934 American Journal of Economic and Management Business e-ISSN: 2835-5199 Vol. 4 No. 7 July 2025 The Influence of Company Fundamentals and Technicals on Investment Decisions in the Financial Sector Fahira Dhea Azzahra, Sulastri, Isni Andriana, Mu’izzuddin Universitas Sriwijaya, Indonesia Email: 01012622327028@student.unsri.ac.id, Sulastri@unsri.ac.id, isniandriana@fe.unsri.ac.id, muizzudin@unsri.ac.id Abstract This study aims to examine the influence of micro-level fundamental factors and technical stock indicators on stock returns. To minimize information asymmetry, firms communicate with investors through signals, which are reflected in changes in stock performance. These signals are interpreted by investors as either positive or negative signs. The fundamental factors are analyzed using several financial ratios, including liquidity (current ratio), solvency (debt-to-equity ratio and debt-to-asset ratio), and profitability (net profit margin, return on assets, and earnings per share). In addition, technical indicators such as the moving average and stochastic oscillator are considered. This study uses secondary data from 41 financial-sector companies listed on the IDX over the period 2019–2023, and data analysis is conducted using EViews 12. The results of this study show that return on assets (ROA) has a significant and negative effect on stock returns, whereas other fundamental factors (CR, DER, DAR, NPM, and EPS) and technical factors (MA and SO) do not show statistically significant impacts on stock returns. Future research could include samples from other sectors and incorporate additional variables such as exchange rate, inflation, company size, and sales growth, among others. Keywords: Stock Return, Current Ratio, Debt to Equity Ratio, Debt to Asset Ratio, Net Profit Margin, Return on Asset, and Earning Per Share INTRODUCTION The global economic turmoil undeniably exerts diverse impacts on national economies, with the economy serving as a crucial determinant of societal welfare (Agustin & Fariono, 2023). According to data from idxchannel.com (2023), Indonesia’s economy ranks as the largest within Southeast Asia, boasting a gross domestic product (GDP) of approximately US$1.4 trillion, accounting for around 36.7 percent of ASEAN’s total GDP. Despite this, Indonesia’s economic growth trajectory is subject to fluctuations driven by both global and domestic instabilities, affecting multiple sectors (Abd’rachim, 2021). Recent economic data from bi.go.id (2024) reveal significant variability: in 2019, economic growth stood at 5.02%, bolstered by consumer purchasing power and relative insulation from Fahira Dhea Azzahra, Sulastri, Isni Andriana, Mu’izzuddin 935 external shocks, alongside a contained inflation rate of 2.72% (Akkaya, 2021; Akpotor et al., 2024). The COVID-19 pandemic and Large-Scale Social Restrictions (PSBB) precipitated a downturn in 2020, with growth dropping to 2.72% and inflation easing to 1.68%. In response, Bank Indonesia decreased the policy BI Rate to 3.75% to stimulate recovery. Subsequently, growth rebounded to 3.70% in 2021 and further improved to 5.31% in 2022 (Arifudin, 2023). Despite ongoing global challenges, such as the Russia-Ukraine conflict, Indonesia maintained economic growth continuity in 2023, with the BI Rate at 5.5% and inflation steady at 5.51% (Aldiena & Hakim, 2019; Almira & Wiagustini, 2020). Investment, particularly within the capital market, remains pivotal to supporting this economic dynamism (Aminah, 2021; Ambarwati & Hwihanus, 2024). The capital market functions as an intermediary between investors and companies, with listed companies offering shares to raise capital (Anderson et al., 2021). The financial sector, notably banking, attracts investor interest due to its foundational role in sustaining economic stability and growth (Anisa et al., 2022; Apriliya et al., 2023; Arifiani & Wijayanti, 2021). Empirical data on the Composite Stock Price Index (JCI) and trading volume document fluctuations from 2019 to 2023, with a marked decline in 2020 attributed to the pandemic, followed by a significant recovery in 2021 and 2022 (Andriani et al., 2025). Asset growth, Return on Assets (ROA), and Capital Adequacy Ratio (CAR) within the banking sub-sector similarly reflect fluctuations but indicate a positive growth trend, underscoring the sector’s resilience amid adversity (Amru Karim Alhabsyi & Hwihanus, 2024). Stock investments are increasingly attractive in Indonesia, evidenced by rising trading volumes and promising sectoral performances. Stock returns fundamentally depend on internal micro-level factors—principally company-specific financial ratios—and external macroeconomic conditions. This research prioritizes internal factors as they hold greater pertinence for individual investment decisions. Consistent with signaling theory, companies communicate critical financial information through their financial statements, serving to mitigate information asymmetry by sending signals that influence investor perception. Positive signals tend to elevate stock prices and returns, whereas negative signals depress them. Hence, investors need proficient comprehension of corporate financial conditions supported by fundamental and technical analyses to make prudent investment choices. From a signaling theory perspective, the Current Ratio (CR) denotes a firm’s liquidity, where a higher ratio signals strong capability to meet short-term liabilities. Conversely, an elevated Debt to Equity Ratio (DER) signals a heightened debt burden, which can be perceived negatively due to increased risk impacting profitability. The Debt to Asset Ratio (DAR) similarly signals financial leverage and potential risk exposure; higher values suggest greater dependence on debt financing. A high Return on Assets (ROA) serves as a positive signal of efficient asset utilization, attracting investors, while a low ROA may arouse concerns about