CC BY-NC-ND
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DOI: https://doi.org/10.17323/j.jcfr.2073-0438.18.2.2024.5-16 JEL classification: G30, G39, M40
fee]
The Determinants of Corporate Cash Holdings: Novel Evidence from Emerging Countries
The aim of this study is to investigate the determinants of corporate cash holdings in emerging countries. The sample comprises non-financial firms from six emerging countries, five of them commonly referred to as BRICS, plus Turkey. The dataset includes the data of 4,769 firms and covers a ten-year period from 2012 to 2021, resulting in a total of 47,690 firm-year observations. We run panel regressions, specifically fixed and random effects models, and conduct the J.A. Hausman [1] test to choose between the latter. We use several firm-specific variables as independent variables and the GDP growth rate and the inflation rate as country-specific control variables. The results reveal that firm size, leverage, capital expenditures, net working capital, operating cash flow, dividend payments, firm age, and research and development (R&D) expenditures are significant determinants of corporate cash holdings, with some differences among countries and/or industries in terms of the sign and the significance levels. The macroeconomic variables showed significant results in some countries and industries, yet they were not consistent enough to make general conclusions. This study provides new empirical evidence on the determinants of corporate cash holdings by using a large dataset from major emerging countries. Our findings have important implications for corporate managers and policymakers in designing cash holding and liquidity policies. A comprehensive understanding of the main determinants of corporate cash holdings enables managers to adopt appropriate financing and investing strategies in the long term, as well as better short-term financial policies.
Keywords: corporate cash holdings, BRICS, emerging countries, liquidity, financial determinants, panel data
For citation: Yilmaz I. (2024) The Determinants of Corporate Cash Holdings: Novel Evidence from Emerging Countries. Journal of Corporate Finance Research. 18(2): 5-16. https://doi.org/10.17323/j.jcfr.2073-0438.18.2.2024.5-16
Ilker Yilmaz
Assistant Professor of Accounting, College of Commerce and Business Administration, Department of Accounting, Dhofar University, Salalah, Oman, iyilmaz@du.edu.om, ORCID
Abstract
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Introduction
Cash holdings play a crucial role for both firm managers and parties analyzing firms for several reasons. Managers must maintain a safe and efficient level of liquidity by holding cash and cash equivalents, ensuring smooth financial management and adhering to risk management policies. Shareholders and potential investors base their investment decisions on valuations, with cash, particularly free cash flow, serving as a fundamental component of many valuation models. Cash is central to both short- and long-term financial decisions. In the short term, a firm's cash conversion cycle is a commonly used measure of working capital management, while in the long term, the present value of expected future cash flows informs the firm's capital budgeting decisions. Although these cases pertain more to cash flows rather than the stock of cash as a balance sheet value, the importance of cash holdings remains unchanged due to the strong association between smooth cash flows and cash holdings. In general, firms tend to hold more cash than their working capital requirement, a tendency supported by empirical evidence worldwide [2-4].
Firms hold cash and cash equivalents for various purposes, including transaction, speculation, and precautionary motives. However, there exists a trade-off for firms in deciding the amount of cash to hold, as cash is not an earning asset and thus incurs an opportunity cost. Despite this, holding cash can offer benefits such as the ability to receive trade discounts from suppliers.
The study makes several contributions to the existing literature. Firstly, it offers novel empirical evidence on the determinants of cash holdings using a comprehensive dataset comprised of emerging countries. Secondly, it provides results for the entire sample, as well as at the country and industry levels.
The remainder of the article is structured as follows: the next section offers a review of pertinent literature. Third section outlines the sample details, data sources, methodology, and model specifications. Fourth section presents the findings and offers a discussion. The final section draws the conclusions.
Literature Review
Cash holdings and their determinants constitute one of the most extensively researched areas in corporate finance literature. Numerous studies across various contexts have explored the factors influencing corporate cash holdings, revealing significant variations across countries and regions. Theoretical frameworks have sought to explain these differences through various approaches. Three theories commonly used in the literature offer explanations for why corporations maintain cash and cash equivalents: the trade-off theory, free cash flow theory, and pecking order theory. According to the trade-off theory, there exists an optimal level of cash where the marginal benefits and costs are balanced [5; 6]. The benefits typically stem from transaction and precautionary motives, while costs may include opportunity costs [7] and agen-
cy costs resulting from the desire to enhance managerial discretion [8; 9]. Pecking order theory [10], on the other hand, suggests that firms prioritize financing sources in a hierarchy, beginning with retained earnings, followed by debt, and finally equity. Cash holdings are viewed as a reflection of the firm's choices regarding potential investments and alternative financing options, suggesting that there is no single optimal level of cash. Free cash flow theory [11] contends that managers often hold cash to increase their discretion over the firm's investments and reduce monitoring by capital providers. However, this increased managerial power may lead to agency conflicts, as managers may prioritize retaining cash over investing in profitable projects, which may not align with shareholders' best interests [12].
