Cash holdings, product market competition, and investment

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Master Thesis

Cash holdings, Product Market Competition, and

Investment

University of Amsterdam

Amsterdam Business School

Msc Finance

Asset Management Track

Supervisor: Dr.Vladimir Vladimirov

Student: Jian Jin 11412313

Email: raffaello1027@gmail.com

Credits:15 ECTS

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Statement of Originality

This document is written by Student Jian Jin who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Acknowledgements

I would like to firstly express my deepest appreciation to my thesis supervisor Dr. Vladimir Vladimirov of Amsterdam Business School at University of Amsterdam for his truthful and critical supports throughout the thesis. He consistently allowed me to be independent, but steered me in the right direction when I encountered problems.

I would like to sincerely acknowledge the second reader of this paper. I am immensely grateful for your valuable comments and suggestions.

I would like present my deepest gratitude my parents and all my friends for spiritually supporting me throughout my year of master study and thesis processes. Thanks for all your constant love and encouragement.

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Abstract

This thesis analyses the interrelations amongst product market competition, corporate cash holdings, and investment of U.S. Compustat firms over the period of 1997 to 2015. The empirical results economically and statistically demonstrate that higher intensity of competition results in an acceleration of cash-to-assets ratio. In particular, the impact of competition is stronger on financially constrained firms. Based on industry structure, this paper further indicates that the association between competition and cash holdings is a significant U-shape, highlighting that firms in oligopolistic hold less cash than in monopolistic and competitive industry. Moreover, this study find that the investment sensitivity is the highest in oligopolistic industry, indicating that firms are more responsive to investment opportunities.

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1. Introduction

During past decades, substantial studies and media have observed that an aggregating cash pile of U.S firms since 1980s. The evolvement of cash holdings has perplexed researchers whether several determinants can be accurately identified as the rationales of reserving a great amount of cash. As the widely accepted explanations for cash holdings, the previous literatures demonstrate that the cash accumulation is motivated by transaction, tax sensitivity, agency problem, and precautionary savings (e.g., Opler et al. 1999; Almeida et al. 2004; Bates et al. 2009). Specifically, past studies emphasise when firm facing uncertainties and under financial distress hoards more cash as a precautionary motive to survive.

On this ground, the empirical studies profoundly stressed on the pattern of firm characteristics, but paid less attention on a broader scope, namely the impact of product market competition on corporate cash policies and its relation with investment. In accordance with recent researches (e.g., Grenadier 2002; Akdogu and MacKay 2008; Boot and Vladimirov 2016), a novel insight that links the corporate cash holdings with competition and investment timing. This theoretical prediction sheds a light on cash holdings as a non-precautionary motive. In particular, the intensive competition results in an investment deferral and further leads to more cash on balance. The rationale is that the higher competition leads to a less profitable investment opportunity. Thus, firms might prefer to delay investment until the uncertain is solved, resulting in holding more cash. Ma, Mello, and Wu (2013) highlight that the first mover as monopolist who successfully enters the market could take advantages of followers, obtain less eroded profit, and further keep rivals out of market. In order to be the first mover, firms hold more cash to timely invest in new project. Combining real options theory and investment, Akdogu and MacKay notice that the effect of product market competition differs across industries, indicating that the firms in oligopolistic industry invest sooner than competitive, and monopolistic industry.

Based on discussion above, a dual effect of competition is predicted that the intensity of competition could accelerate or decelerate investment and hold less or more cash across different industry structures. In particular, the intertwined effect of competition, investment, and cash hoarding have been puzzling researchers whether the first movers accelerate investment to seize the advantage and hold less cash as strategic cash holdings, or the financial constrained firms decelerate investment to wait for another opportunity and hold more cash as precautionary savings. The initial purpose of this paper is to investigate the impact of product market competition on corporate cash holdings across industry structure. Meanwhile, this

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research is dedicated to examine whether the competitive leads to longer investment timing in competitive industry, and shorter in oligopolistic or monopolistic industry.

To verify the empirical implications, this research collects original data from WRDS Compustat Fundamental Annual over the time horizon of 1997 to 2015. The full sample comprises 51,951 firm year observations that excludes financial and regulated firms. The measurements of product market competition are retrieved from Hoberg and Phillips data library. To test the hypothesis, the cross-section regressions between proxies of competition and cash-to-assets ratio with year and industry fixed effects are conducted. The empirical analysis initially focuses on a holistic view through estimating on full sample. In order to link to severity of financial constraints, the sample is further split into group of financially constrained, financially unconstrained. Moreover, to estimate the effect of competition across industries, the sample is also divided into subsamples of low-, mid-, and high-competition. Lastly, to identify the relation between product market competition and investment timing, the mixed proportional hazard model is estimated to capture the time effect. Due to the highly unbalanced penal data, the lagged value of regressors are adopted as instruments to solve potential endogeneity.

The empirical findings of this paper ascertain a positive relation between cash holdings and competition, specifically in a U-shape. This paper also confirms that the magnitude of the competition impact is substantially larger on financially constrained firms. In addition, the empirical evidence reveals that the firms in oligopolistic industry are more responsive to investment opportunities. Notably, the results from duration analysis indicate that the intensity of competition leads to a longer investment timing across industry structures. Yet, the sensitivity of competition is lowest in high concentration industry, indicating that firms in monopolistic industry postpone less time than other industry.

This paper complements the studies on cash holdings in twofold. Firstly, this research identifies the impact of product market competition on cash holdings is non-linear, but a quadratic relation across industry structure. Secondly, the results confirm that investment in oligopolistic industry are more sensitive and invest quicker. However, when intensive competition shocks the market, investment timing is overall slower in all industries.

The remainder of this paper is organized as follows. Section 2 elaborates the related theoretical and empirical literatures. Based on predictions, the hypotheses are developed. Section 3 describes the methodology adopted to examine the potential relations between intensity of competition and cash holdings. Section 4 presents the data collection and summary of statistics. Section 5 demonstrates the empirical results and economic intuitions comparing

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to prior literatures. Section 6 presents the robustness check of empirical evidence. Section 7 concludes the arguments and summarizes the paper.

2. Literature Review

This section reviews the main theories in previous researches. The relevant literatures initially focus on the traditional perspectives of cash holdings. In addition to the effects of firm characteristics, the existing literatures of real option theory, industry structure, and competition further elaborate and show a novel insight, non precautionary cash holdings. Based on the prior studies, the empirical predictions and testable hypotheses are developed.