management effectiveness. Earnings Per Share (EPS) is a critical performance metric; higher EPS reflects strong profitability whereas declining values raise alarms about earnings quality. The Net Profit Margin (NPM) illustrates operational efficiency, with higher margins indicating better control over costs and American Journal of Economic and Management Business Vol. 4 No. 7 July 2025 936 strong profit generation. Literature evidence mixed findings regarding the impact of these financial ratios on stock returns, with some studies highlighting significant roles for DER, ROA, and EPS. This study aims to identify the fundamental and technical factors influencing stock returns in Indonesia’s financial sector, thereby delivering actionable insights for investors. Through an in- depth analysis of corporate financial signals, the research aspires to contribute valuable references for both academic inquiry and practical investment decision-making, fostering a clearer understanding of market dynamics and firm performance. RESEARCH METHODS This study uses a quantitative method with secondary data sources. Secondary data is taken from the annual financial statements (period 2019-2023) of 41 companies listed on the Indonesia Stock Exchange in the main financial sector, while stock return data is obtained from the difference between the closing price of the stock and its opening price, then analyzed to see the influence of independent variables on dependent variables. According to Echdar (2017), the quantitative method is a data analysis-based approach that can be statistically measured with the aim of testing hypotheses. This research involves 6 variables divided into 3 ratios, namely liquidity ratio (variable current ratio), solvency ratio (variable debt to equity ratio and debt to asset ratio), and profitability ratio (variable net profit margin, return on asset, and earnings per share), which is analyzed for its effect on stock returns in the main financial sector listed on the Indonesia Stock Exchange. RESULT AND DISCUSSION Data Analysis Results This study uses the Eviews 12 program and Microsoft excel as data analysis tools, and uses quantitative analysis methods. The data analysis method is part of the analysis process where data is collected and then processed to produce conclusions in decision-making (Novianingtyas & Bagana, 2022). Thus, the data used in this study is in the form of secondary data by taking annual financial statements and daily stock price data of companies through the Indonesia Stock Exchange website specifically for the financial sector. Then the data is processed and transformed into a ratio form using Microsoft Excel to continue processing data Back through the eviews 12 program. The program analyzed 41 companies for the period 2019-2023 and obtained sample data that will be tested as many as 205 samples. The methods used in this study are descriptive statistical calculations, classical assumption tests, and multiple liner regression model tests to determine the significance between independent variables and their dependent variables. Panel Data Regression Model Selection Test This panel data regression test aims to determine the most optimal model in the three available models, namely the Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM) (Sirait, et. al, 2024). The following are the results of the regression model testing in this study: Fahira Dhea Azzahra, Sulastri, Isni Andriana, Mu’izzuddin 937 Chow Test Based on Napitupulu et. al (2021) that the chow test is a test to choose the Common Effect Model (CEM) approach with the Fixed Effect Model (FEM) in estimating panels. The basis for decision-making is as follows: a. If the probability value in the Cross-Section test F is less than 0.05, then the null hypothesis (H₀) is rejected and the alternative hypothesis (H₁) is accepted. This shows that the model that is more suitable to use is the fixed effect model, and then it is necessary to perform a thirst test to determine whether the best model is a fixed effect or a random effect. b. Conversely, if the probability value of Cross-Section F is greater than 0.05, then the null hypothesis is accepted, which means that the corresponding model is the common effect model. Table 1. Regression Model Selection Test Results (Chow Test) Effect Test Statistic d.f Prob. Result Cross-Section F 1.291822 (40.156) 0.1371 Common Effect Mpdel (CEM) Cross-Section Chi-Square 58.652175 40 0.0287 Source: Eviews 12, processed (2024) The assessment criteria in this chow test are seen by comparing the results of the probability value between the cross-section F and the probability value. Based on table 1, the results of the Chow test show that the cross-section probability number F is 0.1371, which indicates a value greater than the significance value of 0.05 and the H1 decision is accepted. So in this case, the best model between the Common Effect Model and the Fixed Effect Model is the Common Effect Model (CEM). Therefore, it can be concluded that the best model in this study is to use the common effect model (CEM) and the next step is to carry out the LM (Lagrange Multiplier) test. LM Test (Lagrange Multiplier) The Lagrange Multiplier test is a test used to choose between a common effect model or a random effect model. This test was developed by Breusch-Pagan to find out the best model between random effect and common effect. According to Tumanggor et. AL (2024), the hypotheses in this test are as follows: a. If the probability values of F and Chi-square are greater than the significance level of 5% (α = 0.05), then (H₀) is accepted (H1) is rejected. Thus, the model used in the panel regression analysis is a common effect model, because there is no significant difference between cross- section units. b. However, if the probability values of F and Chi-square are less than 5%, then the null (H₀) hypothesis is rejected (H1) is accepted. Thus, the right