(Tiểu luận) reviewing theoretical background on the performance of a firm and methodsfor studying this

NATIONAL ECONOMICS UNIVERSITY FACULTY OF MANAGEMENT SCIENCE PROGRAM OF PUBLIC MANAGEMENT AND POLICY IN ENGLISH ASSIGNMENT: GROUP ESSAY SUBJECT: ECONOMETRIC GROUP CLASS: EPMP-7 Họ tên Mã Sinh viên Nguyễn Thị Mai Anh (Nhóm trưởng) 11219596 Nguyễn Thị Nguyệt Ánh 11219598 Phạm Thị Yến Nhi 11214575 Lê Ngọc Quyên 11219617 Nguyễn Minh Trang 11219628 Ngô Thị Hạ Vi 11219632 Ha Noi - 2023 MỤC LỤC Introduction of your work 2 Reviewing theoretical background on the performance of a firm and methods for studying this Specifying the linear models for ROA and ROE and state your hypothesis (expectation) on the relationship between dependent variable and each independent variable 4 Using descriptive statistics to analyze the data (summary statistics, scatter plots, correlation matrix) 5 Model estimation: show your output, check with your hypothesis, explain the results, the test for significance of variables and model 5.1 Dependent variable: ROA (model 1) 5.2 Dependent ROE: (model 2) 9 12 Do the necessary procedures to check for the problems of multicollinearity, heteroscedasticity, functional form, normality of errors (Nhị Phạm) 15 6.1: ROA model: 15 6.2: ROE model 19 Comments and recommendations 22 7.1 Comments 22 7.2 Recommendation 25 1 Introduction of your work The performance of banks is impacted by various financial and macroeconomic factors, and the relationship between these factors and bank performance can be complex However, by studying these relationships, we can identify the factors that have a significant impact on bank performance and develop strategies to improve the profitability and financial health of banks This report analyzes the relationship between the performance of banks, as measured by Returns on Equity (ROE) and Returns on Assets (ROA), and their financial indicators and macro factors like GDP and Inflation The data set used in this analysis was obtained from a website and contains information on various financial indicators such as Capital Adequacy (CA), Logarithm of Size of the company (LogA), Asset Quality (AQ), Asset Composition (AC), Inflation (INF), and GDP Growth (GDP) for different banks The primary objective of this analysis is to identify the significant factors that influence the performance of banks and to develop regression models to study the relationship between these factors and bank performance This study will be useful in understanding the factors that impact the profitability of banks and can be used by policymakers and bank managers to make informed decisions Reviewing theoretical background on the performance of a firm and methods for studying this To further elaborate on the theoretical background of studying the performance of firms, it is essential to understand the various financial indicators that are used to measure the financial health of a company A financial indicator is the return on equity (ROE), which measures the profitability of a company by calculating the amount of net income returned as a percentage of shareholder equity ROE is a key performance indicator that is used by investors and analysts to evaluate the performance of a company Another financial indicator that is commonly used to measure the performance of a company is the return on assets (ROA) ROA measures the efficiency of a company in using its assets to generate profits It is calculated by dividing the net income of a company by its total assets Inflation and GDP growth are macroeconomic factors that can impact the performance of banks Inflation can impact the profitability of banks by reducing the value of their earnings and assets GDP growth can impact the performance of banks by influencing the demand for loans and other financial services There are several methods for studying the performance of a firm, including ratio analysis, trend analysis, and regression analysis Ratio analysis involves calculating financial ratios such as ROE, ROA, and others, to assess the financial health of a company Trend analysis involves analyzing the changes in financial indicators over time to identify patterns and trends Regression analysis is a statistical method that can be used to study the relationship between two or more variables Capital adequacy (CA) refers to the ability of a financial institution to absorb potential losses that may arise from its operations and to meet its financial obligations as they become due In other words, it is the measure of a financial institution's ability to withstand unexpected losses and remain solvent The logarithm of the size of a company (LogA) is a mathematical transformation of the company's size, typically measured in terms of