Abstract: This study empirically examines the influence of corporate diversification, macroeconomic factors on performance of quoted deposit money banks in Kenya within a period of 2007- 2017.The study employs secondary data collected from deposit money banks annual audited financial statements. Employing the use of descriptive statistics, correlation analysis, panel unit root analysis, co-integration test and feasible generalized least squares (FGLS), the data were estimated with the aid of E-views 9.0 econometric statistical package. Using dependent variables (Returns on Assets and Tobin Q), explanatory variables of income diversification, foreign diversification, subsidiary diversification, exchange rate and inflation rate. The findings revealed that income diversification have positive and significant effect on all the performance indicators (ROA and TOBIN Q) used in the study and were significant at 1% significance level. Foreign diversification and Subsidiary diversification have mixed findings under the two performance variables. Exchange rate has a positive and significant impact on performance, and inflation rate was negative and significant. This study therefore recommends, amongst others, that deposit money banks should carefully adopt a strategy that will warranty ideal location forecasting and execution as this will further boost financial annexation in the system.

Growing competition in the business environment all over the globe has encouraged and compelled corporate firms to diversify their businesses to remain active, competitive and dominant in the business space. Hence, corporate diversification is one of the vital and strategic focuses that have gained relevant attention in finance literature. Corporate diversification of deposit money banks has been embraced by most banks and has taken the center stage of most economies in the world. This is so because it helps in building a virile, efficient and robust banking system which can spark performance of the individual banks and lead to the overall growth of the various national economies (Osifo, 2019).

Deposit money banks in every economy plays vital role owing to their distinct financial intermediation function which is the central lubricant to every economy. Corporate diversification and performance are central to the operations of deposit money banks in various aspects. Deposit money banks are financial institutions whose principal goal is profit making through financial intermediation (Ongore & Kusa, 2013). Deposit money banks prior to present day liberalization and deregulation of the financial system derives colossal profit from intermediation process of extending credit from the surplus sector to the deficit sector of the economy. But with increasing competition from other financial institutions (Insurance firms, Microfinance banks, Investment banks, Mortgage banks), it has become imperative for deposit money banks to operate in other industries and in other climes. In pursuit of superior performance, deposit money banks may decide to extend their operations into related and unrelated areas of their core business. This can be manifested in form of income diversification, subsidiary diversification and foreign diversification.

The banking industry does not operate in isolation they are guided by government policies and macroeconomic fundamentals. These macroeconomic factors (interest rate, exchange rate, inflation rate, gross domestic product, etc) can impact on the performance of deposit money banks. The corporate diversification variables are firm specific variables unlike the macroeconomic factors that affect the generality of the market. Hence, this study will be one of the first to attempt to unravel the effect of corporate diversification with macroeconomic factors on performance of deposit money banks.

Firm’s performance stimulation has always been germane in private as well as in public sectors, because of its direct association with the value creation of entity. Firms are persistently contending for better results, influence and competitive advantage (Arasa, 2014). Brealey, Myers and Marcus (2009) see organization’s performance as a measure of how well a firm uses its assets from its core operations and generates revenues over a given period of time. Profitability is the main measure of a bank performance and just like any other business; the traditional measures of profitability (Returns on Asset, Returns on Investment and Returns on Equity) are used in determining the performance of a deposit money bank. The commonly used measure of performance in extant literatures are returns on asset (ROA), returns on equity (ROE), returns on investment (ROI) and net interest margin (NIM). The aforementioned are largely accounting measures of performance. Another measure of firms’ performance that is appealing to researchers (Chen & Ho, 2000; Lang &Stulz, 2004; Doaei &Shavazipour, 2013; Berg, 2016; Manyuru,Wachira & Amata, 2017.) is Tobin Q which is market measure of performance. It is expressed as the ratio between the summation of market value of equities and book value of liabilities to book value of total assets. Hence, this study intends to make use of the market base measure of performance (TobinQ). Thus, this study examines corporate diversification, macroeconomic factors and performance of selected quoted deposit money banks in Kenya.

