The signaling idea was initially put forth by Michael Spence in 1973. It clarifies how the sender, or the person who has the information, provides the investor or recipient with a signal in the form of information that reflects the company's current situation. The actions management takes to demonstrate to investors how they view the company's current and future states are the main subject of signaling theory (Brigham & Houston, 2019:500). It claims that the knowledge asymmetry between a firm's shareholders and management is what drives a corporation to reveal information (Sudana, 2019:15). One side having more accurate, comprehensive, and up-to-date knowledge on a company than the other is known as information asymmetry.

This asymmetry may hinder the latter from making sound investment decisions. To reduce this gap, companies send signals so that decisions can be made appropriately.

Firm value is a certain condition achieved by a company, reflecting public trust in the company after undergoing business activities over the years—from its establishment to the present (Hery, 2017:5). Firm value can serve as a consideration for investors in making investment decisions. A higher firm value reflects greater investor confidence in the management’s capability to lead the company and achieve long-term growth. Conversely, a lower firm value indicates less effective management in achieving sustainable growth.

Credit risk is the potential loss faced by banks due to a borrower's inability and/or unwillingness to fully repay borrowed funds in accordance with the agreed terms, whether on time or after maturity (Sarjana, 2022:59). Research by Anggada & Safitri (2024) found that credit risk, measured using the NPL ratio, negatively affects firm value. These findings are supported by studies conducted by Tjahjadi & Munandar (2022), Wiadnyani & Artini (2023), Yasin et al. (2023), and Jagirani et al. (2023), all of which found a negative relationship between credit risk and firm value. A high NPL ratio may act as a negative signal,

indicating that a bank is facing credit risk due to delayed repayments of disbursed loans, thereby reducing its ability to earn profits and decreasing investor interest, which is reflected in a decline in firm value (Dewi & Badjra, 2020).

H1: Credit risk has a negative effect on firm value

Operational efficiency is a quantitative measure used to evaluate a bank's management ability to control operating costs in relation to business income (Sanjaya & Badjuri, 2024). Research by Rianawati et al. (2024) found that operational efficiency, measured using the BOPO ratio, negatively affects firm value. These results are supported by studies conducted by Anggada & Safitri (2024), Esty et al. (2021), Handayani et al. (2023), and Hidayah & Sakti (2023), which also found a negative effect of operational efficiency on firm value. A high BOPO ratio can serve as a negative signal indicating inefficiency in managing costs related to bank operations, thus decreasing investor interest and reducing firm value.

H2: Operational efficiency has a negative effect on firm value

Liquidity is a measure of a bank’s ability to meet its short-term obligations, such as fulfilling fund withdrawals by depositors and meeting approved credit disbursements (Andrianto, 2019:274). Research by Sakdiyah et al. (2023) found that liquidity, measured using the LDR ratio, negatively affects firm value. These findings are supported by studies from Wiyana et al. (2024), Tjahjadi & Munandar (2022), Chuianda & Duffin (2024), and Esty et al. (2021), all of which found a negative effect of liquidity on firm value. A high LDR ratio can serve as a negative signal because it indicates a potential liquidity risk—more loans are disbursed than third-party funds collected—resulting in inadequate cash reserves to meet withdrawal demands. The lower the bank’s ability to fulfill short-term obligations, the higher the doubt and lower the investor confidence, ultimately reducing firm value (Wiadnyani & Artini, 2023).

H3: Liquidity has a negative effect on firm value

A ratio called profitability is used to assess a company's capacity to turn a profit over a specific time frame (Wiagustini, 2014:86). According to research by Deccasari et al. (2023), business value is positively impacted by profitability as determined by the ROA ratio. Studies by Aprilia & Riharjo (2023), Verawati et al. (2023), Perdana et al. (2023), and Ikhsan et al. (2022) all indicated a positive correlation between company value and profitability, which supports these findings. The bank's great capacity to use all of its assets to create profits is indicated by a high ROA. A higher ROA boosts investor trust in management's ability to allocate corporate resources for long-term profitability, which raises the value of the company.

H4: Profitability has a positive effect on firm value

Figure 2. Conceptual Framework

Y = Firm Value X₁ = Credit Risk

X₂ = Operational Efficiency X₃ = Liquidity

X₄ = Profitability

α = Constant

β₁ = Regression coefficient for Credit Risk

β₂ = Regression coefficient for Operational Efficiency

β₃ = Regression coefficient for Liquidity β₄ = Regression coefficient for Profitability e = Error term

Using the maximum, minimum, mean, and standard deviation values, descriptive statistical analysis is performed to give a summary of the research variables, which include credit risk, operational efficiency, liquidity, and profitability. Table 1 displays the study's descriptive statistical analysis.

Table 1. Descriptive Statistics Results

Source: Processed secondary data, 2025

The results of the descriptive statistical analysis in Table 1 show that the firm value variable, measured by the Price to Book Value (PBV) ratio, has a minimum value of 0.32 and a maximum value of 9.43, with a mean of 1.63 and a standard deviation of 1.74The Non-Performing Loan (NPL) ratio, which measures credit risk, has a mean of 2.85, a standard deviation of 2.29, a minimum value of 0.00, and a maximum value of 14.09. The Operating Expenses to Operating Income (BOPO) ratio, which measures operational efficiency, has a mean of 90.36 and a standard deviation of 32.17. Its range is 41.67 to 287.86. The Loan to Deposit Ratio (LDR), which measures liquidity, has a mean of 85.71 and a standard deviation of 25.87. Its range is 12.35 to 163.19. Last but not least, the Return on Assets (ROA) ratio, which measures profitability, has a mean of 0.84 and a standard deviation of 2.65. It varies from -14.75 to 4.76.

