Retail bank key metrics and stock performance

VIETNAM NATIONAL UNIVERSITY, HANOI INTERNATIONAL SCHOOL GRADUATION PROJECT RETAIL BANK INDUSTRY: KEY METRICS AND STOCK PERFORMANCE Phùng Minh Sơn Hanoi - 2020 VIETNAM NATIONAL UNIVERSITY, HANOI INTERNATIONAL SCHOOL GRADUATION PROJECT RETAIL BANK: KEY METRICS AND STOCK PERFORMACE SUPERVISOR: Dr Lê Đức Thịnh STUDENT: Phùng Minh Sơn CODE: 16071237 COHORT: AC2016C MAJOR: Accounting, Analyzing and Auditing Hanoi - 2020 INFORMATION ON FINAL THESIS Full name: Phùng Minh Sơn Gender: Male Date of birth: August 1998 Place of birth: Hà Nội Official thesis title: Retail bank industry: Key metrics and stock performance Major: Accounting, Analyzing and Auditing Code: 16071237 Guider Lecturer: Dr Lê Đức Thịnh Summary of the findings of the thesis: The study analyze the correlation and p-value between seven key metrics and total shareholders‘ return, the results indicate that Net Loan and Lease to Deposit has more significant linear relationship with TSR which is calculated according to published date of annual report Moreover, NII/AA, NLL/A, NLL/D, Growth rate of Total Asset and ROA have significant impact on stock performance calculated according to fiscal year The result of significant linear relationship of metrics on TSR is more than the results on TSR This study also uses the Regression models which use ROE as a dependent variable and metrics as independent variables which are NI/AA, NLL/A, NLL/D, Growth rate of NLL, Growth rate of EPS and Pre-tax Profit Margin The results show that Net Loan and Lease to Deposit and Pre-tax Profit Margin have very high probabilities of appearing in any regression models Pre-tax Profit Margin has a positive linear relationship with Net Income/Average Equity while Net Loan and Lease to Deposit has a negative linear relationship with ROE The Regression model also uses ROA as dependent variable and metrics independent variables as ROE, the results show that Net Interest Income/ Average Assets and Net Loan and Lease to Deposit both have a 100% probability of appearing in any regression models Net Interest Income/ Average Assets has a positive linear relationship with Net Income/Average Asset while Net Loan and Lease to Deposit has a negative linear relationship with ROA 10 Practical applicability, if any: First of all, the study provides basic understanding and part of financial analysis of retail banks from different region all over the world These banks are on top largest banks in their countries, which represent closely the financial performance and stock price of their regions The study is an academic research which ananlyzes and provides summary of understanding about the impacts of featured and standard metrics on the effectiveness and efficiency of retail banks operating results which reflects on stock price of these banks in different regions This study also includes the research about financial analysis which based on correlation of metrics and total shareholders‘ return so that the investor and financial analyst can have the overview about the important metrics in analyzing the retail banks stock performance and make the decision of investment Date: 27/05/2020 Signature: Full name: Phùng Minh Sơn Acknowledgement First and foremost, I would like to express sincere thanks to my thesis advisor Dr Le Duc Thinh of the International School, Vietnam National University for the continuous support of my study and research, for his helpful guidance, constant advise and feedback during the time when I the thesis Dr Thinh helps me a lot in research, data collection and thesis writing It‘s such an honor and great opportunity for me to have chance to receive advice and support from Dr Thinh I would also like to express my gratitude to my friends at university who gave me continuous advice and support during the time when I analyze data and write thesis They also helped me analyze and comment about the retail banking industry so that I can complete my thesis efficiently and effectively Letter of Declaration I hereby declare that the Graduation Project Retail Bank Industry: Key metrics and stock performance is the thesis of my own research, analysis and has never been published before During the process of this project, I have seriously taken research ethics; all results of this project are my own research and data collection; all references in this project are clearly cited according to regulations I bear full responsibility for the fidelity of the number and data and other contents of my graduation project Hanoi, May 25th 2020 Phùng Minh Sơn List of abbreviation AA Average