Factors Affecting Stock Return of UK Banks - Research Paper Example

Factors Affecting Stock Return of UK Banks Insert your name Insert tutor's name here Insert Institution here Insert a date i. Abstract The objective of this research to survey factors affecting stock return of United Kingdom (UK) banks. The focus the thesis statement on the stock returns variations to different microeconomic and macroeconomic variables by analyzing data using a multi-model approach or model. Basing data from Bloomberg database regarding UK banks between 2007 and 2016 the research assesses different factors. In order to develop regression framework that conforms to data gathered, the research narrowed its data to five UK banks (HSBC Bank, Barclays Bank, Lloyds Banking Group, Royal Bank of Scotland and Standard Chartered Bank). The main finding of the research is that stock returns of UK banks are affected by market volatility. We further observed that in as much as the bulk of the variability of the UK banks stock returns are as a result of cash flow shocks, the margin of variability is higher in smaller banks. Key words: equity, stock returns predictability, cash flow, market risks Table of Contents i.Abstract 2 1.0.Introduction 3 2.0.Literature Review 5 3.0.Data and Methodology 11 Figure1: Bloomberg Data for the Five UK Banks (double click to open) 12 4.0.Research Results 12 Variance for the Companies (Market Returns) 12 Figure 2: Variance for the Companies 13 Monthly Returns and Industrial Performance Index 15 Beta (Measure of Market Risk based on Interest Rate and Exchange Rate on Return) 18 5.0.Conclusion 20 1.0. Introduction UK banking histories stretches beyond 18th century when people were not very keen on aspects such as performance of stock returns. However, Bredin et al. (2007) observed that the significant of banks to the UK had never been questioned. Currently, UK banks are categorized into different types not only to serve different purposes but also help investors and policy makers make decision regarding different issues including investment. These developments have prompted researchers to investigate factors that influence their stock returns since dynamism in financial institutions calls for a financial system and economic conditions that align with specific needs of policy makers and investors (Bauer et al. 2004). The report takes into consideration how the identified banks implement the Basel II rules and regulation. That is, factors connected to a bank’s stock return are well understood from the Basel’s market discipline to the oversight and supervisory processes. Our data analyses therefore assess Bauer et al. (2004) research but incorporate Basel II framework thus accentuating roles market information to the five banks in their prudential monitoring processes. The market prices of UK bank securities, like equities, have been found to provide essential information for investors and other market participants (Pasiouras and Kosmidou 2007). According to the author, research processes in finance require assessment of micro and macro factors affecting UK banks because from this approach, one understand one other issues, banks’ equity price and how they effectively summarise all the information in the public. However, this study stretches the scope of information provided by Kosmidou et al. (2005). The approach to analyse factors affecting the stock return of the identified companies looks at issues such as banks’ securities prices at one point, efficient-market hypothesis and how these banks integrate expectation of both negative and positive future earnings prospects. Previous studies have noted that financial disturbance from one bank have the ability to spread to other channels thus being reflected in stock returns or market (Demirgüç-Kunt and Huizinga 2010). From these studies, the approach of assessing these factors take into consideration the extent the variability in a given bank’s stock price are pushed or driven by common versus bank specific component. The contribution of this paper is to critically provide a better understanding of factors that affect stock return of the UK bank taking case studies on 5 banks as identified. We narrow our factors that relate to unexpected variability in these banks’ equity prices. To this regard, we incorporate empirical methods developed by previous studies (Demirgüç-Kunt and Huizinga 2010). Application of this approaches seek to explicitly distinguish different aspects including changes in rational expectations of future performance and rational expectations of future returns. 