There are several theories (Agency, Resource Base View, Internal Capital Market Theory, Free Cash Flow, Modern Portfolio Theory and Stakeholder Theory) that are relevant to corporate diversification and performance of banks. This study is hinged on Modern Portfolio Theory (MPT) propounded by Markowitz (1952). It is an extension and improvement on traditional investment models. The MPT encourages asset diversification to hedge against market risk as well as the risk that is unique to a specific firm (Osayi, Kasimu &Nkwota, 2018). The MPT also improved on Mean Variance Portfolio theory. The Mean Variance Portfolio theory was developed to find the optimum portfolio when an investor is concerned with return distributions over a single time horizon (Elton, Gruber & Blake, 1997).

The theory is founded on the premise on how risk adverse investors can build their collection of assets or investment in order to maximize their expected returns and minimize risk by diversifying their numerous investments. The deposit money banks relying on this theory will invest in order areas outside the core banking business to increase their income and reduce the risk profile of the organization (Osifo, 2019). This theory is of great relevance to this study because deposit money banks are faced with different unsystematic risk elements.

Studies on corporate diversification and performance of deposit money banks have been carried out extensively. Consensus in their findings and outcomes have been inconclusive as expected owing to the fact that diverse methodologies were adopted and differences in data measurement of variables. For instance, while Ugwuanyi, Ugwu, and Ugwunta (2012), Turkmen and Yigit (2012), Ugwuanyi, and Ugwu(2012), Doaei and Shavazipour (2013), Oweis (2013), Elif, (2015), Aarflot and Arnegård (2017) found a positive relationship between corporate diversification and performance others studies such as Doukas and Kan (2006);Chen, Wei, Zhang and Shi(2013); Jouida and Hellara (2017); Phung and Mishra (2017); Ekanayake and Wanamalie (2017) and Adzobu, Agbloyor and Aboagye (2017) submitted negative relationship. Still, other studies like Colak (2010);Ravichandran and Bhaduri (2015); Mulwa and Kosgei (2016) and Muneer, Jahanzeb and Suwandi (2016) all respectively reported that diversification neither increases nor decreases firms’ performance. In regards to macroeconomic variables and profitability of deposit money banks, Akani, Nwana and Mbachu (2016) revealed a positive but insignificant impact, however, Osundina, Osudina, Jayeoba and Olayinka (2016); Osamwonyi and Chijuka (2014); Owoeye and Ogunmakin (2013) found a negative impact of exchange rate and inflation rate on banks’ profitability within the scope of study.

This study sourced secondary data from the various audited financial statements of sampled deposit money banks in Kenya and World Development Indicator over the period 2007 – 2017. Nine (9) quoted deposit money banks in Nairobi stock exchange (NSE) were used in this study (Barclays bank of Kenya, CFC Stanbic of Kenya, Co-Operative Bank Of Kenya, Diamond Trust Bank Kenya, Equity Group Holdings, Kenya Commercial Bank, National Bank Of Kenya, NicBank and Standard Chartered Bank Kenya.). The choice of the sampled banks is based on the possession and availability of the required data.

This study adopted the use of descriptive statistics, panel unit root analysis, co-integration test and feasible generalized least squares (FGLS). The descriptive statistics is to ascertain the normal characterization of the data; panel unit root is to ascertain the stationarity and normality of the data in the variables in the specified model. Rationalization for the test of stationarity is to guarantee that the data are consistent for the FGLS.

The two models were anchored on the theoretical framework of modern portfolio theory (MPT) as earlier discussed under theoretical consideration. In order to examine the impact of corporate diversification, macroeconomic factors on performance of quoted deposit money banks in Kenya, the functional forms of the models are stated as;

ROA_t-1 = f (ID,SD,FD,EXCH,INT) ………………….......................................(3.1)

TOBINQ_t-1 = f (ID,SD,FD,EXCH, INT) …………………………………….. (3.2)

ROA_t-1 = β₀ + β₁ID_it + β₂SD_it + β₃FD_It + β₄EXCH_it+ β₅INT_it +ε_it …………….(3.3)

TOBINQ_t-1 = β₀ + β₁ID_it + β₂SD_it + β₃FD_It + β₄EXCH_it+ β₅INT_it +ε_it………….(3.4)

