught by loser stocks as it does to catching winner stocks.

4 Dasgupta, Prat, and Verardo (2011a) find that persistent institutional trading negatively predicts long-horizon returns. Edelen et al. (2016) report that US institutions take on positions on the wrong side during the formation of the value-type anomalies.

5 They can also be seen in Onishchenko and Ülkü (2021), Table 1, which uses the same empirical setup to document an anatomy of all investor types’ behavior around the short-term reversal pattern.

6 This description is particularly relevant for order-driven electronic stock markets without dealers where individual and institutional investors comprise comparable shares in trading volume, such as Asian stock markets.

7 Our definition of nonoptimal is “predictably unprofitable after controlling for standard risk factors.” It may or may not encompass irrational behavior. Buying the WML portfolio on day 1 is nonoptimal because, by waiting for a few days, an investor could have served the same motivation without incurring a regret. Note also that the term “unprofitable” in our definition does not require a losing round-trip trade; rather, it implies trading at a price that leads to predictable regret later. (Regret refers to the opportunity cost that emerges after information on best course of action arrives).

8 Also, idiosyncratic return differentials usually do not induce systematic hedging demand or expose institutional investors to systematic client flows.

9 Other similar specifications such as weekly ranking and holding periods yield similar reversals. We employ this version as it provides additional granularity to identify an overshooting day.

10 Based on 13F filings, Blume and Keim (2017) report that institutional investors own 60% of large-cap stocks and 68.2% of micro-cap stocks as of 2010 (see their Figure 2).

11 Assuming 0.05% trading commission per side, a strategy of shorting the WML portfolio at the end of day 1 and closing the position at the end of day 5 yields a small arbitrage profit of approximately 0.20% per trade (or 13.3% annualized) after transaction costs on KOSPI-200 stocks. Mean profit is statistically significant at the 1% level.

12 54% for KRX -computed from daily closing prices- is an understatement: domestic institutions’ average buying price of winners (selling price of losers) is 29 bp above (16 bp below) day 1’s closing price. Hence, the regret measured from their weighted-average trading prices on day 1 amounts to 1.14% per 4 days, or 103.7% annualized.

13 For example, controlling for day 0 market returns, as a remedy against Lo and MacKinlay’s (1990) argument of lead-lag relations, does not affect the significance of reversals (available upon request).

14 Full estimations including factor exposures are presented in the Supporting Information.

15 For other attempts to measure crowding, see Cahan and Luo (2013), Hong et al. (2015), Zhong, Ding, and Tay (2017) and Barroso, Edelen, and Karehnke (2017). Our measure differs in that it identifies overcrowded trading as it occurs, not by its outcomes much later.

16 More precisely, the null is “${\overline{\mathrm{NT}}}_m^{p,d}$ = unconditional mean daily net flow of investor type m”. However, the latter only negligibly differs from zero, and in no case alters the result. Hence, zero is a convenient approximate null.

17 Employing well-diversified portfolios and long sample periods achieves noise dampening in both cross-sectional and time-series dimensions. Using daily marketwide trading flows data from Taiwan, Thailand and Finland, we obtain similar results (available from the authors), indicating that our model’s behavioral assumptions reflect pervasive facts.

18 See Onishchenko and Ülkü (2019) for an analysis of foreign investors’ trading prices and behavior.

19 There is some overlap between return shocks and flow shocks; a switch between return-sorting and net flow-sorting would correspond to alternative Cholesky orderings in a vector autoregression model.