Having reviewed the theoretical underpinnings, this section presents the findings from previous literature regarding the determinants of cash holdings. The discussion is structured according to the variables utilized in our empirical model, and hypotheses are formulated based on both theoretical discourse and empirical observations.
Firm Size
There are conflicting views regarding the relationship between firm size and cash holdings, both theoretically and empirically. On the one hand, some argue that small firms tend to hold more cash while large firms hold less, while others propose the opposite. Small firms may face higher costs related to external financing and bankruptcy due to lower credibility and diversification [13], suggesting a negative relationship. Conversely, large firms often possess better corporate governance mechanisms, leading to lower information asymmetry and external financing costs. Additionally, firm size can serve as a proxy for financial distress, with small firms holding more cash to mitigate such risks [14]. On the other hand, the pecking order theory suggests that large firms tend to have a more profitable history and higher accumulated reserves, suggesting a positive relationship between firm size and cash holdings [6].
Therefore, we formulate two hypotheses to investigate these opposing perspectives:
H1a: There is a positive relationship between cash holdings and firm size.
H1b: There is a negative relationship between cash holdings and firm size.
Leverage
Firms with higher levels of leverage might face increased financing costs due to a reduced borrowing capacity, leading them to hold less cash. This tendency is particularly notable in emerging countries where bankruptcy costs are significant [7; 15]. According to the pecking order theory, we anticipate a negative relationship, as firms would prioritize using liquid resources over issuing new debt when retained earnings are insufficient. Likewise, firms with a surplus may opt to repay existing debt, further reducing leverage. Empirical studies have consistently demonstrated
a negative correlation between cash holdings and leverage [9; 13; 16; 17]. This leads us to formulate the second hypothesis as follows:
H2: There is a negative relationship between cash holdings and leverage.
Profitability
Previous studies have consistently found a positive correlation between cash holdings and profitability, primarily supported by the pecking order theory [6; 7; 18; 19]. Profitable firms are better positioned to meet their financial obligations, including dividends and debt repayment, and are thus able to accumulate greater cash reserves. Conversely, less profitable firms tend to hold lower levels of cash and may rely on debt issuance to fund investments, exhibiting reluctance to issue new equity [20]. Based on these premises, we formulate the third hypothesis as follows: H3: There is a positive relationship between cash holdings and profitability.
Growth Opportunities
Both the trade-off theory and the pecking order theory suggest a positive relationship between cash holdings and growth opportunities. Firms with greater growth prospects are inclined to maintain higher levels of cash reserves to mitigate the risk of illiquidity. This strategy aligns with the transaction cost motive and the precautionary motive, as firms with viable investment opportunities seek to safeguard against cash shortages and potential financial distress [6; 21; 22]. Accordingly, we formulate our next hypothesis as follows:
H4: There is a positive relationship between cash holdings and growth opportunities.
Capital Expenditures
Capital expenditures refer to investments made by a firm to enhance its productive capacity, typically involving the acquisition or construction of non-current assets. These assets can serve as collateral for borrowings, leading to an expansion in debt capacity and a reduced need for cash reserves [21]. Furthermore, in line with the pecking order theory, firms adhere to a financing hierarchy starting with internally generated funds. Accordingly, a firm with viable investment opportunities would prioritize spending on capital expenditures, thereby allocating a smaller portion of its resources to liquid assets. Consequently, we anticipate a negative correlation between cash holdings and capital expenditures:
H5: There is a negative relationship between cash holdings and capital expenditures.
Net Working Capital
Net working capital represents the difference between current assets and current liabilities. While current assets may comprise some less liquid items such as inventory, the majority are comprised of liquid assets that can be readily converted into cash if needed. Consequently, firms with higher net working capital tend to maintain lower levels
of cash reserves [7]. Previous research has similarly identified a negative correlation between cash holdings and net working capital, aligning with the principles of the tradeoff theory [13; 14; 23].
H6: There is a negative relationship between cash holdings and net working capital.
Operating Cash Flow
The net cash flow from operations serves as the primary liquidity source for a healthy firm. Conversely, in cases where this is insufficient, firms may resort to external financing, incurring additional costs and imposing financial constraints. As such, operating cash flow acts as a safeguard against financial constraints [24]. The trade-off theory posits a negative correlation between operating cash flow and cash holdings, contending that firms with stable operating cash flow trends require smaller cash reserves. Conversely, the pecking order theory suggests a positive relationship, as firms prioritize reserves as their primary financing option, thereby accumulating greater cash holdings [9]. Hence, we formulate two hypotheses: H7a: There is a negative relationship between cash holdings and operating cash flow.
