Portfolio management and performance of deposit money banks (DMBs) in Nigeria: 1990-2020

Fajinmi, Christiana; Onwuka, Ifeanyi Onuka; Ayeni, Emmanuel Article Portfolio management and performance of deposit money banks (DMBs) in Nigeria: 1990-2020 Research in Globalization Provided in Cooperation with: Elsevier Suggested Citation: Fajinmi, Christiana; Onwuka, Ifeanyi Onuka; Ayeni, Emmanuel (2023) : Portfolio management and performance of deposit money banks (DMBs) in Nigeria: 1990-2020, Research in Globalization, ISSN 2590-051X, Elsevier, Amsterdam, Vol. 7, pp. 1-8, https://doi.org/10.1016/j.resglo.2023.100139 This Version is available at: https://hdl.handle.net/10419/331069 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). Portfolio management and performance of deposit money banks (Dmbs) in Nigeria (1990–2020) Christiana Fajinmi, Onwuka Ifeanyi Onuka, Emmanuel Ayeni * University of Ibadan, Faculty of Economics and Management Science, Nigeria ARTICLE INFO JEL: G11 G12 G14 G30 G32 Keywords: Portfolio management Risk-return trade-off Profitability Capital adequacy Deposit money banks ABSTRACT There have been a renewed focus on portfolio management of deposit money banks since the global financial crisis of 2007–09. This renewed focus is based on the understanding that an efficient portfolio management reduces risks and loss associated with uncertainty of investment returns which may impact on the performance of banks. In this study, we investigated the connection between portfolio management and performance of deposit money banks in Nigeria. The study essentially sought to ascertain whether portfolio management has predictive value for the out-of-sample predictability of profitability of deposit money banks in Nigeria. The Markowitz portfolio theory underpin the study while time series data on deposit money banks’ liquidity, financial assets, foreign portfolio asset, deposit mix, and private sector concentration were utilized for the analysis. The time series spanned from 1990 to 2020 based on data availability. To increase the robustness of the result, the entire 24 DMBs were included in the study. The unit root test and bound cointegration test were employed to check times series behaviour of the variables. The Autoregressive Distributed Lag (ARDL) was used to estimate both the short-run and long-run dynamics and rapid correction to long-run equilibrium. Our findings reveal that portfolio management and its variants had significant effect on the profit after tax (PAT), return on investment (ROI), asset quality (ASQ), and capital adequacy (CA) of deposit money banks in Nigeria. Introduction Nigeria’s financial system is made up of a variety of markets, instruments, operators, and organizations that interact to offer financial services within the economy. The country’s diverse financial system includes 5,097 bureau de change, 24 deposit money banks, 7 development finance institutions, 6 discount houses, 77 finance companies, 5 merchant banks, 911 micro-finance banks, 1 non-interest bank, and 35 primary mortgage institutions, among others (CBN, 2021). The deposit money banks (DMBs) are the major operators in the Nigeria financial system and are largely responsible for financial intermediation. Financial intermediation involves essentially in linking deposit surplus units with deposit deficit units. In doing this, the DMBs accumulate a portfolio of assets which they must manage efficiently and profitably to remain in business on going-concern basis. To this end, portfolio management consists of asset allocation, diversification, and rebalancing of an asset to a predetermined upper limit. Asset allocation is the division of a portfolio’s holdings into risky and risk-free asset classes. Investing usually necessitates a well-thought-out statement of investment policy that captures the specific demands of the investor. Because it is difficult to predict which subset of an asset class will outperform another, diversification spread risks and subsequently rewards within asset classes. As a result, diversification is defined as the process of increasing the number of assets in different portfolios to reduce investment risk. Institutional portfolio managers in industrialized countries are increasingly interested in banking investments because diversification boosts portfolio returns while lowering risk (Purkayastha et al, 2011). Portfolio management, often known as portfolio diversification, is a risk-reduction and investing approach. It essentially entails holding a variety of assets or asset classes in order to mitigate or eliminate specific risks. The first phase in portfolio management is selection, which entails deciding which assets to acquire, keep, or sell, as well as how much and when to do so (Markowitz & Harry, 1999). In today’s volatile economic environment, a well-organized and effective financial structure is required to specialize in providing services and production, to win and maintain a friendly relationship with investors, and to maintain a competitive advantage in the market in order to increase economic * Corresponding author. E-mail addresses: [email protected] (C. Fajinmi), [email protected] (O. Ifeanyi Onuka), [email protected] (E. Ayeni). Contents lists available at ScienceDirect Research in Globalization journal homepage: www.sciencedirect.com/journal/research-in-globalization https://doi.org/10.1016/j.resglo.2023.100139 Received 23 February 2023; Received in revised form 15 June 2023; Accepted 17 June 2023 Research in Globalization 7 (2023) 100139 2 transactions (Marcia et al, 2014). As a result, having an efficient and stable financial system is crucial. Due to the uncertain economic climate, commercial banks are concentrating on innovative ways to improve their operations by implementing cost-cutting initiatives and diversifying their revenue streams. This is aimed at increasing profitability, lowering