panel regression model is the random effect model, because there is significant variability between units that cannot be explained by common effect. Table 2. Regression Model Selection Test Results (Lagrange Multiplier Test) Cross-Section Test Hypothesis Time Both Breusch-Pagan 0.086550 (0.7686) 0.728837 (0.3933) 0.815387 (0.3665) Honda 0.294194 (0.3843) 0.853720 (0.1966) 0.811698 (0.2085) King-Wu 0.294194 (0.3843) 0.853720 (0.1966) 0.902693 (0.1833) Standarized Honda 0.852730 (0.2045) 1.291631 (0.1153) -3.708197 (0.9999) American Journal of Economic and Management Business Vol. 4 No. 7 July 2025 938 Cross-Section Test Hypothesis Time Both Standarized King-Wu 0.852730 (0.2045) 1.291631 (0.1153) -1.921819 (0.9727) Gourieroux, et. ly -- -- 0.815387 (0.3496) Source: Eviews 12, processed (2024) Based on table 2, regarding the results of the lagrange multiplier test table shows that the Breusch-pagan part obtained a value of 0.086550, with a value greater than the significance of 0.05. That is, (H₀) is accepted (H1) is rejected. Thus, the comparison of the model between the common effect or random effect model is the common effect model (CEM). It can be concluded from the model selection test, it can be concluded that the suitable model to be used in the next test is the common effect model (CEM). Classic Assumption Test Based on Novianingtyas & Bagana (2022), this classical assumption test is used to test the feasibility of a research model whether it is worth testing or not. This classic assumption test consists of: Normality Test The normality test is a test used in a regression model to see whether residual or disruptive variables are normally distributed or not (Tumanggor et. al, 2024). To test whether the residuals in the regression model are normally distributed, one of the methods used is the non-parametric Kolmogorov-Smirnov (K-S) test. If the significant level (p-value) is > 0.05, then the model is normally distributed, and vice versa, if the significant level (p-value) is < 0.05, then the model is not normally distributed (Sirait et. al, 2024). If the data is not distributed normally, it is necessary to perform a logarithmic transformation (Ln) to the regression model, so that the data can be distributed normally. According to Napitupulu et. al (2021) tested normal or non-normal distributed data can also be done with the Jarque-Bera (J-B) test, based on the decision: a. If prob. Jarque-bera > 0.05, H0 is accepted, meaning that the residual has a normal distribution. b. If prob. Jarque-bera < 0.05, H0 is rejected, meaning that the residual does not have a normal distribution. Figure 1. Normality Test Results (Before Logarithmic Transformation) Source: Eviews 12, processed (2024) In figure 1 show the results of the normality test before the logarithmic transformation is performed. This can be seen in the probability (p-value) that is 0.000000 lower than the 0 20 40 60 80 100 120 140 160 0 10 20 30 40 Series : Standardized Res iduals Sample 2019 2023 Observations 205 Mean -8.67e-18 Median -0.412866 Maximum 43.15917 Minimum -3.694408 Std. Dev. 3.290873 Skewness 11.22674 Kurtos is 146.0708 Jarque-Bera 179147.8 Probabi l i ty 0.000000 Fahira Dhea Azzahra, Sulastri, Isni Andriana, Mu’izzuddin 939 significance value of 0.05, then it can be interpreted that the data is not distributed normally. On the other hand, the value of jarque-bera exceeds the significance number so that it adds validity of the residual not distributed normally. To avoid the existence of data that is not well distributed, the researcher transforms logarithmic data (Ln) and chooses to first transform the dependent variable (y) as a step to avoid false regression and produce valid parameters (Widarjono, 2020). Figure 2. Normality Test Results (After Logarithmic Transformation) Source: Eviews 12, processed (2024) Based on table 2. The result of the transformation of the dependent variable, produces a probability value of 0.700680 that exceeds the significance of 0.05, which means that the data is distributed normally. This is supported by the jarque-bera value which shows the number 0.711408, H0 is accepted, which means that the residual is normally distributed in a curve. Multicollinearity Test The multicollinearity test aims to test whether in the regression model there is a high or perfect correlation between independent variables (Novianingtyas & Bagana, 2022). A good regression model should have no correlation between independent variables. Multicollinearity tests between variables can be identified by using correlation values between independent variables. If independent variables are correlated with each other, these variables are not orthogonal. An orthogonal variable is an independent variable whose correlation value between independent variables is equal to zero. According to (Napitupulu et. al, 2021), the way to detect multicollinearity can be seen from the Tolerance value and the Variance Inflation Factor (VIF) value. If the Tolerance value is less than 0.85, it means that there is no multicollinearity and if the Tolerance value is more than 0.85, multicollinearity occurs. Meanwhile, according to Ghozali (2019), the basis for decision-making is as follows: a. If the correlation value > 0.80 then Ho is rejected, there is a multicollinearity problem. b. If the correlation value < 0.80 then Ho is accepted, there is no problem with multicollinearity. Based on table 3, The result of the transformation of the dependent variable, produces a probability value of 0.700680 that exceeds the significance of 0.05, which means that the data is distributed normally. This is supported by the Jarque-Bera which shows the number 0.711408, H0 accepted, which means that the residual is distributed normally in a curve. Table 3. Multicholinity Test Results 0 2 4 6 8 10 12 -4 -3 -2 -1 0 1 2 3 4 5 Series: Standardized Residuals Sample 2019 2023 Observations 92 Mean -5.30e-16 Median 0.075587 Maximum 