H7b: There is a positive relationship between cash holdings and operating cash flow.
Firm Age
Firm age denotes the number of years since the establishment of the firm. It is presumed that there exists a positive correlation between firm age and cash holdings, as older firms typically operate in more mature phases of their life cycle, thereby generating higher cash flows [25]. H8: There is a positive relationship between cash holdings and firm age.
Dividends
The trade-off theory posits a negative relationship between dividend payments and cash holdings. This perspective suggests that firms distributing dividends may opt to reduce or eliminate dividends to access funds when necessary, leading them to maintain lower levels of cash reserves [26].
H9: There is a negative relationship between cash holdings and dividend payments.
Research and Development Expenditures
Research and development (R&D) initiatives typically span long periods and may require significant financial resources over time. Consequently, firms engaged in R&D endeavors often maintain substantial cash reserves to sustain these activities. Empirical studies have indicated a positive correlation between higher levels of R&D expenditure and increased cash holdings [21; 27].
H10: There is a positive relationship between cash holdings and R&D expenditures.
Methodology
Sample and Data
The sample comprises 4,769 non-financial firms from six major emerging countries. Table 1 provides a breakdown of the firms by country and industry. Initially, the sample consisted of 6,505 firms; however, after eliminating those with missing data or outlier values, the final sample comprised 4,769 firms. The dataset spans a 10-year period from 2012
Table 1. Sample Details by Country and Industry
to 2021, totaling 47,690 firm-year observations. The countries included are the BRICS nations (Brazil, Russia, India, China, and South Africa), along with Turkey, another significant emerging economy with similar characteristics. The industries covered are non-financial, excluding sectors such as banking, insurance, and leasing. Additionally, governmental agencies and non-profit organizations were omitted due to their distinct characteristics. The data were sourced from Refinitiv Eikon, formerly known as Thomson Reuters.
Brazil China India Russia South Africa Turkey Total
Basic Materials 24 435 388 42 26 45 960
Consumer Cyclicals 43 398 404 13 22 63 943
Consumer Non-Cyclicals 21 176 128 12 9 33 379
Energy 7 99 36 29 3 5 179
Healthcare 6 208 104 6 2 4 330
Industrials 32 600 254 56 16 31 989
Real Estate 25 143 72 2 12 22 276
Technology 8 340 122 11 15 14 510
Utilities 34 96 24 44 - 5 203
Total 200 2495 1532 215 105 222 4769
Variables
Table 2 displays the variables used in the analysis, along with their measurements and the expected sign of their
relationship with cash holdings. Net cash, which excludes cash and cash equivalents from total assets, is preferred as the proxy for cash holdings.
Table 2. The variables used in the analysis
Variable Calculation Formula Selected References Expected Sign
Dependent Cash Holdings Cash & Cash Equivalent (CCE)/ (Total Assets-CCE) [21] n/a
Size Natural logarithm of Total Assets [2] +/-
Leverage Total Debt/Total Assets [16] -
Profitability Net Profit/Total Assets [19; 20] +
Independent Growth Opportunities Market Price per Share/Book Value per Share [22] +
Capital Expenditures Net Cash Flow of Capital Expenditures/Total Assets [21] -
Net Working Capital Net Working Capital/Total Assets [7; 13] -
Operating Cash Flow
Cash Flow from Operations/ Total Assets
+/-
Independent
Variable
Firm Age
Dividends RD
Calculation Formula
Number of years since establishment
Selected References Expected Sign
[25]
Dummy variable: 1 if paid, 0 if not paid
[26]
R&D Expenditures/Total Assets [27]
+
+
GDP Growth YoY change in GDP [28] +
Control
Inflation Annual rate of inflation [28] -
Dummies for country and Dummy n/a
industry
Model Specification
The model is represented by the following equation: CHlt = + alSIZElt + p2 LEVlt + fi3PROFlt + +fi4GROWlt + ¡35CAPEXlt + p6 NWClt + + fi7OCFlt + AGElt + p9 DIVlt + fiwRDlt + + fiuGDPGR^t + P12 INFLlt + pnCOUNl + + INA +eljt
where CH - cash holdings; SIZE - the firm size measured as the natural logarithm of total assets; LEV - leverage measured as the ratio of total debt to total assets; PROF - the net profit margin; GROW - growth opportunities measured as the ratio of market price per share to book value per share; CAPEX - capital expenditures measured as the ratio of net cash flow of capital expenditures to total assets; NWC -the ratio of net working capital to total assets; AGE -
the firm's age; DIV - the dummy variable for dividends; RD - research and development expenditures divided by total assets; GDPGR - the country's GDP growth rate; INFL - the country's inflation rate; COUN - the country dummy variable; IND - the industry dummy variable.
