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Valuation differences of level 1, level 2 and level 3 financial instruments

ABSTRACT

This research provides thorough discussion as to how financial assets and liabilities affect market value of equity. It starts with a historical informed discourse of conceptual and procedural issues related to the use of the fair value measurement attribute in financial reporting. Next chapter provides a brief history of the fair value measurement attribute and its evolution over the last few decades and then continues with description of existing financial assets fair value hierarchy. As the next step, with the use of regression analysis of 11 global systematically important banks' financial assets and liabilities author explains why and to what extent financial assets are priced differently from their values on the statement of financial position. Also , it is thoroughly explained why financial liabilities have slightly different coefficients compared to financial assets. Sample contains data on financial position of top eleven systematically important banks' annual reports. Research tests whether relevance of Level 1, Level 2 and level 3 financial assets fair value measurements do significantly vary in value.

INTRODUCTION

financial capital valuation value

Financial sector and the way it operates have evolved drastically over the last decades. In XXI century major changes have been made in a way how many aspects of finance are processed such as investments, payments, financial data storage, availability and emergence of new cryptocurrencies. Since 1998 when PayPal disrupted financial industry by providing online payment processing platform, a few other milestones were reached: first cryptocurrency was released in a middle of 2008 financial crisis and launch of ApplePay on IOS and similar services for other software systems. Nevertheless, financial revolution also had in downsides. First of all, as internet was rapidly evolving in the 1990s, on a juncture of two era's new assets class appeared. These stocks of “Internet based companies” led to the burst of the “Tech Bubble” in 2000-2002. On average less than five of every ten `Internet based companies' were able to withstand this fall, while value of others was reduced to zero. Subsequently, as Collateralized Debt Obligations peaked in transaction volume, world experienced financial crisis in 2008. Later on, each year new derivatives and assets classes such as cryptocurrencies or price swap derivatives came to existence and further digitalisation and complication of finance may inevitably cause rigid financial damage that affects economy as a whole, hence everyone is affected by rapidly growing financial sector. This trend boosts existing demand and forces financial organisations and institutions not only use existing financial instruments, but also handcraft custom derivative instruments for specific clients according to their needs. This situation opens the door for possible misevaluations of given financial instruments by both investors and financial institutions, yet investors tend to bear more substantial and untraceable risks. Misevaluation of financial assets implies that either one side of the deal intentionally or haphazardly dilutes fair value of an asset.

Most crises have at least one thing in common, during each new cycle new financial assets appear that disrupt economy in one way or another. Fair value measurement serves as an equalizer since its main priority to estimate fair intrinsic value of any asset and prevent its price' utmost misevaluation. The ASC (Accounting Standards Codification) Glossary defines fair value as “The price that would be received to sell an asset or paid to transfer a liability in an orderly transaction between market participants at the measurement date.” Also, ASC 820 defines fair value using an “exit price” perspective instead of other plausible perspectives, such as entry price or current replacement cost. L. Hodder, P. Hopkins, K. Schipper (2015). Fair Value Measurement in Financial Reporting. Foundations and Trends in Accounting: Vol. 9, pp 7-8 As the matter of fact, fair value measurement is deeply entrenched in not only IFRS or US GAAP, but mostly all profound financial reporting standards and they did not stand still as well, since it has been one of most controversial topics not only in USA, but in the whole world for almost a century. Economic and financial development failed to reconcile opposing prospectives of supporters and opponents of the use of fair value estimations in financial statements. Over the past 20 years, U.S. GAAP has moved towards disclosing and recording more financial assets and liabilities in the financial statements at fair value rather than at historical cost. No wander, the move toward fair value has spurred more debate about the relevance and reliability of fair value estimates, making it more and more relevant nowadays. According to Accounting Standards Codification (ASC), fair value is the price that would be received to sell an asset or paid to transfer a liability in an orderly transaction between market participants at the measurement date. It provides guidance for valuation of different levels of financial instruments as a part of fair value measurement, with the highest priority given to directly quoted active market unadjusted prices of equivalent assets and liabilities (Level 1), second highest priority is given to similar adjusted quoted market prices (Level 2) and the lowest priority given to unobservable model inputs (Level 3). L. Hodder, P. Hopkins, K. Schipper (2015). Fair Value Measurement in Financial Reporting. Foundations and Trends in Accounting: Vol. 9, pp 15 Existing priority establishes the use of quoted prices in active markets for identical assets and liabilities if they exist, however Accounting Standards Codification doesn't have any specific valuation methods and states the next : « when a price for an identical asset or liability is not observable, a reporting entity measures fair value using another valuation technique that maximizes the use of relevant observable inputs and minimizes the use of unobservable inputs». IFRS 13 -- Fair Value Measurement

Source: https://www.iasplus.com/en/standards/ifrs/ifrs13 (15.03.19)

The motivation of the research is to determine whether investors value financial instruments differently than global systematically important banks do and, if yes, conclude to what extent fair value measurement of one side differs from another when it comes to their valuation. Also, tracing possible misevaluations of financial instruments by G-SIBs may lead to higher concern related to such subtle field of study, hence I would like to focus my master thesis on regression models and analysis of existing valuation models as to glimpse on valuation strategies from buy-side prospective. Thus, the research question is determined as “Do investors value financial assets and liabilities differently compared to global systematically important banks (G-SIBs) and, if they are, determine approximately to what extent fair value of each level of an asset or liability is diluted”.