2.0. Literature Review Literatures have agreed that there are different factors that are essential in determining factors that affect the stock return of banks though they do not provide the exact number and how these factors rate (Drehmann 2005). Theoretical models have been proposed to help studies provide an understanding on specific factors that affect stock returns on a given type of bank. Taking a case study from Frederick (2015), the research found that the nature of stock return for banks can be explained by two theories which are: arbitrage pricing theory (APT) and capital asset pricing model (CAPM). In understanding these factors, Chang et al. (2015) found that the two theories provide a link between a given bank’s risk and expected return. However, recent studies have noted that UK banks have operating on a different environment that the two theories may not provide the needed impetus in predicting specific factor. For instance, Reboredo and Rivera-Castro (2014) observed that both APT and CAPM have specific characteristic that has brought them under a lot of arguments noting that market returns was the only factor that was used in the determination of the stock returns fluctuations. Based on these studies, it therefore means that the two theories face some shortcomings thus insufficient in explaining completely the pricing of assets that might be risky (Chang et al. 2015). According to Benbouzid et al. (2017) there are different factors that are potentially essential in explaining the fluctuation of stock returns besides what Benbouzid et al. (2017) term as a single market factor. Based on this research, contemporary scholars have been concerned with creation of multi-factor asset pricing approach or theory such as APT as an alternative model that would help in generating the stock return variation. This study therefore adopts APT model assuming that with the five banks chosen, uncertainty in asset returns will be as a result of common factors specifically, macroeconomic that bank-specific factors. In reference to Benbouzid et al. (2017), countries with forward looking pecuniary schemes in particular the United Kingdom has bank portfolios highly exposed whether unswervingly or circuitously to the real estate sector. Studies have aided radically showing that alterations when it comes to the value of real estate have potential to considerably affect the default risk of financial institutions and their returns consequentially due to high exposure to the stock market. Benbouzid et al. (2017) authenticate that’s the latter is more than ever critical during real estate crises as it was evident following the recent international subprime crisis when losses in banks suffered a tendency to amplify vividly. As a result of this, the entire financial structure was at a risk of collapsing. In accordance with research by Papadamou et al. (2017), majority of studies exploring the essence of real estate market conditions that objectively scrutinize universal risk factors in the United Kingdom bank stock returns applies a model commonly known as ‘two-factor.’ This purports that bank stock returns are influenced by common market situations and also by movements in interest rates thereby suggesting that in the UK, non-attendant risk on loans is moulded by a particular additional factor notwithstanding all bank loans being susceptible to market conditions which is the effect of movements in the real estate market. For banks in the United Kingdom, accounting for the logical influence on market value by the real estate market, the bank stocks evaluation models need to incorporate which reflect the positions in the real estate market. The results of this research points towards the fact that banks in the UK are responsive to the change in real estate conditions concluding that there exists a positive relation between the returns of banks stocks and returns of real estate after critical scheming for general market conditions as well as changes in interest rates. Allegret et al. (2017) says that banks stock returns fluctuate, either by rising or falling with dependence on the business cycle thereby imposing effects on bank equity financing. With respect to this truth is that bank equity financing is discounted in the explosion and high-priced in the period of a recession. This imparts support for discretion that induces enticement for banks to implement capital buffers on occasions when the rate of equity is low. Moreover, facing a higher charge of equity are banks with relatively higher leverage advocating that higher capital fractions are related with lower costs in funding. Spontaneously, the status of the business cycle is capable of influencing bank equity rates by impacting the bank’s assets. In the event of an economic explosion, default rates decline for loans to both households and business firms. In turn, this heightens bank returns and can alleviate investors’ observation of the risks in bank returns. This lessens their expected profit on stocks of the bank (Benbouzid et al. 2017). Recessions on the other hand impose an opposite impact on the value of loans and bank remuneration thereby compelling an increment on the required returns. In actual fact, according to Allegret et al. (2017) this effect is arguably asymmetric in relation to UK banks. In this research, measure of the business cycle is approached in terms of deviation of GDP growth from its trending time. In accordance with this research by Holston et al. (2017) other authors Allegret et al. (2017) find suggestion that for EU banks, particularly banks in the UK for this matter, equity rate developments along with subordinate arrear spreads are essential in envisagement of banking distress as characterized by rating agency relegates. In this research he says that from 2008’s great recession and the ongoing euro sovereign debt crisis (affecting UK) that commenced in early 2010 have ushered in elevated tension in the financial markets. They asses the influence of variables in the business cycle on bank stocks and conclude that returns may be at variance across countries and even types of banks. These studies also agree that in the UK, better capitalised banks make higher stock profits in the scenario of downturns. Allegret et al. (2017) agrees to the opinion that in reference to the model of dividend-discount in equity rating that a company’s stock return can rise if its future profits growth is high which is the fundamental factor and is regularly measured by surplus or if its expected returns are short or in the occurrence of any combination of these two upshots. Regarding financial institutions and for that matter UK, we find that since 2008 equity returns have acted responsively to mainly alterations in progressive sovereign risk and outlook and. For the European sovereign bond investor nerve centre, banks have been a foundation stone holding significant bond portfolios. A raise in sovereign risk is inevitable whenever there is a decline of economic outlook thereby distressing the reimbursement capacity of a country. According to Forbes et al. (2017) Banks’ capability to offer credit and carry on purchasing of sovereign debt is decreased by debit in bond holdings which causes further impairment to the growth outlook and leads to a heightened sovereign spreads affecting the country’s capability to clear its debt. This study by Forbes et al. (2017) found some evidence that banks’ equity returns are made more resilient to inauspicious sovereign risk and economic surprises by towered lower leverage and capitalization. This connotes that equity returns are impacted positively by the ratio of equity-to-asset (Skintzi 2017). However, in this research, statistical magnitude of this