2.1 Theoretical Foundation

Considerable theoretical researches have been devoted to investigate the possible determinants of reserving a great amount of cash. In a traditional view, the corporate cash holdings behaviour is motivated by four aspects, including transaction, precautionary saving, tax sensitivity, and agency problem, as the most important rationale.

In the early stage of study, several researches (eg., Baumol, 1952; Miller and Orr, 1966) find a trade-off effect that the demand of cash is associated with increasing transaction cost of providing liquidity. Due to the effect of economics of scale, the large firms hold less cash and cash equivalent for transaction motive. As for precautionary saving, the cash is reserved to against costly external financing and transaction costs of issuing debt (Myers and Majluf, 1984) In the most quoted literature, Opler et al. (1999) ascertain large firms that can easily access to financial market with high credit ratings, are therefore inclined to have lower cash to non-cash asset ratio, whereas small and firm with potential growth opportunities likely to hold great amount of cash. Bates et al. (2009) reveal the evidence that the sharp increase of average cash-to-assets ratio originates from riskier cash flow, such as intensive future investment, and lower account receivables, resulting in a higher cash flow volatility. Almeida et al. (2004) also indicate that financially constrained firms have propensity to save more cash. In terms of tax sensitivity, most of large firms are multinationals that offshore profits are generated and controlled by overseas subsidies. To avoid the aftermath of repatriating overseas earnings, the large firms are inclined to build-up a cash pile. In addition, the free cash flow hypothesis (e.g., Jensen 1986; Jensen and Meckling 1976) concludes that company with a large amount of free cash flow could possibly squander resources on unpromising investment projects or retain the cash rather than distribute to shareholders, resulting in agency conflicts.

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Pertaining to antecedent researches, the R&D or innovation driven as a new opinion are extensively discussed that also increases the sensitivity of cash holdings. Brown et al., (2009) present evidence that the R&D investments are financed with internal cash and stock issue, insofar previous literatures extensively define the relation between R&D expenditure and cash holdings (eg., Opler et al., 1999; Bates et al., 2009). Importantly, the R&D investment is irreversible, and project maybe suspended or revoked, resulting in financial constraints. The firms are relatively vulnerable while maintaining a high R&D investment level. The nature of R&D investment leads to limited ability to finance innovation from external capital. Thus, to hold surplus cash is considered as a cash buffer to develop new products and prevent potential financial distress (e.g., Schroth and Sazlay 2010; Bolton, Chen, and Wang 2011). In addition to uncertain outcome of R&D investment, another explanation is that innovation as an intangible asset cannot be pledged to sufficiently access external funds (e.g., Arrow 1962; Falato, Kadyrzhanova, and Sim 2013). Recapping the previous literatures, those studies (eg., Opler et al., 1999; Almeida et al., 2004; Gamba and Triantis 2008; Bolton, Chen, and Wang 2013) have explicitly emphasised that firm characteristics are the primary determinants of corporate cash holdings, and further suggested that firms or even high growth firms which accumulate a disproportionate cash are motived by precaution.

On this ground, the vital elements of cash holdings are theoretically and empirically examined in terms of corporate per se, but rarely in a macro scope, namely the product market competition. In the early stage, Bolton and Scharfsein (1990) highlight that the cash policy is considered as a tactic role to set up pricing strategy, such as predatory pricing. Schroth and Sazlay (2010) also confirm that companies with high cash-to-assets ratio have higher probability to be succeed in innovation competition. Admittedly, the corporate cash policy could be affected by intensive competition with rivals. Ma, Mello, and Wu (2013) firstly incorporate the winner’s advantages in innovation industry into a theoretical model, proposing that the first mover who successfully entry the market will possess the extra advantage and capture market share from its rivals. Besides, this model indicates that the external financing for developing new product is time consuming. Intuitively, the firm that firstly launch a brand new product will simultaneously obtain a large profit margin. This type of firm acts as a market leader, or in a monopolistic position, resulting in a natural market barrier that deters new follow-up entrants. It is apparent that the speed of seizing investment opportunities is vitally essential to attain the winner’s advantage and further pre-empt the market. Hence, in order to capture and enjoy the first mover benefits, the firm needs to access capital instantly rather than

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waiting for time consuming external funds which greatly delays the investment. It is undoubted that postponing investment would dramatically offset the winner’s advantage. Therefore, the cash holdings, investment, and R&D are strongly bundled together. This model further precisely reveals that winner’s advantage incentives firms to increase investment expenditure and hold more cash as a preparation for competition. Due to the additional time effect of financing, the longer time needed to access external capital leads to ascending R&D investment and hoarding more cash on balance. The empirical evidence of Ma, Mello, and Wu (2013) confirms the model prediction that the competition incentives all firms to reserve cash in order to rapidly invest in progressive technologies.

Closely related to Ma, Mello, and Wu (2013), Morellec, Nikolov, and Zucchi (2014) derive another dynamic model, which supplements frequency and size of equity issues into the model so that holistically combines the corporate cash policy and financing decisions. It ascertains that the increases in cash holdings and equity issues are driven by competition intensity. Interestingly, the effects of competition are more obvious on small and financially constrained firms, which is in contrast with theories and researches that large and financially unconstrained firms have greater propensity to hold cash. This research indicates that firms in highly competitive market often finance investment through equity market as external funds. Moreover, Morellec, Nikolov, and Zucchi (2014) adopt novel proxies to measure intensity of product market competition, including TNIC HHI and product market fluidity (e.g., Hoberg and Phillips 2010; Hoberg, Phillips, and Prabhala 2012). In accordance with the empirical evidence from Frésard (2010) and Frésard and Valta (2013), it suggests that firm with a higher cash level related to competitors results in substantial market gains while preying on rival’s market share. Especially, firms with higher relative-to-rival cash ratio further expel financially vulnerable counterparts. As a related theoretical study, Della Seta (2013) also confirm that firms hold more cash when the competition is severe, whereas this model assumes that external financing is inaccessible. In addition, Frésard and Valta (2013) and He and Wintoki (2016) show that cash holdings behaviour is not only associated with domestic competition, but also from foreign firms. To this extent, another rationale of hoarding cash can be concluded as entry pre-emption motive.