ROA= Returns on Asset, TOBIN Q= TobinQ, ID =Income diversification, SD = Subsidiary diversification, FD = Foreign diversification, EXCH= Exchange rate, INT = Interest rate

The descriptive statistics of all the series employed in this study are stated and analysed below. Clearly, the mean, median, minimum and maximum values, standard deviation, skewness, kurtosis, Jarque-Bera statistics, as well as their respective probability values are also showed in Table 4A. The mean shows the average of the exact variable, while the standard deviation shows the explosiveness of the variables used in the study. Furthermore, the skewness and kurtosis values signify the asymmetry and peakedness of the dissemination.

The results presented in Table 1 indicate inter alia that, Foreign diversification, inflation rate, and Subsidiary diversification were positively skewed, while Exchange rate, Income diversification, return on asset, and Tobin's Q were negatively skewed in their distributions. In addition, Income diversification, inflation rate, Return on asset and Tobin's Q had positive excess kurtosis values, suggestive of leptokurtic behaviour in their distribution. However, Subsidiary diversification was distributed with a moderate kurtosis value of 3, suggestive of a mesokurtic distribution, while Exchange rate, Foreign diversification and Subsidiary diversification were found to be platykurtic in their behaviours.

Furthermore, exchange rate, foreign diversification, income diversification, inflation rate, return on asset, subsidiary diversification and Tobin's Q in the Kenyan banking sector averaged 85.77, 0.59, 4.70, 9.65, 2.95, 1.75 and 1.08, while their standard deviations were reported as 11.57, 0.72, 1.72, 5.84, 1.21, 0.50 and 0.20 respectively in the course of the estimation. Note that the study considered 9 DMBs and each DMB has a data set of 11 observations. As a result, we had a total of 99 observations in this study and this is considerably large. From the result also, only Exchange rate and Subsidiary diversification followed normal distribution in their behaviours. Table 1 however reports the descriptive statistics of all variables below.

The panel unit root results are presented in the appendix. Table 2A reports the outputs of the stationary tests conducted on all variables employed for the Kenyan banking sector. Essentially, the stationarity tests were in line with the approaches advanced by the Levin, Lin and Chu (which assumes homogeneity in the dynamics of the Autoregression coefficients for all panel members); Im, Pesaran and Shin; ADF-Fisher and PP-Fisher Approach (which allows for heterogeneity in the dynamics). Fundamentally, the study utilised the Im, Pesaran and Shin as well as Levin, Lin and Chu unit root tests approaches. However, the ADF-Fisher and PP-Fisher tests results were also presented for robustness checks.

From the stationarity test results, all the variables employed in this study were found to be stationary at levels as revealed by the Levin, Lin and Chu approach. However, only inflation rate and Tobin’s Q were stationary at levels from the Im, Pesaran and Shin W-stat as well as ADF - Fisher Chi-square. Similarly, all the variables employed in this study (except return on asset) were found to be stationary at levels as revealed by the PP - Fisher Chi-square test approach. From the stationarity tests results, however, all the variables under were stationary at first differences judging from all test approaches. This further authenticates the suitability of our choice of estimation technique, which is predicated on stationarity postulation. In addition, all the variables were found to be stationary at 1% significance level both in the Im, Pesaran and Shin, Levin, Lin and Chu unit root tests, ADF-Fisher as well as PP-Fisher Procedures. The results of the unit root tests are reported at levels and first differences in Table 2A and Table 2B respectively below. The Group unit root test summaries are also reported in Table 2C both at levels and at first differences.

Table 2A. Panel Unit Root Test at Levels- The Levin, Lin and Chu; Im, Pesaran and Shin; ADF - Fisher and PP - Fisher Approaches

Note: Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality.

Table 2B. Panel Unit Root Test at First Difference- The Levin, Lin and Chu; Im, Pesaran and Shin; ADF - Fisher and PP - Fisher Approaches

Note: Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality.