5.219373 Minimum -3.805872 Std. Dev. 1.665619 Skewness 0.182096 Kurtos is 3.230105 Jarque-Bera 0.711408 Probabi l i ty 0.700680 X1 X2 X3 X4 X5 X6 X7 X8 X1 1.000000 -0.491117 -0.313411 0.120248 0.112773 -0.040294 -0.023551 -0.204274 X2 -0.491117 1.000000 0.775036 -0.270134 0.023252 0.196627 -0.063676 0.011898 American Journal of Economic and Management Business Vol. 4 No. 7 July 2025 940 Source: Eviews 12, processed (2024) Based on the results of the multicollety test above, it shows that the correlation value between independent variables is less than 0.80 which in this case means that Ho is accepted. Thus, it can be concluded that there is no multicollaterality between variables in the regression model. Heteroscedasity Test Based on Novianingtyas & Bagana (2022), the heteroscedasticity test is used to test whether in the regression model there is an inequality of residual variance between observations, where if the residual variance between observations is fixed, it is called homoscedasticity, while if it varies, it is called heteroscedasticity. Heteroscedasticity occurs when there is no uniformity of standard deviation of the value of the dependent variable for each independent variable. If heteroscedasticity occurs, this can cause the regression coefficient to be smaller and the Confidence Interval to be wider, which in turn makes the results of the statistical test invalid. Figure 3. Heteroscedasity Test (After Transformation) Source: Eviews 12, processed (2024) Based on figure 3. shows a graph of the presence of certain patterns in the results of the heteroscedasity test. The graph shows one of the blue lines that is above the number 40, and it is assumed that the independent variable does not have the same distribution and variant so that it can be interpreted that there is a symptom of heteroscedasity or does not pass the heteroscedasity test. According to Sihabudin et. al (2021), one way to overcome heteroscedasity is to transform variables so that the heteroscedasity assumption is fulfilled. In this study, the researcher performed a transformation of the dependent variable (y) in the form of LOG. X1 X2 X3 X4 X5 X6 X7 X8 X3 -0.613887 0.775036 1.000000 -0.387295 -0.029917 -0.022562 0.032582 0.087860 X4 0.179511 -0.270134 -0.387295 1.000000 0.489779 0.480415 0.015561 0.014033 X5 0.085382 0.023252 -0.029917 0.489779 1.000000 0.199966 0.057974 0.075401 X6 -0.047539 0.196627 -0.022562 0.480415 0.199966 1.000000 0.173203 0.094933 X7 -0.004055 -0.063676 0.032582 0.015561 0.057974 0.173203 1.000000 0.307493 X8 0.002321 0.011898 0.087860 0.014033 0.075401 0.094933 0.307493 1.000000 -10 0 10 20 30 40 50 1 - 19 3 - 19 5 - 19 7 - 19 9 - 19 1 1 - 1 9 1 3 - 1 9 1 5 - 1 9 1 7 - 1 9 1 9 - 1 9 2 1 - 1 9 2 3 - 1 9 2 5 - 1 9 2 7 - 1 9 2 9 - 1 9 3 1 - 1 9 3 3 - 1 9 3 5 - 1 9 3 7 - 1 9 3 9 - 1 9 4 1 - 1 9 Y Residuals Fahira Dhea Azzahra, Sulastri, Isni Andriana, Mu’izzuddin 941 Figure 4. Heteroscedasity Test (After Transformation) Source: Eviews 12, processed (2024) Figure 4. shows the results of the heteroscedasity test that has been transformed by variable (y) and illustrates that there are no symptoms of heteroscedasity because the blue line on the graph does not pass the 500 and -500 boundaries (Napitupulu et. al, 2021). On the chart, the blue line is still above -5 to below the number 35. On the residual graph (residual heteroscedasticity test) it can be seen that the residual value is between 5 and -3, i.e. it does not cross the limit of 500 and - 500 (5 < 500 and -3 > -500), which means that the residual variance is the same. Therefore, there are no symptoms of heteroscedasity or passing the heteroscedasity test. In all tests, including heteroscedasity tests, the dependent variable has been transformed by 1x, this is because there is abnormal data. Transformation is needed in this study in order to meet the assumptions of the analysis, avoid bias, and make it easier for the data to be interpreted so that the range of numbers in the variables is not much different (Lestari et al., 2021). Autocorrelation Test Table 4. Autocorrelation Test Results R-Squared 0.194687 Mean Dependent Var -1.553345 Adjusted R-Squared 0.117066 S.D. Dependent Var 1.856065 S.E. of Regression 1.744044 Akaike Info Criterion 4.042994 Sum Squared Resid 252.4602 Schwarz Criterion 4.289691 Log Likelihood -176.9777 Hannan-Quinn Criter. 4.142563 F-Statistic 2.508184 Durbin-Watson Stat 1.521760 Prob(F-Statistic) 0.017133 Source: Eviews 12, processed (2024) Based on table 4 shows the Durbin-watson number of 1.521760. The durbin-watson value will be compared with the DW Table, independent variable (k) = 8, the number of observations is 92 (data after transformation) and the significance of the level is 5% (0.05). The durbin-watson number shows a higher value compared to the DL value of 1,489 and lower than the 4-DU of 2,148 (4-1,852), so that (1,489 < 1.521760 < 2,148) it can be concluded that no hypothesis is rejected on the autocorrelation of either positive or negative in the regression model. -4 -2 0 2 4 6 1 - 2 0 2 - 2 1 4 - 2 0 5 - 2 0 7 - 2 2 8 - 2 2 1 0 - 21 1 3 - 21 1 4 - 21 1 5 - 22 1 7 - 22 1 8 - 22 2 0 - 19 2 2 - 20 2 3 - 23 2 6 - 20 2 7 - 22 2 8 - 23 3 1 - 19 3 3 - 19 3 5 - 21 3 7 - 21 3 9 - 19 LOG_Y Residuals American Journal of Economic and Management Business Vol. 4 No. 7 July 2025 942 Regresi Linear Berganda Based on Tumanggor et. AL (2024), that this regression test is carried out to estimate the extent to which the dependent variable will change in response to changes in the value of the independent variable. The regression model used in this study is the common effect model. The following are the results of the linear regression test in this study: Table 5. Multiple Linear Regression Test Results Variable Coefficient Std. Error t-Statistic Prob. C 0.073362 1.603878 0.045741 0.9636 X1 -0.182573 0.238476 -0.765583 0.4461 X2 -0.048520 0.111643 -0.434598 0.6650 X3 -0.948109 