From a related angle, Lyandres and Palazzo (2016) demonstrate that cash holdings act as a commitment device to release firm from outside financing. This research indicates that optimal cash holdings, intensity of product market competition, and degree of financial

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constraints are intertwined. Differently from Ma, Mello, and Wu (2013), it is doubtful that the innovation effect is isolated (e.g., Cockburn and Henderson 1994; Lyandres and Palazzo 2016). In particular, the winner cannot take all benefits and entirely driven weak rivals out of market. Thus, it is contradictory to Ma, Mello, and Wu (2013) that substitutable products also exist in the market and share the market. Furthermore, this literature mentions that the future expected return of R&D investment depends on rivals’ investment strategies and market structure. The firm with higher intensity of R&D reduces the competitors’ inclination of investing in innovation and further shrinks the competitors’ profit. Lyandres and Palazzo (2016) also confirm that a firm increasingly hoard cash can adversely affect cash policies of competitors, especially in market with high intensity of competition. In addition, this research observes that the impact of competition is economically and significantly related to the cash holdings of both financially constrained and unconstrained firms, while the effect on unconstrained firm is slightly weaker. This results are contrary to Morellec, Nikolov, and Zucchi (2014) that argues only the cash holdings of financially constrained firm displays a significant coefficient. This model demonstrates that hoarding cash is a strategic motive to be succeed in competition. In presence of non-precautionary cash hoardings, a novel insight is proposed whether the product market competition would induce an accelerating or postponing on investment, further resulting in more cash holdings, which is contradictory to previous theories and studies (e.g., Akdogu and MacKay 2008; Boot and Vladimirov 2016). The real options theory largely supports the rationale of this motive that a contingent value of delaying irreversible investment exists in situation of uncertainty (e.g., MacDonald and Siegel 1986; Pindyck 1988). This view of investment is also contrary to the theorem of NPV, which defines the investment is valuable if the discounted future cash flow positively larger than initial investment. Pindyck (1988) notes the value of postponing increases with uncertainty. The uncertainty also reduces the value of investment by deferring investment until the next optimal opportunity. Meanwhile, the size of investment project also drops. Several related literatures (e.g., Baldwin 1982; Majd and Pindyck 1987) highlight that whether accelerating or decelerating investment relies on arrival of favourable or unfavourable information. Grenadier (1996, 2002) observes that return of investments is affected by the confrontation with rivals. The literatures of strategic real options (e.g., Grenadier 1996, 2002; Lambrecht and Perraudin 2003) indicate the alignment between investment and industry structure that the value of real options can be diluted with rival action – competition. The value of delaying investment decreases if the competition is rather intensive.

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On this ground, Akdogu and MacKay (2008) extends the relation between investment policy and competition. The empirical evidence is broadly aligned with theoretical predictions that the value of deferring investment diminishes with intensive competition. This research finds that the monopolistic industry presents a lower investment sensitivity, and slower investment timing. Surprisingly, the firms in oligopolistic industry illustrate the highest investment sensitivity and speed. On the basis of HHI, the proxy of industry concentration, Akdogu and MacKay split sample into three group of industries. The higher concentration implies weaker product market competition. Hence, this research reveals a significantly inverse U-shape between investment opportunity and investment expenditure. Moreover, it highlights that the cash holdings also related to competition. Precisely, the coefficient between investment and cash holdings is insignificant in low-concentration industry, the competitive industry. Yet, the investment is negatively associated with cash holdings in monopolistic industry. This paper concludes an effect of wait-lose trade-off as a tactic function of corporate investment.

A more important recent theoretical research by Boot and Vladimirov (2016) relates to this concern, it could be applied to interpret the non-monotonic nexus between product market competition and investment timing. This paper proposes a differential effect of competition, suggesting that the profit erosion induces longer investment timing and hoarding more cash in intensively competitive industry. The winner’s advantage is undermined by rivals, the investment per se is less attractive. Consequently, the firm might wait for better opportunities and access extra financing. On the contrary, the firms in more concentrated industry, such as oligopoly and monopoly, are reluctant to defer but accelerate investment and hold less cash in order to be the first mover, capture substantial market share, and keep followers out. Therefore, dual effects of competition exist on cash holdings phenomenon.

2.2 Empirical Predictions and Hypothesis Development

The prior researches depict a panorama of cash holdings, producing several empirical predictions to be tested in this paper.

Foremost, an increase in the intensity of product market competition, which translates into lower profitability, resulting in an increase in cash holdings. Besides, it was a contradict empirical result whether the effect of increase in competition only appears on financially constrained firms. Intuitively, if the firm is financially constrained, holding more cash as precautionary saving is indispensable for survival. Hence, the competition could greatly affect the level of cash hoardings. The testable hypothesis is developed as follow:

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Hypothesis 1: Product market competition positively induces the increase in cash

holdings, and impact is stronger on financially constrained firms.

As existing literatures confirmed, the product market competition, cash holdings, and investment are interrelated. Firms in higher concentration industries are keen to be the first mover and enjoy the winner’s advantage. In terms of dual effect of competition on cash holdings, it is predictable that the firms defer investment and hoard more cash in high intensity of competition due to profit erosion. On the contrary, firms can relatively secure first mover advantages in oligopolistic or monopolistic industry whilst accelerating investment and holding less cash. Therefore, firms hold less cash, invest more, and more responsive in oligopolistic or monopolistic industry. The testable hypothesis is proposed as follow:

Hypothesis 2: The effect of cash holdings on investment is greater in lower product

market competition industry.

The empirical predictions mentioned above are analysed and further elaborated in following sections.

3. Methodology

The major objective of this research is to understand and ascertain the plausible relation between product market competition and corporate cash holdings over time. In accordance with the empirical predictions in previous section, the expected relation is examined by cross-sectional data across the sample period. To test the hypothesis (1) of the association between product market competition and cash holdings, the empirical specification is estimated as follow:

!"#ℎ_%,' = ) + +_,!-./01212-3_4,'5,+ +₆7_%,'5,+ 8_'+ :₄+ ;_%,' (1) The previous empirical studies adopt several different definitions of dependent and explanatory variables in regression. In line with researches (e.g., Bates et al. 2009 and Opler et al. 1999), the variables are measured in the most common approach.