its revenue, market capitalization, or total assets Taking the logarithm of the size of a company can be useful in statistical analysis and modeling because it can help to normalize the data and make it easier to interpret Asset quality (AQ) refers to the quality or health of a financial institution's assets, such as loans, investments, and other financial assets It is a measure of the risk associated with the institution's assets and their ability to generate income and withstand potential losses Asset composition (AC) refers to the mix of assets held by a financial institution, such as a bank or an investment firm It is a measure of the institution's investment strategy and risk exposure The asset composition of a financial institution can vary depending on its business model, market conditions, and regulatory requirements Regression analysis involves developing a mathematical model that can be used to predict the value of a dependent variable based on the values of one or more independent variables In this analysis, we will use regression models to study the relationship between the financial indicators and macro factors and bank performance We will use a multiple regression model to identify the significant factors that impact bank performance and develop a predictive model that can be used to estimate the performance of banks based on these factors Specifying the linear models for ROA and ROE and state your hypothesis (expectation) on the relationship between dependent variable and each independent variable The linear models for Return on Assets (ROA) and Return on Equity (ROE), with the specified independent variables: ROA = β0 + β1 * CA + β2 * LogA + β3 * AQ + β4 * AC + β5 * GDP + β6 * INF + ε ROE = γ0 + γ1 * CA + γ2 * LogA + γ3 * AQ + γ4 * AC + γ5 * GDP + γ6 * INF + ε Where: ● ROA: Return on Assets ● ROE: Return on Equity ● CA: Capital Adequacy ● LogA: Logarithm of Size of company ● AQ: Asset Quality ● AC: Asset Composition ● GDP: GDP Growth ● INF: Inflation ● β0, γ0: Intercept terms ● β1-β6, γ1-γ6: Coefficients of the independent variables ● ε: Error term Hypothesis (expectation) on the relationship between dependent variables and each independent variable: a) Capital Adequacy (CA): We expect a positive relationship between CA and both ROA and ROE Higher capital adequacy implies better financial stability, which can lead to better profitability metrics ● H1a: CA has a positive effect on ROA (β1 > 0) ● H1b: CA has a positive effect on ROE (γ1 > 0) b) Logarithm of Size of company (LogA): The relationship between company size and profitability could be positive or negative Larger companies may benefit from economies of scale, leading to higher profitability However, larger firms might also face more bureaucracy and inefficiencies, leading to lower profitability In this case, group hypothesized a positive relationship between LogA and ROA and ROE ● H2a: LogA has a positive effect on ROA (β1 > 0) ● H2b: LogA has a positive effect on ROE (γ1 > 0) c) Asset Quality (AQ): We expect a negative relationship between AQ and both ROA and ROE Higher asset quality means that the company spends more on acquisitions and asset upgrades, so profit values are negatively affected ● H3a: AQ has a negative effect on ROA (β1 < 0) ● H3b: LogA has a negative effect on ROE (γ1 < 0) d) Asset Composition (AC): The relationship between asset composition and profitability is unclear, as it depends on the specific composition of assets and their performance A diverse asset composition might lead to better risk management and higher profitability or result in lower profitability if not managed well In this case, group expects banks to have good asset management and therefore, AC has a positive relationship with ROA and ROE ● H4a: AC has a positive effect on ROA (β1 > 0) ● H4b: AC has a positive effect on ROE (γ1 > 0) e) GDP Growth (GDP): We expect a positive relationship between GDP growth and both ROA and ROE Higher GDP growth usually implies a growing economy, which can lead to better business performance and increased profitability ● H5a: GDP has a positive effect on ROA (β1 > 0) ● H5b: GDP has a positive effect on ROE (γ1 > 0) Inflation (INF): The relationship between inflation and profitability is ambiguous Moderate inflation might lead to higher nominal revenues and profitability, while high inflation could increase costs and reduce profitability In this case, inflation indicators are moderate and Group expects a positive relationship between INF with ROA and ROE ● H5a: INF has a positive effect on ROA (β1 > 0) ● H5b: INF has a positive effect on ROE (γ1 > 0) Using descriptive statistics to analyze the data (summary statistics, scatter plots, correlation matrix) Summary statistics: Document continues below Discover more from: trình thuyết KH-112 Đại học Kinh tế… 61 documents Go to course TẬP-ĐỒN- Fedex 42 thuyết trình thuyết trình 100% (4) Tài liệu - 2rf thuyết trình 100% (1) Đánh giá thuyết trình Nhóm thuyết trình None FILE 20220721 213842 an nguyen thuyết trình None 112005 00 - Lý Bằng - ĐỀ CƯƠNG CHI… thuyết trình None VĨNH PHÚC PHẦN 135 ĐÁNH GIÁ KẾT QUẢ… thuyết trình None This output shows summary statistics for variables with 120 observations each: - ROA: The mean return on assets is 0.722 with a standard deviation of 0.550 The minimum and maximum values are -0.594 and 2.619 respectively - ROE: The mean return on equity is 7.839 with a standard deviation of 6.011 The minimum and maximum values are -4.830 and 25.042 respectively - CA: The mean current assets is 9.824 with a standard deviation of 4.323 The minimum and maximum values are 3.257 and 23.838 respectively - LogA: The mean logarithm of assets is 18.409 with a standard deviation of 1.211 The minimum and maximum values are 16.502 and 20.995 respectively - AQ: The mean asset quality is 2.119 with a standard deviation of 1.288 The - AC: The mean asset composition is 0.805 with a standard deviation of 0.187 - GDP: The mean GDP is 6.445 with a standard deviation of 0.628 The minimum and maximum values are 5.490 and 7.080 respectively - INF: The mean inflation rate is 4.323 with a standard deviation of 2.562 The minimum and maximum values are 0.630 and 9.100 respectively minimum and maximum values are 0.340 and 8.437 respectively The minimum and maximum values are 0.363 and 1.154 respectively Scatter plot Figure with ROA: Figure with ROE: Correlation matrix - ROA and CA have a weak correlation (0.2433), indicating a low correlation between return on assets and current assets - ROA and LogA have a very weak correlation (0.0006), indicating no significant correlation between return on assets and natural logarithm of assets - ROA and AQ have a weak negative correlation (-0.1774), indicating a low negative correlation between return on assets and asset quality - ROA and AC have a weak correlation (0.1550), indicating a low correlation - ROA and GDP have a weak negative correlation (-0.2207), indicating a low between return on assets and asset diversity negative correlation between return on assets and GDP - ROA and INF have a moderate correlation (0.3019), indicating a significant correlation between return on assets and inflation rate - ROE and CA have a weak negative correlation (-0.2580), indicating a low negative correlation between return on equity and current assets - ROE and LogA have a moderate correlation (0.4301), indicating a significant correlation between return on equity and natural logarithm of assets - ROE and AQ have a weak negative correlation (-0.2997), indicating a low - ROE and AC have a moderate correlation (0.2951), indicating a significant - ROE and GDP have a very weak correlation (-0.0041), indicating no significant correlation between return on equity and GDP - ROE and INF have a moderate correlation (0.1290), indicating a significant correlation between return on equity and inflation rate negative correlation between return on equity and asset quality correlation between return on equity and asset diversity independent variables and the dependent variable “ROA” are as follows: +) Accept the hypotheses H1a, H2a and H6a because the P_values of the variables CA, LogA and INF in the final model (the model is run for the third time) are all < 0.05 and the Coefficient values of the variables are These variables are all > +) Accept the hypothesis H3a because the P_value of the variable AQ in the last model (the model is run for the third time) < 0.05 and the Coefficient value of this variable < +) Reject hypothesis H4a and H5a because P_value of variable AC (in model run for the first time) = 0.546 > 0.05 and P_value of variable GDP (in model run for the second time) = 0.138 > 0.05 It means, AC and GDP have not effect on ROA The conclusions made after running the regression model times with the dependent variable “ROA” are as follows: +) Prob > F: This value represents the significance level of the F test with hypothesis H0 and H1 as follows: H0: The independent variables predict the dependent variable unreliable H1: : Independent variables have an unreliable predictor of the dependent variable In this case, Prob > F = 0.0000 Reject H0, accept H1 The model is completely statistically significant +) Adj R value – squared = 0.2623 the independent variables in the model (including CA, LogA, AQ, INF) are explaining about 26.23% of the variation of the dependent variable “ROA” in the model The remaining 77.73% are due to out-of-model variables and random errors From the obtained