Assets D Deposit EPS Earning per share NII Net Interest Income NLL Net Loan and Leases ROE Return on Average Equity ROA Return on Average Assets TA Total Assets TE Total Equity TSR Total stock return Table of contents Chapter 1: Introduction .11 The necessity of topic: .11 The goal of topic: .13 Research outcomes: 13 Practical contributions: 13 Chapter 2: Literature review and Research methodology 14 Literature review .14 1.1 Theoretical background 14 1.2 Key metrics 18 Research questions, methodology and scope 20 2.1 Research questions 20 2.2 Methodology 21 2.3 Scope of research .21 Facilities and the difficulty of the researching process 22 3.1 Facilities: 22 3.2 Difficulty: 22 Chapter 3: Main results 23 Descriptive statistics: 23 Analyzing the correlation of metrics with TSR 27 2.1 Summary of Correlation of Companies between featured metrics and TSR 27 2.2 Summary of Correlation of Companies between standard metrics and TSR 30 2.3 Summary of Correlation of Companies between featured metrics and TSR 33 2.4 Summary of Correlation of Companies between standard metrics and TSR 36 2.5 Companies with no significant correlation between metrics and stock performance for both TSR and TSR 38 2.6 Summary of signifcant correlation of metrics and TSR 39 Case study analysis of HSBC Holdings 44 Analyzing the multiple regression 48 4.1 Multiple regression models for ROE .48 Chapter 4: Conclusion, Implication and Recommendation 50 Conclusion and discussion 50 Implications .51 2.1 Literature implications .51 2.2 Practical implications 51 Limitations 51 Recommendation .52 References 53 List of figures and table Figure 1.Real GDP growth by region 2013-2023 Figure Average NLL/A and NLL/D of 20 banks from 2010 to 2019 Figure Average Growth rate of NLL and TA of 20 banks from 2010 to 2019 Figure Average ROA and ROE of 20 banks from 2010 to 2019 Figure Average NII/AA of 20 banks from 2010 to 2019 12 23 24 25 26 Table Summary significant correlations with p-value < 10% of TSR 39 Table Summary significant correlations with p-value < 10% of TSR 41 Table Summary of correlations among standard and featured metrics 43 10 2.6 Summary of signifcant correlation of metrics and TSR In summary, for each retail bank, different metrics of different retail banks have significant impact on TSR The significant correlations which have p-value being less than 10% are summarized in the following table Banks NII/ AA NLL/A NLL/D Growth rate NLL Growth rate TA ROE ROA TE/TA EPS Growth JPMorgan 0.4707 Chase & Co Citibank -0.5925 -0.4893 -0.5837 -0.6284 Bank of 0.7058 America Wells Fargo U.S Bancorp 0.6048 PNC Financial -0.5723 -0.4901 Services Group Bank of New York Mellon Corp Capital One Financial Corporation -0.4266 TD Bank Industrial & Commercial Bank of China 39 Pre-tax Profit Margin Banks NII/ AA NLL/A NLL/D -0.6247 -0.4721 Growth rate NLL Growth rate TA ROE ROA TE/TA EPS Growth Pre-tax Profit Margin -0.6079 -0.4601 China Construction Bank Vietcombank -0.4669 BIDV HSBC 0.5874 Holdings Lloyds Banking Group Santander 0.4423 UK Societe 0.7939 Generale -0.6313 -0.6537 -0.6166 -0.4741 -0.4591 BNP Paribas Amount 0.4867 3 3 Table Summary significant correlations with p-value < 10% of TSR The result of correlation between featured, standard metrics and TSR TSR shows that most of significant indicators are Net Interest Income/ Average Assets, Net Loan and Lease to Assets, Net Loan and Lease to Deposit, Growth rate of Total assets, ROA and EPS Growth The number of significant featured metrics is less than the number of significant standard metrics Societe Generale is the bank that have the most remarkable linear relationships between metrics and stock performance 40 Banks NII/ AA NLL/A NLL/D Growth rate NLL Growth rate TA ROE JPMorgan Chase & Co Citibank -0.5554 -0.4912 -0.4937 Bank of America Wells Fargo 0.4609 U.S Bancorp PNC Financial Services Group Bank of New York Mellon 0.5153 0.4598 -0.5910 Corp Capital One Financial Corporation TD Bank Industrial & Commercial Bank of China China Construction Bank Vietcombank 0.5396 BIDV 41 ROA TE/TA EPS Growth Pre-tax Profit Margin Banks NII/ AA NLL/A NLL/D Growth rate NLL Growth rate TA ROE ROA -0.4440 -0.5772 TE/TA Pre-tax Profit Margin EPS Growth HSBC Holdings Lloyds -0.4603 Banking -0.6372 Group Santander -0.5509 UK Societe -0.4445 Generale BNP Paribas Amount 1 1 0 Table Summary significant correlations with p-value < 10% of TSR For the correlation analysis for TSR 2, Net Loan and Lease to Assets, Net Interest Income, EPS Growth and Pre-tax Profit Margin have the least significant linear relationship with TSR with only one bank in U.S In