This study aims to analyze the effect of Return on Assets (ROA), Current Ratio (CR), and Debt to Equity Ratio (DER) on company value in the packaged food and beverage subsector on the Indonesia Stock Exchange for the period 2018–2023. Using a quantitative approach and multiple linear regression analysis of 144 panel data observations, the results show that ROA and CR have a positive and significant effect on company value, while DER has no significant effect. The research model explains 79.7% of the variation in company value. These findings emphasize the importance of profitability and liquidity in increasing market valuation, and provide practical implications for management in post-pandemic strategic financial management.
Firm value is an important measure for stakeholders to assess management's success in creating wealth for shareholders. Theoretically, a company's value reflects investors' expectations of future cash flow and profitability that the company will earn (Brigham & Houston, 2012). The higher the value of the company, the greater the market's confidence in the management's ability to optimize assets and minimize capital costs (Fahmi, 2015). Jensen and Meckling (1976) explained that increasing company value also contributes to a decrease in agency costs, thereby encouraging transparency and accountability in managerial decision-making. In addition, positive financial performance signals—such as high levels of profitability—will attract investors and increase market valuations (Spence, 1973).
Return on Assets (ROA) as a profitability indicator measures the company's efficiency in generating profits from each unit of assets under management. Various studies confirm the positive influence of ROA on company value. Chandra Wijaya (2022) reported that ROA had a significant positive effect on firm value in companies in the packaged food and beverage subsector for the 2017–2021 period. Harefa, Wijaya, and Simorangkir (2022) also found that ROA directly increased the Price to Book Value of food & beverage companies on the Indonesia Stock Exchange during the same period. International findings support this, where Chen, Wang, and Xu (2019) show that ROA is a strong predictor of market valuation in manufacturing companies in China.
Current Ratio (CR) reflects a company's ability to meet short-term obligations through the use of current assets. The results of the study on the influence of CR on company value show a variety of findings. In Indonesia, Afanny, Ginting, Tarigan, and Hutagalung (2022) concluded that CR does not have a significant direct effect on firm value, but has an indirect positive effect through ROA mediation. Sudiyatno, Artini, and Sugosha (2020) reinforce this finding by stating that although CR does not directly affect Price to Book Value, increased liquidity can improve profitability so that it has an impact on market valuation. On the other hand, García and Martínez (2021) in an international study on manufacturing companies in Spain found that CR had a direct positive effect on the valuation of companies.
The Debt to Equity Ratio (DER) reflects the proportion of funding through debt versus equity, and is a benchmark for a company's capital structure. According to the trade-off theory, the use of debt to the optimal level can increase the value of the company through tax shields, but excessive debt will increase the risk of bankruptcy (Myers, 1984). In the packaged food and beverage subsector, Suhendry, Toni, and Simorangkir (2021) found that DER had a significant positive effect on ROA, but did not have a significant direct effect on company value without ROA mediation. Similar findings were put forward by Afanny et al. (2022) and Harefa et al. (2022), who stated that DER is only effective in increasing firm value if mediated by increased profitability. An international study by Kayo and Kimura (2011) also confirms that in capital-intensive industries such as food manufacturing, moderate DER is more profitable than very low or very high DER.
The packaged food and beverage subsector on the Indonesia Stock Exchange is one of the main contributors to the primary consumer goods sector, with more than 25 companies listed until 2023 (IDX, 2023). The characteristics of this industry — which are highly dependent on economies of scale, product innovation, and sensitivity to fluctuations in raw material prices and exchange rates — demand an adaptive financial management strategy (Putri, 2012). The 2018–2023 period was marked by various external shocks, including fluctuations in food commodity prices, supply chain disruptions due to the Covid-19 pandemic, and global inflationary pressures (BPS, 2024). These changes in macroeconomic conditions have a direct impact on liquidity, profitability, and corporate capital structure policies, so it is necessary to re-analyze the patterns of influence of ROA, CR, and DER on market valuation in the current context.
Previous literature reviews show that most studies ended in 2020 or 2021, so post-pandemic data have not been included until 2023 (Wijaya, 2022; Harefa et al., 2022). As a result, market dynamics that occurred after 2020, including supply chain disruptions and changes in consumer behavior, were not covered in the old analysis. Furthermore, several studies have shown that CR and DER do not have a direct effect on company value, but rather through ROA mediation (Afanny et al., 2022; Suhendry et al., 2021), but there is still little research that retests these findings in more volatile economic conditions. In addition, control variables such as firm size and asset growth are often not taken into account, even though these variables can affect the strength of the relationship between financial factors and market valuation (Sudiyatno et al., 2020).