results, we have the following linear regression equation: ROA = -2.268997 + 0.0598322*CA + 0.1303066*LogA – 0.1551437*AQ + 0.0770741*INF → Thus, the variables CA, LogA, AQ and INF all affect the dependent variable ROA with different degrees of influence Specifically: +) CA, LogA, INF variables have a positive influence on the dependent variable “ROA” When these variables increase by unit, the mean value of ROA increases to 0.0598322, 0.1303066 and 0.0770741 units, respectively +) The variable AQ has a negative effect on the dependent variable “ROA” When this variable increases by unit, the average value of ROA decreases by 0.1551437 units and vice versa ● Test for the significance of the model 11 H0: beta2=beta3=beta4=beta5=0 H1: No H0 (at least coefficient is not equal 0) F=(R^2/(k-1))/((1-R^2)/(n-k))= (0,2871/(5-1))/((1-0,2871)/(120-5)) = 11,57824 F= 11,57824 > F(0,05; 4;115) = 2,46 → reject H0 → the model (1) is significant 5.2 Dependent ROE: (model 2) With independent variables and dependent variable "ROE", group obtained the following regression results: +) Command: reg ROE CA LogA AQ AC GDP INF +) Result: → Comment: The variables CA, AC, GDP have P_value > 0.05 Therefore, these variables in the current model have no effect on the dependent variable “ROE” According to the stepwise regression (backward elimination) method, group removes each variable one by one from the model In which, the variable with the highest P_value is eliminated first Below is the command and the regression results when removing the CA variable: +) Command: reg ROE LogA AQ AC GDP INF +) Result: 12 → Comment: With the results of running the second model, the GDP variable has no effect on the dependent variable “ROE” because this variable has a P_value > 0.05 Group continues to remove this variable from the model Here is the command and the result obtained when removing the GDP variable: +) Command: reg ROE LogA AQ AC GDP INF +) Result: → Comment: All variables in the model including AC, LogA, AQ, INF have an influence on the dependent variable “ROE” because they all have P_value < 0.05 Thus, the results of hypothesis testing on the relationship between the independent variables and the dependent variable “ROE” are as follows: +) Accept the hypotheses H4b, H2b and H6b because the P_values of the variables AC, LogA and INF in the final model (the model is run for the third time) are all < 13 0.05 and the Coefficient values of the These variables are all > +) Accept the hypothesis H3b because the P_value of the variable AQ in the last model (the model is run for the third time) < 0.05 and the Coefficient value of this variable < +) Reject hypothesis H1b and H5b because P_value of CA variable (in the model run for the first time) = 0.526 > 0.05 and P_value of GDP variable (in model run for the second time) = 0.072 > 0.05 It means, CA and GDP have not effect on ROE The conclusions made after running the regression model times with the dependent variable “ROE” are as follows: +) Prob > F: This value represents the significance level of the F test with hypothesis H0 and H1 as follows: H0: The independent variables predict the dependent variable unreliable H1: The independent variables have an unreliable predictor of the dependent variable In this case, Prob > F = 0.0000 Reject H0, accept H1 The model is completely statistically significant +) Adj R value – squared = 0.2978 the independent variables in the model (including AC, LogA, AQ, INF) are explaining about 29.78% of the variation of the dependent variable “ROE” in the model The remaining 70.22% is due to out-of-model variables and random error From the obtained results, we have the following linear regression equation: ROE = -27.93555 + 5.848557*AC + 1.69551*LogA – 1.346786*AQ + 0.6255336*INF → Thus, the variables AC, LogA, AQ and INF all affect the dependent variable ROE with different degrees of influence Specifically: +) Variables AC, LogA, INF have a positive influence on the dependent variable “ROE” When these variables increase by unit, the mean value of ROE increases to 5.848557, 1.69551 and 0.6255336 units, respectively +) The variable AQ has a negative effect on the dependent variable “ROE” When this variable increases by unit, the average value of ROE decreases by 1.346786 units and vice versa ● Test for the significance of the model H0: beta2=beta3=beta4=beta5=0 H1: No H0 (at least coefficient is not equal 0) F= 13,62 > F(0,05; 4, 115) = 2,46 14 → reject H0 → the model (2) is significant Do the necessary procedures to check for the problems of multicollinearity, heteroscedasticity, functional form, normality of errors (Nhị Phạm) 6.1: ROA model: After testing for the significance of each variable in the model, we get the ROA model