Estimation Technique
Following the approach of previous studies on the determinants of corporate cash holdings [6; 9; 28], we employ a static model and conduct estimation in two steps. Initially, we utilize pooled ordinary least squares (POLS) regressions, followed by panel regressions, encompassing both fixed and random effects. To determine whether panel regressions are warranted, and thus ascertain the presence of panel effects, we administer the Breusch - Pagan Lagrange Multiplier (LM) test. Subsequently, we employ the Hausman test to choose between fixed and random effects [1].
Results and Findings
Descriptive Statistics
The descriptive statistics for all variables are presented in Table 3. Table 3. Descriptive Statistics
Variable Mean Std. Dev. Min Max
overall 0.149 0.246 -0.117 4.991
CH between 0.179 -0.002 2.707
within 0.169 -1.478 4.185
overall 19.648 2.048 10.331 26.736
SIZE between 2.005 11.997 26.644
within 0.416 15.502 23.177
overall 0.539 0.362 0.001 14.335
LEV between 0.314 0.020 10.682
within 0.180 -4.030 10.742
overall 0.042 0.122 -3.531 3.411
PROF between 0.071 -0.515 1.201
within 0.099 -3.366 3.408
Variable Mean Std. Dev. Min Max
overall 1.530 1.463 0.002 14.991
GROW between 1.145 0.003 9.057
within 0.911 -4.213 13.845
overall 0.038 0.060 -1.638 0.786
CAPEX between 0.035 -0.543 0.217
within 0.048 -1.412 0.749
overall 0.151 0.294 -3.780 0.985
NWC between 0.245 -1.889 0.888
within 0.163 -2.710 1.891
overall 0.054 0.098 -2.255 2.222
OCF between 0.054 -0.303 0.410
within 0.082 -1.899 2.059
overall 26.792 17.915 1.000 158.000
AGE between 17.685 5.500 153.500
within 2.872 22.292 31.292
overall 0.587 0.492 0.000 1.000
DIV between 0.385 0.000 1.000
within 0.307 -0.313 1.487
overall 0.008 0.016 0.000 0.308
RD between 0.014 0.000 0.233
within 0.007 -0.157 0.260
overall 0.041 0.031 0.010 0.196
INF between 0.025 0.021 0.115
within 0.018 0.001 0.133
overall 0.057 0.033 -0.063 0.114
GDPGR between 0.017 0.005 0.067
within 0.028 -0.058 0.118
The table gives descriptive statistics for the overall sample, including the mean, standard deviation, minimum, and maximum values. For cash holdings, the mean value is 15%, which is considered reasonable for most firms. The minimum value is negative, potentially due to bank overdrafts, while the maximum value reaches almost 5, indicating very high cash holdings. Leverage averages 53.9%, a moderate level for non-financial firms. Profitability, measured by net profit margin, stands at 4.2%, indicating an acceptable level. Growth opportunities, as indicated by the market-to-book price ratio for shares, average 1.5, suggesting generally positive market valuation of firms.
Capital expenditures vary widely, with a mean of 3.8%, a maximum of 78%, and a minimum of -163%. Thus, some firms make huge investments while others decrease their capacities. Net working capital to total assets ratio averages 15%, while operating cash flow to total assets ratio stands at 5.4%. The age of firms averages 27 years, ranging from 1 to 158 years. The dividend dummy variable has a mean value of 0.58, indicating that dividend payment is prevalent among firms. The relative R&D spending (RD) is quite low, with a mean of 0.8%, suggesting limited investment in R&D activities. On a country level, the average inflation rate is 4.1%, with an average GDP growth rate of 5.7%.
Correlation Matrix
Table 4 shows the correlations among the independent variables of the empirical model. Table 4. Pairwise correlations
Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
(1) Firm Size 1.000
(2) Leverage 0.048* 1.000
(3) Profitability 0.067* -0.253* 1.000
(4) Growth Opportunities -0.037* -0.151* 0.113* 1.000
(5) Capital Expenditures 0.090* -0.065* 0.133* 0.064* 1.000
(6) Net Working Capital -0.057* -0.660* 0.266* 0.171* -0.048* 1.000
(7) Operating Cashflow 0.045* -0.074* 0.314* 0.085* 0.201* -0.004 1.000
(8) Firm Age -0.144* 0.092* 0.053* -0.094* -0.066* -0.087* 0.058* 1.000
(9) Dividend Payments 0.315* -0.234* 0.316* 0.117* 0.148* 0.249* 0.172* 0.007 1.000
(10) R&D Spending 0.148* -0.144* 0.004 0.261* 0.051* 0.184* 0.024* -0.182* 0.146* 1.000
(11) Inflation -0.346* 0.093* 0.097* -0.208* 0.004 -0.081* 0.061* 0.295* -0.139* -0.272* 1.000
(12) GDP Growth Rate 0.045* -0.082* -0.002 0.148* 0.057* 0.071* -0.060* -0.177* 0.078* 0.124* -0.287* 1.000
* p<0.01/
The pairwise correlations among the independent variables are relatively low, confirming that the model does not suffer from multicollinearity problems.
