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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue I, January 2025

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The Impact of Short-Term Debt on the Performance of Manufacturing

Companies Listed on the Ghana Stock Exchange

Joseph Kumbankyet

, *Prince Dacosta Anaman

, Christian Donkor

, Benjamin Akyen

1,3,4

University of Education, Winneba, Accounting Department, Winneba, Ghana

Perez University College, Accounting Department, Winneba, Ghana

*Corresponding Author

DOI : https://doi.org/10.51583/IJLTEMAS.2025.140106

Received: 16 January 2025; Accepted: 21 January 2025; Published: 01 February 2025

Abstract: This study examines the impact of short-term debt on the financial performance of manufacturing companies listed on the

Ghana Stock Exchange over an eight-year period (2015–2023). Using a combination of descriptive and causal research designs, the study

utilizes secondary data from audited financial statements to investigate the relationship between short-term debt and firm performance,

measured through Return on Assets (ROA). The findings reveal a significant but negative correlation between short-term debt and ROA,

indicating that higher reliance on short-term financing may adversely affect profitability. Conversely, firm size demonstrates a positive

but weak association with performance. Regression analysis confirms that short-term debt and firm size account for 9.9% of the variations

in financial performance, highlighting the limited but impactful role of short-term debt in the capital structure. The study shows the need

for manufacturing firms to optimize their financing strategies by prioritizing internal funds before resorting to external short-term debt.

Recommendations include exploring alternative investment options, diversifying funding sources, and conducting similar studies across

other sectors to validate these findings. This research contributes to the ongoing discourse on capital structure management in developing

economies, providing insights for policymakers, investors, and corporate managers.

Keywords: short-term debt, financial performance, manufacturing firms, capital structure, Ghana Stock Exchange, Return on Assets

(ROA)

I. Introduction

A company's ideal financial structure should be identified before making judgments about how much money should be borrowed and the

best ratio of debt to equity utilized to fund commercial activities (Al-Slehat et al., 2020; Amraoui et al., 2018). In order to boost its

operations, a company's financial structure is the way it finances its assets through a mixture of equity and debt. This combination of

funding sources is referred to as the capital structure (Gill et al., 2011). As a component of the monetary system, debt with short-term

maturities is an essential component (Sivalingam & Kengatharan, 2018). In a broad sense, the analysis of acts that have an effect on a

company's current assets and current liabilities is what is meant to be covered by the phrase "short-term." When an asset or liability is

expected to be used, liquidated, matured, or paid off within a year of when it was purchased, it is sometimes referred to as "short-term"

(Chandio et al., 2018). According to Ranaldo et al. (2021), in order for short-term assets to be effective, they need be supported by short-

term loans. According to the findings of Shikumo, et al., (2020), the capacity of a company to expand is positively correlated with its

level of short-term debt. Financial success is linked to the utilization of short-term loan financing, based on anecdotal evidence (Devi et

al., 2020; Purba & Septian, 2019). When it comes to financing via short-term debt, the maturity period is one year or less, and the debt

needs to be paid back within ninety to one hundred and twenty days. You are able to take care of your present financial obligations with

the assistance of short-term loans since you are not required to make a commitment for an extended period of time (Lemma et al., 2021).

Short-term loans often have lower interest rates than long-term ones, and most lenders don't start collecting interest until the credit limit

period has expired (Kahl et al., 2015). Because it shows both the status of the organization's finances and the situation of the financial

market, the management of short-term assets and liabilities is an extremely important aspect of every business (Shikumo et al., 2020).

A number of academics and researchers have arrived at the conclusion that a company's financial success can be affected by its level of

short-term debt (Habib et al., 2016; Muturi & Omondi, 2013; Purba & Septian, 2019). Increasing a company's earnings through the use of

short-term finance is possible (Shikumo et al., 2020). In addition, Purba and Septian (2019) found that this factor can have a bearing on

the company's bottom line, with the magnitude of the effect being influenced by the cost of the source of funding utilised by the specific

company. Companies may have specific ratios of short-term liabilities in their financing structure, which Amraoui et al. (2018) state the

companies feel is the most successful in terms of enhancing performance and profitability. In the study conducted by Nunes Serrasqueiro

(2017), the authors found that businesses who had high levels of short-term debt in comparison to their total amount of long-term debt

outperformed their competitors in terms of their overall financial success. As a consequence of this, it is possible that a company's

profitability will benefit from the use of short-term debt. A measure of an organization's overall financial health might be defined as

"financial growth" in a broader meaning (Naz et al., 2016). The value of a company's market capitalization may also be used to gauge

financial growth, according to Buvanendra et al. (2017; Buvanendra et al., 2017). A company's market capitalization is equal to the total

dollar market value of its outstanding shares (Le, 2019). Dollars are used to express this number (Buvanendra et al., 2017). Return on

investment, return on assets, market value, and accounting profitability are all indicators of a company's financial health (Ongore & Kusa,

2013; Usman & Lestari, 2019). For a corporation, financial growth is a measure of how well it manages its debt capital in order to

generate profits. A company's primary business mode generates money by effectively utilizing its assets, which is what is meant by

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financial growth (Usman & Lestari, 2019). As a result of using short-term obligations, such as trade payables and accruals, to fund a

company's activities, it can have an influence on profitability. Trade receivables and accruals may be more cost-effective for the firm than

long-term sources of financing (Naz et al., 2016). Additionally, because short-term finance sources have less contractual obligations, this

may have an impact on a company's profitability. It's because short-term funding sources are utilised for a shorter length of time

(Krishnamurthy & Vissing-Jorgensen, 2013). The company's cost of capital would rise, according to Chaleeda et al. (2019), if it takes on

short-term debt with a short maturity time span.