The research should have internal and external validity. Internal validity proves that the variations in the outcome variable result from changes in the independent variables, and not from other confounding factors Engberg S., Berben L., (2012). Selecting Instruments: Reliability and Validity Considerations. Journal of Wound Ostomy & Continence Nursing, 39(1), pp 16-19, so it is relevant in studies, in which causal relationship is to be established.

This research consists of several integral parts. First of all, introduction starts with a quick review of the topic and contains the main information of the research, namely its aim, goal, relevance et cetera. Second chapter contains information on related literature and theory and presents comprehensive overview of closely linked researches. Chapter three is devoted to the report on the results and their discussion on main findings. The findings of the research are of a particular use for the industry-specific analysis and will reinforce the existing knowledge of the fair value measurement.

1.THEORETICAL ASPECTS OF FAIR VALUE MEASUREMENT

Fair value measurement has been developing for the last century with its highs and lows and, as the consequence, it attracted a lot of attention from scientific part of the world. Lately, the move toward fair value has spurred much debate about the relevance and reliability of fair value estimates, making it a popular topic in academic research. Studies have found that fair value estimates of both financial and non-financial assets are value relevant, and that investors perceive fair values as more value relevant than historical costs. However, the value relevance of fair value estimates has been found to vary with the reliability of the inputs used (e.g., Petroni and Wahlen 1995; Nelson 1996; Cotter and Richardson 2002) and fair value estimates have been shown to be susceptible to managerial opportunism (e.g., Dietrich, Harris, and Muller 2000; Chandar and Bricker 2002; Ramanna and Watts 2012).

Prior to the release of FAS No. 157 Fair Value Measurements in 2006 (post-codification ASC 820--Fair Value Measurement), there were different definitions of fair value within U.S. GAAP and limited guidance for applying these definitions. ASC 820 establishes a single definition of fair value and provides a framework for measuring fair value. Fair value is defined as the price that would be received to sell an asset or transfer a liability in an orderly transaction between market participants at the measurement date. ASC 820 further establishes a hierarchy of fair values (Level 1, Level 2, and Level 3) with corresponding inputs to be used in fair value measurement, from most to least verifiable and representationally faithful. Level 1 inputs are defined as “quoted prices (unadjusted) in active markets for identical assets or liabilities that the reporting entity has ability to access at the measurement date.” Level 2 inputs are “inputs other than quoted prices included within Level 1 that are observable for the asset or liability, either directly or indirectly”. This level includes such instruments as OTC derivatives, many investment-grade listed bonds, some collateral debt obligations (CDO), credit default swaps (CDS) and many less-liquid equities. Level 3 inputs are defined as “unobservable inputs for the asset or liability.” Instruments classified in this category have an element which is unobservable, and which has a significant impact on fair value. This level includes such instruments as more-complex OTC derivatives, distressed debt, highly-structured bonds, illiquid asset-backed securities (ABS), illiquid CDO's (both cash and synthetic), monoline exposures, many commercial real estate (CRE) loans etc. Note that in some circumstances Level 2 and Level 3 inputs can be used for both Level 2 and Level 3 fair values; however, Level 1 inputs can only be used for Level 1 fair values.

Level 1 inputs are viewed as “the rough equivalent of accounting nirvana” (Ryan 2008) and are generally considered to be of maximum faithful representation when obtained from liquid markets. Level 2 inputs include: (1) quoted market prices in active markets for similar assets and liabilities, or quoted market prices in inactive markets for identical assets and liabilities; and (2) mark-to-model measurements that are disciplined by observable market prices (e.g., exchange rates and interest rates). Level 3 inputs, viewed as the lowest in the hierarchy in terms of verifiability, are mark-to-model measurements that are undisciplined by observable market prices but rather reflect firm-made assumptions that should reflect what market participants would use to price assets and liabilities. Hence, the verifiability of fair values is most difficult for Level 3 inputs, making them more susceptible to measurement error and managerial opportunism. However, when transactions are no longer considered to be `orderly', concerns are also raised about the faithful representation of Level 1 and Level 2 inputs due to the poor quality of observable market inputs.

