The cross‐sectional return predictability of employment growth: A liquidity risk explanation

• Published date: 01 February 2022
• Author: Weimin Liu,Di Luo,Seyoung Park,Huainan Zhao
• Date: 01 February 2022
• DOI: http://doi.org/10.1111/fire.12279

DOI: 10.1111/fire.12279

ORIGINAL ARTICLE

The cross-sectional return predictability of

employment growth: A liquidity risk explanation

Weimin Liu1,2Di Luo3Seyoung Park4HuainanZ hao5

1Nottingham University Business School,

University of Nottingham, Nottingham, UK

2Nottingham University Business School

China, University of Nottingham, Ningbo,

China

3Business School, University of Southampton,

Southampton, UK

4Nottingham University Business School,

University of Nottingham, Nottingham, UK

5School of Business and Economics,

LoughboroughUniversity, Loughborough, UK

Correspondence

DiLuo, Business School, University of

Southampton,Southampton, SO17 1BJ, UK.

Email:d.luo@soton.ac.uk

Fundinginformation

Ministryof Education of the Republic of

Korea;National Research Foundation

ofKorea, Grant/Award Number: NRF-

2019S1A5A2A03054249

Abstract

Employment growth (EG) is related to liquidity fundamen-

tals of investment opportunities, firm health, and informa-

tion environment and quality. This, in turn, implies that liq-

uidity risk may play a role in explaining the relation between

EG and stock returns. Wefind strong empirical evidence sup-

porting the link between EG and liquidity risk. Stocks of high-

EG firms are more liquid and exposed to lower liquidity risk

than stocks of low-EG firms. After adjusting for liquidity risk,

EG loses its power to predict returns.

KEYWORDS

employment growth premium, labor hiring, liquidity risk

JEL CLASSIFICATIONS

G12, G14, G30

1INTRODUCTION

Belo et al. (2014) is the first study to examine the relation between firms’ employment growth (EG) and their stock

returns. They find that EG predictsstock returns and argue that the negative relation between them reflects the shock

to the labor adjustment costs. In this paper,we show that liquidity risk explains the EG–return relationship.1

In our study, we conjecture that liquidity risk has the potential to explain the return predictability of EG. On the

one hand, liquidity risk appears to be a priced state variable important for asset pricing. Studies show that investors

require a premium to compensate for their exposure to liquidity risk (e.g., Amihud & Noh, 2020;Liu,2006; Pastor &

1Prior studies havereported a positive association of market liquidity with labor market employment (Levine & Zervos, 1998; Næs et al., 2011; Rocheteau

& Rodriguez-Lopez, 2014; Yépez, 2017). Gomis-Porqueras(2020) shows that labor market conditions affect asset prices in the presence of illiquidity. Thus,

the return predictability associated with the changes in firm’s employmentis likely related to variations in market liquidity. Recent studies also highlight the

role of labor market frictions in asset pricing, for example, Uhlig (2007), Favilukisand Lin (2013), Belo et al. (2017), Hall (2017), Kilic and Wachter (2018),

Donangeloet al. (2019), and Belo et al. (2020).

Financial Review. 2022;57:155–178. wileyonlinelibrary.com/journal/fire ©2021 The Eastern Finance Association 155

156 LIU ET AL.

Stambaugh, 2003; Sadka, 2006).2On the other hand, a firm’s hiring and firing activities are likely related to its invest-

ment opportunities, health conditions (such as financial constraints and/or distress), 3and information environment

and quality,which are fundamental sources affecting stock liquidity (Kerr et al., 2020; Lang et al., 2012;Liu,2006).