Table 5. Variance Inflation Factor (VIF)
SQRT R-Squared Eigenval Cond. Index
Variable VIF VIF Tolerance
(1) Firm Size 1.34 1.16 0.747 0.253 2.433 1.000
(2) Leverage 1.85 1.36 0.540 0.459 1.751 1.179
(3) Profitability 1.32 1.15 0.754 0.245 1.444 1.298
(4) Growth Opportunities 1.17 1.08 0.857 0.142 1.086 1.497
(5) Capital Expenditures 1.1 1.05 0.912 0.087 0.941 1.608
(6) Net Working Capital 1.94 1.39 0.514 0.485 0.877 1.665
(7) Operating Cashflow 1.18 1.08 0.850 0.149 0.790 1.755
(8) Firm Age 1.15 1.07 0.871 0.128 0.688 1.881
(9) Dividend Payments 1.35 1.16 0.743 0.256 0.651 1.934
(10) R&D Spending 1.19 1.09 0.842 0.157 0.557 2.091
(11) Inflation 1.44 1.2 0.695 0.304 0.461 2.297
(12) GDP Growth Rate 1.13 1.06 0.888 0.112 0.321 2.754
Mean VIF 1.34 Condition Number 2.754
Table 5 presents the variance inflation factors (VIF) for the independent variables used in the model. All VIF values for the independent variables are below 10, with a mean VIF of 1.34. This confirms the absence of multicollinearity issues within the model. Additionally, the condition index values are very low, and the overall condition number is 2.754, which is less than 30, further corroborating the VIF findings.
Regression Results
This section presents the results of the regressions and provides a discussion of these findings. Table 6a displays the results for the entire sample and broken down by country, while Table 6b presents the results by industry. The industries are numbered from 1 to 9, as outlined in Table 1.
Table 6a. Regression Results for the Entire Sample and by Country
CH SIZE LEV PROF GROW CAPEX NWC OCF AGE DIV RD INF GDPGR CONS OBS
All -0.05*** 0.11*** -0.02 0.01 -0.14*** 0.37*** 0.22*** -0.00*** 0.03*** -1.29*** -0.14** -0.02 1.09*** 47,690
Brazil -0.01 0.03** 0.03 -0.01 -0.07 0.08*** 0.05* -0.00 0.02*** -0.28 -0.06 -0.14* 0.39 2,000
China -0.04*** 0.26*** -0.10** -0.02 -0.18*** 0.79*** 0.40*** -0.01*** 0.01*** -1.42*** 0.12 0.13* 0.83*** 24,950
India -0.01** 0.02*** -0.01 0.05** -0.14*** 0.08*** 0.10*** 0.00*** 0.01* -0.11 0.09* -0.01 0.15 15,320
Russia -0.01 0.07** -0.04* 0.09 -0.08 0.14*** 0.32*** 0.00*** -0.00 0.59* 0.03 -0.02 -0.01 2,150
S. Africa -0.02 0.12* 0.05 0.03** -0.18* 0.34** 0.19* 0.00 0.00 1.11* -0.20 -0.12 0.26 1,050
Turkey 0.02 0.13** -0.02 0.05 -0.09 0.24*** 0.23*** 0.00*** 0.03** 1.07 0.01 0.29*** -0.59** 2,220