Different authors suggest that fair value management can be a both applicable and biased as it can show true intrinsic value of an asset or tend to discount or add premium to a price. Since ASC 820 became effective for fiscal-years beginning after November 15, 2007, several studies have investigated the decision usefulness of the fair value hierarchy. Using fair value estimates reported by 431 banks in 2008, Song et al. (2010) examine the value relevance of Level 1, Level 2, and Level 3 fair value estimates. Their analysis employs standard value relevance regressions that entail regressing banks stock prices on fair value estimates, along with control variables for assets and liabilities that are not measured at fair value.

Their results in Table 3 (p. 1389) indicate that Level 1 and 2 fair value assets are priced at 97 cents on the dollar, while Level 3 fair value assets are priced at only 68 cents on the dollar. Using similar approaches and samples Kolev (2008) and Goh, Ng, and Yong (2009) also find that Level 3 fair value estimates are priced at a significant discount to Level 1 fair value estimates. While these findings cast doubt on the reliability of Level 3 fair value estimates, research pre-dating the introduction of ASC 820 raises concerns about drawing inferences from value relevance studies where only a subset of assets are recorded at fair value. Ahmed and Takeda (1995) highlight the importance of controlling for fair value changes in on-balance sheet assets recorded at amortized cost as well as the fair values of off-balance sheet net assets. Failure to do so can result in significant correlated omitted variables problems, resulting in biased fair value regression coefficients. Carroll et al. (2003) demonstrate that such limitations appear to have caused earlier value relevance research to reach incorrect inferences. Hence, as previously mentioned, an important limitation of the foregoing fair value hierarchy value relevance studies is that the assets that are measured at fair value constitute only a small proportion of total assets.

Among other similar researches, one of the highest forecast accuracy was obtained by Alastair Lawrence (2015) who used regression analysis to reevaluate relevance of level 1, level 2 and level 3 fair measurements using the closed-end fund setting, in which these fair value measurements are available for substantially all assets. The author stated that closed-end funds are ideal investment vehicles for investing in illiquid securities, because they are not subject to large and unexpected redemptions. Consequently, these closed-end funds invest in a variety of securities, ranging from liquid Level 1 securities to illiquid Level 3 securities.

Other scientific papers suggest that there's no misevaluations present and present certain reasons for that. For example, A. Lawrence and R. Sloan (2015) research concludes that Level 3 fair values are of similar value relevance to Level 1 and Level 2 fair values. Their findings suggest that results of previous researches and possible misevaluations are attributable to correlated omitted variable bias arising from the absence of fair value data for most assets. Specifically, they find that Level 1, Level 2, and Level 3 fair values are all priced over 90 cents on the dollar at 95 cents, 91 cents, and 97 cents, respectively, which stands in stark contrast to Song et al. (2010) who find that Level 3 fair values are priced at 68 cents on the dollar. We also find that Level 3 fair values are less timely than Level 1 and Level 2 fair values, but that such differences are relatively small, and that the ability of closed-end fund premia to predict future stock returns is fairly similar across all three levels of the fair value hierarchy.

This research will differ from existing works in several parameters. Firstly, the research sample will consist of global systematically important banks who deal with financial instruments while A. Lawrence and R. Sloan (2015) research was solely based on closed-end funds. Secondly, all the data in this research will be up to date and present one of the latest insights in contemporary situation in fair value management of financial instruments of all levels specifically based on biggest banks that deal with large amounts of the predetermined assets classes. Thirdly, master thesis will contain extent to which investors price various level financial assets and liabilities differently compared to G-SIB's.

METHODOLOGY

In many papers that exist in the sphere of finance research the main concern are fair value estimations mostly on close-end funds. The assets that are mainly analyzed from N-2 form and represent smaller amount of financial instruments, while this research is focused on annual reports of main global systematically important banks which are determined by methodology designed by the Basel Committee on Banking Supervision. In 2011 the Financial Stability Board (FSB) published an integrated set of policy measures that tend to address the systemic and moral hazard risks associated with systemically important financial institutions. In that publication, the FSB identified as global systemically important financial institutions an initial group of global systemically important banks (G-SIBs), using a methodology developed by the BCBS. FSB Policy Measures to Address Systemically Important Financial Institutions, 4 November 2011

Source: www.fsb.org/2011/11/r_111104bb/ (25.04.19) Sample of the predetermined banks is more relevant than close-end funds or any other banks for several reasons. First of all, these banks tend to have larger volume of level 1, level 2 and especially level 3 assets since smaller banks normally either have infinitesimal number or none at all. Therefore, pricing of Level 3 assets is especially relevant for these banks. Secondly, all of 11 chosen banks have never been dropped from global systemically important banks' list. Thirdly, each bank has to match thorough criteria, such as: high capital buffer, Resolvability, high supervisory expectations et cetera. This makes them stand out from other financial institutions of all dimensions. Another reason to use data from statements of financial position of global systemically important banks is higher percent of fairly measured assets to total assets. On average basis, G-SIBs use fair value to estimate price of 65.3% their assets and liabilities which is more than 4 times higher than the number provided in Song et al. (2010) research (see Appendix 1).