Source: Processed secondary data, 2025

The normality assumption is met and the data utilized in this study are normally distributed, according to the findings of the second normality test in Table 3, which also shows that the Asymp. Sig. (2-tailed) value is higher than the significance threshold (0.200 > 0.05).

Source: Processed secondary data, 2025

According to Table 4's autocorrelation test findings, the Durbin-Watson value is 1.890. With a sample size (n) of 140, four independent variables (k = 4), and a 0.05 significance level, the Durbin-Watson table produces dU = 1.7830 and 4 − dU = 2.217. Since the regression model in this study satisfies the requirement dU < dW < (4 − dU) or 1.7830 < 1.890 < 2.217, the outcome shows that it is devoid of autocorrelation symptoms.

Source: Processed secondary data, 2025

The results of the multicollinearity test in Table 5 show that the tolerance values for all independent variables are greater than 0.10 and the VIF values are less than 10:

Credit Risk: Tolerance = 0.952, VIF = 1.051

Operational Efficiency: Tolerance = 0.811, VIF = 1.233

Liquidity: Tolerance = 0.757, VIF = 1.322

Profitability: Tolerance = 0.660, VIF = 1.515

This indicates that the independent variables in this study do not exhibit multicollinearity, and the regression model is free from multicollinearity issues.

Heteroscedasticity Test

To determine if the variance of residuals inside the regression model varies from one observation to another, the heteroscedasticity test is used. By regressing the absolute residual values on the independent variables, this test applies the Glejser test. If a regression model's significance value is higher than 0.05, it is considered to be heteroscedastic.

Source: Processed secondary data, 2025

The significant values for credit risk (0.250), operational efficiency (0.667), liquidity (0.199), and profitability (0.539) are all higher than the 0.05 significance level, according to the findings of the heteroscedasticity test, which are displayed in Table 6. This suggests that there are no signs of heteroscedasticity in the regression model.

Source: Processed secondary data, 2025

The results of the multiple linear regression test in Table 7 yield the following regression equation:

Y = 9.866 − 0.043X₁ − 1.556X₂ − 1.060X₃ + 0.980X₄ (2)

Interpretation of the results in Table 7 is as follows:

Model Feasibility Test (F-Test)

Source: Processed secondary data, 2025

The F-test results in Table 8 show a significance value of 0.000 < 0.05, indicating the regression model is statistically valid. Therefore, credit risk, operational efficiency, liquidity, and profitability together significantly affect firm value.

Coefficient of Determination Test (R² Test)

Source: Processed secondary data, 2025

Table 9 shows an Adjusted R² of 0.673. This means 67.3% of the variability in firm value is explained by the independent variables (credit risk, operational efficiency, liquidity, and profitability), while the remaining 32.7% is due to other factors outside the model.

Credit risk does not significantly influence the firm value of banking sub- sector firms listed on the IDX from 2021 to 2024 (p = 0.299 > 0.05), thus H1 is not supported. This suggests that changes in credit risk levels do not meaningfully impact firm value. Credit risk is viewed as an inherent banking risk already priced into stock valuations. Investors accept NPL fluctuations within regulatory limits (<5%) and consider them expected.

During 2021–2024, post-COVID accommodation via OJK/BI regulations allowed banks to defer recognizing impaired assets without hurting performance, mitigating the impact of NPLs on firm value. Risk management under Basel III and POJK rules (e.g., provisioning, stress testing, portfolio diversification) reassures investors.

This finding deviates from signalling theory: NPL disclosures alone aren’t perceived as strong signals. Instead, investors weigh broader performance and risk controls. This aligns with similar findings from Manurung et al. (2023), Kusuma & Ruslim (2022), Miranti et al. (2024), Purnamasari & Sitorus (2023), and Mawarti et al. (2022).

Operational efficiency negatively and significantly affects firm value (p = 0.000 < 0.05), so H2 is supported. A lower BOPO ratio (higher efficiency) signals effective cost control and higher profit margins. Increased profitability strengthens fundamentals, boosts investor confidence, raises demand for shares, and increases firm value.

This supports signalling theory: a low BOPO ratio is a credible signal of good management. Investors interpret it as evidence of strong operational capabilities and long-term profitability, increasing stock valuation. This result aligns with studies by Tahmat et al. (2023), Mustikaweni et al. (2024), Anggada & Safitri (2024), Wahyuni & Choirul Umam (2023), and Rianawati et al. (2024).

Liquidity has a negative, significant effect on firm value (p = 0.000 < 0.05), supporting H3. A high LDR means a higher loan-to-deposit ratio, reducing liquidity buffers and increasing liquidity risk. While high lending boosts interest income, it raises investor concerns about cash availability for withdrawals and obligations.

Even with regulatory reserve requirements (GWM), investors favor banks with liquidity cushions beyond mandated levels. A high LDR is viewed as imprudent risk-taking, undermining confidence and firm value. This supports signalling theory: a high LDR sends a negative signal. Under BI regulation No. 17/11/PBI/2015, healthy LDR ranges from 78% to 92%; exceeding that signals poor prudence. Research by Bijak et al. (2024), Tjahjadi & Munandar (2022), Damayanti & Sucipto (2022), Devi & Suardana (2022), and Naibaho et al. (2024) concurs.

Profitability has a positive, significant impact on firm value (p = 0.000 < 0.05), confirming H4. Higher ROA indicates efficient asset use and sustainable profitability, which reflects strong managerial performance and increases investor confidence and firm value.

This supports signalling theory: high ROA is a positive signal that the bank can generate stable cash flows. Investors tend to value such firms more, expecting long-term growth—evidenced by studies from Devi & Suardana (2022), Damayanti & Sucipto (2022), Lasdao Mara & Munandar (2024), Keter et al. (2024), and Ikhsan et al. (2022).