No matter how big or small a company is, the impact of debt financing on its financial performance and profitability is of the utmost

significance. The capital structure, as opposed to the debt structure of organisations, has been the focus of a significant amount of study

that has been conducted on the subject of the financial structure of businesses and companies (Purba & Septian, 2019; Sivalingam &

Kengatharan, 2018). As a direct consequence of this fact, there is not a solitary integrated theory that can be taken into consideration

while attempting to evaluate the effect that debt financing has on the monetary performance of businesses in the here and now. When a

corporation makes purchases or investments in new or existing assets, this influence will lead to current outcomes and repercussions.

According to the findings of certain studies, carrying a debt load has a negative effect on a company's capacity to generate profits.

According to a number of academics, including Adesina et. al., 2015; Batchimeg, 2017, Nassar, 2016; Vătavu, 2015 a high amount of

debt has a detrimental impact on a company's capacity to satisfy its monetary commitments. The opposite has been shown by Afolabi et

al. (2019) and Gamayuni (2015). A study by Sunardi and colleagues (2020) found no statistically significant impact on industrial

enterprises in the United States.

Non-financial enterprises listed on the Ghana Stock Exchange (GSE) have, according to Bunyaminu et al. (2019), suffered from poor

financial performance in large numbers. In the past, investors were wary of investing in such firms because of this concern. Creditors are

less likely to lend money to enterprises in this situation because of the fall in their financial performance. The capital's organizational

structure was the primary focus of much of the research (Akomeah et al., 2018; Antwi et al., 2012; Ganiyu et al., 2019; Musah, 2018).

Consequently, this study was deemed relevant due to the fact that studies on debt funding got a lower level of attention than studies on

capital structure. This study was also prompted by a lack of agreement amongst previous empirical studies on the impact of short-term

debt on public company financial performance. This inconsistency in the findings of earlier empirical studies is another incentive for this

study. Examining the effect that short-term debt has on the financial growth of manufacturing companies that are publicly listed on the

Ghana Stock Exchange is the purpose of this study, which aims to fulfil the research objective of "closing this conceptual gap."

II. Literature Review

Theoretical Review

Agency Theory

Agency theory was created by Jensen and Meckling (1976) who claimed that a corporation's financial structure may reduce agency costs

resulting from disagreements between managers and shareholders of the organization. This was one of the central hypotheses of the

theory. The decline in the number of conflicts involving the agency would lead to a reduction in the expenditures incurred by the agency,

which would in turn lead to improved financial performance. According to the results of Jensen and Meckling (1976), the utilisation of

debt in a company may serve as a potential assistance in the management and monitoring of managers inside the firm. This is done with

the intention of ensuring that managers work toward goals that are beneficial to the company. According to Kahl et al. (2015), the

inclusion of debt into a company's financial structure offers a motive for managers to drive the growth of a firm to produce cash flows that

can be utilized to repay loans. Several pieces of research go in the direction that this idea is correct. As a result, this makes a contribution

toward the enhancement of the company's profitability (Yusuf et al., 2018). According to this line of thinking, a company's ability to take

on debt, whether it be long-term or short-term debt, or any other kind of debt, will reduce the amount of agency conflicts that exist

between the company's managers and its shareholders, which will lead to increased financial growth for the company (Cruz & Haugan,

2019). The agency theory is of critical significance in the process of decision-making on financing because of the potential for conflicts to

arise between shareholders and holders of debt.

Pecking Order Theory

According to this theory, businesses prefer to raise cash from inside rather than from outside sources. In the event that an organisation

needs financing from outside sources, it will pick debt over stock, and it will only turn to equity as a last choice in these kinds of

circumstances. As a result of the uneven distribution of information, organisations do not have a debt-to-equity ratio that has been

calculated in advance or that is ideal (Martinez et al., 2018). When it comes to dividends, the businesses adhere to established procedures

and make use of debt financing in order to raise the value of the firm. This is done in an effort to maximise shareholder returns. According

to the idea, businesses have a preferred order in which they would want to receive cash for the purpose of financing their activities. This

order might either be chronological or non-chronological. In order to maximize profits and account for the knowledge gaps between the

company and potential investors, the firm would prioritize short-term debt over long-term debt, debt over equity, and debt over retained

earnings (Agyei et al., 2020).

Empirical review

An analysis by Langat et al. (2014) indicated that long-term debt and total debt had a substantial and positive association with the

profitability of Kenya Tea Development Authority processing facilities, while a negative and significant relationship with short-term debt

was observed at 5%. This result was obtained because short-term debt is negatively correlated with long-term debt. It is unable to secure

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finance for tea processing through short-term debt because of the negative connection between short-term debt and the profitability of tea

processing factories Companies listed on the National Stock Exchange were the topic of research by Maina and Ishmail (2014). The

results of the study, which included statistical software and a regression model, showed that NSE-listed companies' financial performance

is strongly influenced by debt and returns to shareholders. An adverse link between financial performance and capital structure might be

regarded statistically significant, according to the statistics. This association is in the opposite direction of what we would expect. If a

corporation relied more heavily on debt to fund its operations, its performance would be negatively affected. In addition, the study found

that firms on the NSE were more likely to employ short-term debt than long-term debt in their financing.