Existing research also generally uses a partial model, so there has been no comprehensive study that assesses the simultaneous influence of ROA, CR, and DER in a single analytical framework over a longer and more recent period. This is an important empirical gap to fill, given the practical need for managers and investors to get a clearer picture of how the combination of these three financial indicators affects a company's value in a post-pandemic situation.
Based on the identification of the gap, this study is designed to evaluate the direct influence of ROA, CR, and DER on the value of companies in the packaged food and beverage subsector on the IDX during the 2018–2023 period. In addition, this study will also examine the mediating role of ROA in the relationship of CR and DER to company value, thereby providing a deeper understanding of the mechanisms by which liquidity and capital structure affect market valuation through profitability. By including control variables such as company size and asset growth, this study is expected to make a more thorough and relevant empirical contribution to current market conditions.
The contribution of this research is twofold. Theoretically, the results of the study will enrich the corporate finance literature with the latest post- pandemic data, while retesting signaling and trade-off theories in the context of the packaged food and beverage industry. Practically, the research findings will provide guidance for corporate financial managers to adjust liquidity strategies and capital structures in order to maximize the company's value. For investors, this analysis offers insight into which financial indicators are most influencing market valuations amid global economic uncertainty.
Thus, this research not only fills empirical gaps in the literature, but also provides practical strategic recommendations for stakeholders in the packaged food and beverage subsector on the Indonesia Stock Exchange, especially in facing challenges and opportunities in the post-pandemic era.
The signaling theory was put forward by Ross (1977) which states that financial information published by a company can provide signals to investors and other stakeholders. Positive signals from a company's financial performance will increase investor confidence and influence investment decisions, which can ultimately drive asset growth. According to Brigham & Houston (2019), a company with good financial performance will give a positive signal to the market, which can attract investment and accelerate business expansion. In the context of this study, financial ratios such as ROA, CR, and DER can provide signals to investors regarding the potential growth of the company's value.
The relationship between signal theory and company value growth is to explain that good financial information can increase the trust of investors, creditors, and other stakeholders. This trust then leads to increased investment and better access to funding, which ultimately drives the company's value growth.
Return on Asset is part of the profitability ratio in the financial performance report. Return on Asset is a financial ratio used to measure the extent to which the asset has been used to generate profits (Heikal & Gaddafi, 2014). Return on Asset is able to measure a company's ability to generate profits in the past to be projected in the future. A high return on asset indicates that the company is able to manage its assets well to generate profits. The profits can be used to invest in new assets, thus driving the company's growth.
Current ratio is a ratio that plays a role in measuring a company's ability to pay debts that are due soon or short-term obligations at the time of total collection (Kasmir: 2016). In practice, the current ratio size that is considered standard is 200% (2:1) which is a satisfactory or good enough size for the company. In other words, the current ratio indicates how much short-term liabilities a current asset is able to cover. The current ratio can be referred to as the margin of safety or a form of measurement of the company's level of security. It is calculated by comparing the total current assets with the total current liabilities. In paying off liabilities, the company is said to lack capital if the current ratio shows a low value. However, the high ratio shown by the company does not necessarily indicate that the company is in good condition. This high value can be caused by the idle cash due to not being used as much as possible.
According to Kasmir (2016), DER is the ratio used in the assessment of liabilities to equity which is calculated by comparing total liabilities to total equity. The function of the rasio.ini is to find out the capital itself that is used as collateral. In addition, it serves to find out how much funds are provided creditor to owner company.
Company value is an investor's perception of the company's success rate which is reflected in its share price, where this value is the main benchmark in assessing corporate performance (Sujoko & Soebiantoro, 2007). Conceptually, the value of a company can be grouped into face value, market value, intrinsic value, and book value; Intrinsic value is mainly obtained from the calculation of the present value of the company's future cash flow (Risman, 2021). This definition is in line with the view of Salvatore (2005) and Ebert & Griffin (2017) that a company is an entity that adds value by producing goods and services to meet market needs. According to Fahmi (2015), the value of a company can be measured as the ratio of market value to book value—the higher this ratio, the greater the confidence of investors in the company's growth prospects. In the financial management literature, Fama and French (1998) assert that every investment, funding, and dividend decision will affect the value of the company through the mechanism of changes in cash flow and market risk perception. Therefore, increasing the value of a company not only reflects operational efficiency, but also maximizes shareholder prosperity as the main goal of the corporation (Dewi, Yuniarta, & Atmadja, 2014).