Essentially, cointegration tests are carried out to determine the existence (or otherwise) of a long run relationships among the variables in a regression model. The study adopted the method of Engle-Granger and Philip-Ouliaris Single-EquationCointegration Tests, which assess possible cointegration on a single-equation basis. The co-integration tests results proof the existence of a co-integrating association as shown by the significance of the Panel tau-Statistic and Panel z-statistic from both Engle-Granger and Philip-Ouliaris tests approaches. The results indicate that all the series employed in the study are all relevant at the conventional co-integration test levels as shown in Table 3, see appendix.

Generally, the Engle-Granger single-equation cointegration test result indicated the presence of 3 cointegrating equations, while the Philip-Ouliaris single-Equation cointegration test result showed the existence of 5 cointegrating equations at 1% significance level respectively. Specifically, both test approaches revealed evidence of a long run relationship among the ROA as well as Tobin’s Q models variables at 1% significance level. The test was conducted to determine the possibility of proceeding on panel pooling of the various DMBs data and analysis. Consequently, the results from the co-integration tests tend to support panel pooling procedure for evaluation in this study. The co-integration tests results are however presented in Table 3, see appendix.

Table 3. Cointegration Test Results- Eagle-Granger and Philip-Ouliaris Single-Equation Approaches

4.4. Empirical Analysis of the Relationship between Corporate Diversification and Banks Performance in Kenya

The major task of evaluating the relationship between corporate diversification and banks performance in Kenya was carried out using the panel data technique. The panel estimation results for the overall Kenyan banking sector are reported in Table 4. Essentially, results from the feasible generalized least squares (FGLS) technique for both the ROA and Tobin’s Q models are produced for comparative analysis. In addition, results of the cointegration tests showed evidence of a long run association among the variables of the two models considered in this study. Thus, this necessitated the simulation of the associated long run models with a view to producing the long run coefficients. The long run estimates from the two models were therefore produced and presented alongside their short run counterparts, for robust purposes.

For all intents and purposes, the study utilized the cross section seemingly unrelated regression (SUR) framework as the generalized least squares weighting method with a view to controlling for both serial correlation and the manifestation of heteroskedasticity in the estimated models. This was further complemented by cross section SUR; panel corrected standard error, as the coefficient covariance estimation method.

From the results in Table 4, the coefficient of exchange rate was negative and statistically significant at 1% level in all the estimation (Short Run and Long run Estimates) results for both ROA and Tobin’s Q models. Specifically, the result implies that a unit increase in the rate at which the Kenyan domestic currency is exchanged for other currencies, would occasion a corresponding decrease in ROA by 0.012 unit in the short run and 0.01 unit in the long run respectively. This would further lead to a decline in Tobin’s Q of the Kenyan banking sector by 0.01 unit both in the Short Run and Long run respectively.

Also, the coefficient representing foreign diversification was found to be positive in the ROA model results both in the Short Run and in the Long run. It however turned negative in the case of Tobin’s Q model outputs both in the Short Run and in the Long run. In addition, it was statistically significant at 1% in the ROA Short Run result as well as Tobin’s Q Long run equation. Precisely, the result suggests that a unit increase in foreign diversification in the Kenyan banking sector, would occasion a corresponding rise in ROA by 0.61 unit in the short run and 0.03 unit in the long run respectively. This would also translate into a decline in Tobin’s Q by 0.04 unit and 0.09 unit in the Short Run and Long run respectively.

From the results in Table 4 also, the coefficient of income diversification was positive and statistically significant at 1% level in all the estimation (Short Run and Long run Estimates) results for both ROA and Tobin’s Q models. Explicitly, the result indicates that, a unit increase in income diversification would occasion a corresponding increase in ROA by 0.50 unit in the short run and 0.68 unit in the long run respectively. This would further lead to a rise in Tobin’s Q by 0.06 units both in the Short Run and Long run respectively.

In addition, the coefficient representing inflation rate was found to be negative and statistically significant at 1% level in the ROA as well as Tobin’s Q model results both in the short run, and in the long run. Specifically, the result reveals inter alia that, a unit increase in inflation rate in Kenya would occasion a corresponding shrinkage in her banking sector ROA by 0.0086 unit in the short run and 0.0087unit in the long run respectively. In addition, a similar increase in the general price level in the country by 1 unit is likely to bring about a decline in Tobin’s Q by 0.004 unit and 0.005unit in the short run and long run correspondingly.