I've decided to focus on regression analysis since it is indeed the most popular and convenient way of conducting fair value measurement researches. For example, previous research by Song et al. (2010) regresses stock prices on accounting-determined book values that comprise of a combination of fair values and historical costs. The Song et al. (2010) study uses bank data from the 2008 financial crisis where the assets being fair valued average less than 15% of total assets and the regressions only include contemporaneous quarterly net income to proxy for the fair value of banks' off-balance sheet assets L. Hodder, P. Hopkins, K. Schipper (2015). Fair Value Measurement in Financial Reporting. Foundations and Trends in Accounting: Vol. 9, pp 19.

In this research, value relevance tests employ the standard regression of dollar amount of level 1, level 2 and level 3 assets on accounting- determined fair values divided by shares outstanding for each bank for the last 10 years. Since Song et al. (2010) used bank data from 2008 where less than 20% of total assets were fairly valued and their regression model merely included contemporaneous quarterly net income to proxy for bank off-balance sheet assets' fair value. These characteristics in the Song et al. (2010) regression analysis can lead to misspecification of the model (Carroll et al. 2003). Data from global systemically important banks' annual reports provides a distinctive advantage as it is comprehensive, thorough and contains no off-balance sheet assets. The value relevance tests simply involve regressing Market Value of Entity (MVE) on Level 1 ($A1/SO), Level 2 ($A2/SO), Level 3 ($A3/SO) fair values of assets, ($L1/SO), ($L1/SO), ($L1/SO) fair values of liabilities and net income (NI/SO), net assets not measured by fair value ($NFNA/SO) and dummy variable (DR) for region where country resides. Each of them except DR is expressed in per share basis:

MVE = в_{0 +} в₁*($A1/SO) + в₂*($A2/SO) + в₃*($A3/SO) - в₄*($L1/SO) - в₅*($L2/SO) - в₆*($L3/SO) + в₇*(NI/SO) + в₈*($NFNA/SO) + в₉*EU + в₁₀*US + в₁₁*ASIA

See appendix A for detailed information. In this model, we store panel data of dollar amounts of level 1, level 1 and level 3 financial assets and liabilities of each global systemically important bank for 10 years and divide it by shares outstanding to transform it into per share basis. As the result, we obtain $A1/SO, $A2/SO, $A3/SO, $L1/SO, $L2/SO. and $L3/SO variables, but we also add several control variables that will enhance the model precision and prevent omitted variables bias. These variables are net income divided by shares outstanding (NI/SO), dollar amount of net assets not measured by fair value divided by shares outstanding ($NFNA/SO) and dummy variable for region of the bank (DR). Song et al. (2010) deduced that both в₁ and в₂ are close to one however в₃ is only priced 0.68 cents per dollar. The primary goal of our study is to revaluate these results using the G-SIBs setting.

As to ensure applicability, verifiability and robustness of regression analysis, several tests are going to be employed. First of all, as to understand whether fixed or random effects shall be used, Hausman specification test (1978). Secondly, Wooldridge test (2002) for autocorrelation in panel data with the null hypothesis of no first-order autocorrelation will be applied. Thirdly, Modified Wald test (1997) for groupwise heteroskedasticity in fixed effect regression model. Provided that the model passes these three tests, it can be deduced that it can be taken as a fair approximation of MVE

DATA AND DISCRIPTIVE STATISTICS

Data for this paper was collected from several sources. First of all, for each observation financial data was collected from a respective Annual report of a bank. Secondly, certain missing parts like shares outstanding and currency exchange rates were taken either from Reuters terminal or investing.com.

Data sample consists of 110 observations of 11 global systematically important banks for the last 10 years or, more precisely, from the last global financial crisis in 2008. Each observation shows dilution of value of equity on per-share basis. Throughout analysis market value of equity per share was compared to sum of net financial assets of first, second and third level, Net Income and Net Assets not valued at Fair Value. As to undertake the analysis, a few currency adjustments had to be made since GSO-B's represent 3 regions which are Asia, North America and Europe.

Figure 1. Predetermined currencies exchange rate from 2008 to 2018 year.

As it can be seen on graph one, assets, liabilities and net income nominated in EUR, JPY, GBP and HKD had to be denominated in dollar amounts.