In this model, the dependent variable, !"#ℎ%,' as firm’s cash-to-assets ratio, broadly

relates to !-./01212-3_4,'5, that proxies the intensity of product market competition in certain industry ? in year 1 − 1. The 7%,'5, is the vector of control variables, such as market-to-book

value, book debt, book equity, cash flow, net working capital, which are widely verified in prior empirical researches (e.g., Bates et al. 2009; Opler et al. 1999). Besides, 8' and :4

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coefficient +_, are highly notable while adopting various proxies of product market competition. Due to the strongly unbalanced panel data that several firms only lists a few years. As Akdogu and MacKay (2008) propose that the lagged value of regressors are adopted as instruments to solve potential endogeneity bias. Since the panel data are strongly unbalanced with gaps, the lags can alleviate the bias of endogeneity and increase the prediction power of model, resulting more accurate results.

This paper additionally incorporates the Tobin’s Q in the regression to measure sensitivity of investment. The explanatory variables and control variables are measured as follows:

Cash. The ratio of cash and short-term investment to total assets Firm Size. The natural logarithm of total assets.

Credit Rating. The indicator variable equals to 0 if the S&P quality ranking is missing

or in non-investment grade, and 1 for otherwise.

Pay-out Ratio. The sum of dividends and purchase of common and preferred stock,

divided by operating income before depreciation.

Market-to-Book value. The ratio of market value of firm plus the book debt to total

assets.

Cash Flow. The ratio of operating income before depreciation minus tax and interest to

total assets.

Tobin’s Q. It is calculated as the sum of market value of equity and book value of debt,

scaled by total assets.

Net Working Capital. Working capital minus cash and short-term investment scaled by

total assets.

Industry Cash Flow Volatility. It is defined as the mean of standard deviation of

corporate cash flow deflated by total assets over ten years in the same industry if exists at least three year of observations at four-digit SIC industry code.

Capex. The ratio of capital expenditure to total assets.

Leverage. The book debt scaled by total assets less book equity plus market value. R&D. The ratio of research and development expenses to total assets.

Acquisitions. The acquisition scaled by total assets.

Tangibility. The ratio of net property, plant and equipment to total assets.

Dividends. The dummy variable equals to 1 if dividend-common reported, 0 for

otherwise.

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To test whether the product market competition illustrates a non-linear relation with cash holdings, the following quadratic model is proposed:

!"#ℎ_%,' = ) + +_,!-./01212-3_4,'5,+ +₆!-./01212-3_4,'5,6+ +_A7_%,'5,+ 8_'+ :₄ + ;_%,' (2)

In this specification, the equation (2) additionally incorporates !-./01212-3_4,'5,6 as a quadratic explanatory variable into model that can be applied to examine the U-shape relation between intensity of competition and cash holdings (e.g., Akdogu and MacKay 2008). This model is also controlled by year and industry fixed effects.

To test the hypothesis (1) of the relation between product market competition on cash holdings with severity of financial constraint, the model specification is developed as follow:

!"#ℎ_%,' = ) + +_,!-./01212-3_4,'5,+ +₆!-3#1C"231_%,'5,+ +_A7_%,'5,+ 8_'+ :₄ + ;_%,' (3)

In equation (3), the dependent variable !"#ℎ_%,' and explanatory variable !-./01212-3_4,'5, are identically defined as cash-to-assets ratio in previous regressions. !-3#1C"231_%,'5, gauges the financial constraint of firm 2 in year 1 − 1 . The severity of financial constraint is measured by five methods, including firm’s pay-out ratio, size, credit rating, Size Age index, and Whited and Wu index. In order to verify the relation between intensity of competition and severity of financial constraints, the sample are further split into two subsamples, financially constrained and unconstrained firms, on the basis of four mentioned criteria.

In terms of testing hypothesis (2), it predicts the firms in high concentration industry hoard less cash and invest more in order to move first and obtain winner’s advantage. The model is estimated as follow:

!"/0E_%,' = ) + +_,!"#ℎ_%,' + +₆F_%,'+ 8_'+ :₄+ ;_%,' (4) The equation (4) forecasts the potential relation between cash-to-asset ratio, !"#ℎ_%,', and capital expenditure to asset ratio, !"/0E_%,', for firm 2 in year 1. The coefficient estimates of +_, is importantly notable in this test. F_%,' represents a vector of control variables, which are suggested by Akdogu and MacKay (2008), including Tobin’s Q, cash flow, size, debt-to-asset ratio, and cash flow volatility. The Tobin’s Q is calculated as the ratio of market value of equity plus the book debt to total assets. !"/0E_%,' is measured as capital expenditure divided by total assets. This model is also controlled by 8' and :4 as the year fixed effects and industry fixed

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effects. In this specification, the full sample is split into tertile according to TNIC HHI that proxies the industry concentration.

Turning to the estimate the relation between investment timing and product market competition. Existing literatures have proved several empirical inconsistency and measurement error regarding Tobin’s Q that Q theory only explains a little cross-sectional variation in corporate investment and biasedly measures investment sensitivity (e.g., Erickson and Whited 2000; Peters and Taylor 2017). Hence, it is inadequate to link the product market competition and investment sensitivity. In accordance with real options theory, a certain value can be captured in delaying investment. To examine this association, this paper complies with the multivariate duration analysis as several prior studies proposed (e.g., Akdogu and MacKay 2008; Leary and Roberts 2005; Morellec, Nikolov, and Zucchi 2014). The duration analysis precisely tackles the situation that measures the length of time by hazard rate until a certain event occurs. Besides, this model can directly analyse the relation between investment timing and competition, which eliminates the modelling issues related to Tobin’s Q. In particular, it is unnecessary to add in extra regressors. In terms of unobserved heterogeneity, duration analysis solves it by plugging in likelihood function that specifies the censorship in time-to-event. To take advantage of this feature, the mixed proportional hazard model is employed to examine the last hypothesis. The equation for firm 2 in year 1 is estimated below:

H_% 1 = I_% H_J 1 0E/ E_% 1 K_{+ (5)}

In this specification, the hazard function is defined by H_% 1 .Where 1 is the time to the event that firm achieves its estimated investment threshold. This model is a semi-parametric function, including a parametric component of + and I% and a non-parametric component of

H_J 1 .