2.036257 -0.465614 0.6427 X4 6.911450 5.442946 1.269799 0.2077 X5 -0.663758 0.247385 -2.683096 0.0088 X6 -0.555958 0.438263 -1.268547 0.2081 X7 0.009354 0.030383 0.307882 0.7589 X8 -0.008977 0.014776 -0.607545 0.5451 Source: Eviews 12, processed (2024) Table 5 shows the results of the panel regression test which is processed with Eviews, The following are the results of the panel data regression equation processed using Eviews: LOG(Y) = 0.073362 - 0.182573*X1 - 0.048520*X2 - 0.948109*X3 + 6.911450*X4 - 0.663758*X5 - 0.555958*X6 + 0.009354*X7 - 0.008977*X8 The explanation of the purpose of the results of the panel data regression data processing is as follows (Tumanggor et. al, 2024): 1) The constant value of 0.073362 indicates that if the independent variables current ratio (X1), debt to equity ratio (X2), debt to asset ratio (X3), net profit margin (X4), return on asset (X5), earning per share (X6), moving average (X6), and stochastic oscilliator (X8) are constant (zero), then the stock return variable (Y) is 0.073362. 2) The value of the variable beta coefficient in the current ratio (X1) is -0.182573, if the value of other variables is constant and the variable X1 increases by 1%, then the stock return variable (Y) will decrease by 0.182573. 3) The value of the variable beta coefficient in the debt to equity ratio (X2) is -0.048520, if the value of other variables is constant and the variable X2 increases by 1%, then the variable return of shares (Y) will decrease by 0.048520. 4) The value of the variable beta coefficient in the debt to asset ratio (X3) is -0.948109, if the value of other variables is constant and the variable X3 increases by 1%, then the variable return of shares (Y) will decrease by 0.948109. 5) The value of the beta coefficient variable in the net profit margin (X4) is 6.911450, if the value of other variables is constant and the X4 variable increases by 1%, then the stock return variable (Y) will increase by 6.911450. 6) The value of the variable beta coefficient on the return on asset (X5) is -0.663758, if the value of other variables is constant and the variable X5 increases by 1%, then the variable return of the stock (Y) will decrease by 0.663758. Fahira Dhea Azzahra, Sulastri, Isni Andriana, Mu’izzuddin 943 7) The value of the variable beta coefficient on earnings per share (X6) is -0.555958, if the value of other variables is constant and the variable X5 increases by 1%, then the variable return of shares (Y) will decrease by 0.555958. 8) The value of the variable beta coefficient on the moving average (X7) is 0.009354, if the value of other variables is constant and the variable X7 has increased by 1%, then the stock return variable (Y) has increased by 0.009354. 9) The value of the variable beta coefficient on the stochastic oscilliator (X8) is -0.008977, if the value of other variables is constant and the variable X8 increases by 1%, then the stock return variable (Y) will decrease by 0.008977. 10) The value of e (error term) in the table is 0.882934 or 88.2934% (1- Adjusted R Square), which indicates that in addition to being influenced by the current ratio (X1), debt to equity ratio (X2), debt to asset ratio (X3), net profit margin (X4), return on asset (X5), earning per share (X6), moving average (X6), and stochastic oscilliator (X8) and stock return variable (Y) is still influenced by other variables of 88.2934%. Hypothesis Test Coefficient of Determination Test (R2) The Coefficient of Determination (R2) aims to measure the extent to which the model can explain variations from dependent variables (Novianingtyas & Bagana, 2022). The value of this coefficient ranges from zero to one. If the value is low, it means that independent variables have limited ability to explain dependent variables. Conversely, if a value close to one indicates that independent variables are almost fully capable of providing the information needed to predict dependent variables. Table 6. Determination Coefficient Test Results (R2) R-Squared 0.194687 Adjusted R-Squared 0.117066 S.E. of Regression 1.744044 F-Statistic 2.508184 Prob(F-Statistic) 0.017133 Source: Eviews 12, processed (2024) Table 6 explains that the Adjusted R Square value is 0.117066 or 11.7066%. The value of the determination coefficient shows that the independent variables consisting of current ratio (X1), debt to equity ratio (X2), debt to asset ratio (X3), net profit margin (X4), return on asset (X5), earning per share (X6), moving average (X7), and stochastic oscilliator (X8) are only able to explain the stock return variable (Y) of 11.7066%. While the remaining 88.2934% (1 – adjusted R Square value) was influenced by other factors that were not included in this study model. In this case, it can be concluded that there are other factors (external) that are able to explain the complex variable of stock returns. Statistical Test F Based on Nikmah et. al (2021), that the F Test was conducted to test the feasibility of the regression model used to explain the overall influence of independent variables on their dependent American Journal of Economic and Management Business Vol. 4 No. 7 July 2025 944 variables. In addition, based on Ghozali (2019), the F statistical test is also carried out to test regression models that meet the requirements of goodness of fit, namely: a. If the significance value > 0.05, then the regression model does not meet the goodness of fit requirements. b. If the significance value < 0.05, then the regression model meets the goodness of fit requirements. Table 7. Statistical Test Results F R-Squared 0.194687 Adjusted R-Squared 0.117066 S.E. of Regression 1.744044 F-Statistic 2.508184 Prob(F-Statistic) 0.017133 Source: Eviews 12, processed (2024) Based on the results of table 7 shows the F-value calculated as 2.508184 > F-table which is 2.05201 and the prob value (F-Statistic) 0.017133 < 0.05. Therefore, based on these results, H0 was rejected and Ha