Another important issue that had to be addressed which underscores relevance of using global systematically important banks in this research is average percent of fairly valued assets. Song et al. (2010) in his research mentioned that in his sample banks on average had only 15% of fairly valued assets. As it can be seen on the Graph 2, each GSO-B's during the predetermined time interim had higher index of fairly valued assets. In a midst of a crisis, global systematically important banks had on average 4 times more fairly valued assets than the banks that were used in Song et al. (2010).

Figure 2. Percent of fairly valued assets on average and for each bank individually from 2008 to 2018 year.

Figure 3. Proportions of level 1, level 2 and level 3 financial assets to total amount of financial assets listed in the financial statements

Each bank has its own peculiarities related to acquisition of financial assets of different levels. Nevertheless, as it can be seen on Figure 3, most of the banks have much more of second level' financial assets compared to the first and third. Moreover, for most of the banks in the sample, level 1 assets significantly exceed amount of level 3 financial assets due to their higher liquidity. On average, through the predetermined time interim, global systematically important banks had the next proportions of their financial assets: 28% in level 1, 66% in level 2 and 6% in level 3 financial assets.

2.RESULTS AND DISCUSSION

For the regression analysis all of the factors have been chosen that, should have influenced the MVE. Even though the R² equals 0,8985, what represents an excellent quality description of the dependent variable by it regressors, financial assets of the first level, EU dummy variable and net assets not fairly valued per share appear to be insignificant in the model provided that the p(value) equals to 0.258, 0.263 and 0.196 respectively. Moreover, region dummy variable ASIA was omitted due to high extent of existing collinearity.

Table 1: Regression results with the following factors: financial assets of level 1, financial assets of level 2, financial assets of level 3, financial liabilities of level 1, financial liabilities of level 2, financial liabilities of level 3, net income, net assets not fairly valued each presented on per-share basis and 3 region dummy variables, EU, USA and ASIA.

R-sq: overall = 0.8985				Wald chi2(8) =	972.7
				Prob > chi2 =	0
MVE	Coef.	Std. Err.	z	P>z	[95% Conf.	Interval]
A1SO	0.8186476	0.0305151	1.13	0.258	0.152956	1.0437115
A2SO	0.673542	0.0391415	5.21	0	0.3287934	0.7842834
A3SO	0.590499	0.4905707	3.97	0	0.3864466	1.007921
L1SO	-0.8583282	0.0189414	-3.15	0.002	-0.948187	-0.0221135
L2SO	-0.8917915	0.0382333	-5.33	0	-0.9831833	-0.23089
L3SO	-0.979082	0.3327654	-3.27	0.001	-1.735699	-0.4342634
NISO	2.281748	0.5244172	6.58	0	1.224516	2.860194
NFNASO	0.3346653	0.0130385	1.29	0.196	-0.0087087	0.0824014
EU	-16.42255	3.659585	-1.12	0.263	-11.2732	3.07211
ASIA	0	(omitted)		0
US	17.86527	3.444514	7.96		13.02077	21.52361
_cons	0.7533154	2.777526	2.08	0	0.0423157	1.464315

Since EU and ASIA dummy variables appear to be insignificant according to the data in table 1, they are removed from the model.

Another variable with high P-value is net assets that are not fairly valued. According to table 2, it has high multicollinearity with the several independent variables.

In this model autocorrelation- and heteroscedasticity-robust standard errors, so high correlations of NFNASO with A1SO, A3SO and L1SO makes no difference, however it was also omitted from the model to see the results without several independent variables.

Table 2. Correlation box for all the the independent variables

Correlation	A1SO	A2SO	A3SO	L1SO	L2SO	L3SO	NISO	NFNASO	EU	US
A1SO	1
A2SO	0.4201	1
A3SO	0.3107	0.2367	1
L1SO	0.2917	0.3862	0.4719	1
L2SO	0.4199	0.3808	0.2151	0.4515	1
L3SO	0.3581	0.2634	0.3691	0.4659	0.4015	1
NISO	0.4615	0.3813	0.3006	0.2868	0.12	0.4135	1
NFNASO	0.787	0.3427	0.5612	0.5271	0.2975	0.4776	0.2327	1
EU	0.436	0.3114	0.2685	0.3359	0.4602	0.3253	0.1093	0.5287	1
US	-0.1398	0.0203	-0.0053	-0.1291	-0.1654	0.0402	0.3541	0.2778	-0.4278	1
ASIA	-0.323	-0.3858	-0.3032	-0.2211	-0.3179	0.3237	-0.3309	0.5674	-0.2887	-0.5164

If net assets not fairly valued, EU and ASIA dummy variables are omitted, the next more satisfactory model results appear:

Table 3: Enhanced model

R-sq: within = 0.9180				Wald chi2(8) =	933.95
					Prob > chi2 =	0
MVE	Coef.	Std. Err.	z	P>z	[95% Conf.	Interval]
A1SO	0.9135218	0.0233866	2.51	0	0.3128108	1.0344844
A2SO	0.8393453	0.027583	7.59	0	0.5552836	0.963407
A3SO	0.729489	0.1713783	-6.54	0	0.456394	0.984604
L1SO	-0.9267282	0.0319414	-2.12	0.034	-1.0303321	-0.4051242
L2SO	-0.9517915	0.0260105	-8.14	0	-1.262771	-0.1608119
L3SO	-1.069082	0.2271857	8.8	0	-1.553806	-0.644358
NISO	1.981748	0.4547406	7.44	0	2.490473	4.273024
US	18.35817	3.444514	5.19	0	11.11415	24.6164
_cons	1.648288	2.777526	0.59	0.214	-3.795564	7.092139

As to ensure that fixed effects is the right specification, the hausman test was used. Since p-value of 0.0672 is statistically significant with 10% degree of freedom it can be deduced that fixed effects model was the right fit.

Table 3. Results of Hausman test to check whether the fixed or random effects models shall be used.

Test Ho:	difference in coefficients not systematic
	chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B)
	3.32
	Prob>chi2 = 0.0672

Another very important test must be applied to ensure the equality of regression coefficients that were generated in the second model, it is possible by running command “test”. As it can be seen in table 4, significance level of the test is 0, hence so we can definitely reject the hypothesis of no difference between the regions

Table 3. equality of regression coefficients test' output

1	A1SO = 0
2	A2SO = 0
3	L3SO = 0
4	L1SO = 0
5	L2SO = 0
6	A3SO = 0
F( 6, 20) = 49.87
Prob > F = 0

Enhanced model is better than previous for several reasons. First of all, the amount of variance of Y explained by X didn't change significantly while two-tail p-values of each of our independent variables reject the null hypothesis, because the coefficients are less than 0.05. Moreover, F test results ensure that all the coefficients in the model are different than zero. As to accept this second model, several tests must be applied to ensure its robustness. First of all, heteroskedasticity test will show whether error terms of the variables re constant. Secondly, autocorrelation test will ensure that there's no similarity in the model compared with its own lagged version over predetermined time interims. Thirdly, endogeneity test will ensure that the independent variables are not correlated with error term.

First of all, heteroscedasticity test was run where the zero hypothesis implies the homogeneity of variance. Since our P-test is above 0.05, it can be concluded that error terms have constant variance. Moreover, the model uses robust option as to obtain heteroskedasticity-robust standard errors.

Table 3. Results of Modified Wald test to check whether error terms have constant variance.

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model
H0: sigma(i)^2 = sigma^2 for all i
chi2 (21) = 30.71
Prob>chi2 = 0.1186

The second very important test that has to be applied is the test for serial correlation. Woolridge test has null hypothesis of no first-order autocorrelation. Serial correlation itself makes the standard errors of the coefficients to be smaller than they actually are and presents higher R-squared, hence presence of serial correlation causes rigid damage to model output.

Table 3. Results of Wooldridge test for autocorrelation in panel data

Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation
F( 1, 6) = 0.174 Prob > F = 0.41192

Since the existing model has no issues related to heteroscedasticity and autocorrelation, it can be taken as a fair approximation of MVE. Table 3 shows the coefficients of the independent variables. Where p-value of the factors in model are all significant at 0.05 significance level. Only constant term has high P-value on figure 3, but it is not used to predict the value of MVE. Moreover, the R² (amount of variance of dependent variable explained by independent variables) of this regression model is excellent and accounts to the 0,8962.

A1SO: represents fairly valued financial assets of 1 level on per-share basis, since it is the most liquid type of assets, it has the highest coefficient among other assets in the model. If fairly valued assets of the first level are increased by 1, market value of equity is increased by 0.9135218 provided that other variables stay constant.

A2SO: Fairly valued financial assets of 2 level per share are of slightly less relevance and, as it was expected, have coefficient of 0.8393453. This variable has P-value that equals to 0.000, hence it is statistically significant. Since A2SO are either illiquid or have some unobservable inputs, investors price them at almost 84 cents per dollar i.e. an increase of 1 in A2SO will lead to marginal increase of MVE by 0. 8393453.

A3SO: Even though fairly valued financial assets have successfully passed p-test, A3SO has the smallest coefficient of fairly valued assets that equals 0.729489. This happens due to large amount of unobservable input, assets may just be OTC or illiquid. Here, with a 1 unit increase in A3SO marginal change in MVE will be equal to 0.729489.