H_J 1 denotes the baseline hazard function that could be shifted upward or downward by 0E/ (E% 1 K+) as time independent explanatory variable which comprises a vector of

time-varying covariates E_% 1 K_{and unknown coefficient +, which dramatically avoids the bias over}

estimates. In addition, I_% defines a random variable that measures unobserved heterogeneity. This model assumes the I% and E% 1 K independent and follow gamma distribution with mean

of zero and maximum likelihood. In particular, a goodness-of-fit test M6_{is applied to examine}

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4. Sample Selection and Descriptive Statistics

4.1 Sample Selection

The sample is constructed from WRDS Compustat Fundamental Annual database that includes all active and inactive U.S public companies. The time horizon of this research is from 1997 to 2015. In accordance with Bates et al. (2009) and Opler et al (1999), this research excludes all financial firms with SIC code from 6000 to 6999 and regulated utility firms with SIC code from 4900 to 4999.

The dataset includes all firms with positive assets, sale, and capital expenditure in sample period. The variables with missing value are removed, including SIC code, total assets, cash and cash equivalent, operating income before depreciation. In order to minimize the influence of spurious outlier observations, the sample is further adjusted by winsorizing variables of cash flow, cash flow volatility, ratio of capital expenditure to assets, and investment sensitivity Q in the 99th percentile.

In terms of product market competition, the text-based network industry classification (TNIC) based HHI (Herfindahl-Hirschman Index) as industry concentration index, are employed as a proxy of product market competition, noting that higher HHI represents lower competition. Hoberg and Phillips (2010) suggest another proxy of competition, total similarity, which is negatively related to product differentiation. Hoberg, Phillips, and Prabhala (2014) also generate product market fluidity to measure intensity of competition. All of TNIC HHI and total similarity data are accessible in the Hoberg and Phillips data library. All measurement of intensity of product market competition are updated to 2016. The final observations consist of 51,951 firm-years.

4.2 Intensity of Product Market Competition

To measure the intensity of product market competition, the three proxies are employed in this paper. The first proxy is TNIC HHI (Herfindahl-Hirschman Index), which measures the industry concentration that higher HHI links with lower intensity of competition. Comparing to widely accepted HHI, the TNIC HHI is an advanced derivative that based on 10-K text-based industry concentration. As Hoberg and Phillips (2011) suggest that the TNIC HHI has better predict power to explain the cross-sectional variation of firms. This dataset is accessible in Hoberg and Phillips data library from year of 1996 to 2016.

The second measurement of product market competition is the total similarity, which is also developed by Hoberg and Phillips. It is also measures the market structure and market power. In particular, each firm has a specific group of competitors. Hence, the total similarity is customised to each firm while total similarity negatively relates to product differentiation,

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indicating that higher total similarity is associated with stronger product market competition. Same as TNIC HHI, this data is updated until December of 2016.

The last proxy for intensity of competition is the product market fluidity. It gauges the changing intensity of competition around firm over years, such as the uncertainties in the market and product threats from rivals. In the same vein, the product market fluidity also tailored for each firm. Higher product market fluidity implies higher intensity of competition. This proxy is also available in in Hoberg and Phillips data library from year of 1997 to 2016.

4.3 Severity of Financial Constraint

To test the hypothesis on the relation between financial constraint and cash holdings, it is crucial to appropriately define the measurement of financial constraint. In accordance with existing literatures, a variety of plausible methods (e.g., Almeida et al., 2004; Fazzari, Hubbard, and Petersen 1988) are suggested to proxy financial constraints. Although there is no final agreement which approach is the most proper measurement. Amongst a number of proxies, this paper mainly focuses on five widely common approaches, including pay-out ratio, corporate size, credit rating, Size Age index, and Whited and Wu index.

The first proxy is the pay-out ratio, which is computed as the sum of dividends (DVC) and purchase of common and preferred stock (PRSTKC), divided by operating income before depreciation (OIBDP). Intuitively, the firms less likely distribute more to investors in financial distress. In the sample period, the firms are sorted according to pay-out ratio in each year. The financially constrained firms are defined as in the bottom three deciles of annual pay-out ratio, whereas the unconstrained firms are classified as in the top three deciles. Intuitively, the firms less likely distribute more to investors in financial distress.

The second scheme is based on firm age and size. As Erickson and Whited (2000), Almeida et al., (2004), and Gilchrist and Himmerlberg (1995) suggested, it is predictable that the small firms are young, less accessible to capital market and obtain outside funds. Besides, small firms are also relatively less competitive in the market. It is therefore likely to be financially constrained. On the contrary, the large firms are formidable in the market, and easier to obtain external financing. On the basis of firm size, the firms are annually categorised over the sample period. The financially constrained and unconstrained firms are defined as in the bottom and top three deciles of firms’ size distribution respectively. Moreover, this paper also uses Hadlock and Pierce (2010) financial constraint index that solely based on firm characteristics as an additional proxy. Higher value of SA index is associated with severer financial constraint. Formally, the SA index is calculated as follow:

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In this model, N2T0 stands for the log normalised and inflation adjusted book

assets. OU0 represents the number of years that firm listed with continuous stock price. The sample is split according to the annual distribution, the financially constrained firms are defined as in top three deciles, while financially unconstrained are in bottom three deciles. The third proxy is the credit rating to measure the severity of financial constraints (e.g., Gilchrist and Himmelberg 1995). According to S&P domestic long term credit rating, the observations are assigned as financially constrained if the credit rating is missing or below investment grade, BBB-. The financially unconstrained firms are identified as being in investment grade rating.

Finally, following Whited and Wu (2006), the last proxy of firms’ financial constrains is the Whited and Wu Index, which is estimated through generalised method of moments of Euler equation. It is a linear combination of firm characteristics related to external financing. Notationally, the model of Whited and Wu index is represented below:

VV = −0.091!X_%,'− 0.062Z[\]^N_%,'+ 0.021_`_Z_%,'− 0.044`a_O_%.' + 0.102[Nb_%,' − 0.035Nb%,'

To be more specific, !X_%,' denotes the cash flow, Z[\]^N_%,' identifies the indicator variable of paying dividends, _`_Z_%,' defines the ratio of long term debt to assets, `a_O_%.' stands for log normalised total assets. [Nb_%,' and Nb_%,' represents the industry and firm sale growth rate respectively. The high WW index implies small firms with lower growth rate. Thus, on the basis of annual distribution, the financially constrained firms are defined as in top three deciles while, financially unconstrained are classified in bottom three deciles.

4.4 Descriptive of Statistics

The table 1 presents the summary statistics of dependent and explanatory variables. The following results are broadly aligned with existing literatures (e.g., Bates et al.,2009; Morellec et al, 2004).

