The retail habitat

时间：2025-11-07

1. Hypothesis development

In this section, we outline a model and our empirical predictions.

1.1. Motivation

Consider a model in the spirit of Kyle (1985) with multiple securities. An informed insider, representing institutional investors, has a total attention budget allocated across stocks to maximize their total expected trading profits. Apportioning more attention to a given stock increases the insider’s signal precision, but learning about any given asset entails diminishing marginal returns. We allow the securities to be heterogeneous in two ways: (A) in the level of noise trading intensity, standing in for differences in the intensity of retail trading, and (B) in the cost of generating a signal of a given precision, standing in for the difficulty of valuing the stock. Finally, the model features fixed participation costs that informed investors must pay if they want to trade a given security. Within this model we ask: where in the cross-section would the institutional investor find it most profitable to produce information?

When assets differ only in the intensity of noise trading, informed investors allocate more attention to those with higher retail activity. This reflects the standard intuition that noise traders help mask informed investors’ trades. However, cross-sectional differences in the informed investor’s ability to generate a signal can overturn this relationship. Faced with different costs of signal production, all else equal, the informed investor would allocate more attention to the stocks with lower information production costs. To the extent that the stocks retail investors prefer are precisely the ones with high information production costs, this second force can outweigh the expected benefits of hiding among retail order flow.

The two-period (Kyle, 1985) model described in Online Appendix A.1 allows us to illustrate this point. We specify the model with five assets featuring different levels of noise trader order flow volatility and, potentially, differences in information acquisition costs and explore the resulting attention allocation of the informed investor.

First, we consider the case with no cross-sectional differences in the cost of producing a signal of a given precision. As illustrated in the top left panel of Fig. 1, the informed investor’s optimal attention allocation increases monotonically with noise trading intensity. This relationship aligns with the standard intuition from Kyle (1985): all else equal, an informed trader’s expected profits are increasing in noise trader activity. As a result, when equating the marginal benefits of learning across assets, the informed investor concentrates on those with higher noise trading intensity. Introducing fixed participation costs at the asset level reinforces this emphasis. As shown in the bottom left panel of Fig. 1, with positive participation costs, the stocks with the lowest noise trading share can receive zero attention. These fixed participation costs aim to capture several features of the investment process, such as the effort required to identify viable investments before conducting deeper research, maintain coverage of existing positions, and manage the operational burden of justifying underperforming positions.

Second, we consider the case with cross-sectional differences in information production costs. We introduce the parameter

a_{i}

, which captures the ease of producing a signal of a given precision in stock

i

. Low values of

a_{i}

make signals of a given precision costlier to obtain, capturing the concept of a hard-to-value stock. What is more, while we still model retail investors’ order flow as uncorrelated with fundamentals, we allow their trading intensity to be correlated with the difficulty of valuing a particular stock. In other words, we specify low values of the parameter

a_{i}

for precisely the stocks where noise trading is the highest. A correlation between difficulty-to-value and retail interest might stem from various behavioral effects, such as hard-to-value stocks being more attention grabbing, or it could stem from retail investors evincing a degree of sophistication and recognizing their informational disadvantage among relatively easy-to-value securities.

The top right panel of Fig. 1 shows that such retail investor focus on hard-to-value stocks can flip the results in the top left panel. In this particular parametrization, the informed investor’s attention allocation monotonically decreases with noise trading intensity, the opposite pattern relative to the set-up with equal learning costs across assets. While the informed trader would still prefer to trade in securities with more noise traders, the signal production costs of these stocks are high enough to push them into allocating most of their attention to low noise trader intensity stocks. Therefore, equalizing the marginal benefits of learning across the five assets results in the informed investor devoting more attention to the low noise trading stocks.

Like before, the introduction of fixed costs can exclude certain securities from the informed investor’s consideration altogether. The bottom right panel of Fig. 1 shows that, as fixed costs increase from 0 to 2, the informed investor stops learning about the highest retail interest securities #4 and #5. This phenomenon illustrates our notion of the retail habitat: a subset of stocks that—due to being hard to value in the sense that signal generation is costly—are heavily traded by retail investors and avoided by institutional investors.

To summarize, the model in Online Appendix A.1 highlights two competing forces: informed investors’ desire to hide their trades among noise traders versus the precision of their signal. The results from the model suggest that which of these forces dominates depends on whether retail investors have a persistent habitat of hard-to-value stocks and therefore is an empirical question.