risk, raising debt capacity, boosting growth, and extending the business’ life cycle.The goal of portfolio management is therefore, to protect firms from fluctuations in the returns of various investment possibilities. In an ideal world, a portfolio manager would enhance the portfolio’s systematic risk ahead of a market upturn and decrease the beta ahead of a market downturn. Investing in various investments increases a company’s chances of earning a favorable return, albeit this may not be guaranteed due to the risk involved in the investments. According to Ross (1977, p.34), “specific techniques should be developed and deployed while constructing an optimal portfolio holding based on the investor’s risk appetite, objectives, circumstances and horizon”. In Nigeria, portfolio management is offered by asset management companies, pension fund administrators, and other financial institutions. These entities manage portfolios based on the investment objectives and risk tolerance of their clients. To diversify risk in their portfolio, banks and other non-banks utilized buying and selling of bonds, treasury bills, treasury certificates, and other securities in the money market. Portfolio management increases the liquidity of banks as a fund manager or portfolio manager can select a portfolio that can easily be converted into cash within a relatively short period and this increase the fund available to lenders which play a crucial role in the economy for allocation of resources. Similarly, the fund borrowed could be channeled to investment which increases aggregate expenditure and gross domestic product. In Nigeria, the Central Bank of Nigeria (CBN) and the Security and Exchange Commission (SEC) issued guidelines that govern portfolio management activities, including the selection and management of liquid assets. For instance, in 2019, the CBN issued a guideline that required DMBs to tackle credit concentration in their portfolios and to deploy information technology infrastructure that will enable them effectively monitor credit concentration (CBN, 2019). Considering portfolio management, Nigeria restructured the financial system and portfolio of financial intermediaries which include banks, insurance companies, merchant banks, and others. This led to the establishment of the Asset Management Corporation of Nigeria (AMCON) in July 2010 which was a collaboration of CBN and the Minister of Finance, the asset transfer operation of AMCON successfully lower the non-performing loan level in Nigerian banking and reduce risk in the banking sector. In 2008, the NDIC also implemented Differential Premium Assessment System (DPAS) for DMBs and Merchant banks. The DPAS is a risk-based premium assessment system that considers the risk profile of each bank in its portfolio. In 2012, 2017, and 2021 respectively, Non-Interest Banks (NIBs), Microfinance Banks, and Payment Service Banks were also required to adopt the DPAS framework. Several studies have shown that efficient portfolio management could add a firm’s profitability and bottomline. For instance, studies such as Chakrabatrtriet al. (2007); Oyatoye and Arilesere (2011); Purkayastha et al. (2011); Sanya and Wolfe (2012); Perez (2015); Aglobiet al. (2020) show that efficient portfolio management is crucial for a firm’s portfolio returns risk minimization. According to Oyatoye and Arilesere (2011), portfolio management is an essential component of banking, with individual banks and financial institutions structuring their asset collections to add value to their investments. The assets kept are designed to maximize expected profits while minimizing expected risk. Banks can diversify their risks and increase their earning potential by building an effective portfolio (Sanya & Wolfe, 2012). This is in line with the modern portfolio theory espoused by Harry Markowitz (1952), capital asset pricing model (Sharpe 1964, Lintner 1965) and the theory of active portfolio management by Grinold (1989). Fundamentally, the modern portfolio theory illustrates the possibility of constructing a portfolio that contains multiple securities which are supposed to maximize investor returns for an associated risk level. Also, portfolio management approach is highly dynamic, entailing active company managers trying to optimize company returns and perform above the market standards. However, this approach to managing portfolio is also contingent on both stock risk and exposure risk as well. Active managers typically seek out beneficial information from research experts in order to profit from market inefficiencies, such as buying inexpensive companies and short-selling those that are overvalued. In other occasions, a mutual fund’s purpose, hedge fund, or investment portfolio entails reducing risk lower than the benchmark index, which is accomplished by active portfolio management. The theory of capital asset pricing is a theory that attempts to link the price of a risky asset to its expected returns and the returns from a relatively less risky asset. According to the capital asset pricing model, an investor’s portfolio selection criteria should derive from risk-return profiles of the portfolios in order to maximize expected returns and minimize risk. Commercial banks, according to Perez (2015), need assets that generate greater income, especially in this era of increasing adoption and use of technology-enabled products and services. This is predicated on the reality that different assets perform differently when exposed to diverse economic environments, and the performance generated from such assets appears to have no relationships. The banks organizes those asset portfolios in such a way that they maximize returns while also expanding their product base in order to attract more clients and therefore boost their profitability (Aglobiet et al. 2020). Diversification of asset classes also helps financial institutions