The term "long-term debt" refers to money that is owing to lenders for more than a year. Baid (2009) found that there is no statistically

significant association between a high return on assets and a high amount of long-term debt. Long-term loans are the preferred mode of

debt financing for well-established corporate entities because of the value of their assets and collateral. This is because the high value of

these assets and collateral makes them the most attractive sources of debt financing in the majority of circumstances. The capacity of

small and medium-sized businesses (SMEs) to grow and do well in the financial markets has been hindered by the huge decline in

financing received from major financial institutions. According to Masiega and co-authors (2013), they set out to find out how much the

capital structure of publicly traded firms on the National Stock Exchange influences their financial performance (NSE). During a period

of five years beginning in 2001, data was collected from thirty firms that were traded on the National Stock Exchange (NSE). The results

of the analysis show that there is a statistically significant association between a company's total assets and its long-term debt. Githaig and

Kabiru (2015) found empirical evidence that long-term indebtedness has a negative impact on the financial performance of small and

medium-sized enterprises (SMEs). After analyzing the data, the writers came to this conclusion.

Long-term loans have been shown to improve productivity in the past, but our analysis demonstrates that this is not the case in this

situation. According to Huang and Song (2006), long-term debt can have a detrimental impact on return on assets (ROA). Debt in the long

run is beneficial to one's financial well-being, according to a study by Abor (2005). The existing research on long-term debt is divided,

however several studies, such as Ebaid (2009) and Huang and Song (2006), have indicated that long-term debt has a negative impact on

financial performance. As a result, we have a knowledge gap that has to be filled. According to the findings of Jaramillo and Schiantarelli

(2002), a study conducted in Ecuador found evidence that a shorter maturity did not favour better productivity. According to their

findings, a company's ability to obtain long-term debt was critical to its success. Delaying debt repayment might have the unintended

consequence of increasing output in some circumstances.

During the global financial crisis of 2013, Fosberg published his results on short-term loan finance. The global financial crisis had a

significant impact on the United States and other countries' capital and credit markets in the second part of this decade. Short-term loan

financing grew from 1.3 percent of assets in 2006 to 2.2 percent of assets in 2008, according to the data provided. This tendency

accelerated between 2006 and 2008. When short-term debt financing rose at the end of 2009 and quickly returned to its pre-crisis levels,

we may conclude that it was not a deliberate effort. By the end of 2009, the expansion in short-term debt financing had been completely

reversed, confirming this. The decline in long-term debt and equity financing, as well as a reduction in the amount of capital obtained

through accounts receivable from suppliers, have all contributed to a rise in the usage of short-term loan financing. Another factor that led

to the requirement for extra short-term debt financing, which had not been accessible before, was the significant reduction in the quantity

of assets that were sold. The financial crisis, rather than the concomitant recession, appears to be the cause of the surge in the use of short-

term debt as a source of funding, as shown by the findings of a regression analysis.

Habib, Khan and Wazir (2016) performed a research on how debt affects the profitability of Pakistani firms, relying on data acquired

from the country's non-financial sector. The non-financial sector of Pakistan was chosen to conduct the study, however, several businesses

were not included since there was insufficient information on how they functioned. After excluding the 340 companies that traded on the

Karachi Stock Exchange (KSE) between 2003 and 2012 from the dataset, the examination focused on the remaining companies. In order

to conduct this research, a panel research methodology was utilized. The profitability of a corporation is calculated using the dependent

variable, return on assets. When calculating the overall debt to asset ratio, we don't consider any one of the other debt-to-asset ratios.

Instead, we look at each one separately. The size of the company, the rate of sales growth, and the potential for growth are all examples of

control variables. Utilizing random effect regression analysis helps researchers estimate how much of an impact debt has on a company's

capacity to turn a profit. There was found to be a correlation that was not only statistically significant but also negative between total and

net asset returns, as well as between short-term and long-term debt.

Conceptual framework

The conceptual framework lays out the interconnections that exist between the many conceptions that are of interest to the research. The

model illustrates the connection between the independent variable, short-term debt, and the dependent variable, firm performance, as it

relates to manufacturing enterprises that are listed on the Ghana Stock Exchange.

Figure 1: Conceptual framework

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The framework shows the hypothesised impact of short-term debt and interest on the financial performance of manufacturing firms on the

Ghana stock exchange.

III. Methodology

Research design

In this study, the descriptive and causal research designs were utilised. A descriptive study design is a method that is used in the scientific

community, and it entails watching and describing the behaviour of a subject without having any impact on that behaviour in any manner

(Martyn, 2017). According to Brains, Willnat, and Rich (2011), causal research is the analysis of cause-and-effect interactions. Causal

research is utilised when sufficient evidence is available for examining the cause and effect link in a phenomenon. For the purpose of this

study, descriptive research was helpful in attempting to explain the phenomenon in its current state. On the other hand, a causal research

design was also appropriate for determining whether a change in one of the variables being studied causes a change in the dependent

variable.

Sources of data

Secondary data were utilised for this study. These data were gathered from the yearly audited financial statements of the concern

businesses that were published on the Ghana Stock Exchange Market. In order to conduct statistical analysis, the data that was obtained

was examined to ensure that it was both comprehensive and consistent. The research was conducted over a span of eight (8) years, from

2015 to 2023, which is the time frame that is being deemed appropriate for determining the study's dependability (effects of short – term

debt on the performance of registered manufacturing companies in Ghana). In order to be eligible for consideration for data collection, the

firms in question must have been actively engaged in the trade industry during the past twelve years.