L1SO: A different situation appears when liabilities are analyzed. Fairly valued financial liabilities of 1 level have passed p-test, however p-value it close to the rejection boundary with index of 0.0340. Here, L1SO have a slightly higher coefficient than A1SO indicating that investors price liabilities higher than assets. This indicates that investors think that assets are slightly overvalued whereas liabilities are undervalued.

L2SO: Fairly valued financial liabilities of 2 level move in the same direction, but with a higher magnitude. Each increase in L2SO by 1 unit has a negative marginal change in market value of equity by 0.9517915.

L3SO: Since fairly valued financial liabilities of 3 level have a lot of unobservable inputs and priced by internal bank models which appear to be less appealing to investors. As the result, for each increase in L3SO market value of equity decreases by 1.069082.

NISO: net income also tends to play significant role in determining MVE. It is statistically variable and for each 1 unit change in net income, MVE moves by 1.981748 which is the most significant change in the model.

US: Dummy variable for USA proved to be both statistically important and significant. When it comes to American banks, they tend to have higher MVE than banks in other regions by 17.86527.

CONCLUSION

This paper contributes to the existing research in three ways. First of all, research expands analysis of financial assets to 2018 which makes this study up to date compared to many others. For example, research of L. Hodder, P. Hopkins, K. Schipper (2015) related to fair value management of financial assets in some time will be outdated due to the pace this field is expanding. Secondly, relevance of financial assets fair values has never been analyzed for global systematically important banks that appear to be more significant in terms of financial assets' analysis due to their higher amount on the statement of financial position. Thirdly, not only value relevance of financial assets was tested, this research also did include thorough analysis of investors attitude towards fairly valued financial liabilities.

According to performed analysis, output suggests that global systematically important banks have approximately the same coefficients that Song et al. (2010) had in his research. First of all, financial assets of 1 level are priced 0.8393453 per dollar, whereas abovementioned authors concluded that their bank sample priced each dollar of a first level financial assets by 95 cents. Nevertheless, level 3 financial assets are priced higher than in Song et al. (2010): according to the data in this research investors price level 3 liabilities at 0.73 cents per dollar while Song et. Al (2010) suggest that their value drops to 67 cents per dollar.

Another interesting finding suggests that investors tend to price assets and liabilities differently. As for the first level, liabilities are priced by 1.3% higher than the assets and the spread only increase. Second level of financial liabilities are even less discounted as they're priced at 95 cents per dollar while assets have 0.83 coefficient. The last and most delicate level is the third where the spread increased again: investors value financial liabilities at above-stated level at 106 cents per dollar while 3 level financial assets are discounted by 28%.

This result suggests than investors tend to underestimate declared fair values of financial assets as they tend to be tricky. The more uncertainty is added to fair value estimation of an asset's price, the more this value tends to be discounted. Yet, the results for liabilities are diametrically opposite: as the level of uncertainty rises, investors tend to price financial liabilities even above their declared value. For example, level 3 assets are priced 6% higher than they are on the statement of financial position which lead to conclusion that investors think that financial assets tend to be overpriced and financial liabilities are occasionally undervalued.

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APPENDIX

Percent of Fairly measured assets for GSI-B's according to the data presented in their annual reports.

Bank	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
DB	31.16%	36.61%	39.92%	42.04%	44.87%	41.13%	40.15%	41.61%	43.21%	85.37%	84.77%
JPM	63.01%	79.90%	81.29%	91.12%	88.97%	85.08%	85.24%	76.63%	73.64%	63.03%	62.63%
Citigroup	72.89%	83.49%	81.08%	89.78%	89.12%	87.01%	90.04%	83.66%	81.49%	72.93%	72.37%
HSBC	57.57%	49.14%	50.80%	61.68%	48.13%	56.79%	60.06%	58.84%	78.46%	77.25%	44.90%
BOA	89.61%	97.33%	97.01%	95.20%	95.89%	83.24%	85.18%	77.14%	73.61%	66.05%	62.62%
GS	75.84%	88.39%	88.17%	89.09%	85.91%	84.20%	84.01%	80.58%	78.09%	74.11%	73.12%
BOC	45.86%	50.71%	48.20%	53.00%	50.43%	50.40%	48.95%	47.81%	46.19%	47.50%	52.68%
WF	56.05%	59.82%	63.75%	68.40%	66.97%	65.04%	63.76%	62.68%	62.50%	57.71%	55.56%
Barclays	59.26%	65.59%	64.47%	67.30%	65.75%	63.85%	67.44%	67.84%	65.48%	64.02%	64.46%
BNB P	64.09%	64.02%	68.64%	32.31%	66.76%	56.96%	58.31%	59.70%	57.97%	49.82%	49.87%
Mitsubishi	34.00%	41.00%	56.35%	57.58%	54.44%	56.38%	53.50%	57.49%	57.48%	66.21%	55.10%
Average	59.03%	65.09%	67.24%	67.95%	68.84%	66.37%	66.97%	64.91%	65.28%	65.82%	61.64%
AV all											65.38%

Appendix contains data on relative amount of fairly valued assets for each GSI-B. Here, all fairly valued assets were compared to total assets. Two last rows indicate amount of fairly valued assets on average yearly and average for the whole predetermined period.