in poor nations improve their performance (Chakrabatrtriet et al. 2007). Portfolio rebalancing is an example of monetary policy’s risk-taking channel, applied to a specific scenario of asset purchase program (Albertazzi et al, 2018). The goal is to reduce excessive or excessive concentration in one asset class. Over-concentration and non-performance in particular asset classes are to blame for many financial institutions’ poor performance (Hoshi et al. 2010). It should be noted that portfolio management is one area where there are significant research shortages both globally and locally (Campbell, 2002). Due to the high significance of portfolio management to policy and development action, scholars, policymakers, and donors are now paying closer attention to its consequences on the performance of financial institutions. According to Chakrabartet al. (2007), portfolio management helps to improve performance in developing institutional systems and portfolio diversity does not in any way reduce a firm’s value. According to Ishak and Napier (2006), when portfolio diversity levels improve, the firm’s value also rises. The Nigerian banking industry has grown dramatically as a result of deregulation and liberalization. This, combined with the introduction of shadow banking and fin-tech, has resulted in fierce competition in the sector. To thrive in this competitive market, banks have had to diversify their asset portfolios in order to remain profitable. However, as observed by Perez (2015), some of these diversified assets may be non-performing in terms of their ability to generate direct incomes. To this end, it could be said that revenue generated by trading assets is insecure, and improper trading leads to losses. As a result, banks must segregate their investments into subgroups based on the performance variability attributable to the various market conditions, and banks must assess the history and expected outlook of each investment outlets in terms of risk, return, and correlation. Extant literature has shown that emphasis has been placed disproportionately on profitability as a factor to commercial bank’s success. For instance, a study by Agblobi et al. (2020) on the impact of portfolio management on deposit money banks in Ghana showed that holding government securities and investing in subsidiaries have a considerable beneficial impact on banks profitability in Ghana. Hailu and Tassew (2018) examined the impact of investment diversification on commercial banks’ financial performance in Ethiopia and found that investment diversification had a beneficial impact on the financial performance of C. Fajinmi et al. Research in Globalization 7 (2023) 100139 3 deposit money banks in Ethiopia. Again, Arnety (2016) examined the impact of portfolio diversification on the financial performance of Kenyan commercial banks. The results from the study showed that portfolio diversification accounted for 68 percent of changes in the financial performance of commercial banks’ in Kenya, and most banks diversify their investments, allowing them to boost earnings and performance by so doing. In Nigeria, several studies have been carried out on portfolio management and firm performance. Some of these studies include: Jeroz (2007); Miriti (2008); Tanui (2010); Oyedijo (2012); Micheni (2013); Oyewobi et al. (2013); and Mutega (2016). Most of these studies, however, focused on a single variable of interest, mostly on profitability. Only Oyedijo (2012) combined market and product diversity in his empirical analysis. Our current study differed from these earlier studies by using a wider array of deposit money banks performance metrics, namely: liquidity, financial assets, foreign portfolio, deposit mix, and loan concentration to the private sector. Moreover, our study engaged with current data and in a more robust context, and expectedly, with a more robust outcome. It is expected that the findings of the study will be of significance to deposit money banks (DMBs) in Nigeria in terms of designing and implementing effective asset diversification and portfolio management strategies. Other industry players, such as mutual fund managers, equity investors, venture capitalists, and stock brokers, will also benefit from the findings of the study as the performance of deposit money banks is critical to their own successes. The rest of the paper is organized as follows; the methodology is discussed in the next section. In section 3 is the discussion of results and in section 4 is the conclusion and recommendations. Methodology The study aimed to investigate the nexus between portfolio management and performance of deposit money banks in Nigeria. The study utilized data from the 24 deposit money banks licensed by Central Bank of Nigeria (CBN) and insured by Nigeria Deposit insurance Commission (NDIC) as at 2020. The time series spanned a period of 31 years (1990 – 2021). The rationale for using banks is because it is one of leading financial investment institutions in the financial system coupled with inherent risk of their nature of business and a long period to capture trends and short term dynamics necessary to restore the long term equilibrium. The time series data were collected from CBN statistical bulletin and NDIC respectively for portfolio management indicators which include liquidity, financial assets, foreign portfolio asset, deposit mix, and loan to private sector as indicators of bank performance which constitute net profit after tax, capital adequacy, asset quality and return on investment. The rationale for all the variables is based on portfolio theories and indicators of bank performance as issued by CBN. In this section, we formulate our empirical model that examines the impact of portfolio management on performance of deposit money banks (DMBs) in Nigeria. There are already numerous studies analyzing the relationship between portfolio