Population, Sample and sampling technique

The primary demographic of interest for this study consisted of the manufacturing businesses that are traded on the Ghana Stock

Exchange (GSE). Purposive sampling was used to choose the sample for the study since the major goal was to establish whether or not

having short-term debt had an impact on the overall performance of manufacturing companies listed on the Ghana Stock Exchange. This

selection strategy is commonly used when dealing with tiny samples, such as in case study research, and when selecting examples that are

particularly informative (Neuman 2005).

Variable measurements

Short-term debts

To determine the quantity of short-term debt, Githaiga et al. (2015) utilized the ratio of short-term loans to total loans as the basis for their

calculation. Short-term debt, as defined by Magoro and Abeywardhana (2017), was defined as the percentage of total assets that equaled

to debt that was due to be repaid within a year of the investigation. According to Ma'aji et al. (2018), the ratio of short-term obligations to

total assets is short-term debt. A different way to calculate short-term debt was to use this metric. The amount of short-term debt in this

inquiry was determined by comparing the amount of overall assets to the amount of current liabilities.

Research model

As part of this research, a multivariate regression model was utilized to examine the relationship between short-term debt and business

financial performance on the Ghana Stock Exchange. There was an application of a multiple regression model, which consisted of two

independent variables, namely interest rate and short-term debt. The level of profitability served as the dependent variable. This approach

sought to adhere to the paradigm proposed by Rajan and Zingales (1995), as well as Tale (2014).

ROA

= α + β

STDA

+ β

Size

+ β

Lev

+ e

i ………………………………..1

ROA

= α + β

INTit + β

Size

+ β

Lev

+ e

i ……………………………………2

Where,

ROA = firm financial performance

STDA = Short – term debt

Size = Firm size

Lev = Leverage

e = error term

Data analysis

Following the completion of the data gathering, it was necessary to organise the information so that it could be used to make informed

decisions. Observing and characterising the behaviour of the variables that were the focus of the study required the use of descriptive

statistics. These statistics include the minimum, maximum, mean, and standard deviation of the data. The data that was collected was

evaluated with inferential statistics in order to obtain inferences that are relevant from the data. The study utilised inferential statistics,

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such as Pearson's Coefficient and Regression Analysis, to determine the amount of reliability and consistency of the findings in relation to

the primary purpose of the study.

Multicollinearity test

The problem of multicollinearity may appear if two or more variables were found to have a strong correlation between them. It is possible

that the estimate of the regression parameters was impacted as a result (Hair et al., 2010). Examining the correlation matrix allowed the

researcher to test for the presence of multicollinearity. The Variance Inflation Factor (VIF), was used to do this. There is no

multicollinearity problem if the VIF ratio is less than five, which is the ratio of the true disparity percentage to the total disparity (Fox,

1991).

Table 1: Coefficients

Model

Collinearity Statistics

Tolerance

VIF

STD/A

.688

1.454

Rate

.971

1.030

Leverage

.883

1.133

FIRM SIZE

.739

1.353

a. Dependent Variable: ROA

The multicollinearity coefficient result, as presented in the table indicates that the items for measuring the constructs in the study are less

than the conventional acceptable (5) (Fox, 1991). Thus, in essence, the multicollinearity value of 1.454, 1.030, 1.133 and 1.353 for

STD/A, Rate, Leverage and Firm Size respectively as in the case of this study is an indication that there no problem of multicollinearity

among the variables.

IV. Findings

Descriptive analysis

Included in this section the descriptive statistics of all the variable under study. This gave a pattern ranging from minimum to

maximum values as well as the mean scores and standard deviation of the variables to find out how they related to firm performance.

The Table 1 provides a summary of the descriptive statistics of the dependent and independent variables for the sample of firms.

Table 2: Descriptive statistics

Min

Max

Mean

S. D

ROA

-0.14

0.47

0.06

0.14

STD/A

0.01

0.89

0.38

0.25

INTEREST RATE

0.13

0.26

0.20

0.05

FIRM SIZE

15.21

20.40

18.37

1.41

LEVERAGE

-7.51

55.09

2.34

7.14

From 2015 through 2023, the average ROA was 6%. The maximum and minimum values for ROA were 47% and -14%, respectively.

This implies that listed companies did poorly during the time period under review. The findings also show that there was a huge

discrepancy in corporate performance, such that although some organizations made significant profits, others lost a lot of money. With

short-term debt to total asset ratios ranging from as low as 0.01 percentile units to as high as 89 percentile units on our short list, it's clear

that most Ghanaian businesses are dependent on short-term loan funding. As seen in the table, businesses pay an average interest rate of

20%, with rates ranging from 13% to 26%. The average size of the organizations researched is 18.37. From -7.51 to 55.09, the debt-to-

equity ratio (leverage) went up to 2.34 with a standard deviation of 7.14.

Correlation analysis

Prior to conducting a multiple regression analysis, a correlation matrix was created to analyse the correlations between the independent

variables. The purpose of this was to help in the development of a multiple prediction model. The use of correlation analysis was

beneficial in identifying any potential instances of multicollinearity. A correlation value of 0 indicates that there is no link between the

variable that is being dependently measured and the variable that is being independently measured. On the other side, if the correlation is

less than one, it indicates that the connection is either perfectly positive or perfectly negative (Hair et al., 2010). The table below shows

the normal Pearson’s correlation without the control variables (firm size and growth rate).