impact diminishes when projections are altered for cross-sectional dependence. Yearly cross-segment regressions put forward that the outperformance of banks having higher objectivity to assets was narrowed to 2008. Forbes et al. (2017) made a research on banks stock returns by analyzing daily proceeds using the daily sum return index for stocks from a case study DataStream involving a sample range of 37 countries. A classification was developed by IFC( International Finance Corporation) according to which 13 of the countries are advanced in terms of their economic positions (UK, Greece ,Denmark ,Hong Kong ,Italy ,Japan ,Sweden ,Spain ,Finland ,Hungary ,Belgium). This study covers the market feedback of bank stock prices in relation to changes in sovereign rating. Frederick (2015) found that normally bank stock prices fall following sovereign rating downgrades and are insignificantly responsive to upgrades on sovereign rating. More vitally, the research finds that noteworthy cross sectional disparity in the response of bank stock prices to variations in sovereign ratings. This study intimate that stock market reactions towards variations in sovereign credit ratings is aptly elucidated by an incorporation of factors. In contradiction with other studies, Forbes et al. (2017) has their centre of attention on stock prices reactions in the course of short windows about relatively distinct events, for instance sovereign rating changes. This method helps to exclude additional factors from the analysis that may perplex the pragmatic correlation between sovereign credit and bank performance. In accordance to Frederick (2015) the aid by government to banks is comprehended to not only equity holders but also profit bank debt holders as well. Expected bailouts by the government may boost value of shareholder by plummeting the cost of debt subvention for banks and lowering the chances of financial strain. In other terms, expected government aid may increase returns in good states and lower the prospects of bad states. Nonetheless, a few studies have concluded that the expected bank bailouts may step up risk- taking when it comes to banking sector. All in all Frederick (2015) found that banks expected to take delivery of fervent aid from their governments experience a large negative stimulus on their stock returns in result of sovereign debt rating downgrades. This outcome is more evident for banks in superior economies where governments hold a better standing to offer that support. Drehmann (2005) findings that the expected return shocks are comparatively more essential for larger banks than for smaller ones while the enormity of the inconsistency of EU banks’ stock returns subsist owing to cash flow shocks. 3.0. Data and Methodology This research project will collect data from Bloomberg database, PRIMO and Google Scholar on the five UK based banks (HSBC Bank, Barclays Bank, Lloyds Banking Group, Royal Bank of Scotland and Standard Chartered Bank). The sample period will be from 2007 and 2016. The research will focus on financial ratio and Statistical Package for Social Sciences (SPSS) data analysis. All the information will from FTSE 100 that can make the data quality and accuracy. SPSS will be the primary process of data analysis where the research will reduce and organise data to produce findings that require interpretation by the researcher. Just like Frederick (2015) found, the scores of the tests will be processed through SPSS software and used in the quantitative analysis. The qualitative analysis of the data obtained regarding the five banks will involve interpreting each aspect of the factor. Means and frequencies will be utilised in the analysis. This research will include different variables like profits, book value, growth, dividend, volatility, business cycle, interest rate and among other variables. Using these data to analysis what factors affecting the stock return of UK banks. The use of SPSS to carry out the quantitative analysis could strengthen the validity and reliability of the research. The research data for the five companies is as attached below: Figure1: Bloomberg Data for the Five UK Banks (double click to open) 4.0. Research Results Variance for the Companies (Market Returns) The research calculated variances for the 5 companies comparing their data based on market returns to assess the extent to which market return was a factor in determining stock return. The Excel sheet attached below provides statistical analysis of the variance. Figure 2: Variance for the Companies Pi1 Pi2 Pm Ri1 Ri2 Rm 68.04 1.89 3.95 68.23 1.88 2.96 0.0028 (0.0053) (0.2506) 69.12 1.78 3.71 0.0130 (0.0532) 0.2534 68.12 1.8 3.66 (0.0145) 0.0112 (0.0135) 68.99 1.8 3.28 0.0128 - (0.1038) 69.22 1.8 3.33 0.0033 - 0.0152 69.06 1.96 3.8 (0.0023) 0.0889 0.1411 69.8 1.96 4.25 0.0107 - 0.1184 70.59 1.94 5.13 0.0113 (0.0102) 0.2071 71.61 1.94 4.24 0.0144 - (0.1735) 72.23 1.71 4.57 0.0087 (0.1186) 0.0778 73.36 1.71 4.06 0.0156 - (0.1116) 73.35 1.71 4.63 (0.0001) - 0.1404 73.59 1.71 4.13 0.0033 - (0.1080) 73.09 1.39 3.78 (0.0068) (0.1871) (0.0847) 72.34 1.39 4.23 (0.0103) - 0.1190 72.4 1.39 4.52 0.0008 - 0.0686 72.61 1.39 4.37 0.0029 - (0.0332) 72.51 1.39 4.64 (0.0014) - 0.0618 73.57 1.39 5.18 0.0146 - 0.1164 74.44 1.39 5.85 0.0118 - 0.1293 74.16 1.99 5.39 (0.0038) 0.4317 (0.0786) 75.36 1.99 4.23 0.0162 - (0.2152) 76.61 1.99 3.76 0.0166 - (0.1111) 77.12 1.99 3.04 0.0067 - (0.1915) 77.21 1.99 2.57 0.0012 - (0.1546) 77.16 1.84 2.7 (0.0006) (0.0754) 0.0506 78.08 1.84 2.34 0.0119 - (0.1333) 78.57 1.84 2.12 0.0063 - (0.0940) 78.6 1.84 2.47 0.0004 - 0.1651 78.2 1.84 2.85 (0.0051) - 0.1538 79.07 1.84 2.48 0.0111 - (0.1298) 79.8 1.87 2.57 0.0092 0.0163 0.0363 79.86 1.94 3.26 0.0008 0.0374 0.2685 80.84 1.94 4.36 0.0123 - 0.3374 80.97 2.35 4.85 0.0016 0.2113 0.1124 81.12 2.35 5.52 0.0019 - 0.1381 81.03 2.35 5.55 (0.0011) - 0.0054 81.08 2.67 5.73 0.0006 0.1362 0.0324 80.37 2.67 5.67 (0.0088) - (0.0105) 79.82 2.81 5.63 (0.0068) 0.0524 (0.0071) 80.24 2.81 5.8 0.0053 - 0.0302 80.31 2.81 6.21 0.0009 - 0.0707 81.12 2.81 7.05 0.0101 - 0.1353 79.56 2.81 7.3 (0.0192) - 0.0355 78.25 2.92 7.02 (0.0165) 0.0391 (0.0384) 78.55 3.01 5.96 0.0038 0.0308 (0.1510) 77.87 3.01 6.71 (0.0087) - 0.1258 78.74 9.67 6.22 0.0112 2.2126 (0.0730) 79.42 1.71 6.73 0.0086 (0.8232) 0.0820 79.18 1.71 8.39 (0.0030) - 0.2467 78.68 1.39 10.75 (0.0063) (0.1871) 0.2813 79.9 1.84 8.27 0.0155 0.3237 (0.2307) 80.27 1.84 8.74 0.0046 - 0.0568 80.14 1.87 9.19 (0.0016) 0.0163 0.0515 80.78 1.39 8.5 0.0080 (0.2567) (0.0751) 81.63 1.39 9.55 0.0105 - 0.1235 81.7 1.39 8.53 0.0009 - (0.1068) 81.47 1.99 7.35 (0.0028) 0.4317 (0.1383) 82.4 1.99 5.61 - 81.95 1.99 6.35 - Ri1 Ri2 Rm 0.0031 0.0401 0.0202 Vari1 Vari2 Varm 0.0001 0.1087 0.0194 SDi1 SDi2 SDm 0.0085 0.3297 0.1391 Ri1 A Ri2 A Rm A (0.2000) (0.2011) (0.1895) The data above reveal stock data with the highest return depending on the market returns. The calculation indicates that Pi is standing at 0.003 and on the other hand, P2 is projected at 0.00041. This is an indication that the more there are market return the more the stock returns. This finding conforms to previous studies such as Frederick (2015). Furthermore, the standard deviation (SD) shows that Pi has 0.0085 while P2 is having 0.3297 meaning that for the five banks, their stock return were higher with higher market return. Monthly Returns and Industrial Performance Index Following studies such Skintzi (2017), the study analysed data on the five companies’ monthly return and how they compared with industrial performance index. Based on the data above, there is relationship between industrial production and lagged real stock returns of the 5 banks. This finding is supported by previous studies such as Benbouzid et al. (2017) who found that there is positive long run and short run relationship between industrial production and stock prices. Pi1 Pi2 Pm Mr1 Rr2 Rm 78.04 1 3.95 78.23 1.5 2.96 0.0024 0.5000 (0.2506) 79.12 1.68 3.71 0.0114 0.1200 0.2534 68.12 1.6 3.66 (0.1390) (0.0476) (0.0135) 78.99 1.7 3.28 0.1596 0.0625 (0.1038) 79.22 1.7 3.33 0.0029 - 0.0152 79.06 1.66 3.8 (0.0020) (0.0235) 0.1411 69.8 1.66 4.25 (0.1171) - 0.1184 60.59 1.64 5.13 (0.1319) (0.0120) 0.2071 61.61 1.84 