The first line indicates the mean if cash-to-assets ratio is 0.185, and the standard deviation is 0.214, which is slightly higher than Opler et al. (2004) and Morellec et al. (2004). The following three rows are the proxies of competition, the mean and standard deviation of TNIC HHI is 0.257 and 0.225 respectively, while the mean of product market fluidity is 6.787 with

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standard deviation of 3.174. The univariates results are consistent with Morellec et al. (2004) and Hoberg, Phillips, and Prabhala (2014).

With regards to measurement of financial constraints, the mean value of pay-out ratio is 0.338 which is consistent with Morellec et al. (2004), whereas the standard deviation of 16.56 is somewhat higher than previous research, indicating a wider spread of data. In terms of firm size, the mean is 5.810 with standard deviation of 2.124. The mean of S&P bond credit rating is 0.129. As for Whited and Wu index, the mean is -0.294 with standard deviation of 1.780. The mean of Size Age index is -3.214 with standard deviation of 0.652.

Turning to the control variables, the mean of cash flow volatility is 0.108 with standard deviation of 0.412. The mean and standard deviation of market-to-book ratio is 1.814 and 2.670 respectively. Both of cash flow volatility and market-to-book ratio are higher than Morellec et al. (2004). Comparing to prior studies, the slight variation originates from different research period in this paper. The mean of R&D to assets ratio is 0.0943 with standard deviation of 0.236. The results of control variables are widely consistent with Lyandres and Palazzo (2015).

5. Empirical Results

5.1 Cash Holdings and Competition

This section comprehensively investigates the effect of product market competition on corporate cash holdings. To test hypothesis (1) as a general view that an increase in intensity of competition results in holding more cash, the model specification is estimated through equation (1).

Table 2 presents the regression results of hypothesis (1), indicating the intensity of product market competition as measured by TNIC HHI increases cash-to-assets ratio. Since the TNIC HHI is negatively related to competition, the regression outcome shows that a one-unit increase in intensity of product market competition leads to 0.128% increase in cash holdings. The baseline regression also illustrates that the size effect on cash holding is not statistically significant, but the sign of coefficient is reasonable. Regarding the corporate bond rate, as a proxy of severity of financial constraints, is statistically and negatively significant at 1% confidence level, which is consistent with previous literatures (e.g., Opler et al. 1999; Bates et al 2009; Morellec et al. 2004; Lyandres and Palazzo 2015). In terms of other control

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variables, this empirical results are also in line with prior studies. In particular, the signs of coefficient are identical, such as positively significant market to book ratio R&D to assets, and negatively significant capital expenditure and leverage. This baseline model also implies that firms with higher R&D to assets ratio have higher propensity to hoard cash in order to financing project efficiently.

In addition to baseline, the column (2) represents that the results are also robust by controlling year and industry fixed effect across industries. This specification clarifies the industry variations, noting that the effect of competition is statistically significant at 1% confidence level. Specifically, a one-unit increase in competition leads to 0.067% increase in cash-to-assets ratio. Notably, the effect of firm size is negatively and statistically different from zero at 1% significance level that explains the large firms hold less cash on balance. Across control variables, the sign and significance of estimated coefficients are identical to baseline specification with relatively equal magnitude.

The specification (3) reveals the non-linear relation between corporate cash holdings and intensity of competition. As Akdogu and MacKay (2008) highlight that an inverse U-shaped relation appears between investment opportunity, measured by Tobin’s Q, and investment expenditure by splitting sample into three subgroup of industry concentration. On the basis of equation (2), the quadratic regression function captures the slope and curvature of model that the coefficient of !-./01212-3_4,'6 is statistically significant at 1% confidence level, showing that the quadratic term provided an improvement over multiple linear regression. By inspecting the coefficient of determination, R-square of the quadratic model is 0.553 which is higher than other specifications. In the same vein, this result confirms that a quadratic relation also exists between corporate cash holdings and product market competition. It sheds a light on testing following hypotheses.

The Figure 1 illustrates the quadratic relation between corporate cash holdings and intensity of product market competition, as measured by TNIC HHI. It verifies that the relation is a convex shape. Interestingly, the U-shape is asymmetric, plotting that the right tail is lower than the left side. This figure reveals the distinct pattern of cash holdings across industry structure. In particular, the firms in high concentration industry (as in monopoly or oligopolistic) generally hold less cash than counterparts in low concentration (as in competitive industry).

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Intuitively, firms in weaker competition industry are more likely to remain high profit and access external capital market. Therefore, the size of cash holdings of the firms in high concentration is almost twice lower than in low concentration.

The Table 3 evidences the similar results as equation (1) estimated through using an alternative proxy of product market competition. The total similarity defines the proximity between products, which negatively relates to product differentiation. Specifically, a one-unit increase in total similarity leads to 0.0148% growth in cash holdings. In line with previous studies (e.g., Bates et al. 2009), the impact of firm size on corporate cash holdings is statistically significant at 1%, noting that one-unit increase in firm size results in 0.004% drop in cash holdings. As another proxy of financial constraints, the bond credit rating entails a negative relation that the higher credit rating, such as in the investment grade (i.e. higher than BBB-), induces hoarding less cash. In light of economic view, corporates with higher credit rating are easier to access external financing rather than depleting cash and cash equivalents. In terms of other control variables, the estimated coefficient of tangibility indicates that firms with more tangible assets holds less cash. The possible rationale is that physical assets can be readily pledged as collateral in order to obtain external capital. Regarding the net loss dummy, the firms that suffer greater loss are inclined to hold more cash as precautionary saving. In the other specifications, the coefficients and significance of the intensity of competition are similar to column (1). Specification (2) and (3) confirms that the relation between competition and cash holdings after controlling for time trends and invariant differences across industries. In comparison with R-square, the regression specifications with proxy of total similarity is in range of 0.53 to 0.59 that are more explanatory than models in Table 2.

The regression bias would rise with employing similar proxies. Table 4 additionally demonstrates a test via using product market fluidity as a proxy of competition. The product market fluidity measures the threats and uncertainties around the firm and market over years. Thus, the equation (1) and (2) are repeated on the same sample. The results are consistent with literature predictions and prior results that the intensity of product market competition is economically and statistically significant. To this extent, the empirical evidences ascertain that

Insert Table 3 Here

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the product market competition, as estimated by TNIC HHI, total similarity, and product market fluidity, induces the increases of corporate cash holdings.