management and performance of DMBs especially profitability on the basis of the risk-return hypothesis and other portfolio management theories (see Oyedijo, 2012; George et al., 2013; Nadanalingam & Larojan, 2015; Ayodele, Afolabi, & Olaoye, 2017; Hail & Tassew, 2018; Osayi, Dibal & Ezuem, 2019). We construct a model that connects high risk investments to high risk assets according to Harry Markowitz’s (1952) portfolio framework that determines the minimum level of risk for an expected return. The theory assumes that investors will favour a portfolio with a lower risk level over a higher risk level for the same level of return.We specify our model to control for endogeneity bias, which may result from omitting other predictors of DMBs performance, conditional heteroskedasticity effect, due to the use of high frequency data and persistence, which is unique to most financial and economic time series. Furthermore, the proxy for performance of banks which is the dependent variable are profit after tax, asset quality, capital adequacy and return on investment. The rationale for employing the performance indicator variables is because they are indicators and requirement set by CBN (2021) for determining financial soundness and stability of banks in Nigeria and few of them have been utilized by previous studies (see Ayodele, Afolabi, & Olaoye, 2017; Hail & Tassew, 2018; Osayi, Dibal & Ezuem, 2019)). Similarly, the inclusion of liquidity, financial assets, foreign portfolio, deposit mix and loan to private sector is because they are risk factor metrics linking the expected risk and expected returns as well as various portfolio management tools to reduce the expected risk in an investment in line with the modern portfolio theories (Merton, 1973; Fama, 1992 and Camp, 2002). Following Ngari (2018), we construct our base model: Y=β0+β1X1+β2X2+β3X3+β4X4+β5X5+Et(1) Where; Y =Profitability measured by Absolute Profit before Tax. X1 =Liquidity measured by the absolute cash and cash equivalents. X2 =Financial Assets measured as a ratio of the total banks’ financial Assets such as treasury bills, bonds, commercial papers to the total assets. X3 =Foreign Portfolio Asset measured by Banks Foreign Asset. X4 =Deposit Mix measured as ratio of Current Accounts – Savings Account (CASA) to the total deposits. X5 =Private Sector Concentration measured as Amount of Loans to Private Sector. β 0 =regression constant. β1, β2, β3, β4, β5 =coefficients associated with independent variables. e =Residual (error) term. Expressing eq. (1) in logarithmic form, with other variants from the base model, we have: LOGPAT =β0+β1LOGLIQ +β2FA +β3LOGFP +β4DM +β5LOGLPS (2) ASQ =β0+β1LOGLIQ +β2FA +β3LOGFP +β4DM +β5LOGLPS (3) CA =β0+β1LOGLIQ +β2FA +β3LOGFP +β4DM +β5LOGLPS (4) ROI =β0+β1LOGLIQ +β2FA +β3LOGFP +β4DM +β5LOGLPS (5) Where: LOGPAT =Natural Logarithm of Profit after Tax. ASQ =is measured as non - performing loans/ total loans and advances. CA =Measured by (Tier one Capital +Tier Two Capital) ÷(Risk- Weighted Assets). ROI =is measured as profit after tax (PAT)/ total asset (TA). LOGLIQ =Natural Logarithm of liquidity. FA =Financial Asset. LOGFP =Natural Logarithm of Foreign Portfolio. DM =Deposit Mix. LOGLPS =Natural Logarithm of loans to private sector. β 0 =Intercept of relationship in the model. β 1 – β 5 =Coefficients of independent variables. µ =Stochastic Error term. The Augmented Dickey Fuller (ADF) and the Philips Perron (PP) tests were both used to verify for unit root or stationarity in the time series data analysis. The short-run and long-run dynamics, as well as quick adjustment to long-run equilibrium, are reflected or estimated using Autoregressive Distributed Lag (ARDL). The overarching objective of the Augmented Dickey Fuller Unit root test is to determine the order in which the time series variable and co-integrated Durbin Watson Statistic are integrated.The aim of any statistical analysis is to draw inference regarding the configuration of the population using sample observations. The PP test,being a modified Dickey Fuller test takes into consideration error autocorrelation and heteroskedasticity. One advantage of the C. Fajinmi et al. Research in Globalization 7 (2023) 100139 4 PP tests over the ADF tests is that they are resistant to a wide range of heteroskedasticity in the error term. Moreover, with the PP test, there is no need to account for a lag length when using the test regression. Following the framework provided by Pesaran, Shin, and Smith’s (1996), Pesaran and Shin (1999), and Pesaran, Shin, and Smith’s (2001), the study utilized the Autoregressive Distributed Lag (ARDL) in analysing the data. In comparison to other co-integration strategies, the ARDL method has a number of advantages. First, it allows for the estimation of long-run relationships utilizing variables that are combined or integrated in various orders. I(0) and I(1) variables, in particular, can be incorporated in the same co-integrating equation. Again, the utility of the ARDL is that the variables do not need to be unit root tested to be fit for analysis. All that is required is for the variables to be integrated in either order 0 or 1. Using the ARDL model, we hypothesized as follows: Hypothesis One: PAT =f(LIQ,FA,FP,DM,LPS)(6) ΔLOGPATt=β0+∑ m1 i=1 β1ΔLOGPATt−1+∑ m2 i=0 β2ΔLIQt−i+∑ m3 i=0 β3ΔFAt−i +∑ m4 i=0 β4ΔLOGFPt−i+∑ m5 i=0 β5ΔDMt−i+∑ m6 i=0 β6ΔLOGLPSt−i + α 1(LOGLIQ)t−1+ α 2(FA)t−1+ α 3(LOGFP)t−1+ α 4(DM)t−1 + α 5(LOGLPS)t−1+∊t (7) Hypothesis Two ASQ =f(LIQ,FA,FP,DM,LPS)(8) ΔASQt=β0+∑ m1 i=1 β1ΔASQt−1+∑ m2 i=0 β2ΔLIQt−i+∑ m3 i=0 β3ΔFAt−i +∑ m4 i=0 β4ΔLOGFPt−i+∑ m5 i=0 β5ΔDMt−i+∑ m6 i=0 β6ΔLOGLPSt−i + α 1(LOGLIQ)t−1+ α 2(FA)t−1+ α 3(LOGFP)t−1+ α 4(DM)t−1 + α 5(LOGLPS)t−1+∊t (9) Hypothesis Three CA =f(LIQ,FA,FP,DM,LPS)(10) Hypothesis Four ROI =f(LIQ,FA,FP,DM,LPS)(6) ΔROIt=β0+∑ m1 i=1 β1ΔROIt−1+∑ m2 i=0 β2ΔLIQt−i+∑ m3 i=0 β3ΔFAt−i +∑ m4 i=0 β4ΔLOGFPt−i+∑ m5 i=0 β5ΔDMt−i+∑ m6 i=0 β6ΔLOGLPSt−i + α 1(LOGLIQ)t−1+ α 2(FA)t−1+ α 3(LOGFP)t−1+ α 4(DM)t−1 + α 5(LOGLPS)t−1+∊t 13) Where: LOGPAT =Natural Logarithm of Profit after Tax. CA =Capital Adequacy. LIQ =Liquidity. ROI =Return on Investment. ASQ =Asset Quality. FA =Financial Asset. LOGFP =Natural Logarithm of Foreign Portfolio. DM =Deposit Mix. LOGLPS =Natural Logarithm of loans to private sector. β 0 =Intercept of relationship in the model. β 1 – β 6 =Coefficients of independent variables. µ =Stochastic Error term. ∊t =Stochastic error term. Δ =Difference operator. β =are short-run parameter. M 1 , M 2 , M 3 , M 4 , M 5 and M 6 =Respective optimal lag length for each differenced term. t- 1 =Variable Lagged by one period. α 1, α 2 α 3 α 4and α 5 are long-run parameters. A-priori expectations:.