INSIDER DEMAND AND INDUSTRY TRENDS

• DOI: http://doi.org/10.1111/jfir.12192
• Date: 01 December 2019
• Author: Kainan Wang,Collin Gilstrap,Alex Petkevich
• Published date: 01 December 2019

The Journal of Financial Research Vol. XLII, No. 4 Pages 713–733 Winter 2019

DOI: 10.1111/jfir.12192

INSIDER DEMAND AND INDUSTRY TRENDS

Collin Gilstrap, Alex Petkevich, and Kainan Wang

University of Toledo

Abstract

We investigate the time‐series patterns in industry return predictability conditioned

on insider demand from 1996 to 2016. Current insider demand within an industry is

positively associated with higher future industry returns. This relation is primarily

driven by the buy side of insider trades and is more pronounced during periods of

economic recession and high market volatility where uncertainty and information

asymmetry are relatively high. Our results are consistent with the notion that

corporate insiders have an informational advantage that can be used as an indicator

for industry portfolio selection.

JEL Classification: G11, G14

I. Introduction

Corporate insiders’trades predict future stock performance because of better

information about their own company and the overall industry. The former is well

established in the literature (Alldredge and Cicero 2015; Cline, Gokkaya, and Liu

2017), and the latter is explored in this article. Under certain conditions, the aggregate

trades of corporate insiders contain information relevant to predicting future returns

(Seyhun 1988, 1992; Massa et al. 2015; Chung, Sul, and Wang 2019). Although it is

possible that corporate insiders trade only with firm‐specific information in mind, we

argue that aggregate intra‐industry insider demand is informative about the

performance of industries as a whole. Firms do not exist in a vacuum but rather

are connected to other firms through economic and contractual ties. Firms within the

same industry exist in the same regulatory environment and share similar risks because

of similar production inputs and customer demand. Our argument is supported by a

significant body of literature that examines industry information environments and

intra‐industry information transfers between firms. This literature suggests that pricing

information relevant to one firm within an industry can reveal information about other

firms within the same industry (Foster 1981; Akhigbe, Madura, and Martin 2015).

With this information transfer effect in mind, we examine the return predictability of

industry‐level trading patterns of corporate insiders.

We thank Colin Campbell, Doina Chichernea, Kershen Huang, and Pavel Teterin for insightful comments

and suggestions. Any errors or omissions are the authors’alone.

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© 2019 The Southern Finance Association and the Southwestern Finance Association

Firms in the same industry usually engage in a related economic activity, have

similar manufacturing processes, rely on similar technologies, have a similar supply chain

network, and are subject to similar regulations. Thus, firms within the same industry tend

to experience similar economic, regulatory, and technological shocks (Mitchell and

Mulherin 1996; Harford 2005). In fact, there is a large body of research motivated by these

links that examines intra‐industry information transfers. Foster (1981), Baginski (1987),

and Kim, Lacina, and Park (2008) find that new information about a particular firm’s

future cash flows (earnings announcements, management forecasts, and forecast revisions)

predicts future returns across the announcement firm’s industry, even when peer firms do

not have an information event. Lang and Stulz (1992) and Jorion and Zhang (2007) find

that bankruptcy announcements and credit events predict future returns within the industry.

Given their access to detailed, private information about the firm, corporate insiders should

be able to accurately assess the impact of an industrywide shock to their own firm.

Following this line of thought, we hypothesize that an analysis of industry‐level insider

trading removes idiosyncratic firm‐level trades and emphasizes systematic trends in the

industry. Relatedly, we examine whether insiders provide information about industrywide

trends and whether they can forecast future industry returns.

Given the emphasis on industry analysis from a practitioner’s point of view,

academic research on predicting future industry returns is relatively sparse and

generally focuses on using historical returns to predict future returns. Fama and French

(1988) document predictable return reversals in industry portfolios over a two‐to five‐

year horizon, and Moskowitz and Grinblatt (1999) show that industries exhibit a 12‐to

24‐month momentum effect after accounting for individual stock momentum. Hong,

Torous, and Valkanov (2007) present evidence that some industries lead aggregate

market performance because information diffuses slowly at the market level. Hou

(2007) examines intra‐industry information flows and finds that the lead–lag return

relation within industries is more pronounced in industries that are neglected by

analysts and institutions, have higher information asymmetry, and experience lower

trading volume. One notable exception to using prior returns for forecasting future

industry performance is DellaVigna and Pollet (2007). The authors find that

predictable shifts in demographic cohorts can successfully predict returns for age‐

sensitive industries. A second exception is Hou and Robinson (2006), who show that

industry concentration is negatively related to future returns. These studies suggest that

aggregate industry characteristics provide important information for predicting

industry‐level returns and should not be ignored. In our article, we expand this

literature to industry‐level insider trading patterns.

Although research has found that aggregate insider demand predicts broad market

performance (Seyhun 1988, 1992; Jiang and Zaman 2010), the primary focus has been on

insider trading patterns at the firm level. For instance, studies have focused on parsing the

information content of insider trades to predict future returns, earnings surprises, and

unexpected news about the firm (Lakonishok and Lee 2001; Chan et al. 2012; Beneish and

Vargus 2002; Fidrmuc, Goergen, and Renneboog 2006). The recent literature shows that

only certain subsets of insider trades are informative, given that many insiders simply trade

for personal liquidity. Examples of informed subsets include nonroutine traders (Cohen,

Malloy, and Pomorski 2012), historically profitable insiders (Cline, Gokkaya, and Liu

714 The Journal of Financial Research