contrast, Net Loan and Lease to Deposit and Growth rate of Total Asset have the most significant correlation with TSR The result of two tables of significant correlation with p-value less than 10% of both TSR and TSR states that standard and featured metrics have more significant linear relationship on total shareholders‘ return calculated by fiscal year than TSR calculated by published date of annual report The table below illustrate correlation among metrics 42 NII/AA NLL/A NLL/D Growth rate NLL Growth rate TA ROE ROA TE/TA EPS Growth Pre-tax Profit Margin Growth rate NLL NII/AA NLL/A 0.16275 -0.32627 0.671989 NLL/D 0.25382 0.204519 -0.03097 0.188289 0.099958 0.199929 -0.03814 0.645632 -0.04961 0.808238 -0.0067 -0.11732 -0.32948 -0.53142 -0.58354 -0.05766 ROE ROA EP Gro TE/TA -0.01849 0.084492 -0.04266 0.148146 Growth rate TA 0.807436 0.230004 0.197274 0.133949 0.108955 0.43567 0.113859 0.126073 0.216159 0.687173 -0.0071 -0.07062 -0.14919 0.254244 0.279268 -0.01808 -0.08798 -0.08666 0.53626 0.203371 0.013442 -0.11 Table Summary of correlations among standard and featured metrics According to the analysis of correlation among ten, Net Loan and Leases to Assets and Deposit have significant correlation to each other Growth rate of Total Asset has significant relationship with Growth rate of Net Loan and Lease, RA has significant correlation with Net Interest Income to Average Assets and Total Equity/ Total Asset has significant correlation with ROA and Net Interest Income to Average Assets 43 Case study analysis of HSBC Holdings13 Net Interest Income/ Average Assets is the key metrics having significant positive correlation with stock performance of HSBC Holdings Net interest income is a basic measure of earnings among banks Net interest income is the difference between the revenue generated by assets — loans, mortgages, and securities — and the interest costs on liabilities, such deposits in checking and savings accounts In 2010, Reported net interest income fell by 3% to US$39bn This was driven by the exceptionally low interest rate environment and by the effect of repositioning our customer assets towards secured lending as we reduced our higher risk and higher yielding portfolios The interest expense on debt issued by the Group fell, largely due to a decline in average balances in debt securities in issue as HSBC Finance‘s funding requirements continued to decrease in line with the run-off of the residual balances in Mortgage Services and Consumer Lending and the sale of the vehicle finance portfolios Net interest spread decreased due to lower yields on loans and advances to customers, partly as a result of the greater focus on secured lending In 2011, Net interest income was US$40.7bn, 3% higher than in 2010 Interest income from short-term funds and loans and advances to banks also increased, attributable to higher average balances with central banks This reflected higher deposit requirements by central banks in certain markets, together with the placement of excess liquidity in Asia with central banks In 2012, Reported net interest income decreased by 7% This was driven by lower interest income on customer lending, including loans classified within ‗Assets held for sale‘, due in part to the loss of interest income from disposals during 2012, 13 https://www.hsbc.com/investors/results-and-announcements/all-reporting/group?page=1&take=20 44 principally in the US These disposals also led to a change in the composition of our lending book as the decline in higher yielding card balances was replaced by volume growth in relatively lower yielding products, mainly residential mortgages and term lending In 2013, reported net interest income of US$35.5bn decreased by 6% compared with 2012 This was driven by lower interest income from customer lending, including loans classified within ‗Assets held for sale‘ Yields on financial investments and cash placed with banks and central banks declined as the proceeds from maturities and sales of availablefor-sale debt securities were invested at prevailing rates, which were lower This was partly offset by growth in customer deposits leading to an overall increase in the size of the Balance Sheet Management portfolio In 2014, Reported interest income was broadly unchanged, as decreases in interest income from customer lending were offset by increases in income from shortterm funds Increased interest income on customer lending in Asia is driven by growth in term lending volumes and, to a lesser extent, residential mortgages during the year In 2015, reported net interest income of $32.5bn decreased by $2.2bn or 6% compared with 2014 Interest income by type of asset and interest expense by type of liability, and the associated average balances as set out in the summary tables above, were affected by the reclassification in June 2015, of our operations in Brazil to ‗Assets held for sale‘ in ‗Other interest-earning assets‘ and liabilities of disposal groups held for sale in ‗Other interest bearing liabilities‘, respectively Interest expense on customer accounts fell marginally despite growth in average balances Europe was affected by downward movements in market rates in the eurozone This was partly offset by rising costs in North America, in line with promotional deposit offerings Interest expense on debt issued also fell, primarily in Europe as new debt was issued at lower prevailing rates and average outstanding balances fell as a result of net redemptions Interest 45 expense also fell on repos, notably in Europe, reflecting the managed reduction in average balances In 2016, Net interest income of $29.8bn decreased by $2.7bn or 8% compared with 2015 This was partly the impact of the disposal of our operations in Brazil on July 2016, which reduced net interest income by ($1.2bn), and adverse effects of currency translation differences Income increased in Europe as the effect of growth in average balances, primarily an increase in term lending volumes, more than offset the effect of lower yields on both term lending and mortgages, reflecting competitive pricing in the market and lower interest rates in the eurozone In 2017, Interest income decreased by $1.4bn compared with 2016, including the adverse effects of the significant items and foreign currency translation totalling $3.7bn Excluding these, interest income increased by $2.3bn mainly driven by higher income on surplus liquidity and reverse repurchase agreements Interest income on loans and advances to customers was marginally higher, excluding the adverse effects of the UK customer redress programme In 2018, Net interest income of $30.5bn increased by $2.3bn or 8% compared with 2017 This included the minimal effects of significant items and foreign currency translation differences Net interest margin of 1.66% was basis points (‗bps‘) higher than in 2017 This included the minimal effects of significant items and foreign currency translation differences The rise in net interest margin mainly reflected the effect of rate rises on asset yields, notably on term lending in Asia and on surplus liquidity in most regions This was partly offset by the higher cost of customer accounts, notably in Asia and Europe, and the higher cost of debt issued to meet regulatory requirements The increase in net interest margin in 2018 includes the fourthquarter impact of increased liquidity requirements in Europe and the increased cost of customer accounts in Asia 46 In 2019, Net interest income (‗NII‘) of $30.5bn was broadly unchanged compared with 2018 Interest income associated with the increase in average interestearning assets (‗AIEA‘) of 5% was offset by higher funding costs, reflecting higher average interest rates compared with the previous year Interest income on loans and advances to customers increased by $2.3bn This was mainly driven by higher average interest rates compared with the previous year In summary, Net interest income is a financial performance measure that reflects the difference between the revenue generated from a bank's interest-bearing assets and expenses associated with paying on its interest-bearing liabilities According to the analysis of case study for HSBC Holdings, Net Interest Income could be lower because of financing policies from government and central banks Moreover, Net Interest Income was also affected by the market rates in eurozone or the expectation of customers and the exchange rates of currency In addition, Net Interest Income could increase which depends on the increase of interest earnings asset of banks In the combination with the analysis of correlation of Net Interest Income/ Average Assets with stock performance, it can be concluded that Net Interest Income/ Average Assets ratio can be affected by the market rates, changes in earning assets and the policies of government or central banks, which influence the expectation of investors on stock price As a result, the change in Net Interest Income/ Average Asset (depends mainly on the change on Net Interest Income) has positive relationship with stock performance of HSBC Holdings 47 Analyzing the multiple regression 4.1 Multiple regression models for ROE Regression models: choose Net Income/Average Equity (y) as the dependent variable (this is basically ROE); metrics as independent variables: Net Interest Income/ Average Assets (x1), Net Loan and Lease to Assets (x2), Net Loan and Lease to Deposit (x3), Growth rate of Net Loan & Leases (x4), EPS Growth (x5), Pre-tax Profit Margin (x6) We use a sample of 150 observations from the following 15 companies: JP Morgan Chase & CO, Citibank, Bank of America, Wells Fargo, U.S Bancorp, PNC Financial Services Group, Bank of New York Mellon Corp, Capital One Financial Corporation, Industrial & Commercial Bank of China, China Construction Bank, HSBC Holdings, Societe Generale, Lloyds Banking Group, BNP Paribas, Santander UK The BMA package gives us the following best models with cumulative posterior probability = 0.8041: Model 1: y = 0.03079 + 0.31802(x1) - 0.01706(x3) + 0.07618(x6); R^2 = 0.264 Model 2: y = 0.04031 - 0.02154(x3) + 0.07401(x6); R^2 = 0.351 Model 3: y = 0.02439 + 0.51201(x1) - 0.22250(x2) + 0.08486 (x6); R^2 = 0.363 Model 4: y = 0.04053 - 0.02160 (x3) + 0.01931(x4) + 0.07014 (x6); R^2 = 0.361 Model 5: y = 0.01407 + 0.45547(x1) + 0.08108(x6); R^2 = 0.337 The BMA package shows that Net Loan and Lease to Deposit (x3) and Pre-tax Profit Margin (x6) have very high probabilities of appearing in any regression models Pre-tax Profit Margin (x6) has a positive linear relationship with Net Income/Average 48 Equity (y) while Net Loan and Lease to Deposit (x3) has a negative linear relationship with (y) Similarly, we can also consider Net Income/ Average Total Asset as the dependent variable (this is basically ROA); and independent variables are the same as above The BMA package also gives us best models with cumulative posterior probability = 0.8885 Here are the first models: Model 1: y = 5.082e-03 + 2.209e-01(x1) - 5.458e-03(x3) + 5.236e-03(x6); R^2 = 0.563 Model 2: y = 6.620e-03 + 1.662e-01(x1) + 8.466e-03(x2) - 9.721e-03(x3); R^2 = 0.561 Now the BMA package shows that Net Interest Income/ Average Assets (x1) and Net Loan and Lease to Deposit (x3) both have a 100% probability of appearing in any regression models Net Interest Income/ Average Assets (x1) has a positive linear relationship with Net Income/Average Asset (y) while Net Loan and Lease to Deposit (x3) has a negative linear relationship with (y) 49 Chapter 4: Conclusion, Implication and Recommendation Conclusion and discussion This thesis collects and analyzes annual date of twenty banks in US, Europe, Asia and UK in ten-year period from 2010 to 2019 to evaluate the positive or negative linear relationtionship between seven key metrics including featured metrics for bank industry and standard metrics in corporate finance and total shareholders‘ return The study analyze the correlation and p-value between ten key metrics and total shareholders‘ return, the results indicate that Net Loan and Lease to Deposit has more significant linear relationship with TSR which is calculated according to published date of annual report Moreover, NII/AA, NLL/A, NLL/D, Growth rate of Total Asset, ROA and EPS Growth have significant impact on stock performance calculated according to fiscal year The result of significant linear relationship of metrics on TSR is more than the results on TSR Most of Net Interest Income/ Average Asset show that it has positive linear relationship with stock performance according to fiscal year while Net Loan and Lease to Deposit has negative linear relationship with stock performance according to both fiscal year and published date of annual report This study uses the Regression models which use ROE as a dependent variable and metrics as independent variables: Net Interest Income/ Average Assets, Net Loan and Lease to Assets , Net Loan and Lease to Deposit, Growth rate of Net Loan & Leases, EPS Growth, Pre-tax Profit Margin Net Loan and Lease to Deposit and Pretax Profit Margin have very high probabilities of appearing in any regression models Pre-tax Profit Margin has a positive linear relationship with Net Income/Average Equity while Net Loan and Lease to Deposit has a negative linear relationship with ROE This study also uses the Regression models which use ROA as the dependent variable; and independent variables are the same as above Net Interest Income/ 50 Average Assets and Net Loan and Lease to Deposit both have a 100% probability of appearing in any regression models Net Interest Income/ Average Assets has a positive linear relationship with Net Income/Average Asset while Net Loan and Lease to Deposit has a negative linear relationship with ROA Implications 2.1 Literature implications This thesis is an academic