β1,β2,β3,β4,β5,β6⋯⋯⋯.β12 >0 The expected signs are positive and this follows the portfolio management theories and it is expected that increase in portfolio management will increase the performance of bank because portfolio strategy of diversification of portfolio, shifting of financial assets (assets allocation) to reduce the expected risk and increase the expected returns of DMBs. Data and preliminary analysis The descriptive statistics of the variables used in the study, as shown in Table 1 reveals that the average value of profit after tax is 25,130.86 billion naira whereas, the maximum profit after tax is recorded at 35,810.20billion naira and the minimum generated as profit after tax is 5,780.4billion naira. Also, the average return on investment in Nigeria is 37.48billion naira; however, the maximum of return on investment for the time scope covered is 419.39billion naira. On average, the total loan to private sector is pegged at 7,651.04billion naira. This shows that private sector is one of the predominant sectors where a large of chunk of bank loans is deployed to. Likewise, the average liquidity available in form of cash is pegged at 996 billion naira; nonetheless, the peak of liquidity is to the tune of 3,193.47billion naira for the periods considered. The result shows that investment of banks in foreign portfolio on average is 828.23billion naira, interestingly, the minimum of bank foreign portfolio for the study period is 6.55billion naira, and the maximum is 2,618 billion naira. This indicates that there is tendency that foreign portfolio are more appealing to banks than the local portfolios. In addition, the financial asset to the total asset on average is 0.11 billion naira; this value at its maximum is 0.18 billion naira. This shows that majority of total asset are concentrated in non-financial asset as against the financial asset. The deposit mix of banks on average is −0.001. This implies that much of accounts held in banks are savings relative to current accounts. Capital adequacy on average is 20.52, indicating that the total capital of tier one and tier two bank relative to risk-weighted assets is low. The average asset quality of banks is 0.06. This means that the value of non-performing loans in deposit money ΔCAt=β0+∑ m1 i=1 β1ΔCAt−1+∑ m2 i=0 β2ΔLIQt−i+∑ m3 i=0 β3ΔFAt−i+∑ m4 i=0 β4ΔLOGFPt−i+∑ m5 i=0 β5ΔDMt−i+∑ m6 i=0 β6ΔLOGLPSt−i + α 3(LOGFP)t−1+ α 4(DM)t−1+ α 5(LOGLPS)t−1+∊t 11) C. Fajinmi et al. Research in Globalization 7 (2023) 100139 5 institutions is low in comparison to total loans and advances. Theasymmetry (Skewness) of the series’ distribution around the mean showed mixed results. It is skewed to the left if the value is negative, and it is slanted to the right if the value is positive. Except for profit after tax, financial asset, and deposit mix, all the other variables studied were skewed to the right.Kurtosis is used to determine if the distribution is peaked or flat. It has an expected value of three for a normal distribution. Approximately, Loan to private sector, foreign portfolio asset, deposit mix are platykurtic because their values are less than three, while all others are leptokurtic because their values are greater than three. In order to avoid a spurious result, it is important to test for unit root in the variables. This process is conventional to time series. To this end, the result as presented in Table 2 using the ADF and PP tests showed that ASQ, PAT. ROI and CA are stationary at levels. Thus, the null hypothesis of unit root is rejected. This implies that the series of ASQ, PAT. ROI and CA is mean reverting at levels, and it standard deviation is constant at levels. On the other hand, DM, FA, and LPS has unit root at levels. However, after differencing the series once, there is stationary in the series. Hence, we reject the null hypothesis at first difference. For the series of FP and LIQ, there is aninconsistency between the ADF and PP test. The PP test suggests that the series of FP and LIQ are stationary at levels, but the ADF test suggests that these series are stationary at first difference.Thus, this study prioritizes the PP test because it is robust to serial correlation. The variables in this study show a mixed integration in general, that is I(0) and I(1). This further substantiates the choice of Table 1 Descriptive Statistics. N Mean Median Maximum Minimum. Std. Dev. Skewness Kurtosis Jarque-Bera PAT 31 25130.86 25133.80 35810.20 5780.400 6703.944 −0.493351 3.737070 1.959270 ROI 31 37.48348 6.013797 419.3928 0.112543 79.99698 3.730782 18.01228 363.0144 LPS 31 7651.036 1838.390 29051.61 33.54770 9256.250 0.884747 2.331128 4.622230 LIQ 31 995.6818 932.9301 3193.470 23.18850 856.8810 0.684472 2.782768 2.481546 FP 31 828.2296 450.4487 2618.010 6.550200 841.3266 0.593667 1.896524 3.065324 FA 31 0.110379 0.121846 0.180540 0.000000 0.052726 −0.753725 2.677157 3.069820 DM 31 −0.001054 0.039446 0.342734 −0.323336 0.192879 −0.178321 1.922213 1.503621 ASQ 31 0.058610 0.059537 0.150838 0.000000 0.033628 0.467530 3.221399 1.192668 CA 31 20.52296 17.66000 118.6839 2.970000 4.161661 2.014145 6.908954 