Similarly, the coefficient representing subsidiary diversification was found to be negative in the ROA model results both in the short run and in the long run. It however turned positive in the case of Tobin’s Q model outputs both in the short run and in the long run. In addition, it was statistically significant at 10% in the ROA long run result, while such effect became statistically significant at 1% level in the Tobin’s Q short run and long run equations. Explicitly, the result submits that a unit increase in subsidiary diversification in the Kenyan banking sector would cause a fall in its ROA by 0.06 unit in the short run and 0.08 unit in the long run. Additionally, a similar increase in subsidiary diversification by 1 unit would translate into a decline in Tobin’s Q by 0.16 unit and 0.14 unit in the short run and long run respectively.

Furthermore, the Durbin-Watson statistic reported in the lower segment of Table 4 fails to reject the null hypothesis of no autocorrelation. The implication of the above finding is that, the estimated results are free from the problem of autocorrelation. Therefore, the parameters estimates are adjudged efficient, consistent and reliable. The R-squared values of 0.996,0.98,0.80 and 0.76 indicate that, about 99.6% and98%of the systematic variations in ROA in the short run and long run were respectively explained by exchange rate, foreign diversification, income diversification, inflation rate and subsidiary diversification. In addition, about 80% and 76% of the systematic variations in Tobin’s Q in the short run and long run were explained respectively by exchange rate, foreign diversification, income diversification, inflation rate and subsidiary diversification in the Kenyan banking sector.

Given the set of variables utilized in the study, the coefficient of determination (R-squared values) are appropriate for the reason that, in theory, there are other well-known variables that explain banks’ performance (proxied with returns on assets as well as Tobin’s Q) but are extraneous to this study. The goodness-of-fit models are well accentuated by the explanatory power (high coefficient of determination). This means that the independent variables are collectively significant. This was further validated by the judiciously large Fisher ideal statistic (F-statistic). The results are reported simultaneously in Table 4 below.

The fundamental role corporate diversification plays in enhancing financial sector growth in both developing and advanced countries has been theoretically recognized and emphasized in the literature. In recognition of the potential role of corporate diversification, many enterprises are actively trying to attract both foreign and domestic investors in a bid to stimulate their performance objectives. In the Africa region though, the issues of whether corporate diversification stimulates bank performance seems to have been largely flouted in the process of financial policy formulation in the region. It is on this note that, this study examined the impact of corporate diversification on the banking sector performance within the purview of macroeconomic factors in the context of Kenya.

Generally, bank performance was found to be determined partly by corporate diversification and macroeconomic fundamentals such as exchange rate and inflation rate. The foregoing findings tend to challenge the orthodox perception, arguing that banking sector performance in general is driven majorly by factors other than corporate diversification. Rather our findings tend to submit that, corporate diversification components like income, subsidiary and foreign Assets are just as crucial as other economic factors and inducements in stimulating banking sector performance in Kenya.

It follows thus that, if the current freezing out of the relevant corporate diversification policies is to be inverted, then the same recognition should be accorded to both economic and non-economic fundamentals in the conception, formulation and implementation of financial policies aimed at banking sector-led growth in the long-run.

For Kenya’s banking sector to mitigate the negative impacts of subsidiary diversification and fully benefit from such activities, tactical efforts should be made in identifying areas that require urgent injections as well as those compelling restraints. The commercial bank should carefully adopt a strategy that will warranty ideal location forecasting and execution as this will further boost financial annexation in the system.

Osagie Osifo is a lecturer with the department of Banking and Finance in the prestigious University of Benin, Nigeria. He holds B. Sc in Banking and Finance, M. Sc in Finance and PhD in Finance all from University of Benin. He has published in reputable journals both in Nigeria and international.

Christopher Ighodaro is a lecturer currently pursuing a higher academic qualification of a PhD in Finance in the department of Banking and Finance, University of Benin. This is after obtaining B.Sc and M.Sc degree in the same discipline making first class and distinction respectively.