was accepted so that the variables of current ratio (X1), debt to equity ratio (X2), debt to asset ratio (X3), net profit margin (X4), return on asset (X5), earning per share (X6), moving average (X7), and stochastic oscilliator (X8) had a significant influence on stock returns in 41 financial sector companies simultaneously and were said to be feasible models or met the requirements of goodness of fit. Based on the results of these conclusions, the researcher continued with the partial test (t-test). Statistical Test t Based on Tumanggor et. al (2024), that this partial test is used to evaluate the significance of dependent variables to independent, both partially and individually. The model used in this partial test is the common effect model. Here are the results of the t-test: Table 8. Statistical Test Results t Variable Coefficient Std. Error t-Statistic Prob. C 0.073362 1.603878 0.045741 0.9636 X1 -0.182573 0.238476 -0.765583 0.4461 X2 -0.048520 0.111643 -0.434598 0.6650 X3 -0.948109 2.036257 -0.465614 0.6427 X4 6.911450 5.442946 1.269799 0.2077 X5 -0.663758 0.247385 -2.683096 0.0088 X6 -0.555958 0.438263 -1.268547 0.2081 X7 0.009354 0.030383 0.307882 0.7589 X8 -0.008977 0.014776 -0.607545 0.5451 Source: Eviews 12, processed (2024) The research hypotheses in this partial test are as follows: a. H0 = Independent variable has no effect on dependent variable b. H1 = Independent variable affects dependent variable Based on table 5. shows the results of the t-test or the relationship between independent and dependent variables. The value of the table is 1.986675 (df = n – k) which is obtained through a sample that has been transformed by n = 92 and the number of variables or k = 9 with a confidence level of 5%. Here are the significant results: Fahira Dhea Azzahra, Sulastri, Isni Andriana, Mu’izzuddin 945 1) The Effect of Current Ratio (CR) on Stock Returns: Based on the results of the t-test using eviews 12, it is known that the current ratio (X1) has a tstatistic of 0.765583. Comparing with the result of ttable 1.986675, it is obtained that the ttable < statistic is 0.765583 < 1.986675. Meanwhile, judging from the probability, the probability of > 5% = 0.4461 > 0.05, H1 is rejected and H0 is accepted. This can be interpreted as the current ratio variable does not have a significant effect on stock returns in 41 companies in the financial sector. 2) The Effect of Debt to Equity Ratio (DER) on Stock Returns: Based on the results of the t-test using eviews 12, it is known that the debt to equity ratio (X2) has a statistic of 0.434598. Comparing with the result of ttable 1.986675, it was obtained that the ttable < statistic is 0.434598 < 1.986675. Meanwhile, judging from the probability, the probability of > 5% = 0.6650 > 0.1844, H1 is rejected and H0 is accepted. This can be interpreted as the debt to equity ratio variable does not have a significant effect on stock returns in 41 companies in the financial sector. 3) The Effect of Debt to Asset Ratio (DAR) on Stock Return: Based on the results of the t-test using eviews 12, it is known that the debt to asset ratio (X3) has a statistic of 0.465614. Comparing with the results of ttable 1.986675, it was obtained that the ttable < statistic is 0.465614 < 1.986675. Meanwhile, judging from the probability, the probability of > 5% = 0.6427 > 0.05, H1 is rejected and H0 is accepted. This can be interpreted as the debt to asset ratio variable does not have a significant effect on stock returns in 41 companies in the financial sector. 4) The Effect of Net Profit Margin (NPM) on Stock Returns: Based on the results of the t-test using eviews 12, it is known that the net profit margin (X4) has a statistic of 1.269799. Comparing with the result of ttable 1.986675, it was obtained that the ttable < statistic is 1.269799 < 1.986675. Meanwhile, judging from the probability, the probability of > 5% = 0.2077 > 0.05, then H1 is rejected and H0 is accepted. This can be interpreted as the net profit margin variable does not have a significant effect on stock returns in 41 companies in the financial sector. 5) The Effect of Return on Asset (ROA) on Stock Return: Based on the results of the t-test using eviews 12, it is known that the return on asset (X5) has a statistic of 2.683096. Comparing with the result of ttable 1.986675, it was obtained that the ttable < statistic is 2.683096 > 1.986675. Meanwhile, judging from the probability, the probability of < 5% = 0.0088 < 0.05, H1 is accepted and H0 is rejected. This can be interpreted as the variable return on assets has a significant effect on stock returns in 41 companies in the financial sector. 6) The Effect of Earning per Share (EPS) on Stock Returns: Based on the results of the t-test using eviews 12, it is known that earnings per share (X6) has a statistic of 1.268547. Comparing with the result of ttable 1.986675, it was obtained that the ttable < statistic is 1.268547 < 1.986675. Meanwhile, judging from the probability, the probability of > 5% = 0.2081 > 0.05, H1 is rejected and H0 is accepted. This can be interpreted as the earning per share variable does not have a significant effect on stock returns in 41 companies in the financial sector. American Journal of Economic and Management Business Vol. 4 No. 7 July 2025 946 7) The Effect of Moving Average (MA) on Stock Returns: Based on the results of the t-test using eviews 12, it is known that the moving average (X7) has a tstatistic of 0.307882. Comparing with the result of ttable 1.986675, it was obtained that the ttable < statistic is 0.307882 < 1.986675. Meanwhile, judging from the probability, the probability > 5% = 0.7589 > 0.05, then H1 is rejected and H0 is accepted. This can be interpreted as the moving average variable does not have a significant effect on stock returns in 41 companies in the financial sector. 