Market value of equity, amounts of financial assets and liabilities, net income and not fairly valued assets for each bank for the predetermined time interim.

Bank	Year	MVE	$A1	$A2	A3	$L1	$L2	$L3	NI	NFNA
Deutche Bank	2008	13,975	184,743	2,062,448	125,358	111,553	1,752,695	49,133	(5,571)	209,957
Deutche Bank	2009	36,122	173,688	1,160,465	83,255	88,744	920,901	25,982	7,090	134,116
Deutche Bank	2010	51,931	196,419	1,400,159	66,718	81,074	1,130,660	18,594	3,332	163,950
Deutche Bank	2011	37,859	190,299	1,648,707	68,029	68,620	1,388,457	19,192	6,186	159,471
Deutche Bank	2012	45,815	201,088	1,544,267	54,387	190,299	1,648,707	68,029	452	156,504
Deutche Bank	2013	46,866	178,959	1,145,160	41,517	63,585	836,051	13,641	974	129,977
Deutche Bank	2014	39,971	189,558	1,212,218	44,750	44,589	948,257	12,026	2,418	1,241,968
Deutche Bank	2015	24,675	224,428	1,014,026	45,115	65,372	785,901	14,021	(9,684)	1,140,391
Deutche Bank	2016	27,461	203,858	907,850	37,568	79,055	743,879	14,715	1,939	1,272,437
Deutche Bank	2017	37,905	220,906	733,832	31,491	89,818	587,656	10,209	(1,051)	822,659
Deutche Bank	2018	18,353	180,902	681,302	35,198	75,082	511,524	11,031	488	922,677
JP Morgan	2008	89,846	300,693	1,096,216	71,290	92,576	940,822	43,346	5,605	804,569
JP Morgan	2009	151,087	334,065	1,863,728	127,648	52,615	1,540,114	55,627	11,728	404,365
JP Morgan	2010	178,726	353,826	1,858,762	110,639	62,949	1,511,940	46,097	17,370	396,252
JP Morgan	2011	146,216	307,573	2,243,022	113,489	47,674	1,672,243	34,609	18,976	201,139
JP Morgan	2012	179,825	211,646	2,158,838	99,148	47,674	1,672,243	34,609	21,284	326,339
JP Morgan	2013	211,198	209,207	1,596,518	69,310	69,310	1,200,452	33,958	17,886	360,436
JP Morgan	2014	206,535	197,652	1,754,920	50,914	152,791	1,464,899	35,054	21,745	379,760
JP Morgan	2015	224,553	179,065	1,316,893	31,227	59,164	996,533	25,528	24,442	549,675
JP Morgan	2016	312,285	223,943	1,258,736	23,240	79,025	946,870	25,402	24,733	656,702
JP Morgan	2017	413,752	208,164	901,387	19,216	74,702	597,700	32,271	24,441	936,555
JP Morgan	2018	353,349	236,291	899,182	17,165	85,483	574,090	34,784	32,474	979,929
Citigroup	2008	174,504	144,547	1,459,767	145,947	46,886	1,314,533	81,541	(27,684)	525,473
Citigroup	2009	885,346	194,500	1,105,575	96,874	57,400	876,270	37,333	(1,606)	306,465
Citigroup	2010	131,168	188,992	1,111,027	70,784	57,379	876,270	37,333	10,600	362,131
Citigroup	2011	84,179	152,892	1,482,566	61,299	62,188	1,130,968	24,602	11,100	191,495
Citigroup	2012	119,823	184,011	1,619,372	49,312	58,137	1,250,013	20,448	7,500	202,870
Citigroup	2013	134,951	233,316	1,234,714	45,685	57,222	894,749	21,109	13,700	244,190
Citigroup	2014	133,445	261,637	1,322,433	42,393	63,164	982,447	23,524	7,300	143,691
Citigroup	2015	118,545	260,938	1,013,230	32,637	50,970	706,117	21,371	17,200	282,797
Citigroup	2016	147,270	282,885	992,034	19,526	76,343	699,230	22,619	14,900	331,703
Citigroup	2017	194,703	292,700	790,217	11,059	82,437	494,879	23,334	15,800	498,683
Citigroup	2018	150,668	294,151	818,051	10,314	95,133	528,228	19,625	18,000	529,656
HSBC	2008	105,062	361,433	848,507

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