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Table 3: Correlations

ROA

STD/A

Rate

Leverage

Firm Size

ROA

Pearson Correlation

-.257

-.263

-.196

.072

Sig. (2-tailed)

.028

.024

.097

.545

STD/A

Pearson Correlation

-.257

-.037

.306

.509

Sig. (2-tailed)

.028

.759

.008

.000

Rate

Pearson Correlation

-.263

-.037

-.164

-.061

Sig. (2-tailed)

.024

.759

.166

.606

Leverage

Pearson Correlation

-.196

.306

-.164

.157

Sig. (2-tailed)

.097

.008

.166

.183

FIRM

SIZE

Pearson Correlation

.072

.509

-.061

.157

Sig. (2-tailed)

.545

.000

.606

.183

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

In the table above, the correlations between the dependent and independent variables are shown. The short-term to asset (STD/A) and

return on asset (ROA) have a negative correlation coefficient. This suggests that there is a weak and negative association (correlation)

between (STD/A) and the return on asset. The test's p-value is 0.028, which is less than the 0.05 level of significance. As a result, the

association between ROA and (STD/A) is statistically significant (using a 5 percent significance level). Interest Rate (Rate) has a negative

correlation coefficient with return on asset (ROA). There is a modest link (correlation) between the interest rate and the asset's return. The

test's p-value is 0.024, which is higher than the 0.05 level of significance. As a result, the link shown between ROA and rate is strong

(using a 5 percent significance level). This suggests that the performance of a company is influenced by changes in the interest rate.

Leverage and ROA have a negative association, as evidenced by the correlation coefficient .196, this shows that the growth rate and asset

return have a weak and negative link (correlation). P-value for the test is 0.097, which is larger than the 0.05 level of significance required

for the test. According to this, the association between ROA and leverage is insignificant (using a 55 percent significance level). In the

analysis of company size and return on asset (ROA), a positive link was found. According to the correlation coefficient of 0.072, there is a

modest and positive association (correlation) between business size and asset return. The test's p-value is 0.545, which is higher than the

0.05 level of significance. The association between ROA and size is therefore not substantial, as this suggests (using a 5 percent

significance level). This suggests that the performance of a business is unaffected by its size.

Regression analysis

A multiple regression analysis was carried out to investigate the possible effect of one predictor variable on another. The study made use

of a multiple regression analysis with Return on Assets (ROA), and it comprises an analysis of variance along with model coefficients and

a model summary (ANOVA).

ROA

= α + β

STDA

+ β

Size

+ β

Lev

+ e

Table 4: Model Summary

Model

R Square

Adjusted R Square

Std. Error of the Estimate

.370

.137

.099

.12877

a. Predictors: (Constant), STD/A, Leverage, FIRM SIZE

The modified R squared coefficient of determination tells us how much the dependent variable changes as a result of changes in the

independent variable. Short-term debt, leverage and business size were shown to have a 9.9 percent impact on the financial performance

of listed manufacturing enterprises in Ghana at a 95 percent confidence interval. These findings were based on the findings presented in

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the table that was presented earlier. This demonstrates that short-term debt, leverage, and company size might be responsible for

accounting for 9.9 percent of the fluctuations in the financial performance of listed manufacturing businesses. R is the correlation

coefficient, and it illustrates the degree to which the variables under examination are related to one another. According to the data, there

was a somewhat favourable connection between the several factors that were investigated, as shown by the value of 0.370.

Table 5: ANOVA

Model

Sum of Squares

Mean Square

Sig.

Regression

.181

.060

3.643

.017

Residual

1.144

.017

Total

1.325

a. Dependent Variable: ROA

b. Predictors: (Constant), STD/A, Leverage, FIRM SIZE

The significant level of the processed data, which reflects the population parameters, was 0.017, according to the ANOVA statistics

shown in the table above. Since the significance level is less than 5%, the data can be used to draw conclusions about a population

parameter. The fact that the calculated F

count

was greater than the critical value (3.643) showed that the financial performance (ROA) of

listed manufacturing companies in Ghana was considerably affected by STD, leverage and firm size. Because the significance value was

lower than 0.05, it may be deduced that the model was significant from a statistical point of view.

Table 6: Coefficients

Model

Unstandardized Coefficients

Standardized Coefficients

Sig.

Std. Error

Beta

(Constant)

-.350

.218

-1.603

.113

Leverage

-.002

.002

-.129

-1.102

.274

FIRM SIZE

.026

.013

.274

2.108

.039

STD/A

-.194

.073

-.357

-2.647

.010

a. Dependent Variable: ROA

At a significance level of 0.05, it can be shown from Table that STD/regression’s coefficient (-0.194) and the coefficient significance test

(tcount) are both equal to -2.647 with an error probability of (p) = 0.10. According to the p value (0.010), which is less than the

significance threshold (0.05). As a result, the ROA variable is adversely and considerably influenced by the STD/A variable to some

extent. A look at Table shows that the regression coefficient for company size is 0.026, and that in a coefficient significance test using the

t statistic, it is 2.108 tcounts at the significance level of 0.05. P-value (0.039) is below than significant threshold (0.10), according to the

results (0.05). As a result, the ROA variable is favourably and significantly influenced to some extent by the firm size variable. At the

significance level of 0.05, an error probability of (p) = 0.27 (4%) is calculated for the regression co-efficients of leverage, which is

equivalent to -0.002 (see Table). According to the results, the p value (0.274) is higher than the significance threshold (0.05). (0.05). As a

result, the ROA variable is only slightly impacted by the leverage variable.