5.2 Financial Constraints and Competition

To test hypothesis (1) on the relation between product market competition and severity of financial constraints, the following results are estimated according to equation (3). On the basis of four proxies of financial constraints in Section 4.3, the sample is further divided into two groups, the financially constrained and financially unconstrained.

The Table 5 represents the results of examining whether the impact of product market competition on cash holdings is greater on financially constrained firms than unconstrained. In terms of the empirical prediction, the impact of product market competition and cash holdings is anticipated to be greater on financially constrained than unconstrained firms. Turning to the empirical results, it confirms that the estimated coefficient of cash holdings to competition is much larger on financially constrained firms. In particular, the results are consistent across four sub-samples with distinct measurements of financial constraints. The economic implication is substantial. Specifically, coefficients of competition intensity are statistically significant at 1% confidence level on financially constrained firms amongst all proxies of severity of financial status. For instance, a one-unit increase in competition leads to 0.101% increase in cash holdings of constrained firms. Similar to Morellec et al. (2004), the impact of product market competition is statistically insignificant on financially unconstrained firms, apart from the measurement of pay-out ratio. Therefore, the financial status can dramatically affect the influence of competition on corporate cash holdings. Nonetheless, Lyandres and Palazzo (2016) detail that coefficient of either financially constrained or unconstrained firms is economically and statistically significant at 1% confidence level. The inconsistency stems from the different proxy of product market competition. Lyandres and Palazzo use a pair-cohort-level innovation proximity that measures the shared citation of patents between two firm.

In Table 5, the estimated coefficients of control variables are generally aligned with existing literatures. Akin to Bates et al. (2009), this results report the negative coefficient on firm size, capital expenditure, net working capital, merge and acquisition, and leverage whilst positive coefficients on market-to-book ratio and R&D expenses. Moreover, the empirical evidence also reveals that the higher tangibility significantly leads to lower cash-to-assets ratio

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across all proxies of competition at 1% confidence level. In the same vein, the effect on financially constrained firms is larger. Intuitively, if constrained firms have higher tangibility, the tangible assets can be urgently transferred as pledged assets whilst facing financial distress.

5.3 Investment and Cash holdings

According to empirical prediction, the firms in lower intensity of product market competition are eager to pre-emptively retain winner’s advantages as the first mover, and keeping new entrains out of market. To test the hypothesis (2) and further identify the effect of product market competition and investment on cash holdings, the equation (4) is estimated across three measurements of competition through splitting into three sub-samples of low, mid, and high product market competition. Specifically, the three sub-samples represent competitive competition industry (low competition), oligopolistic industry (mid competition), and monopolistic industry (high competition).

Table 6 illustrates that the estimated coefficients of cash-to-assets ratio are statistically and economically significant at 1% confidence level, and vary across different industry structures. In view of TNIC HHI measurement, the effect of cash holdings on investment is the strongest in mid competition industry, indicating that the one percent increase in cash holdings leads to 0.049 decrease in investment. It implies that the firms in oligopolistic industry holds less cash in order to grasp the investment opportunity. In terms of industry structure measured total similarity, the firms in mid competition industry also indicate the highest cash-to-assets amongst other two industries. However, in the industry split by product fluidity, the firms in high competition industry hold rather less cash.

The results indicate that the impact of cash holdings on investment depends on industry structure. It is somewhat ambiguous while only inspecting the estimates of cash holdings. Table 6 presents that the sensitivities of investment, as Tobin’s Q measured, are also statistically significant across industry structures. Explicitly, as classified by TNIC HHI, the investment sensitivity is 0.0028 and 0.0017 in low and high competition industry respectively. This empirical evidence entails that firms in monopolistic industry are highly responsive to the investment opportunity than the firms in competitive industry. Based on additional specification, the results are robust, noting that the investment sensitivity in low competition industry is significantly higher than counterparts in high competition industry. Notably, the

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firms in mid competition – oligopolistic industry, reveal a salient feature that the highest investment sensitivity is reported at 0.0097 and 0.0099 under the category of TNIC HHI and total similarity.

In perspective of economic insights, the empirical findings indicate that firms in the lower competition industry are acting as the actual market leader. In particular, these firms are approximately in a monopolistic or oligopolistic status. Generally, this type of firms is in the dominate position of market through holding advanced technology, human resources, and capital. If the firm can seize the first mover advantages, they could keep high profit and threaten rivals’ investments. Therefore, the firms in monopolistic and oligopolistic are more proactive in responding investment opportunities, resulting in a higher Tobin’s Q ratio. Furthermore, Akdogu and MacKay (2008) propose two possible explanations of the highest investment sensitivity in oligopolistic industry. The first one is based on real option theory. It emphasises a strategic role that firms are eager to initiate investment to prevent potential threats from competitors. Secondly, the industrial organisation theory suggests that firm’s investment acts as a scheme to affect competitors’ investment decision. Akdogu and MacKay further explain that the strategic function of investment is probably neglected by other market participants in monopolistic and competitive industry as utmost situation. Specifically, the intrinsic value of postponing investment would not sharply drop in monopolistic industry, because of lower competition. On the contrary, the strategic value of investment would dramatically plunge in competitive industry, because no one can easily avoid conflicts with other participants. Therefore, the firms in oligopolistic industries observe that the advantage of being the first mover is rather valuable than deferring investment and waiting for another opportunity. Comparing to past studies (e.g., Almeida et al. 2004; Opler et al. 1999), these researches merely focus on the firm characteristics and corporate cash holding, but hardly on the investment sensitivity, confirming that confirm that firms with financial constraints and higher cash flow volatility hold more cash. The empirical findings are contradicting to Akdogu and MacKay (2008) that states firms in low competition are inclined to delay investment. However, the results are consistent with the theoretical prediction that firms in competitive industry defer the investment and hold more cash due to profit erosion and uncertainties (see e.g., Boot and Vladimirov 2016). Meanwhile, firms in oligopolistic and monopolistic industry are rather responsive to investment opportunity and accelerate investment.

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6. Robustness Check

6.1 Financial Constraints and Competition

In this section, the relation between competition and financial constraints are reviewed by using various proxies of competition and financial constraints. Table 7 illustrates the estimation results.