research which provides the summary of featured and standard metrics used in the evaluation of effectiveness and efficiency of banks industry The metrics depends on the indicators that reflects the financial situation and performance of banks with 18 banks from U.S, UK, Europe and Asia in 10 years The metrics calculation and analysis indicates that Net Loan and Leases to Deposit has significant linear relationship with stock performance of banks calculated according to both fiscal year and published date of annual report 2.2 Practical implications According to the practical implication of the study results, this thesis provides part of the financial analysis and stock performance evaluation For the investors and financial analysts, it is necessary to have overall understanding about key metrics, ratio and the relationship with stock performance of the banks The study analyzes the correlation of metrics and total shareholders‘ return so the investors can consider which metrics are important and have negative or positive impact on stock price In addition, this study analyze the financial performance of different banks from different regions in the world, it can provides the part of direction for the investments in different regions The study also analyze the metrics as dependent and independent variable and analyzes the relationship between seven ratios so that the financial analyst can understand the impact of financial ratio on ROE Limitations 51 This study analyzes a wide range of data which includes data of eighteen banks in 10 years so it takes time to collect all the data Format and presentation of annual report and financial statement of every bank is presented differently so it is difficult to find out the information Last but not least, there is one bank which is BIDV is not listed on the stock market from 2010 to 2013 so the results of this bank from 2010 to 2013 are rejected Moreover, there is not enough date to use the regression model to have a result on TSR and Recommendation The next study can consider to use different standard and featured metrics or use more featured metrics to evaluate the impact of these metrics on stock performance of banks Moreover, it also can reduce the amount of U.S banks to test the correlation between these metrics and TSR 52 References Kimberly Amadeo (2019) Retail Banking, Its Types and Economic Impact Available at: https://www.thebalance.com/what-is-retail-banking-3305885 Deloitte 2019 Banking Industry Outlook Available https://www2.deloitte.com/global/en/pages/financial-services/articles/gx-bankingindustry-outlook.html at: World Economic Outlook, October 2018, International Monetary Fund Necmi K Avkiran & Hiroshi Morita (2010) Predicting Japanese bank stock performance with a composite relative efficiency metric: A new investment tool Beverly J Hirtle & Kevin J Stiroh (2007) The return to retail and the performance of US banks Norazidah Shamsudin, Wan Mansor Wan Mahmood, and Fathiyah Ismail (2013) The Performance of Stock and the Indicators Aggeliki Liadaki & Chrysovalantis Gaganis (2009) Efficiency and stock performance of EU banks: Is there a relationship? Reference in person: Nguyen Ngoc Lam – Graduated Student covering airline industry in U.S Corpgov.law.harvard.edu (2018) Performance Metrics and Their Link to Value [online] Available at: https://corpgov.law.harvard.edu/2013/02/20/performancemetrics-and-their-link-to-value/ 10 Top 100 Banks in the World Available at: https://www.relbanks.com/worlds-topbanks/assets Business Insider: Alicia Phaneuf (2019) List of the largest banks in the United States by assets in 2020 Available at: https://www.businessinsider.com/largest-banks-us-list 11.Finance.yahoo.com (2019) https://finance.yahoo.com/ Yahoo Finance [online] Available 12 Eighteen banks official websites for investors and information publication 13 HSBC Holdings annual report Available at: https://www.hsbc.com/investors/results-and-announcements/allreporting/group?page=1&take=20 53 at: ... and key metrics with each analyzed retail bank as a sample 20 2.2 Methodology  Qualitative: study key featured metrics for retail bank such as NI/AA; NLL/A; NLL/D; Growth rate of NLL and standard... correlation between TSR and 10 key metrics for retail bank are calculated by the metrics used the original currency which was reported on the annual report of the retail bank and the metrics are calculated... between key metrics including financial ratios or featured indicators and total stock return of retail banks to see whether those metrics has any significant impacts on the fluctuation of retail banks‘