Our results confirm the association between a firm’s EG and liquidity fundamentals. As expected, EG is related to

firm’s financial health: Low-EG firms appear to be financially distressed or constrained whereas high-EG firms tend

to be financially healthier. A firm’s EG is positively related to Tobin’s q, investment rate, and asset growth, indicating

more investmentopportunities for high-EG firms as compared to low-EG firms. A firm’s EG is also positively correlated

with information measures such as the number of institutional investors, institutional ownership, advertising growth,

and earnings quality, meaning a lower level of information asymmetry for high-EG firms than for low-EG firms. This

evidence suggests that high-EG firms are likely to be more liquid and, hence, exposed to lower liquidity risk than are

low-EG firms.

Indeed, using different liquidity proxies, we observe that low-EG stocks are thinly and infrequently traded com-

pared to high-EG stocks, and trading on low-EG stocks incurs high transaction costs and has large impact on price as

compared to trading on high-EG stocks. The liquidity betas of the Liu (2006) liquidity-augmented capital asset pricing

model (LCAPM) decrease steadily and almost monotonically from low- to high-EG portfolios (Figure 1). This pattern

is largely consistent throughout the paper, which demonstrates the impact of EG on liquidity risk. After taking into

account the two risk sources (market and liquidity) of the LCAPM, we find that the power of EG in predicting stock

returns diminishes. Yet, non–liquidity-based pricing models such as the CAPM, the Fama and French (1993) three-

factor model (FF3FM), the momentum-extendedFF3FM, the Fama and French (2015) five-factor model (FF5FM), and

the Hou et al. (2015) q-factor model (HXZqFM) are not capturing the EG effect. Consistent with our conjecture, the

EG–return predictability stems from the liquidity risk.

We perform various robustness tests to check our results. We examine the performance of portfolios formed by

sequential double sorts on investment rate and EG, and on industry competition/transparencyand EG; we separately

examine NYSE/Amexand NASDAQ stocks; we test results in subperiods including periods of recessions and periods

of decimalization of stock prices. Our main results are consistent throughout these tests.

The ability of liquidity risk in explaining the EG–return relation is economically intuitive. Firms with high EG are

expanding(owing to more investment opportunities, for example), are healthier,are more transparent, and have higher

earnings quality.These firms are more attractive to investors and, thus, are more liquid than firms with low EG. When

the economy is haunted by uncertainty and liquidity squeeze, the returns of high-EG firms are less sensitive to liq-

uidity shocks than those of low-EG firms. As a result, they relieve investors from states of negative economic shocks

while low-EG firms undermine investors’ ability to cushion the deterioration in economic conditions. Consequently,

investors require high returns to hold securities of low-EG firms due largely to their exposureto high liquidity risk.

Our study makes severalcontributions to the literature. First, we provide a liquidity risk explanation to the cross-

sectional return predictability of EG (Belo, et al., 2014). Second, we extend the aggregate relation between unemploy-

ment rate and liquidity of Næs et al. (2011) by showing novel evidence at the firm level. Third, we extend previous

studies on the importance of liquidity in firms’ health such as distress risk (Liu, 2006), credit risk (Das & Hanouna,

2009), leverage(Fang et al., 2009), and information quality (Ng, 2011).

The remainder of the paper proceeds as follows. In Section 2, we describe the data. In Section 3, we conduct empir-

ical analyses and perform robustness tests. Finally,Section 4concludes the paper.

2Chanand Faff (2005) show supporting evidence for a liquidity-augmented Fama–French model in Australia. Cheng et al. (2013) highlight the role of liquidity

riskin real estate markets. Liu et al. (2016) find that liquidity risk is priced in the liquidity-extended Epstein-Zin Model.

3Asness et al. (2000) show that firms that have recently cut jobs are in distress. Brown and Matsa (2016)findthatanincreaseinanemployer’sfinancial

distressleads to fewer and lower quality j ob seekers.These results provide support to our liquidity-risk–based explanation as liquidity risk captures distress

risk (Liu, 2006). The link of liquidity to labor hiring is also related to prior research showing that firms with higher leverageratios are likely to cut employees

(e.g.,Hanka, 1998;Sharpe,1994) and that illiquid stocks have high leverage ratios (e.g., Fang et al., 2009).