40.69659 Source: Authors’ computation using E-view software. N ¼Number of years. Table 2 Unit Root Results. Variables At Level First Difference Order of Integration ADF PP ADF PP ASQ −3.9088*** −3.8420*** —————————— —————————— I(0) DM −1.1577 −1.1444 −5.6445*** −5.6445*** I(1) FA −2.5975 −2.5473 −7.0068*** −7.1316*** I(1) FP −1.9990 −3.2876** −7.3926*** ————————— I(0) LIQ −2.0616 −5.0100*** −6.6720*** ————————— I(0) LPS −2.3546 −2.4973 −3.8124*** −3.8124*** I(1) PAT −3.4922** −3.1490** —————————— —————————— I(0) ROI −10.5957*** −9.3082*** —————————— —————————— I(0) CA −7.5154*** −6.9629*** —————————— —————————— I(0) Note: ***, and ** represent 1% and 5% level of significance. Source: Authors’ computation using E-view software. Table 3 ARDL Results. PAT ASQ CA ROI Panel A: Short Run LIQ −0.2881** (0.0971) 0.0840*** (0.0134) −1.4572*** (0.1628) —————————— FA −2.2241** (0.7702) −0.2145*** (0.0657) 1.5310** (0.4205) −2.2691*** (0.7332) FP 0.1834* (0.1034) −0.0668*** (0.0119) 0.7440*** (0.0821) —————————— DM 1.3462** (0.5241) −0.2382*** (0.0589) 1.0077* (0.3213) 2.8188*** (0.5747) LPS −0.7904*** (0.2375) −0.0458 (0.0315) −1.7919*** (0.2648) −1.3104*** (0.1382) ECT (-1) −0.7790*** (0.2693) −0.9384*** (0.1785) −0.9985*** (0.0995) −0.9116*** (0.1749) Panel B: Long Run LIQ −0.1678 (0.1028) 0.0802*** (0.0129) −0.8910 (0.9553) −0.1683** (0.0743) FA −1.0785 (0.7362) −0.0177 (0.0835) 2.6343 (3.1988) −1.1126* (0.6088) FP 0.3489** (0.1419) −0.0708*** (0.0188) 1.4587 (1.0421) 0.1169 (0.1250) DM −0.3716 (0.5397) −0.0582 (0.0567) −1.9873 (1.0145) −1.6730*** (0.4655) LPS −0.0570 (0.1237) 0.0477*** (0.0136) −0.7403 (0.4926) −0.4412*** (0.1083) Panel C: Diagnostics Bound Test 5.3571*** 13.8797*** 4.8229*** 6.5111*** Adjusted R 2 0.7383 0.8604 0.9268 0.7697 Serial Correlation 1.4730 [0.2679] 0.1428 [0.8687] 1.539699 [0.4951] 2.8637 [0.0962] Heteroskedasticity 0.7097 [0.7127] 0.6992 [0.7347] 1.656044 [0.3781 0.2853 [0.9785] Note: ***, **, * represent 1%, 5%, and 10% level of significance. Values in () are standard error, while values in [ ] are probability. Source: Authors’ computation using E-view software. C. Fajinmi et al. Research in Globalization 7 (2023) 100139 6 ARDL used in this paper. Empirical model estimation results Bound test as seen in Table 3 shows that there is long-run relationship between the variables for each of the study objectives. Also, it is noted that for the four objectives, the models are notauto correlated and the variance of the error term is constant across the four objectives. Discussion of results Main findings 1 In this study, we investigated the connection between portfolio management and the performance of deposit money banks in Nigeria. To achieve this, we formulated four objectives. The findings of the study showed that holding all other factors constant, liquidity, financial assets, foreign portfolio asset, loan to private sector concentration have significant effects on the profit after tax in the short run. A 1% increase in liquidity will lead to 0.288% decrease in profit after tax. This means that as commercial banks decides to hold more liquid cash, rather than to invest the cash to both risk free (money market) and risky investment (capital market) or assets that will generate more returns or higher profitability. This reflect the risk appetite of the Nigeria banking system leading to their highest holding portfolio holding in the money market specifiacally treasury bills. Since 2012, the commercial banks in Nigeria dominated the money market and governmet bond. In the year 2012, the top three banks have ₦948 billion in treasury bills with Zenith Bank leading with ₦542 billion. This shows that the commercial banks in the country are risky averse. Also, a percent increase in financial asset will lead to 2.22% decrease in PAT. A percent increase in foreign portfolio asset will lead to 1.88% increase in PAT. Thus, the more investments deposit money banks make in foreign portfolios, the better their performance. A percent increase in deposit mix will lead to 1.34% increase in PAT. A percent increase in loan to private sector concentration will lead to 0.79% decrease in PAT ceteris paribus. The inverse relationship between loan to private sector concentration and profit after tax may be attributable high non-performing loan inducing low profitability or low returns from investment. The investment portfolio of the commercial banks are dominanted by money market instrument thereby crowing out for available funds. Commercial banks charge higher interest rate on loan leading to adverse selection and moral hazard. Investments with higher risk are mostly finance which leads to increase in non-performing loans in the loan portfolio of commercial banks. This result is in tandem with the findings by Ngari (2016), who asserted that there is an inverse relationship between loan to private sector and profit after tax. Also while there is an inelastic relationship between FP and PAT, the relationship between DM and PAT is elastic. In the long run, the result shows that only FP significantly influence PAT in the periods studied. Specifically, there is a negative relationship between FP and PAT, that is, a percent increase in FP reduces PAT by 0.34 percent. This could be due to exchange rate volatility. In general, the result shows that aside from FP which has long run impact on PAT, other variables (that is, LIQ, FA, DM, and LLPS) had an impact on the PAT only in the short run. Again, the adjusted R-square indicates that 74 percent of the variations in PAT is accounted for by the variables in the model.This contrasted with the findings in a study by Opler, Lee, Rene and Rohan (2001). The authors in their study argued that companies that had large liquidity bases performed well in turbulent times. For the second objective, the findings of the study showed that LIQ has a positive and significant impact on ASQ in both the