This study uses a quantitative approach with an associative method. This approach aims to analyze the relationship between independent variables and dependent variables, so that the influence of each variable can be known partially or simultaneously. In this study, the independent variables used were Return on Assets (ROA), Current Ratio (CR), and Debt to Equity Ratio (DER), while the dependent variable was Growth of Value of Manufacturing Companies.
This research was conducted using secondary data obtained from the company's financial statements that met the sample selection criteria. The analysis technique used in this study is multiple linear regression to measure the influence of ROA, CR, and DER on the value growth of manufacturing companies.
Research Population:
The research population is all companies in the food and beverage subsector listed on the Indonesia Stock Exchange from 2018 to 2023. According to IDX data, at the end of 2023 there were 28 companies included in this subsector. The population was chosen because the
packaged food and beverage sector has unique characteristics— dependent on the economies of mass production, complex supply chains, and sensitive to changes in raw material prices—that are relevant for testing the variables of liquidity, profitability, capital structure, and company value.
Research Sample:
Samples were taken by purposive sampling technique based on the following criteria:
Actively listed in the food and packaged beverage
subsector on the IDX during the 2018–2023 period.
Publish complete (audited) financial statements every
year within the research period.
Not experiencing delisting or discontinuation of
operations that have a significant impact on data
continuity.
Has a calculable value of equity and debt whose DER is
not zero or negative).
Based on the criteria above, out of 28 population companies, 24 companies met the criteria. With a six-year observation period (2018– 2023), the number of panel observations became 24 × 6 = 144 annual data. This number is considered adequate for multiple regression analysis and mediation with a minimum observations-to-variables ratio of 10–15:1 (Hair et al., 2010).
Data Collection Techniques
The data used in this study is secondary data, which is obtained through the documentation of the company's financial statements. Data sources include:
Annual financial statements published by each
company.
Other sources of documentation relevant to the research
variable. The data collected in this study includes
information related to
Return on Assets (ROA), Current Ratio (CR), Debt to Equity Ratio (DER), and the growth of the value of manufacturing companies. This data is then processed and analyzed to obtain conclusions about the relationship between the variables studied.
Data Analysis Techniques
Data analysis in this study was carried out through several stages, namely:
Descriptive Statistics
Descriptive statistics are used to provide an overview of the characteristics of the data used in the research. This analysis includes the calculation of the mean value, standard deviation, minimum value, and maximum value of each research variable.
Classical Assumption
Test Before conducting regression analysis, several classical assumption tests are performed to ensure that the regression model is eligible. The tests carried out include:
Normality Test: To test whether residual data is normally
distributed.
Multicollinearity Test: To ensure there are no strong
linear relationships between independent variables.
Heteroscedasticity Test: To test whether there is a
residual variance that is not constant.
Autocorrelation Test: To find out if there is a
relationship between residuals in regression models.
Multiple Linear Regression Analysis
Multiple linear regression analysis is used to determine the influence of independent variables (ROA, CR, and DER) on dependent variables (Asset Growth) both simultaneously and partially. The regression equations used in this study are:
Y = β0 + β1(ROA) + β2(CR) + β3(DER) + e
Where:
Y = Asset Growth
β0 = Constant
β1, β2, β3 = Regression coefficients
ROA, CR, DER = Independent variable
e = Error term
Hypothesis Test
t-test (Partial): To test the influence of each
independent variable on the dependent variable
individually.
F Test (Simultaneous): To test whether independent
variables together have a significant effect on
dependent variables.
Coefficient of Determination (R²): To measure how
much an independent variable contributes in explaining
the variation of the dependent variable.
Through this stage of analysis, the research aims to uncover the influence of ROA, CR, and DER on the value growth of manufacturing companies and provide insights for decision-making in the field of corporate finance.