4.24 0.0168 0.1220 (0.1735) 62.23 1.91 4.57 0.0101 0.0380 0.0778 63.36 1.71 4.06 0.0182 (0.1047) (0.1116) 63.35 1.81 4.63 (0.0002) 0.0585 0.1404 63.59 1.61 4.13 0.0038 (0.1105) (0.1080) 63.09 1.69 3.78 (0.0079) 0.0497 (0.0847) 62.34 1.89 4.23 (0.0119) 0.1183 0.1190 62.4 1.89 4.52 0.0010 - 0.0686 62.61 1.89 4.37 0.0034 - (0.0332) 52.51 1.39 4.64 (0.1613) (0.2646) 0.0618 53.57 1.99 5.18 0.0202 0.4317 0.1164 54.44 1.89 5.85 0.0162 (0.0503) 0.1293 64.16 1.79 5.39 0.1785 (0.0529) (0.0786) 75.36 1.89 4.23 0.1746 0.0559 (0.2152) 76.61 1.99 3.76 0.0166 0.0529 (0.1111) 77.12 1.89 3.04 0.0067 (0.0503) (0.1915) 77.21 1.99 2.57 0.0012 0.0529 (0.1546) 77.16 1.84 2.7 (0.0006) (0.0754) 0.0506 78.08 1.84 2.34 0.0119 - (0.1333) 78.57 1.84 2.12 0.0063 - (0.0940) 78.6 1.84 2.47 0.0004 - 0.1651 78.2 1.84 2.85 (0.0051) - 0.1538 79.07 1.84 2.48 0.0111 - (0.1298) 79.8 1.87 2.57 0.0092 0.0163 0.0363 79.86 1.94 3.26 0.0008 0.0374 0.2685 80.84 1.94 4.36 0.0123 - 0.3374 80.97 2.35 4.85 0.0016 0.2113 0.1124 81.12 2.35 5.52 0.0019 - 0.1381 81.03 2.35 5.55 (0.0011) - 0.0054 81.08 2.67 5.73 0.0006 0.1362 0.0324 80.37 2.67 5.67 (0.0088) - (0.0105) 79.82 2.81 5.63 (0.0068) 0.0524 (0.0071) 80.24 2.81 5.8 0.0053 - 0.0302 80.31 2.81 6.21 0.0009 - 0.0707 81.12 2.81 7.05 0.0101 - 0.1353 79.56 2.81 7.3 (0.0192) - 0.0355 78.25 2.92 7.02 (0.0165) 0.0391 (0.0384) 78.55 3.01 5.96 0.0038 0.0308 (0.1510) 77.87 3.01 6.71 (0.0087) - 0.1258 78.74 9.67 6.22 0.0112 2.2126 (0.0730) 79.42 1.71 6.73 0.0086 (0.8232) 0.0820 79.18 1.71 8.39 (0.0030) - 0.2467 78.68 1.39 10.75 (0.0063) (0.1871) 0.2813 79.9 1.84 8.27 0.0155 0.3237 (0.2307) 80.27 1.84 8.74 0.0046 - 0.0568 80.14 1.87 9.19 (0.0016) 0.0163 0.0515 80.78 1.39 8.5 0.0080 (0.2567) (0.0751) 81.63 1.39 9.55 0.0105 - 0.1235 81.7 1.39 8.53 0.0009 - (0.1068) 81.47 1.99 7.35 (0.0028) 0.4317 (0.1383) 82.4 1.99 5.61 - 81.95 1.99 6.35 - Beta (Measure of Market Risk based on Interest Rate and Exchange Rate on Return) This study adopted beta as the measure of the sensitivity of the stock return based on the two factors (interest rate and exchange rate). Basing on approaches taken by Benbouzid et al. (2017), we sought to find the effects on the Market Risk by dividing variance of the market by covariance of the banks’ stock to the market. - (0.0536) (0.0202) 0.0011 Covi2,m Corri2,m Betai2 (0.0076278) (0.1626) (0.3940527) Coefficients Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .027 .031   .867 .396 R2 -.073 .060 -.258 -1.223 .235 a. Dependent Variable: Rm Market risk premium 3% E(Ri2) 2.3013% = rf + Betai2 * market risk premium a) Change in free risk rate Market risk premium 4.5% E(Ri2) 3.8013% = rf + Betai2 * market risk premium From the calculation above, where there is increase in market free rate then there will be increase in in stock return. However, if it reduces by 1.5% as indicated then the stock return for the five banks reduces. This finding conforms to Benbouzid et al. (2017) who found that the riskiness of stock reduces with increase in market free rate. The research found that the factor was the most variable factor that affected the stock performance of the five banks. This finding conforms to Benbouzid et al. (2017) who found that market returns remain as the most essential and positively related variable to stock returns. This is the same trend with other factors however; other issues that could be included to ascertain such factors are money supply. 5.0. Conclusion The aim of this paper was to critically analyse factors affecting stock return of United Kingdom banks. Working on parameters such as return on assets, book value, dividend value and their volatilities we found that there are four factors that affect stock return on the identified banks. These factors include market return, industrial production index, monthly returns and market risks. We also observe that the factors listed are economic variables that show statistical significance with the volatility of the returns of the five banks. 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Determinants of stock-bond market comovement in the Eurozone under model uncertainty. Read More