公司的投资决策是否会受到同行股价的影响：公司是否对同行股价有学习效应

Foucault and Fresard - JFE - 2014 - Learning from peers’ stock prices and corporate investment

主要关注点

公司的投资会受到同行的市场估值（股票价格）的影响。具体而言，我们检验了一个假设，即公司同行的市场估值会影响其投资，因为这种估值可以告知管理者公司的增长机会，从而补充管理者可获得的其他信息。例如，管理人员可以从专注于此活动的公司的股票价格中了解特定活动中增长机会的其他信息。

实证方法

检验公司投资和自己公司股价以及同行公司股价的关系

To estimate the covariation between a firm’s investment and (i) the stock prices of its peers and/or (ii) its own stock price, we include peers’ characteristics in a standard linear investment equation. Specifically, we consider the following baseline specification:
$I _ { i , t } = \alpha _ { i } + \delta _ { t } + \eta Q _ { - i , t - 1 } + \beta Q _ { i , t - 1 } + \varphi \mathbf { X } _ { - i , t - 1 } + \mathbf { \Phi } \mathbf { X } _ { i , t - 1 } + \varepsilon _ { i , t } \tag{1}$
the subscript -i represents an equally weighted portfolio of peer firms based on the TNIC industries (firm B in the model)

The dependent variable, $I_{it}$, is the ratio of capital expenditure in that year scaled by lagged fixed assets (property, plant, and equipment).

The explanatory variable $Q_{it-1}$ is the Tobin’s Q of firm i in year t-1 as defined above and $Q_{-it-1}$ , is the Tobin’s Q of firm i’s peers, computed as the average Q of all firms belonging to the same TNIC industry as firm i in year t - 1, excluding firm i.

The vectors $\mathbf { X }$ include control variables known to correlate with investment decisions.

用标准差对解释变量做了标准化，于是系数 $\eta$ 和 $\beta$ 分别是公司投资和自己股价和同行股价的协方差。同时，这样标准化后，系数的经济意义直接就是效果的经济显著性，而非仅仅是统计显著性

We scale all independent variables by their standard deviation. Hence, coefficients $\eta$ 和 $\beta$ are the empirical counterparts of $\operatorname { cov } \left( I , p _ { \mathrm { B } 1 } ^ { \mathrm { s } } \right)$ and $\operatorname { cov } \left( I , p _ { \mathrm { A } 1 } ^ { \mathrm { s } } \right)$(see the discus- sion in Section 2.4). Another advantage of this scaling is that the magnitude of the estimated coefficients is directly informative about the economic significance of the effects.

具体估计分两步：

We use estimates of these coefficients to test the main implications of the model. We proceed in two steps.

首先，估计模型(1)，显示 $\eta$ 和 $\beta$ 都是统计显著的。这是“学习性同行效应“成立的必要条件，但不是充分条件

First, in Sections 4.1 and 4.2, we establish that estimates for $\eta$ and $\beta$ are both statistically significant in our sample. This is indeed a necessary condition for the “learning from peers” channel to play a role (see Corollary 3). This condition is not sufficient, however.

$Q_i$ and $Q_{-i}$有两层含义：(i) 都是管理层关于投资机会私有信息的代理变量 (ii) 都是管理层从股价中学习信息的决定因素。因此， $\eta$ 和 $\beta$ 显著可能并非是由于学习效应，而仅仅是因为私有信息的作用

Indeed, as highlighted by the model, $Q_i$ and $Q_{-i}$ play a dual role in Eq. (1): they are both (i) proxies for unobserved managers’ private information about their investment opportunities and (ii) determinants of investment if managers learn information from stock prices. Thus, a significant association between investment and Tobin’s Q might exist even if managers do not learn from stock prices, simply because Tobin’s Qs act as a proxy for unobserved managers’ signals (the correlated information channel).

为了解决上述的Concern，我们检验了锚定于 “learning from peers” 场景下的模型显著性。即检验价格的信息含量，管理层信息，公司股价相关性是否影响 $\eta$ 和 $\beta$ 的显著性。

Hence, in a second step (Section 4.3), we test the predictions of the model about $\eta$ and $\beta$ that are specific to the “learning from peers” scenario. That is, we test whether proxies for the model’s parameters (price informativeness, managerial information, and the correlation in demand shocks between firms) affect $\eta$ and $\beta$ as Table 1 predicts when managers learn from their peers, with a special focus on the five predictions specific to this scenario.

具体来说，检验投资者和同行股价之间的相关性是否受到

• 知情交易(informed trading)
• 排除股价的管理层信息($\gamma$)
• 公司股票与其同行股票之间需求量的相关性($c(\rho)$)

的影响(引入交乘项)：
$\begin{aligned} I _ { i , t } = & \alpha _ { i } + \delta _ { t } + \eta _ { 0 } Q _ { - i , t - 1 } + \eta _ { 1 } \left[ Q _ { - i , t - 1 } \times \phi _ { i , t - 1 } \right] + \beta _ { 0 } Q _ { i , t - 1 } \\ & + \beta _ { 1 } \left[ Q _ { i , t - 1 } \times \phi _ { i , t - 1 } \right] + \cdots \end{aligned}$
Specifically, we study how the covariation between investment and own’s and peers’ stock prices varies with measures of (i) informed trading ($\pi_A$ and $\pi_B$), (ii) managerial information other than stock prices($\gamma$), and (iii) the correlation in demand shocks between a firm and its peers ($c(\rho)$).