Aarflot, S. & Arnegaard, L. (2017). The effect of industrial diversification on banks’ performance: a case study of the Norwegian banking market. SNF working paper no 09/17.

Adzobu, L. D.; Agbloyor, E. K. &Aboagye, A. (2017). The effect of loan portfolio diversification on banks’ risks and return: evidence from an emerging market. Managerial Finance, 4(1), pp. 28-44.

Akani, H. W.; Nwana, I. & Mbachu, A. (2016). Effects of selected macroeconomic variables on commercial banks performance in Nigeria. International Journal of Banking and Finance Research, 2(3), pp. 34-68.

Arasa, S. O. (2014). Diversification strategy and performance of Kenya commercial bank group. A master’s thesis submitted to the Department of School of Business. University of Nairobi.

Berg, J. V. (2016). Corporate diversification and firm performance: The effect of the global financial crisis on diversification in India. Master’s thesis submitted to the department of business administration. University of Groningen.

Brealey, R. A.; Myers, S. C. & Marcus, A. J. (2009). Fundamentals of Corporate Finance. 6th ed. McGraw –Hill.

Chen, S. & Ho, K. W. (2000). Corporate diversification, Ownership structure and firm value. International Review of Financial-Analysis, 9(3), pp. 315-326.

Chen, Y.; Wei, X.; Zhang, L. & Shi, Y. (2013). Sectoral diversification and the banks’ return and risk: evidence from Chinese listed commercial banks. Procedia Computer Science, 18, pp. 1737-1746, available at: https://doi.org/10.1016/j.procs.2013.05.342 (accessed May 3, 2016).

Çolak, G. (2010). Diversification, refocusing and firm value. European Financial Management, 16, pp. 422–448

Doaei, M., & Shavazipour, B. (2013). Corporate diversification’s effects on efficiency and productivity: case study of manufacturing firms listed in Bursa Malaysia. International Journal of Business and Development Studies, 5(1), pp. 77-96.

Doukas, J. A. & Kan, O. B. (2006). Does global diversification destroy firm value? Journal of International Business Studies, 37(3), pp. 352–371.

Ekanayake, N. (2017). Income diversification and bank risk-return trade-off: evidence from an emerging economy. Asian Economic and Financial Review, 7(7), pp. 644-655.

Elif, A. (2015). Corporate diversification and firm value: evidence from emerging markets. International Journal of Emerging Markets, 10(3), pp. 294-310.

Elton, E. J.; Gruber, M. J., & Blake, C. R. (1997). Modern Portfolio Theory, 1950 to Date. NYU Working Paper No.FIN-97-003. Available at SSRN: https://ssrn.com/abstract=1295211.

Jouida, S.; &Hellara, S. (2017). Diversification, capital structure, and performance: A simultaneous equation approach. Manage Decision Economics. 2, pp. 1–14.

Lang, L., &Stulz, R. (1994). Tobin’s Q, corporate diversification, and firm performance. Journal of Political Economy, 102(6), pp. 1248 – 1280.

Manyuru, A.; Wachira, M. &Amata, E. (2017). The impact of corporate diversification on firm value in Kenya. African Journal of Business Management, 11(11), pp. 241-2419.

Markowitz, H. (1952). Portfolio selection.Journal of Finance, American Finance Association, 7(1), pp. 77-91.

Muneer, S.; Jahanzeb, A. & Suwandi (2016). Income diversification and performance: a comparison of commercial and Islamic banks of Pakistan. Abasyn Journal of Social Sciences, Special Issue, pp. 471 - 485

Ongore, V. O. & Kusa, G. B. (2013). Determinants of financial performance of commercial banks in Kenya. International Journal of Economics and Financial, 3(1), pp. 237-252

Osamwonyi, I. O. &Chijuka, I. M. (2014). The impact of macroeconomic variables on the profitability of listed commercial banks in Nigeria. European Journal of Accounting Auditing and Finance Research, 2(10), 85-95

Osayi,V. I.; Kasimu, A. & Nkwonta, H. C. (2018). Financial market derivatives and the performance of deposit money banks in Nigeria. International Journal of Economics, Commerce and Management, VI (11), pp. 382-397.