with independent variables including: CA, LogA, INF, AQ · Multicollinearity checking The "vif" command is used to calculate the Variance Inflation Factor (VIF) for each independent variable in a regression model, which measures the degree of multicollinearity between the independent variables After using Variance Inflation Factor (VIF) measures for testing how much multicollinearity exists in a regression model, we get following result: 15 In Stata, the "vif" command is used to calculate the Variance Inflation Factor (VIF) for each independent variable in a regression model, which measures the degree of multicollinearity between the independent variables In your output, the VIF values for each independent variable are less than 5, which suggests that there is no significant multicollinearity in the model According to the general rule of thumb, a VIF value greater than indicates the presence of multicollinearity, which may affect the reliability and interpretability of the regression coefficients Furthermore, the mean VIF value is 1.73, which is also an indication of low multicollinearity in the model In general, a mean VIF value greater than 1.5 or suggests some degree of multicollinearity, whereas a value below 1.5 or suggests that there is no significant multicollinearity Therefore, based on the VIF values in your output, we can conclude that there is no significant multicollinearity in the ROA model with four independent variables: CA, LogA, INF, and AQ · Heteroscedasticity checking We use White’s test is used to test for heteroscedastic (“differently dispersed”) errors in this model We have hypothesis: ● H0 : doesn’t exist heteroscedasticity ● H1 : exist heteroscedascity After using “estat imtest, white” command, we get the following result: 16 The White's test result shows that the chi-square statistic is 28.86 with 14 degrees of freedom, and the corresponding p-value is 0.0109 This indicates that we can reject the null hypothesis of homoscedasticity at the 5% level of significance Therefore, we can conclude that there is evidence of heteroscedasticity in the ROA model Furthermore, the results show that the major source of heteroscedasticity is the heteroscedasticity itself with a chi-square statistic of 28.86 and 14 degrees of freedom The other two sources of heteroscedasticity, skewness and kurtosis, are not significant at the 5% level of significance In summary, the White's test result indicates that there is evidence of heteroscedasticity in the ROA model → To overcome the phenomenon of variable variance, group uses the Robust Standard errors method +) Command: reg ROA CA LogA AQ INF,robust +) The model results have been corrected for the phenomenon of variable variance: 17 ● Normality of errors checking We use Skewness/Kurtosis tests to test for the normality of the errors to ensure that the assumptions underlying statistical tests and confidence intervals based on the model are valid, by the "sktest" command in Stata We have hypothesis: ● H0 : the errors follow a normal distribution ● H1 : the errors not follow a normal distribution Variables "CA", "LogA", and "INF" have non-normal distributions because the pvalues for the Skewness and Kurtosis tests are less than 0.05, which indicates that the null hypothesis of normality is rejected at the 5% significance level On the other hand, the variable "AQ" has a normal distribution, as the p-values for both Skewness and Kurtosis tests are greater than 0.05, indicating that the null hypothesis of normality is not rejected at the 5% significance level If these variables are used as predictors in a regression model, the non-normality may violate the assumption of normality of errors and may produce biased parameter 18 estimates and unreliable hypothesis tests Therefore, we should transform the variables or use a different modeling technique that does not assume normality of errors 6.2: ROE model After testing for the significant of each variable in model, we get ROE model with independent variables including: AC, LogA, AQ, INF · Multicollinearity testing In this case, the VIF values for all the independent variables in the ROE model are less than 5, ranging from 1.07 to 1.25 This suggests that there is no severe multicollinearity in the model, and the independent variables are not strongly correlated with each other The mean VIF for all variables in the model is 1.16, which is close to 1, further 19 indicating the absence of significant multicollinearity Therefore, we can conclude that multicollinearity is not a major issue in this ROE model · Heteroscedasticity testing; We have hypothesis: ● H0 : doesn’t exist heteroscedasticity ● H1 : exist heteroscedasticity After using “estat imtest, white” command, we get