Interest rate on Debt on ROA model …………………………2

ROA

= α + β

INTit + β

Size

+ β

Lev

+ e

Table 7: Model Summary

Model

R Square

Adjusted R Square

Std. Error of the Estimate

.360

.137

.039

.12878

a. Predictors: (Constant), FIRM SIZE, Rate , Leverage

The modified R squared coefficient of determination tells us how much the dependent variable changes as a result of changes in the

independent variable. Adjusted R squared (0.039) indicates that listed manufacturing enterprises in Ghana's financial performance (ROA)

can be affected by interest rate, leverage, and company size at a 95 percent confidence interval, which is consistent with the data in the

table above. This demonstrates that variations in the financial performance of publicly traded manufacturing businesses can be accounted

for by interest rate, leverage, and firm size to the tune of 3.9 percent. R is the correlation coefficient, and it illustrates the degree to which

the variables under examination are related to one another. According to the data, there was a somewhat favourable connection between

the several factors that were investigated, as shown by the value of 0.360.

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Table 8: ANOVA

Model

Sum of Squares

Mean Square

Sig.

Regression

.181

.060

3.641

.017

Residual

1.144

.017

Total

1.325

a. Dependent Variable: ROA

b. Predictors: (Constant), FIRM SIZE, Rate , Leverage

The significant level of the processed data, which reflects the population parameters, was 0.017, according to the ANOVA statistics

shown in the table above. Since the significance level is less than 5%, the data can be used to draw conclusions about a population

parameter. For listed Ghanaian manufacturing enterprises with a turnover (ROA) greater than the critical value (3.641), variables such as

interest rates, leverage, and company size had a significant influence on financial performance (ROA). Because the significance value was

lower than 0.05, it may be deduced that the model was significant from a statistical point of view.

Table 9: Coefficients

Model

Unstandardized Coefficients

Standardized Coefficients

Sig.

Std. Error

Beta

(Constant)

.074

.214

.346

.731

Rate

-.889

.336

-.300

-2.646

.010

Leverage

-.005

.002

-.260

-2.263

.027

FIRM SIZE

.009

.011

.095

.834

.407

a. Dependent Variable: ROA

Table shows that the interest rate's regression coefficient is -0.889, and the t statistic's coefficient significance test is tcount = -2.646, with

an error probability of (p) = 0.010 at a significance level of 0.05. These values are significant at the 0.05 level of significance. According

to the p value (0.010), which is less than the significance threshold (0.05). As a result, the ROA is influenced both adversely and

significantly by the interest rate variable. Regression leverage co-efficient are -0.002 in the table, and the coefficient significance test

using the t statistic yields an error probability of (p) = 0.27, which is significant at a level of 0.05, as can be seen. According to the results,

the p value (0.274) is higher than the significance threshold (0.05). As a result, the ROA variable is only slightly impacted by the leverage

variable. It is also clear from the data in Table that the regression coefficient for company size is just under zero (0.009), and the

corresponding coefficient significance test (p) equals just under four-hundredths of one at the significance level of 0.05, using the t

statistic. According to the results, the p value (0.407) is higher than the significance threshold (0.05). Since ROA was favourably

influenced by company size but not considerably, this may be concluded.

V. Discussions of findings

According to the descriptive statistics produced from the data gathered, firms' ROA performance suggests an average of 6.0 percent with

a standard deviation of 14 percent. For example, the variable STD/A is found to have an average value of 38%, while the variable interest

rate has an average value of 20%, with a standard deviation of 5 percentage points. Finally, the natural log of total assets yields an average

firm size of 18.37. Return on asset (ROA) and short-term debt (STD/A) have a negative association for the average person. It's for this

reason that ROA and STD/A are inversely proportional. A modest association (correlation) exists between the STD/A and asset return,

but it is a negative correlation. Mirza and Javed (2013), who studied Pakistan's macro- and microeconomic factors of financial

performance, came to the same result. They observed that the short-term asset-to-total asset ratio was a large and negative one. It

contradicts Goyal (2013) and Yegon, Cheruiyot, Sang, and Cheruiyot (2014) who found a positive and statistically significant correlation

between the STD/A ratio and ROA when investigating the link between the firm's capital structure and profitability. Capital structure and

profitability were studied by Goyal (2013), Yegon (2014), Cheruiyot (2014), Sang (2014), and Cheruiyot (2014).

Furthermore, the correlation coefficient between interest rate and return on asset (ROA) is shown to be negative. This shows that interest

rates and return on assets have a relationship (correlation), but it is a weak and negative one. This supports the findings of Mnang'at et al.

(2016), who found that the interest rate and the financial success of micro businesses in Kenya are closely linked. Firm size and return on

asset have a positive correlation value of 0.072, which suggests that the two variables have a very weak and positive association

(correlation). The interest rate has a strong negative impact on stock market returns in Ghana, according to Barnor (2014). Finally, a

negative correlation of 0.196 has been found between leverage and return on asset (ROA). As a result, there appears to be a weak and

negative link between leverage and asset return. The p-value for the test was 0.005 at the 0.05 level of significance when the association

between STD/A and ROA was adjusted for firm size and leverage. Although the interest rate-ROA correlation fell to -0.303, the test's p-

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value was 0.005 at a significance level of 0.05, suggesting that the result is statistically significant. These results are significant because of

this fact: STD/A, interest rate and ROA are all associated with each other (using a 5 percent significance level).