Descriptive statistics provide a summary of the research data, which includes the lowest, maximum, mean, and standard deviation values for each variable studied in the study. The next results of the descriptive statistical analysis are as follows:
Table 1. Descriptive Statistical Analysis
| 7,36 | 2,71 | 2,40 | 14,60 | |
| 1,36 | 0,41 | 0,95 | 3,69 | |
| 0,83 | 0,21 | 0,35 | 1,40 | |
| 1,53 | 0,59 | 0,75 | 4,36 |
An average ROA of 7.36% indicates that most companies have managed to generate a net profit of around 7.36 cents for every rupiah of their assets.
The range of 2.40–14.60% reflects a disparity in operational efficiency; some companies are superior in asset utilization (logically companies with higher production automation have a ROA close to 14.6%) while others still need to improve asset management.
An average CR of 1.36× (> 1) indicates the adequacy of current assets to meet short-term liabilities.
However, a minimum value of 0.95× indicates that there is a company with potential liquidity difficulties (current assets slightly below current liabilities) – this is a signal for management to speed up inventory conversion or receivables collection.
DER averages 0.83× (assets financed by equity greater than debt), indicating a relatively conservative leverage position.
A maximum value of 1.40× indicates that some companies are opting for a more aggressive structure, but still below the general limit (DER = 2×).
PBV of 1.53× means that the market values the company at an average of
1.53 times its book value.
The range of 0.75–4.36× illustrates a highly variable market perception; companies with consistent growth tend to have high PBVs.
Normality tests are performed to confirm whether the residual in the regression model follows the normal distribution. This study used the Kolmogorov-Smirnov and Shapiro-Wilk tests to assess normality.
|
|
|---|---|
0,809 |
1,92 × 10⁻³ |
Zero (H₀) hypothesis: Normal distributed residuals.
Result: p < 0.05 → minus H₀.
Interpretation: Residual is not normally distributed. To
improve, you can make transformations (e.g. logs) on skewed
variables or use robust (White) estimation.
The multicollinearity test aims to ensure a strong correlation between independent variables in the regression model. Multicollinearity causes considerable interdependence among independent variables, resulting in distortions in regression results.
|
|
|---|---|
const |
1,03 |
ROA |
5,82 |
CR |
1,67 |
DER |
4,53 |
In this study, multicollinearity was tested using Variance Inflation Factor (VIF) and Tolerance. The test results show that:
Criteria: VIF < 10 → no serious multicollinearity.
Interpretation: Although the VIF_ROA and VIF_DER are
close to 5, the number is still below the threshold of 10,
so the correlation between the predictors is considered
moderate and does not threaten the stability of the
regression coefficient.
The heteroscedasticity test was performed to find out whether there is an inconsistency of variance from the residual in the regression model, which can cause regression estimation to be inefficient. In this study, the heteroscedasticity test was performed using the Glejser Test, in which independent variables (ROA, CR, and DER) were regressed to the residual absolute value.
|
|||
|---|---|---|---|
35,76 |
8,43 × 10⁻⁸ | 16,58 | 3,72 × 10⁻⁸ |
H₀: Constant residual variance (homokedasticity).
Result: p < 0.05 → minus H₀ → heteroscedasticity
exists.
Solution: Use heteroskedasticity-robust standard errors
on regression coefficients.
The autocorrelation test aims to find out if there is a relationship between residual values in the regression model. If autocorrelation occurs, then the regression model can produce inefficient estimates, especially in time-series data.
In this study, autocorrelation was tested using the Durbin-Watson Test. Durbin-Watson Test ResultsDurbin-Watson Score = 1.014
Durbin-Watson value interpretation:
If the value is close to 2, then there is no
autocorrelation.
If the < value is 1.5 or > 2.5, then there is a
possibility of a positive or negative autocorrelation.
In this study, the Durbin-Watson value of 2,043 showed
that there was no autocorrelation in the regression
model.
Since the Durbin-Watson value is close to 2, it can be concluded that the regression model does not suffer from autocorrelation problems. Thus, this model can be used for further analysis without the need for improvements to autocorrelation.
The Summary Model provides information about the strength of the relationship between independent variables (ROA, CR, DER) and dependent variables (Asset Growth).
|
|
|
|
|---|---|---|---|
| 0,797 |
0,794 |
214,98 |
< 0,001 |
Results obtained:
R² = 0.797: 79.7 % PBV variation is explained by ROA, CR,
and DER.
Adjusted R² = 0.794: takes into account the number of
predictors and sample sizes, indicating a very robust
model.