short run and long run horizon. Specifically, a percent increase in LIQ on the average increases ASQ by 0.08 percent. This has an important implication as deposit money banks must be liquid to improve their asset quality. The study also showed thatin the short run, a percent increase in LLPS reduces ASQ by 0.05 percent, a percent increase of LLPS in the long run increases ASQ by 0.05 percent. The implication is that in the short run, loan concentration to the private sector is detrimental to the deposit money bank’s asset quality but significant in the long run. Financial asset and deposit mix have significant effects in the short run but no significant effect on asset quality in the long run. Thus we can safely conclude that portfolio management has a significant impact on the asset quality of deposit money banks in Nigeria. For the third objective, results indicated that although there exist long run relationship among CA and other variables, this relationship is not significant on CA. That is, the impact of LIQ, FA, FP, DN, and LPS is insignificant the short run. Specifically, the result shows that both LIQ and LPS are negatively related and significant to CA. That is, an increase in LIQ and LPS reduces CA by 1.46 percent and 1.79 percent respectively. Whereas, a percent increase on the average in FA, FP, and DM increases CA by 1.53 percent, 0.74 percent, and 1.01 percent respectively. In general, the result shows that while the relationship of CA with LIQ, FA, DM, and LPS is elastic, that of CA with FP is inelastic. Also there is sufficient evidence through the adjusted R-square that 93 percent of variation in CA is accounted for by the explanatory variables in this model. Finally, the result shows that both LIQ and FA are not related whatsoever with ROI in the short run. Nevertheless, there exist a positive relationship between DM and ROI, while a negative relationship existsbetween FA and ROI, as well as LPS and ROI in the short run. Specifically,in the short run, a percentage increase in FA and LLPS reduces ROI by 2.27 percent and 1.31 percent respectively; while a percentage increase in DM increases ROI by 2.81 percent. Meanwhile in the long run, when LIQ increases by a percent, it reduces ROI by 0.16 percent on average. Also, a percent increase in FA, DM, and LPS in the long run, significantly reduces ROI by 1.11 percent, 1.67 percent, and 0.44 percent respectively. The adjusted R-square value indicates that 77 percent of variation in the value of ROI is captured in this model. Post estimation tests It is expected that the model is not serially correlated, thus no ARCH effect and normally distributed model. Based on Table 4 above, serial correlation of the model residuals is determined by Breusch-Godfrey Serial Correlation LM Test. A decision is made based on the probability value of the observed R-squared. The result show that P-value is greater than 5%, for hypothesis 1–4, thus, the null hypothesis is accepted and the alternative hypothesis that there is no serial association or correlation between money liquidity, financial asset, foreign portfolio asset, deposit mix, loan concentration to private sector and profit after tax, asset quality, capital adequacy and return on Table 4 Post Estimation Results. Serial Correlation Test Heteroskedasticity Test Hypothesis One Obs*R-squared 1.4730 Obs*R-squared 0.7097 Prob. Chi-Square (1) 0.2679 Prob. Chi-Square (1) 0.7127 Hypothesis Two Obs*R-squared 0.1428 Obs*R-squared 0.6992 Prob. Chi-Square (1) 0.8687 Prob. Chi-Square (1) 0.7347 Hypothesis Three Obs*R-squared 1.539699 Obs*R-squared 2.8637 Prob. Chi-Square (1) 0.4951 Prob. Chi-Square (1) 0.0962 Hypothesis Four Obs*R-squared 1.656044 Obs*R-squared 0.2853 Prob. Chi-Square (1) 0.3781 Prob. Chi-Square (1) 0.9785 Source by: Author’s Computation. 1 Detailed estimation results including the ARDL are available on request. C. Fajinmi et al. Research in Globalization 7 (2023) 100139 7 investment respectively is rejected.The model is not serially correlated, according to our null hypothesis. Thus, there are no flaws in the model, and it is fit. The observed R-square for the heteroskedasticity test is also higher than the 5% acceptable level for the four hypotheses. Given that the p-value is more than 5%, our null hypothesis is that there is no ARCH effect. Thus, we accept the null hypothesis and reject the alternative. As a result, the model is appealing. Test of hypotheses Hypothesis one The ARDL was utilized to determine if portfolio management has a positive and significant effect on Profit after Tax (PAT) of Deposit Money Banks in Nigeria. It is revealed that the indicators of portfolio management (liquidity, financial assets, foreign portfolio asset, deposit mix and private sector concentration) all have significant effects on the Profit after Tax of Deposit Money Banks in the short run but only foreign portfolio asset has a positive relationship and significantly influence the Profit after Tax of Deposit Money Banks in the long run for the period considered. In the short run, deposit mix and foreign portfolio asset have a positive relationship with profit after tax while the others have a negative relationship. Thus, we reject the null hypothesis and accept the alternative. Hypothesis Two ARDL was used to determine if portfolio management has positive and significant effect on asset quality of deposit money banks in Nigeria. In the short run, the indicators of performance management all have significant relationship with asset quality except private sector concentration, which also became significant in the long run. Therefore, the null hypothesis is rejected. Hypothesis three ARDL was used to determine if portfolio management has positive and significant effect on capital adequacy of deposit money banks in Nigeria. In the short run, all variables are significant, only liquidity and private sector concentration have negative