The coefficient of determination (R) for regression (model one) is 0.370, as shown in the model description above, which was produced

from the SPSS result. Return on Asset (ROA) and short-term debt, leverage, and business size appear to have a moderately significant

positive association. The conclusions of this study are in line with those of Safa and Maulana (2017), who showed that short-term

borrowing had a positive impact on firm profitability. Both Safa and Maulana confirmed the existence of such a bond. Regression model

2 has an R-value of 0.360, indicating a fairly positive and statistically significant relationship between Return on Asset and the interest

rate, the leverage, and the size of the company. The findings of Kanwal and Nadeem (2013) support their conclusion that the real interest

rate and ROA have a strong positive association. Despite this, Obamuyi and Olorunfemi (2011) showed that financial reform and interest

rate adjustments have a major impact on Nigeria's economic development.

VI. Conclusion and Recommendations

Whatever the size of the business, the impact of debt financing on its financial performance and profitability is critical. Determining

whether to use an optimal or ideal debt-to-equity ratio can have an impact on a company's financial performance as well as its market

value. Including short-term debt in the entire financial system is vital. Short-term debt financing has been shown to have a significant

influence on the financial performance of industrial businesses quoted on the Ghana Stock Exchange since 2015. Research shows that the

majority of publicly listed manufacturing firms do not make a profit, and descriptive data shows that these firms are heavily dependent on

short-term capital. Debt in short-term terms was similarly shown to be negatively correlated with company success; however, when the

variables that influence this correlation were controlled, it was revealed that short-term loans had a modest but significant association with

firm performance. Consequently, the relationship between short-term indebtedness and total financial success is both negative and large.

Thus, it is recommended for publicly traded companies to deplete their retained earnings completely before considering the use of

alternative kinds of investment, such as debt or stock. This will guarantee that the greatest amount of funds is accessible and that a proper

choice of investment is made, while also limiting the amount of money that is wasted. Also, the upper management of a company have to

investigate many investment options before settling on the finest investment to make. This will guarantee that the firm's investments in

prioritised areas are based on the finances available, which will boost the firm's ability to utilise available money to their fullest potential.

It is also recommended that a similar research study be carried out in a different industry, such as the financial sector or Small and

Medium Scale Enterprises (SMEs) in Ghana, in order to determine whether or not the same results will be obtained despite the differences

in debts and equity acquired by these companies. This will allow for a more informed conclusion to be drawn regarding the findings. It is

also recommended that future researchers carry out a comparable study when a significant amount of time has passed, such as twenty

years, because of the rapid pace at which technical advancements and legal frameworks are evolving. In light of this, it is important to

conduct a comparison and arrive at factually sound conclusions.

References

1. Adesina, J. B., Nwidobie, B. M., & Adesina, O. O. (2015). Capital structure and financial performance in Nigeria. International

Journal of Business and Social Research, 5(2), 21–31.

2. Afolabi, A., Olabisi, J., Kajola, S. O., & Asaolu, T. O. (2019). Does leverage affect the financial performance of Nigerian firms?

Journal of Economics & Management, 37, 5–22.

3. Agyei, J., Sun, S., & Abrokwah, E. (2020). Trade-off theory versus pecking order theory: Ghanaian evidence. SAGE Open,

10(3), 2158244020940987.

4. Akomeah, E., Bentil, P., & Musah, A. (2018). The Impact of capital structure decisions on firm performance: The case of Listed

non-financial institutions in Ghana. International Journal of Academic Research in Accounting, Finance and Management

Sciences, 8(4), 1–15.

5. Al-Slehat, Z. A. F., Zaher, C., Fattah, A., & Box, P. O. (2020). Impact of financial leverage, size and assets structure on firm

value: Evidence from industrial sector, Jordan. International Business Research, 13(1), 109–120.

6. Amraoui, M., Jianmu, Y., & Bouarara, K. (2018). Firm’s capital structure determinants and financing choice by industry in

Morocco. International Journal of Management Science and Business Administration, 4(3), 41–50.

7. Antwi, S., Mills, E. F. E. A., & Zhao, X. (2012). Capital structure and firm value: Empirical evidence from Ghana. International

Journal of Business and Social Science, 3(22).

8. Batchimeg, B. (2017). Financial performance determinants of organizations: The case of Mongolian companies. Journal of

Competitiveness, 9(3), 22–33.

9. Bunyaminu, A., Tuffour, J. K., & Barnor, C. (2019). Assessing the determinants of business failure of companies listed on the

Ghana Stock Exchange. Journal of Accounting and Finance, 19(4), 39–54.

10. Buvanendra, S., Sridharan, P., & Thiyagarajan, S. (2017). Firm characteristics, corporate governance and capital structure

adjustments: A comparative study of listed firms in Sri Lanka and India. IIMB Management Review, 29(4), 245–258.

11. Chaleeda, M., Islam, A., Ahmad, T. S. T., & Ghazalat, A. N. M. (2019). The effects of corporate financing decisions on firm

value in Bursa Malaysia. International Journal of Economics and Finance, 11(3), 127–135.

12. Chandio, A. A., Jiang, Y., Wei, F., & Guangshun, X. (2018). Effects of agricultural credit on wheat productivity of small farms

in Sindh, Pakistan: are short-term loans better? Agricultural Finance Review.