*** ** * Significance at 1%, 5%, and 10% levels, respectively. OBS: Number of observations.
Table 6b. Regression Results by Industry
CH SIZE LEV PROF GROW CAPEX NWC OCF AGE DIV RD INF GDPGR CONS OBS
1 -0.04*** 0.06*** -0.13 0.01 -0.12** 0.29*** 0.16*** 0.00 0.02*** -0.87** 0.15 0.01 0.74*** 9,600
2 -0.05*** 0.05** 0.04** 0.01*** -0.05** 0.20** 0.16** -0.00*** 0.02*** -0.57 -0.13* -0.11** 1.02 9,430
3 -0.03** 0.15** 0.00** 0.01*** -0.35 0.37* 0.26* -0.00*** 0.03** -0.49 0.08 0.18* 0.68 3,790
4 -0.04** 0.06** 0.02** 0.01** -0.14 0.26* 0.15* 0.00*** 0.03** 0.53 0.32 0.17 0.84 1,790
5 -0.07** 0.39* -0.04* -0.01*** -0.11* 0.82 0.31 -0.01*** 0.02** -3.28 -0.78 -0.11 1.37 3,300
6 -0.04*** 0.21** -0.03** -0.00*** -0.13* 0.42** 0.29** -0.01*** 0.03*** -0.83 -0.38 -0.02** 0.94 9,890
7 -0.02*** -0.05** 0.06** 0.00*** 0.03* 0.16** 0.25** 0.00*** 0.02** -2.77 -0.04* 0.04* 0.35 2,760
8 -0.07** 0.28* -0.04** -0.01*** -0.28 0.78* 0.24* -0.01*** 0.03** -1.11 -0.58 -0.17 1.57 5,100
9 -0.04** 0.07** 0.04* 0.01** -0.00* 0.22* 0.29* 0.00*** 0.00*** -3.07 -0.05 0.01* 0.94 2,030
***, **, * Significance at 1%, 5%, and 10% levels, respectively. OBS: Number of observations.
Discussion of Results for the Entire Sample and by Country
The regression results for the entire sample and by country are presented in Table 6a. As described in the methodology section, we employed pooled OLS regressions and panel regressions using fixed effects (FE) and random effects (RE) models. Based on the LM test, panel models were deemed more appropriate in the initial step. Subsequently, the Hausman test [1] determined that the FE model was preferable over the RE model. Therefore, the results in both Tables 6a and 6b are derived from the FE model.
The findings indicate that firm size has a significantly negative impact on cash holdings, contradicting the pecking order theory yet aligning with the notion that smaller firms tend to hold more cash due to lower credibility and restricted access to financing compared to larger counterparts. This trend is observed in China and India at the country level, while insignificance is noted in other countries. Across the entire sample and all countries therein, a positively significant relationship between leverage and cash holdings is identified, contrary to our hypotheses, the pecking order theory, and previous empirical findings.
Profitability is generally insignificant, except for China and Russia, where a negative relationship is observed, contrary to our expectations.
Regarding growth opportunities, insignificance is generally noted, except for India and South Africa, where a negative relationship is observed, consistent with our hypotheses and the pecking order theory.
Capital expenditures display a negative significant relationship with cash holdings in the overall sample and in China, India, and South Africa. In other countries, this relationship is insignificant. This suggests that increased capital expenditures diminish cash holdings, consistent with our hypotheses, the pecking order theory, and prior research [9; 17].
We found a highly positive and significant relationship between net working capital and cash holdings across the entire sample and at the country level for all countries. This finding aligns with the pecking order theory yet contradicts the trade-off theory and our hypotheses.
Similarly, a positive relationship between operating cash flow and cash holdings was observed for the entire sample and at the country level for all countries. This result is consistent with the pecking order theory. Regarding firm age, we obtained mixed results with very low coefficients. For the entire sample and China, a negative relationship was found, while a positive relationship was observed in India, Russia, and Turkey. In Brazil and South Africa, the results were insignificant.
Concerning dividend payments, a significant positive relationship was identified for the entire sample and all countries except Russia and South Africa at the country level. This finding is intriguing as it contradicts both the pecking order theory and the trade-off theory. Theoretically, dividend payments are expected to have a negative effect on
cash holdings. However, in our model and in most empirical studies, the dividend variable is included as a dummy variable rather than a continuous variable. This approach may impact the results and presents a potential area for future research.
Our findings regarding relative R&D expenditures yielded mixed results. We observed a significant negative relationship for the entire sample and China, while a positive relationship was identified for Russia and South Africa. In other countries, the relationship was deemed insignificant. As for macroeconomic variables, our results also varied. The inflation rate demonstrated a negative relationship for the entire sample. In individual countries, this relationship was mostly insignificant, except for India, where a positive relationship was observed. The negative relationship for the overall sample suggests that firms tend to hold less cash during periods of higher inflation, possibly due to decreased purchasing power.
The GDP growth rate showed an insignificant relationship with corporate cash holdings for the entire sample, although significant coefficients were detected in three countries, each with different signs.
Discussion of Results by Industry
Table 6b presents the regression results by industry. Across all industries, firm size exhibited a significantly negative relationship with corporate cash holdings, consistent with the findings for the entire sample and at the country level. This contradicts the pecking order theory but aligns with the perspective that smaller firms tend to hold more cash relative to their larger counterparts within the same industry.