Consistent with prior results, the estimated coefficients of financially constrained firm are statistically significant at 1% confidence level on all measures of competition and financial constraints. For instance, one unit change in TNIC HHI is associated with 0.0861% change in corporate cash holdings as SA index measured. In conclusion, the status of financial constraints can dramatically enhance the impact of competition on corporate cash holdings. Besides, some of coefficients of financially unconstrained are also significant at 10% to 1% confidence level, but the magnitude is significantly weaker than financially constrained.

6.2 Duration Analysis

To re-examine the effect whether the product market competition leads to a shorter investment timing in high concentration industry and longer investment in low concentration industry, the equation (5) is estimated to capture the effect of competition on investment timing. As section 3 mentioned, the Tobin’s Q is partially biased because of potential measurement error and ambiguous interpretation. Hence, the mixed proportional hazard model is dedicated to allay the issues related to Q theory and provide more accurate results. As mentioned in the methodology, the duration analysis mainly follows the Akdogu and MacKay (2008). The dependent variable is the number of year that the ratio of investment is smaller than fixed investment threshold at 5%. The censor code is identified as 1 if event happened or 0 if no event occurred as right censored.

Table 8 represents the empirical results of duration analysis for entire sample. In general, the results statistically confirm that estimated coefficient of product market competition inevitably shifts up the probability of not exceeding the investment threshold amongst all industry structures, indicating that the intensity of competition leads to a longer time in investment, namely investment deferral. This outcome indicates that the impact of product

Insert Table 8 Here Insert Table 7 Here

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market competition is substantial, noting that results in a longer investment time across all industry. Although intensity of competition results in delaying investment, the magnitude of competition differs across industries. In particular, one standard deviation increase in TNIC HHI leads to an increase of hazard rate at 3.9% (i.e. (0E/ 0.177×0.220 − 1)×100) in high concentration industry. Notably, the impact on high concentration industry (monopolistic industry) is rather weaker than oligopolistic and competitive industry. The number of year of not exceed investment threshold is less sensitive to competition in monopolistic industry, but more sensitive in competitive and oligopolistic industry.

Table 9 reports the robustness of proportional hazard model with fixed investment threshold at 10% and 15%. Corroborate prior results in table 8, firms in monopolistic and oligopolistic industry are less sensitive to impact of competition, resulting a shorter investment period.

As economic intuition, the firms in competitive industry are less reactive to the potential investment project. The threats of rivals exist every moment. Hence, firms in competitive industry still need to postpone investment in order to tackle uncertainties. In terms of mid concentration, firms in oligopolistic industry competes with less rivals that are more discreet to investment decisions. Once competition shocks the market, the firms in oligopolistic industry might delay investment as a strategic role that avoids potential loss in R&D and competition. The impact on firm in monopolistic industry is less dramatic. The possible explanation is that lower degree of competition exists between rivals. Therefore, the intention to be the first mover and take the advantage of other competitor is higher. In this situation, firms in monopolistic industry a less sensitive to intensity of competition, and postpones less time than other two industries. In terms of control variables, the results indicate that the deferral of investment significantly increases with corporate cash holdings in competitive and oligopolistic industry. In addition, the length of investment timing decreases with firm size.

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7. Conclusion

This study examines whether the product market competition affects corporate cash holdings of U.S. firms during 1997 to 2015. In general, the results support the theoretical predictions (see Morellec et al. 2004; Lyandres and Palazzo 2016). This paper demonstrates a strong evidence that cash holdings are also driven by intensity of competition, noting that higher competition leads to acceleration of cash-to-assets ratio. The empirical evidences complement literatures of cash holdings that not only the firm characteristics (see, e.g., Bates et al. 2009; Opler et al.1999), but also the intensity of competition can be identified as an additional determinant. As inspired by Akdogu and MacKay (2008), the association between intensity of product market competition and corporate cash holdings is further analysed in aspect of industry structure. The results highlight that the pattern of cash holdings differs across industry structure. In particular, the relation is a statistically significant convex shape, through modelling a quadratic regression. This U-shape relation indicates that firms in oligopolistic and monopolistic industry hold less cash than in competitive industry, especially firms in oligopolistic have the lowest cash-to-assets ratio. Besides, several previous studies states that large and financially unconstrained firms hoard more cash as a strategic motive to pre-empt market and keep new entrants out. On this ground, this paper further tests whether the competition greatly impacts financially constrained or unconstrained companies. Consistent with Morellec et al. (2004), the results confirm that the smaller, lower credit grade, and financially constrained firms are substantially affected by product market competition. In addition, as prior theories predict that the investment, cash holdings, and competitions are intertwined. The regression results illustrate that the investment sensitivity, as measured by Tobin’s Q, is the greatest in mid- concentration (oligopolistic) industry, indicating that firms are more responsive to investment opportunities. Hence, firms in oligopolistic industry have more propensity to accelerate investment and hold less cash. To avoid potential measurement errors and interpretation issue, this research additionally examines the investment timing through conducting mixed proportional hazard model as a duration analysis. The regression outcome illustrates that the effect of competition leads to a longer investment timing across all industry structures while firms in monopolistic industry are less sensitive and with shorter deferring time. It is inconsistent with Akdogu and MacKay (2008), they observe that the investment speed is the quickest in mid-concentration industry.

Admittedly, each model is merely an approximate simulation of actual economic activity. It is notable that several limitations also might influence the results of this thesis. Firstly, due to the unbalanced panel data, this paper only uses lagged value of regressor to resolve potential

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endogeneity issue. Secondly, to identify the industry structure, this study simply splits the full sample into tertiles according to industry concentration, as measured by TNIC HHI. Thus, the industry structure might not be very accurate to fully capture the effect in each section. In addition, this research mainly focuses on cross-sectional analysis, but rarely on the time series variation, such as secular increases during past decades, financial crisis, or other uncertain shocks. Lastly, the sample only comprises U.S. firms. Therefore, the results cannot be generalised in worldwide that different countries or regions may present distinct cash holdings features.

On the basis of this research, a vitally essential implication is that the corporate cash policies, even the financial decisions, should not merely concentrate on firm’s vision, but on a broader horizon, namely the competitors and industry structure. Corporate cash policies can be dynamically associated with investment opportunities and external rivals’ action. In terms of future researches, the relation between product market competition and corporate cash holdings can be also investigated in following aspects: time-varying differences, cross country analysis, and corporate governance impacts.

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Cash holdings, product market competition, and investment

Master Thesis