13. Cruz, A. M., & Haugan, G. L. (2019). Determinants of maintenance performance: A resource-based view and agency theory

approach. Journal of Engineering and Technology Management, 51, 33–47.

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue I, January 2025

www.ijltemas.in Page 50

14. Devi, S., Warasniasih, N. M. S., Masdiantini, P. R., & Musmini, L. S. (2020). The impact of COVID-19 pandemic on the

financial performance of firms on the Indonesia stock exchange. Journal of Economics, Business, & Accountancy Ventura,

23(2), 226–242.

15. Gamayuni, R. R. (2015). The effect of intangible asset, financial performance and financial policies on the firm value.

International Journal of Scientific and Technology Research, 4(1), 202–212.

16. Ganiyu, Y. O., Adelopo, I., Rodionova, Y., & Samuel, O. L. (2019). Capital structure and firm performance in Nigeria. African

Journal of Economic Review, 7(1), 31–56.

17. Gill, A., Biger, N., & Mathur, N. (2011). The Effect of Capital Structure on Profitability: Evidence from the United States.

International Journal of Management, 28(4), 3–15.

18. Githaiga, P. N., Nyauncho, J., & Kabiru, C. G. (2015). Foreign Direct Investments and Economic Growth: The Primary Drivers.

19. Habib, H., Khan, F., & Wazir, M. (2016). Impact of debt on profitability of firms: Evidence from non-financial sector of

Pakistan. City University Research Journal, 6(01).

20. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure.

Journal of Financial Economics, 3(4), 305–360.

21. Kahl, M., Shivdasani, A., & Wang, Y. (2015). Short‐term debt as bridge financing: Evidence from the commercial paper market.

The Journal of Finance, 70(1), 211–255.

22. Krishnamurthy, A., & Vissing-Jorgensen, A. (2013). Short-term debt and financial crises: What we can learn from US Treasury

supply. Unpublished, Northwestern University, May.

23. Le, B. (2019). Working capital management and firm’s valuation, profitability and risk: Evidence from a developing market.

International Journal of Managerial Finance.

24. Lemma, T. T., Lulseged, A., & Tavakolifar, M. (2021). Corporate commitment to climate change action, carbon risk exposure,

and a firm’s debt financing policy. Business Strategy and the Environment, 30(8), 3919–3936.

25. Magoro, K., & Abeywardhana, D. (2017). Debt capital and financial performance: A study of South African companies.

International Journal of Scientific Research and Innovative Technology, 4(4), 71–84.

26. Martinez, L. B., Scherger, V., & Guercio, M. B. (2018). SMEs capital structure: trade-off or pecking order theory: a systematic

review. Journal of Small Business and Enterprise Development.

27. Musah, A. (2018). The impact of capital structure on profitability of commercial banks in Ghana. Asian Journal of Economic

Modelling, 6(1), 21–36.

28. Muturi, W., & Omondi, M. M. (2013). Factors affecting the financial performance of listed companies at the Nairobi Securities

Exchange in Kenya. Research Journal of Finance and Accounting, 4(15), 99–104.

29. Nardi, P. M. (2018). Doing survey research: A guide to quantitative methods. Routledge.

30. Nassar, S. (2016). The impact of capital structure on Financial Performance of the firms: Evidence from Borsa Istanbul. Journal

of Business & Financial Affairs, 5(2).

31. Naz, F., Ijaz, F., & Naqvi, F. (2016). Financial performance of firms: evidence from Pakistan cement industry. Journal of

Teaching and Education, 5(01), 81–94.

32. Nunes, P. M., & Serrasqueiro, Z. (2017). Short-term debt and long-term debt determinants in small and medium-sized hospitality

firms. Tourism Economics, 23(3), 543–560.

33. Ongore, V. O., & Kusa, G. B. (2013). Determinants of financial performance of commercial banks in Kenya. International

Journal of Economics and Financial Issues, 3(1), 237–252.

34. Purba, J. H. V., & Septian, M. R. (2019). Analysis of Short Term Financial Performance: A Case Study of an Energy Service

Provider. Journal of Accounting Research, Organization and Economics, 2(2), 113–122.

35. Ranaldo, A., Schaffner, P., & Vasios, M. (2021). Regulatory effects on short-term interest rates. Journal of Financial Economics,

141(2), 750–770.

36. Shikumo, D. H., Oluoch, O., & Wepukhulu, J. M. (2020). Effect of Short-Term Debt on Financial Growth of Non-Financial

Firms Listed at Nairobi Securities Exchange. ArXiv Preprint ArXiv:2011.03339.

37. Sivalingam, L., & Kengatharan, L. (2018). Capital structure and financial performance: A study on commercial banks in Sri

Lanka. Asian Economic and Financial Review, 8(5), 586–598.

38. Sunardi, N., Husain, T., & Kadim, A. (2020). Determinants of Debt Policy and Company’s Performance. International Journal of

Economics and Business Administration, 8(4), 204–213.

39. Usman, B., & Lestari, H. S. (2019). Determinants of bank performance in Indonesia. Jurnal Minds: Manajemen Ide Dan

Inspirasi, 6(2), 193–204.

40. Vătavu, S. (2015). The impact of capital structure on financial performance in Romanian listed companies. Procedia Economics

and Finance, 32, 1314–1322.

41. Yusuf, F., Yousaf, A., & Saeed, A. (2018). Rethinking agency theory in developing countries: A case study of Pakistan.

Accounting Forum, 42(4), 281–292.