With regard to leverage, a positively significant relationship with cash holdings was observed in all industries except for real estate, mirroring the results at the country level.
The relationship between profitability and cash holdings varied across industries, being positive in five industries, negative in three industries, and insignificant in one industry. This variability could stem from differences in trade credit policies and supplier-customer relationships. In most industries, growth opportunities exhibited a positively significant relationship with cash holdings, in line with our hypotheses. This indicates that growing sectors tend to hold more cash to finance their expansion.
Capital expenditures showed a significantly negative relationship with cash holdings in the majority of industries, consistent with our hypotheses and country-level findings. Net working capital and operating cash flow both displayed a positive relationship with cash holdings across all industries except for healthcare, aligning with country-level results.
The relationship between firm age and cash holdings varied across industries, with some showing positive coefficients and others negative.
All industries demonstrated a positively significant relationship between dividend payments and cash holdings,
suggesting a need for increased cash reserves to accommodate dividend payouts.
R&D expenditures yielded mostly insignificant results across industries, except for the basic materials sector, which showed a negative relationship. Inflation and GDP growth rates did not produce consistent results at the industry level, with varying significance across different sectors.
Conclusion
This study aimed to explore the determinants of corporate cash holdings using a comprehensive panel dataset spanning major emerging countries over a decade-long period from 2012 to 2021. The regression results, presented for the overall sample and dissected by country and industry, shed light on the factors influencing corporate cash reserves. Across the board, firm size, leverage, capital expenditures, net working capital, operating cash flow, firm age, R&D expenditures, and dividend payments emerged as significant determinants of cash holdings, albeit with some variations observed at the country and industry levels. Notably, mac-roeconomic variables such as inflation and GDP growth rate exhibited limited significance compared to firm-level determinants. These findings give preference to the pecking order theory over the trade-off theory in elucidating the drivers of corporate cash holdings.
The results of this study carry significant implications for both corporate managers and policymakers. Understanding the determinants of corporate cash holdings can help managers to formulate effective short and long-term financial strategies tailored to their specific circumstances. Policymakers, particularly those in governmental regulatory bodies, can leverage these insights to shape new regulations aimed at fostering liquidity at both the firm and market levels. These findings hold particular relevance in view of the pivotal role of cash holdings in firm liquidity, particularly in less developed financial systems and emerging economies. While our study focused on major emerging countries, its implications extend to other regions with similar characteristics, highlighting the broader applicability of our findings across diverse economic contexts.
Cash holdings play a central role in the liquidity management of the firms, while firm liquidity is crucial for the financial soundness of firms and for global financial markets, insofar as liquidity problems are among the primary causes of macroeconomic crises. Cash holdings are particularly important for developing countries where the financial system is relatively less developed. While our sample was limited to major emerging economies, the findings are also applicable to other emerging countries with similar characteristics.
Our study has several limitations that pave the way for future research avenues. Firstly, our analysis employed a static model to investigate the determinants of corporate cash holdings. Future studies could expand upon this by incorporating dynamic models.
Secondly, our study did not incorporate variables related to
corporate governance characteristics. Future research endeavors could take such factors as board size, board gender
diversity, and CEO duality into account.
References
1. Hausman J.A. Specification tests in econometrics. Econometrica. 1978;46(6):1251-1271. https://doi. org/10.2307/1913827
2. Al-Najjar B. The financial determinants of corporate cash holdings: evidence from some emerging markets. International Business Review. 2013;22(1):77-88. https://doi.org/10.1016Aj.ibusrev.2012.02.004
3. Chireka T., Fakoya M.B. The determinants of corporate cash holdings levels: evidence from selected South African retail firms. Investment Management and Financial Innovations. 2017;14(2):79-93. https:// doi.org/10.21511/imfi.14(2).2017.08
4. Ramirez A., Tadesse, S. Corporate cash holdings, uncertainty avoidance, and the multinationality of
firms. International Business Review. 2009;18(4):387-
403. https://doi.org/10.1016/jj.ibusrev.2009.02.013
5. Myers S.C. Determinants of corporate borrowing. Journal of Financial Economics. 1977;5(2):147-175. https://doi.org/10.1016/0304-405X(77)90015-0
6. Opler T., Pinkowitz L., Stulz R., et al. The determinants and implications of corporate cash holdings. Journal of Financial Economics. 1999;52(1):3-46. https://doi.org/10.1016/S0304-405X(99)00003-3
7. Ferreira M.A., Vilela A.S. Why do firms hold cash? Evidence from EMU countries. European Financial Management. 2004;10(2):295-319. https://doi. org/10.1111/j.1354-7798.2004.00251.x
8. Saddour K. The determinant and the value of cash holdings: Evidence from French firms. 2006. https://www.finance.dauphine.fr/wp-content/ uploads/2015/07/cereg200606.pdf (accessed on 13.10.2023)
9. Guizani M. The financial determinants of corporate cash holdings in an oil-rich country: evidence from Kingdom of Saudi Arabia. Borsa Istanbul Review. 2017;17(3):133-143. https://doi.org/10.1016/jj. bir.2017.05.003
10. Myers S.C., Majluf N.S. Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics. 1984;13(2):187-221. https://doi. org/10.1016/0304-405X(84)90023-0
11. Jensen M.C. Agency costs of free cash flow, corporate finance, and takeovers. The American Economic Review. 1986;76(2):323-329.