F(3.176) = 214.98; p < 0.001: the model is
simultaneously significant.
The t-test is used to find out whether each independent variable (ROA, CR, DER) has a significant influence on the dependent variable (Asset Growth) partially.
Results of the t-test:
Interpretation:
ROA (β₁ = 0.058; p = 0.002):
The increase in ROA of 1 percentage point was
associated with an increase in PBV of 0.058 units.
Showing profitability plays a significant role:
companies that are more efficient in generating profits
attract market interest so that their valuation
increases.
CR (β₂ = 0.985; p < 0.001):
An increase in CR per unit (e.g. from 1.2× to 2.2×)
increases PBV by 0.985 units.
The effect is very strong, confirming the
significance of liquidity: investors value companies
that are able to meet short-term obligations easily as
companies with lower risk.
DER (β₃ = –0.125; p = 0.559):
It is insignificant, meaning that the variation in
capital structure (debt-to-equity ratio) has no direct
effect on PBV in this sample.
Perhaps because DER ranges in a conservative range,
so there is no sharp difference that the market
perceives.
The results that ROA has a significant positive effect on PBV are in line with Pramudita & Gantino (2023), who found that profitability ratios (even though they use ROE) significantly boosted the market valuation of companies in the food and beverages—cosmetics—household subsector on the IDX for the period 2016–2021^11. The findings of Yunita, Ridloah, & Humaira (2024) also confirm the positive effect of ROA on firm value in the food and beverage industry in Indonesia in 2017–2021 (β > 0; p < 0.05)^12. Theoretically, it supports profitability signals in signaling theory; The increase in profitability is perceived as a signal of a higher future outlook for investors, so the PBV increases. Arifin & Asyik (2020) added that profitability provides a cushion against macroeconomic risks, so the market rewards companies that can maintain consistent positive profit margins^14.
This study showed that CR had a stronger positive effect (β = 0.985; p < 0.001). Pramudita & Gantino (2023) also reported a significant positive liquidity effect on PBV (although the sample context was slightly different)^11, while Firzaalpi et al. (2023) revealed that short-term cash flow (CR) strengthened shareholder confidence, thereby increasing valuation (β = 0.276; p < 0.01)^15. Birohmatika & Ekowahjudi (2024) affirm this in the food and beverage sector, which emphasizes the importance of working capital management—quick receivables collection, inventory optimization—to keep the CR stable above 1.2× to attract institutional investors^13. Theoretically, high liquidity reduces asymmetric information and lowers perceived risk, so that firm value rises.
The finding that DER was insignificant confirmed the results of Dewi (2022), who also did not find a significant effect of DER on PBV in the 2016–2020 period in the food and beverage subsector (p > 0.05)^16. Ikatama & Wiagustini (2019) reported varying leverage effects—positive in some large companies, negative in others—due to differences in debt policies and debt costs (β between –0.132 and 0.147; p varied)^17. Yunita et al. (2024) even found a significant negative effect of DER on firm value in a sample of 33 companies in 2017–2021 (β
= –0.058; p < 0.05)^12, in contrast to this study which was non-significant, likely because our sample companies tended to maintain moderate DER (mean = 0.83×). Theoretically, according to pecking order theory, companies first use internal funding (retained earnings), then debt; if the DER is relatively low, the effect on market valuation becomes less strong.
Agency Theory: Jensen & Meckling (1976) assert that debt can be a bonding mechanism to reduce free cash flow and mitigate managers'
moral hazards through interest and principal debt payment obligations^18. However, in this sample, a moderate DER may not be large enough to provide a bonding effect, so the coefficient is not significant.
Financial Distress Mediation: A study by Apriyono et al. (2024) shows that financial distress mediates the relationship between ROA and PBV; high profitability reduces distress, which in turn increases firm value^19. Since our data show heteroscedasticity and autocorrelation, the possible mediating effects of financial distress are also relevant to be analyzed in future studies.
Signaling and TradeOff Theories: Good profitability and liquidity signal positively (signaling theory) and close to the optimal tradeoff between bankruptcy costs and tax benefits (tradeoff theory), so market valuations appreciate both.
Thus, this study corroborates the evidence that profitability and liquidity are the main drivers of company value in the packaged food and beverage subsector, while capital structure requires a more in-depth analysis of the optimum points of leverage and mediation of financial stress.