8) The Effect of Stochastic Oscilliator (SO) on Stock Returns: Based on the results of the t-test using eviews 12, it is known that the stochastic oscilliator (X8) has a tstatistic of 0.607545. Comparing with the result of ttable 1.986675, it was obtained that the ttable < statistic is 0.607545 < 1.986675. Meanwhile, judging from the probability, the probability of > 5% = 0.5451 > 0.05, H1 is rejected and H0 is accepted. This can be interpreted as the stochastic oscilliator variable does not have a significant effect on stock returns in 41 companies in the financial sector. The following are the results of the t-test that show the significance of the independent variable to its dependent variable: Table 9. Summary of Variable Significance Test Results Variabel TStatistic TTable Itself. Std. Sig R-Square Results Current Asset_X1 -0.765583 1.986675 0.4461 0.05 0.000198 Insignificant Debt to Equity Ratio_X2 -0.434598 1.986675 0.6650 0.05 0.051937 Insignificant Debt to Asset Ratio_X3 -0.465614 1.986675 0.6427 0.05 0.056817 Insignificant Net Profit Margin_X4 1.269799 1.986675 0.2077 0.05 0.000035 Insignificant Return on Asset_X5 -2.683096 1.986675 0.0088 0.05 0.111855 Signifikan Earning per Share_X6 -1.268547 1.986675 0.2081 0.05 0.030967 Insignificant Moving Average_X7 0.307882 1.986675 0.7589 0.05 0.008098 Insignificant Stochastic Oscilliator_X8 -0.607545 1.986675 0.5451 0.05 0.013780 Insignificant Source: Eviews 12, processed (2024) The Effect of Current Ratio (CR) on Stock Return The t-test results reveal that the current ratio (CR) has no significant effect on stock returns (t-statistic = -0.765583 < t-table 1.986675, p-value = 0.4461 > 0.05), contradicting some studies (Arifudin, 2023; Anderson et al., 2021) which suggest an inverse relationship between CR and returns, as financial sector analysis shows increasing short-term debts outpacing current assets. Signaling theory explains this outcome, as CR fails to serve as a strong investor signal - while Rahayu (2022) notes high CR doesn't guarantee debt-paying ability due to potential uncollectible receivables, Razak (2019) warns low CR indicates liquidity issues, both negatively impacting investor perceptions (Munthe et al., 2022). Consistent with Putra's (2022) findings and the semi- strong market efficiency hypothesis, investors prioritize more reliable liquidity measures (quick/cash ratios) as public CR information is already price-reflected, supported by multiple studies (Kampongsina et al., 2020; Faidh et al., 2021; etc.), though conflicting with research showing CR's significance (Hidayat, 2020; Sinaga & Astini, 2021; etc.). Fahira Dhea Azzahra, Sulastri, Isni Andriana, Mu’izzuddin 947 The Effect of Debt to Equity Ratio on Stock Returns The t-test results indicate that the debt to equity ratio (DER) has no significant effect on stock returns (t-statistic = -0.434598 < t-table 1.986675, p-value = 0.6650 > 0.05), as financial sector analysis reveals higher debt proportions than equity, which according to Hartinah et al. (2020) and Sari & Kurniasih (2021) increases company risk and investor aversion due to potential bankruptcy costs and reduced tax benefits. Signaling theory explains this relationship, where high DER signals declining profitability (Tezar, 2019) and deters investors (Rahayu, 2022), though in the financial sector, DER's interpretation varies - while Handayani et al. (2022) note investor concerns about debt burdens, Qotimah et al. (2023) argue debt can fuel operations and growth, suggesting DER's impact depends on additional factors like profitability and risk management. These findings align with research by Kampongsina et al. (2020), Anggraini & Wijayanto (2021), and others showing DER's insignificance, but contradict studies by Hertina & Saudi (2019), Tezar (2019), and colleagues demonstrating DER's influence, highlighting contextual complexities in debt-equity dynamics. The Effect of Debt to Asset Ratio on Stock Returns The t-test results show the debt to asset ratio (DAR) has no significant effect on stock returns (t-statistic = -0.465614 < t-table 1.986675, p-value = 0.6427 > 0.05), with financial sector analysis revealing high debt proportions relative to equity. While signaling theory suggests high DAR signals greater risk and deters investors (Rasyad et al., 2020), interpretations vary - Munthe et al. (2022) note debt can improve performance if properly managed, though excessive debt increases financial risk, whereas Andriani et al. (2025) argue low DAR indicates safer financing that may boost returns. Investor perceptions remain divided, as Hermansyah & Sihombing (2022) find high debt reduces investor confidence due to perceived risk, while others acknowledge debt's role in financial sector operations when profits remain stable. These findings align with research by Nur & Tjahjono (2021), Munthe et al. (2022) and others showing DAR's insignificance, though they contradict studies by Ristyawan (2019) and Rasyad et al. (2020), suggesting additional factors like profitability, risk management, and market sentiment (Qotimah et al., 2023) mediate DAR's relationship with returns. The Effect of Net Profit Margin on Stock Returns The t-test results indicate that net profit margin (NPM) has no significant effect on stock returns (t-statistic = 1.269799 < t-table 1.986675, p-value = 0.2077 > 0.05), as while signaling theory suggests NPM reflects company performance (Spence, 1973), with high NPM potentially indicating operational competence and low NPM signaling financial deterioration, Anderson et al. (2021) and Arramdhani & Cahyono (2020) argue NPM merely shows income returns without significantly influencing investment decisions, since it doesn't account for sales dynamics and represents historical rather than predictive data. This aligns with the efficient market hypothesis, where NPM information is already price-reflected (Karo & Wafa, 2024), explaining why investors often disregard NPM as a primary investment metric, especially as Nikmah et al. (2021) note its American