and significant relationship with capital adequacy; financial asset, foreign portfolio asset and deposit mix have a positive and significant relationship with capital adequacy of deposit money banks in Nigeria.We therefore, reject the null hypothesis. Hypothesis four All variables except liquidity and foreign portfolio asset have significant effect on ROI in the short and long run except foreign portfolio asset which is not significant to ROI in the long run. Foreign portfolio asset does not have a significant effect on ROI. Therefore, the null hypothesis is rejected. Conclusion and recommendations The goal of this study is to evaluate the effect of portfolio management on performance of Deposit Money Banks in Nigeria. Based on empirical findings, portfolio management have significant impact in the performance of Deposit Money Banks in Nigeria. Thus, besides concentrating on the profitability of DMBs as a measure of her performance; return on Investment, asset quality and capital adequacy should also be considered, which is one of the major gaps filled by this study. Deposit Money Banks’ profitability, asset quality, capital adequacy, and return on investment are all affected by liquidity, financial assets, foreign portfolio assets, deposit mix, and loan concentration to the private sector in Nigeria.It is essential that the associated risk and return of each portfolio be evaluated vis a vis its overall effect on the bank’s performance. Based on the findings of the study, we recommend as follows: a) Deposit Money Banks should reduce the level non– performing loans through enhancing the skills of staff, strengthen its due diligence procedures and intensify its monitoring activities. b) Policy makers and the government institutions that regulate the banking sector in Nigeria should provide a conducive regulatory environment that support portfolio management efforts by Deposit Money banks. This should involve a multi-sectoral approach by all stakeholders to review the regulations, analyze the impact and finally review them based on the findings. c) Deposit money banks should always weigh the risk and return associated with holding liquid assets in terms of return on investment while capital adequacy ratios should be monitored continuously to ensure optimal mix that generates the highest returns to stakeholders. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Agblobi, K., & Asamoah,. (2020). Portfolio Management and Profitability of Commercial Banks. Journal of Business and Economic Development. http://www.sciencepublish ingg. Albertazzi, Ugo; Becker, Bo &Boucinha, Miguel (2018). Portfolio rebalancing and the transmission of large-scale asset programmes: Evidence from the euro area. European Central Bank. Working Paper Series. No. 2125/January 2018. Ayodele, E. A., Afolabi, B., & Olaoye, A. C. (2017). Impact of Interest Rate on Portfolio Management in Nigeria. Asian Journal of Economics, Business and Accounting. CBN (2021). Financial stability report. Chakrabarti, A., Singh, K., & Mahmood, I. (2007). Diversification and performance: Evidence from East Asian firms. Strategic Management Journal, 28(2), 101–120. George, G. E., Miroga, B., Ngaruiya, N, J. (2013). An Analysis of Loan Portfolio Management on Organization Profitability: Case of Commercial Banks in Kenya. Research Journal of Finance and Accounting ISSN 2222-1697. Hailu and Tassew (2018), The impact of investment diversification on financial performance of commercial banks in Ethiopia: http://hdl.handle.net/10419/ 231666. Ishak,I and Napier, C (2006): Expropriation of minority interests and corporate diversification. Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets”. Review of Economics and Statistics., 47(1), 13–37. Merton, R. C. (1973). An inter-temporal capital asset pricing model. Econometrica, 41(5), 867–887. Mutega (2016), MS.c Thesis on the Effect of Asset Diversification On The Financial Performance Of Commercial Banks In Kenya. Nadanalingam, S., &Larojan, C., (2015). The impact of Portfolio Structure on Financial Performance of listed private Commercial Banks in Sri Lanka. Osayi, V. I., Dibal, H. S., & Ezuem, M. D. (2019). Risk Management Approach and Banks’ Portfolio Investment Performance in Nigeria. Research Journal of Finance and Accounting www.iiste.org. ISSN 2222–1697 (Paper) ISSN 2222–2847. Owolabi, Windapo and Cattell (2013); Impact of Business Diversification on South African Construction Companies’ Corporate Performance. Journal of Financial Management of Property and Construction. DOI 10.1108/JFMPC-12-2012-0045. Retrieved from http://smallbusiness.chron.com/benefits-financial-transactions- assets-. Oyatoye, E. O., & Arilesere, W. O. (2012). A non-linear programimng model for insurance company investment portfolio management in Nigerian. International Journal ofData Analysis Techniques and Strategies., 4(1), 83–100. Oyedijo (2012): Effects of Product- Market Diversification Strategy on Corporate Financial Performance and Growth: An Empirical Study of Some Companies in Nigeria. Perez, S. (2015). Banking Asset Indicators: Do they Make Analysis Easy?. Retrieved from http://marketrealist.com/2015/03/banking-asset-indicators-make-analysis-easy/. C. Fajinmi et al. Research in Globalization 7 (2023) 100139 8 Purkayastha, S., Manolova, T., & S., and Edelman, Linda, F.. (2011). Diversification and Performance in Developed and Emerging Market Contexts: A Review of the Literature. International Journal of Management Reviews., 14(1), 18–38. Ross, S. A. (1977). The capital asset pricing model (CAPM), short-sale restrictions and related issues. Journal of Finance, 32(177). Sanya, S& Wolfe, S. (2011). Can Banks in Emerging Economies Benefit from Revenue Diversification? JournalFinance Services Research. 40, p. 79–101.https://doi.org/ 10.1007/s106 93-010-0098-z. Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance., 19(3), 425–442. C. Fajinmi et al.