Based on the findings and theories above, here are some practical recommendations:
Lean Manufacturing & Automation: Adoption of lean practices to minimize waste on the production line, as well as process automation to increase output per fixed asset turnover. This has been shown to increase ROA by up to 1–2 percentage points in the medium term (Arifin & Asyik, 2020)^14.
Pricing and Margin Management: The implementation of dynamic pricing strategies based on demand analytics and price elasticity can optimize contribution margins, as discussed by Ross, Westerfield, & Jaffe (2019)^8.
JustinTime Receivables & Inventory Collection:
Accelerating the cash cycle through sales discount policies
for faster repayment and implementing a JIT system to reduce
inventory holding costs, improving CR without increasing
working capital requirements (Pramudita & Gantino,
2023)^11.
Cash Buffer & Treasury Management: Set a minimum CR
target (e.g. ≥ 1.3×) and monitor real-time cash positions
using a treasury management system to avoid the risk of
illiquidity.
Compliance Order Pecking: Prioritize internal financing (retained earnings) for investments, then debt when needed, and equity last. Align
DER with industry benchmarks to ensure market perceives manageable leverage (Myers & Majluf, 1984)^7.
Debt Covenants & Flexibility: When negotiating debt, include covenants that are less restrictive but maintain payment commitments, so that the company still has refinancing options when market conditions change.
Robust Standard Errors: Because heteroscedasticity and autocorrelation are detected, use White's heteroscedasticity‐consistent standard errors and NeweyWest corrections when compiling financial projections and investment analysis (White, 1980)^10.
Stress Testing & Scenario Analysis: Conduct periodic stress tests on projected cash flows and DSCR ratios to anticipate possible financial distress that could weaken valuations.
Transparency of the ROA/CR Measurement Methodology: Describe the assumptions of the ratio calculation in the annual report to minimize asymmetric information (Ross et al., 2019)^8.
Roadshow & ESG Reporting: Engage investors in roadshows and increase ESG disclosure, especially when it comes to waste management and sustainability—practices that are increasingly being viewed positively by the market.
Financial Distress Mediation Analysis: Investigate the role of mediation of market conditions and financial distress in the ROA/CR–PBV relationship to design proactive liquidity readiness policies (Apriyono et al., 2024)^19.
Industry Benchmarking: Compare the performance of financial ratios with peer groups to set realistic ipso facto targets and increase competitiveness.
Based on the results of this study, it can be concluded that profitability measured through Return on Assets (ROA) and liquidity measured through Current Ratio (CR) have been proven to have a positive and significant influence on company value (PBV) in the consumer goods industry in the packaged food and beverage subsector for the 2018–2023 period, where every increase of one percentage point of ROA and one unit of CR drives an increase in PBV of 0.058 and 0.985 units, respectively. Meanwhile, the capital structure measured by the Debt to Equity Ratio (DER) showed no significant effect, indicating that the variation in the DER in the moderate range in this study sample has not affected market perception. The multiple linear regression model used was able to explain about 79.7% of PBV variations, although violations of classical assumptions in the form of heteroscedasticity, positive autocorrelations, and residual nonnormalities were found, so it is necessary to apply a more robust estimation method to support the validity of the findings.
Based on the findings of this study, the authors propose some practical recommendations for corporate management and suggestions for future research:
Increase Profitability: Focus on operational efficiency, supply chain optimization, and cost control to increase ROA.
Strengthen Liquidity: Strictly manage working capital, accelerate receivables collection, and optimize inventory to keep CR above safe threshold (≥ 1.2×).
Monitor Capital Structure: Although the influence of DER is not significant, keep leverage at a moderate level to maintain financial flexibility.
Improve Financial Reporting: Apply robust estimation techniques for more rigorous data processing and internal auditing to mitigate the effects of heteroscedasticity and autocorrelation.
Variable Extensions: Consider including mediation or moderation variables, such as financial distress or corporate governance, to capture the mechanisms more deeply.
Alternative Methods: Use dynamic data panel approaches (e.g. GMM) or nonlinear methods to address autocorrelation and heteroscedasticity issues.
Sample Expansion: Conduct research on other industries or longer periods to test the consistency of findings.
Transform Testing: Exploration of variable transformations (e.g. logarithms) to address nonnormalities and improve model fit.
By applying the above recommendations, it is hoped that financial managers can increase the company's value, while researchers can develop a more comprehensive study of the determinants of company value.