12. Orens R., Reheul A.M. Do CEO demographics explain cash holdings in SMEs? European Management Journal. 2013;31(6):549-563. https://doi. org/10.1016/j.emj.2013.01.003
13. Bugshan A., Alnori F., Bakry W. Shariah compliance and corporate cash holdings. Research in International Business and Finance. 2021;56:101383. https://doi.org/10.1016/j-.ribaf.2021.101383
14. Ozkan A., Ozkan N. Corporate cash holdings: an empirical investigation of UK companies. Journal of Banking and Finance. 2004;28(9):2103-2134. https:// doi.org/10.1016/j.jbankfin.2003.08.003
15. Al-Najjar B. The interrelationship between capital structure and dividend policy: Empirical evidence from Jordanian data. International Review of Applied Economics. 2011;25(2):209-224. https://doi.org/10.10 80/02692171.2010.483464
16. Choi P.M.S., Choi J.H. Why do firms hold cash? Evidence from Korean stock listings. Investment Management and Financial Innovations. 2016;13(3):311-321. https://doi.org/10.21511/ imfi.13(3-2).2016.03
17. Marwick A., Hasan M.M., Luo T. Organization capital and corporate cash holdings. International Review of Financial Analysis. 2020;68:101458. https:// doi.org/10.1016/j.irfa.2020.101458
18. Al-Najjar B., Clarck E. Corporate governance and cash holdings in MENA: Evidence from internal and external governance practices. Research in International Business and Finance. 2017;39(A):1-12. https://doi.org/10.1016/jj.ribaf.2016.07.030
19. Yang X., Han L., Li W., et al. Monetary policy, cash holding and corporate investment: evidence from China. China Economic Review. 2017;46:110-122. https://doi.org/10.1016/jj.chieco.2017.09.001
20. Dittmar A., Mahrt-Smith, J. Corporate governance and the value of cash holdings. Journal of Financial Economics. 2007;83(3):599-634. https://doi. org/10.1016/j.jfineco.2005.12.006
21. Bates T.W., Kahle K.M., Stulz R.M. Why do US firms hold so much more cash than they used to? The Journal of Finance. 2009;64(5):1985-2021. https://doi. org/10.1111/j.1540-6261.2009.01492.x
22. Lozano M.B., Yaman S. The European financial crisis and firms' cash holding policy: an analysis of the precautionary motive. Global Policy. 2020;11:84-94. https://doi.org/10.1111/1758-5899.12768
23. Ramezani C.A. Financial constraints, real options and corporate cash holdings. Managerial Finance. 2011;37(12):1137-1160. https://doi. org/10.1108/03074351111175074
24. Chen H., Yang D., Zhang J.H., et al. Internal controls, risk management, and cash holdings. Journal of Corporate Finance. 2020;64:101695. https://doi. org/10.1016/j.jcorpfin.2020.101695
25. Bigelli M., Sanchez-Vidal J. Cash holdings in private firms. Journal of Banking and Finance. 2012;36(1):26-35. https://doi.org/10.1016/jjbankfin.2011.06.004
26. Al-Najjar B., Belghitar Y. Corporate cash holdings and dividend payments: evidence from simultaneous analysis. Managerial and Decision Economics. 2011;32(4):231-241. https://doi.org/10.1002/ mde.1529
27. D'Mello R., Krishnaswami S., Larkin P.J. Determinants of corporate cash holdings: Evidence from spin-offs. Journal of Banking & Finance. 2008;32(7):1209-1220. https://doi.org/10.1016/j. jbankfin.2007.10.005
28. Hall T., Mateus C., Mateus I. What determines cash holdings at privately held and publicly traded firms? Evidence from 20 emerging markets. International Review of Financial Analysis. 2014;33:104-116. https://doi.org/10.1016/jj.irfa.2013.11.002
The article was submitted 21.12.2023; approved after reviewing 15.02.2024; accepted for publication 27.02.2024.