Journal of Economic and Management Business Vol. 4 No. 7 July 2025 948 weak credibility in reflecting true profit potential, supported by research from Sari (2020), Mahadianto et al. (2020) and others, though conflicting with studies by Aminah (2021), Abd'rachim (2021) and colleagues demonstrating NPM's significance. The Effect of Return on Asset on Stock Return The t-test results demonstrate that return on assets (ROA) significantly negatively affects stock returns (t-statistic = -2.683096 > t-table 1.986675, p-value = 0.0088 < 0.05, regression coefficient = -0.663758), indicating that while ROA reflects a company's profit-generating ability (Rahayu & Riharjo, 2022) and should theoretically signal good prospects to investors (Faizaturrahmi & Handajani, 2024), this study reveals an inverse relationship where increasing ROA correlates with decreasing stock returns, suggesting that companies may struggle to efficiently convert asset growth into proportional profitability (Hertina & Saudi, 2019). Although ROA explains 11% of stock return variation (with 88% influenced by external factors like exchange rates according to Anggraini & Wijayanto, 2021), investors closely monitor asset management efficiency, as declining ROA erodes confidence and depresses stock prices, consistent with findings by Senewe et al. (2020), Wijaya & Sedana (2020) and others, highlighting that while improved ROA through effective management (Aldiena & Hakim, 2019) should theoretically boost returns, actual market responses may diverge due to complex valuation dynamics and unobserved macroeconomic factors. The Effect of Earnings per Share on Stock Returns The t-test results reveal that earnings per share (EPS) has no significant effect on stock returns (t-statistic = -1.268547 < t-table 1.986675, p-value = 0.2081 > 0.05), as while signaling theory positions EPS as a key indicator of company performance and dividend potential (Spence, 1973; Qotimah et al., 2023), investors increasingly recognize that high EPS doesn't necessarily translate to strong future prospects (Sinaga & Astini, 2021) or guarantee dividends (Rahman & Cahyono, 2024). The efficient market hypothesis explains this insignificance, suggesting EPS information is already reflected in stock prices, while fundamental investors now scrutinize profit quality beyond EPS figures, examining whether earnings stem from core operations rather than accounting adjustments. These findings align with research by Christianto & Firnanti (2019), Faidh et al. (2021) and others showing EPS's limited predictive power, though they contradict studies by Sausan et al. (2020) and Nurhuda et al. (2023) demonstrating EPS's influence, highlighting how modern investors increasingly rely on comprehensive financial assessments rather than single metrics when evaluating stock potential. The Effect of Moving Average on Stock Returns The t-test results show that moving averages (MA) have no significant effect on stock returns (t-statistic = 0.307882 < t-table 1.986675, p-value = 0.7589 > 0.05), as while MA serves as a technical analysis tool to identify price trends (Hasan et al., 2024), its effectiveness diminishes in uncertain market conditions due to being a lagging indicator that follows rather than predicts Fahira Dhea Azzahra, Sulastri, Isni Andriana, Mu’izzuddin 949 price movements. Signaling theory suggests MA provides historical trend information, but investors often disregard these signals as unreliable for forecasting returns, consistent with Smith's view (in Hutama, 2020) that past price changes don't predict future movements, and supporting Fama's (1970) efficient market hypothesis that historical data contains no actionable information. This explains why investors increasingly favor fundamental analysis over technical indicators like MA (Nugraheni, 2019), as evidenced by studies from Cahyani & Mahyuni (2020), Jauhari (2022), and Wijanarto & Lantara (2024) confirming MA's limited predictive power, particularly for long- term investment decisions in the volatile financial sector where stock prices fluctuate significantly. The Effect of the Stochastic Oscilliator on Stock Returns The t-test results indicate that the stochastic oscillator (SO) has no significant effect on stock returns (t-statistic = -0.607545 < t-table 1.986675, p-value = 0.5451 > 0.05), as while this leading indicator theoretically provides early signals for price movements (Putri & Hardiansyah, 2023), its effectiveness is limited in the volatile financial sector where stock prices fluctuate unpredictably and information asymmetry makes it difficult to distinguish between high- and low- quality firms. Although SO can generate capital gains through short-term buy/sell signals (Siahaan et al., 2024), it often produces false signals and lacks predictive power for long-term returns, aligning with Fama's (1970) efficient market hypothesis that historical price information cannot generate excess returns. These findings support research by Muis et al. (2021), Jauhari (2022), and others demonstrating SO's inaccuracy in determining stock returns, as investors increasingly recognize its limitations in assessing fundamental company performance and long-term prospects, despite its occasional utility for short-term technical analysis. CONCLUSION Based on the research findings, return on assets (ROA) has a significant and negative effect on stock returns, indicating that investors consider ROA a critical fundamental factor when making investment decisions. Specifically, if a company fails to efficiently utilize its assets to generate profitability, investors perceive this as a negative signal, leading to reduced confidence, decreased investor interest, and ultimately a decline in stock prices and returns. This suggests that investors closely monitor asset management efficiency as a key indicator of a company’s future stock performance. 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