Osifo, O. (2019). Corporate diversification and performance of quoted deposit money banks in selected sub-Sahara African countries: The effect of the global financial crisis on diversification in India. Unpublished PhD thesis submitted to the department of banking and finance. University of Benin. Benin City.

Osundina, C. K.; Osundina, J. A.; Jayeoba, O. O. & Olayinka, I. M. (2016). Exchange rate volatility and banks performance: Evidence from Nigeria. International Journal of Banking and Finance Research, 2(4), pp. 1-11.

Oweis, A. (2012). The impact of corporate diversification on the financial performance of U.S. Bank holding companies pre and post the financial services modernization act of 1999. CGU theses & dissertations, 58. http://scholarship.claremont.edu/cgu_etd/58. doi: 10.5642/cguetd/58.

Owoeye, T. & Ogunmakin, A. A. (2013). Exchange rate volatility and bank performance in Nigeria. Asian Economic and Financial Review, 3(2), pp. 178-185.

Phung, D. N., & Mishra, A. V. (2017). Corporation diversification and firm performance: Evidence from Vietnamese listed firms. Australian Economic Papers, 55(4).

Ravichandran, A. & Bhaduri, D. S. (2015). Diversification and firm performance: A study of Indian manufacturing firms. Retrieved December 18, 2015, from Munich Personal RePEc Archive: https://mpra.ub.unimuenchen.de/id/eprint/68013.

Turkmen, S. Y. & Yigit, I. (2012). Diversification in banking and its effect on bank performance: Evidence from Turkey. American International Journal of Contemporary Research, 2(12), pp. 110-119

Ugwuanyi, G. O.; Ani, W. U.; Ugwu, J. N. & Ugwunta, D. O. (2012). Impact of corporate diversification on the market value of firms: A study of deposit money banks Nigerian. European Journal of Business and Management, 4 (8), pp. 47-56.

Ugwuanyi, G. O. & Ugwu, J. N. (2012). The effect of corporate diversification on the profitability of the financial services sector in Nigeria. International Scholarly and Scientific Research & Innovation, 6(7), pp. 78 – 83.

1PhD, Lecturer, Department of Banking and Finance, University of Benin, Nigeria, Address: Benin City, Nigeria, Tel: +2347038823825, Corresponding author: osagie.osifo@uniben.edu.

2 Lecturer, Department of Banking and Finance, University of Benin, Nigeria, Address: Benin City, Nigeria, Tel: +23480602770683, E-mail: christopher.ighodaro@uniben.edu.

AUDŒ, Vol. 16, no. 3/2020, pp. 38-52

Method	Levels		First Difference
Method	Statistic	Prob.**	Statistic	Prob.**
Levin, Lin & Chu t*	-3.49	0.0002	-31.12	0.0000
Im, Pesaran and Shin W-stat	-7.26	0.0000	-28.67	0.0000
ADF - Fisher Chi-square	81.6702	0.0000	140.475	0.0000
PP - Fisher Chi-square	82.1143	0.0000	140.475	0.0000
Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality.

Dependent	Eagle-Granger Single-Equation Cointegration Test Result					Philip-Ouliaris Single-Equation Cointegration Test Result
Dependent	tau-stat	Prob.*	z-stat	Prob.*	tau-stat		Prob.*	z-stat	Prob.*
EXCH	-2.07	0.996	-9.52	0.996	-8.53		0.00***	-86.13	0.00***
FD	0.28	1.00	0.35	1.00	-4.36		0.36	-25.66	0.62
ID	-9.51	0.00***	-94.82	0.00***	-9.56		0.00***	-94.86	0.00***
INFL	-2.06	0.996	-8.35	0.998	-15.94		0.00***	-118.3	0.00***
ROA	-9.21	0.00***	-91.53	0.00***	-9.26		0.00***	-92.45	0.00***
SD	-5.07	0.11	-41.17	0.10	-5.11		0.11	-41.32	0.10
TOBINQ	-8.33	0.00***	-81.54	0.00***	-8.39		0.00***	-83.21	0.00***