the following result: The output of the White test in Stata indicates that there is evidence of heteroskedasticity in the ROE model with independent variables AC, LogA, INF, and AQ The null hypothesis of homoscedasticity is rejected at the 5% significance level (p < 0.05) since the chi-square test statistic is significant with a probability of 0.0030 Additionally, it also shows that the source of heteroskedasticity comes from sources other than skewness and kurtosis, which are not significant at the 5% significance level → To overcome the phenomenon of variable variance, group uses the Robust Standard errors method 20 +) Command: reg ROE AC LogA AQ INF,robust +) The model results have been corrected for the phenomenon of variable variance: In conclusion, the White test results suggest that there is evidence of heteroskedasticity in the ROE model, which indicates that the assumption of equal variance across the observations is violated So, we should employ some solutions for the ROE model, similar to those used in the ROA model ● Normality of errors checking We have hypothesis: ● H0 : the errors follow a normal distribution ● H1 : the errors not follow a normal distribution The output of the "sktest" command in Stata shows the results of the Skewness/Kurtosis tests for normality for each variable included in the command In this case, the results indicate that the variables "AC", "LogA", and "INF" are 21 potentially non-normally distributed, with the skewness test being significant for "AC", and both the skewness and kurtosis tests being significant for "LogA" and "INF" However, the variable "AQ" appears to have a normal distribution, as neither the skewness nor the kurtosis test is significant Non-normality in the independent variables can cause problems in linear regression models, including biased parameter estimates, incorrect standard errors, and reduced efficiency So we may want to consider transforming the variables "AC", "LogA", and "INF" to achieve normality, or consider using a non-linear model that does not assume normality Comments and recommendations 7.1 Comments With dependent variable ROA model: - Capital Adequacy (CA) has a positive impact on Returns on assets (ROA) Having sufficient capital can increase the stability and credibility of a financial institution, which can lead to increased investor confidence and a lower cost of capital This can, in turn, lead to increased profitability and returns on assets For example, a financial institution with high levels of capital adequacy may be viewed as more creditworthy and stable by investors, which can lead to a lower cost of funds and higher profitability Additionally, having a strong capital base can provide a buffer against unexpected losses, which can help to maintain earnings and protect returns on assets - Logarithm of Size of the company (LogA): Larger companies may benefit from economies of scale, which can lead to lower costs and higher profitability Additionally, larger companies may have access to more resources, such as capital and talent, which can help them to generate higher returns on assets Therefore, the relationship between Logarithm of Size of the company and Returns on assets (ROA) is beneficial - Inflation (INF) can lead to higher nominal interest rates, which can increase the returns on certain types of assets, such as bonds and other fixed-income securities This can also lead to higher profits for companies that are able to pass on their increased costs to customers through higher prices Furthermore, the impact of inflation on returns on assets can depend on the specific economic conditions and the cause of the inflation For example, if inflation is caused by an increase in demand, it may lead to higher profits for companies 22 and higher returns on assets - Asset quality (AQ) has a negative impact on Returns on assets, particularly for financial institutions such as banks and credit unions Asset quality refers to the quality and performance of a financial institution's loan portfolio, and is typically measured by the percentage of loans that are delinquent, in default, or have been written off Poor asset quality can indicate that a financial institution has made risky loans or has not properly managed its credit risk, which can lead to higher loan losses and lower profitability In particular, financial institutions with high levels of non-performing loans or loan loss provisions may need to set aside more capital to cover potential losses, which can reduce their returns on assets Additionally, poor asset quality can damage a financial institution's reputation and lead to increased regulatory scrutiny, which can further impact profitability and returns on assets - Asset Composition (AC) refers to the mix of different types of assets held by a company, such as cash, inventory, property, and equipment Returns on assets (ROA) is a financial ratio that measures a company's profitability by dividing its net income by its total assets The asset composition of a company can affect its financial position and risk profile, but it does not have a direct impact on ROA The ROA formula only considers the total amount of assets held by the company, regardless of their composition Therefore, while asset composition can affect a company's overall financial performance, it does not directly impact ROA - The impact of GDP growth on returns on assets is complex and depends on a variety of factors, including the overall economic environment, the competitive landscape, the company's financial structure and management practices, and other market and regulatory factors Therefore, GDP Growth doesn’t directly affect Returns on assets With dependent variable ROE model: - Asset composition (AC) has a positive effect on Returns on equity (ROE), as the mix of assets held by a company can impact its profitability and overall financial performance For example, companies that have a higher proportion of high-return assets, such as stocks or real estate, may be able to generate higher returns on equity than companies that have a higher proportion of lower-return assets, such as cash or bonds Companies that hold a diversified portfolio of assets may be 23 better able to manage risk and generate consistent returns over time - The impact of the size of a company on returns on equity can depend on the specific industry or sector in which the company operates, as well as other factors such as the company's competitive position, management practices, and financial structure On the other hand, larger companies may benefit from economies of scale, which can lead to lower costs and higher profitability Additionally, larger companies may have access to more resources, such as capital and talent, which can help them to generate higher returns on equity - Similar to Returns on Assets (ROA), inflation (INF) has a beneficial effect on Returns on Equity (ROE) - Asset Quality (AQ) has a positive effect on Returns on Equity Financial institutions with high levels of non-performing loans or loan loss provisions may need to set aside more capital to cover potential losses, which can reduce their returns on equity Conversely, financial institutions with stronger asset quality may be able to generate higher returns on equity by reducing their loan loss provisions and improving their overall profitability - While capital adequacy (CA) is important for the stability and reliability of a financial institution, it does not directly affect the returns on equity A company with a higher capital adequacy ratio may be perceived as more stable and secure, which could potentially attract more investors and improve the company's overall financial performance However, the returns on equity are ultimately determined by the company's profitability, which can be influenced by a wide range of factors such as market conditions, competition, and management decisions - GDP growth and returns on equity are related but they are not directly dependent on each other A company may be operating in a country with high GDP growth, but if it is facing strong competition, poor management decisions, or unfavorable market conditions, it may still have low returns on equity 7.2 Recommendation - Maintaining appropriate levels of capital adequacy relative to the specific risk profile and business objectives of the company This can help to provide a buffer against unexpected losses and maintain earnings and returns on assets because capital adequacy has a positive relationship with returns on assets - Monitoring and optimizing the asset composition of the company's portfolio in order to improve profitability and returns on equity, while also managing risk 24 effectively - Maintaining strong asset quality by making prudent lending decisions and effectively managing credit risk This can help to reduce loan losses and - Monitoring and adjusting strategies in response to economic conditions and improve profitability and returns on equity inflationary pressures in order to optimize returns on assets and returns on equity This may involve adjusting pricing strategies, managing costs effectively, and maintaining a diversified portfolio of assets - Conducting thorough analysis and monitoring of the specific factors that impact returns on assets for the company, including market conditions, competitive environment, management practices, and financial structure, in order to make informed decisions and optimize performance over the long term 25