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The current study aims to examine the determinants of the capital adequacy ratio (CAR) in the context of Jordanian banks through a literature review and analysis of empirical evidence. The aggregate data were obtained from Globaleconomy.com, the Financial Soundness Indicators, the Central Bank of Jordan, and World Bank Data covering the period from 2003 to 2021. The aggregate data were analyzed using autoregressive distributed lag (ARDL), utilizing Econometric Views (EViews) software. The empirical results suggest a short-run causality relationship running from banks’ credit-to-deposits ratio, banks’ leverage ratio, banks’ liquidity ratio, and one-year-lagged ROE to the CAR. The results also suggest the existence of short-run causality running from the capital-to-assets ratio, one-year-lagged capital-to-asset ratio, liquid-assets-to-deposits ratio, and coverage ratio to CAR. In addition, the results show the leverage ratio and liquidity ratio as having positive long-run associations with CAR. A positive and significant long-run association was also found between CAR, on the one hand, and the capital-to-assets ratio and the liquid assets to deposits ratio; the coverage ratio, on the other hand, showed a negative and statistically significant long-run association with CAR. The pairwise Granger causality test results reveal that liquid asset to deposits, money supply, profitability, and the capital-to-assets ratio Granger cause CAR. The study findings emphasize the importance of understanding the factors impacting CAR, the direction of the influence, the magnitude of the influence of the determinants of CAR in emerging economies such as Jordan and taking appropriate measures to safeguard the stability and resilience of the banking industry.

Background This study aims to gain insight into the effect of banks' financial performance on their market performance. We conceptualized the research subject on the assumption that the financial performance of an organization is the most important criterion for triggering movement in its stock price. We explored various models and parameters to evaluate financial performance of banks and found CAMELS being one of the most comprehensive and appropriate model. We considered share price growth of banks to measure their stock market performance Methods We collected financial and stock market data pertaining to 32 listed Indian banks for the period 2018 to 2022. The study has employed multiple linear regression analysis of panel data for evaluating the relationship between independent and dependent variables. We adopted panel regression for data analysis and used the Prais- Winsten regression with panel corrected standard errors, as the data suffers from contemporaneous cross-sectional correlation. Results The results show that net non-performing assets, net interest margins, and return on capital have a significant negative impact on share price growth. The capital adequacy ratio and the current and savings account deposit ratios have a positive insignificant impact. The liquid asset-to-total asset ratio has a negative, insignificant impact. The coefficient of determination indicates that the share price growth of banks is more dependent on other factors which are not included in the regression analysis of this study. Conclusion This study helps investors and bankers understand the limited impact of financial parameters on banks'stock prices and to look for other parameters which explain the stock price movement better.

The recent integration of digital technology and financial services has given rise to the newly emerging modality of digital finance. However, does digital finance improve the efficiency of financial services while influencing the investment behavior of brick-and-mortar businesses? With the help of the data about Chinese listed companies, this paper uses multiple regression analysis, instrumental variables, and other methods to empirically test whether and how digital finance affects the financial asset allocation decisions of brick-and-mortar enterprises. The findings suggest that digital finance has a galvanizing effect on financial asset allocation. However, this effect mainly stems from the fact that firms allocate more illiquid financial assets and has a dampening effect on liquid financial assets. Path analysis shows that easing financing constraints is a causal pathway through which digital finance dampens firms' liquid financial asset allocation. Moreover, rising risk exposure levels partially mediate the stimulus of digital finance, motivating firms to allocate illiquid financial assets. This paper contributes to the research on the economic consequences of digital finance and provides policy recommendations on how digital finance can better serve the real economy.

PurposeThe study examines how the liquid assets holdings among non-financial Indian firms vary due to tightening monetary policy and increasing macroeconomic uncertainty.Design/methodology/approachThe authors analyze 5,640 firms for the period 2011–2021. The authors first estimate India’s monetary policy shocks by decomposing the exogenous shocks from the systematic component of monetary policy changes. The authors then examine the effects of the estimated monetary policy shocks and a range of macroeconomic and policy uncertainty indicators on companies’ cash and bank balances to asset ratios using two-step system generalized method of moments (GMM) estimators.FindingsThe authors find that monetary policy shocks cause the cross-sectional variances for the firms’ liquidity holdings to increase. In anticipation of macroeconomic volatility, companies respond to these shocks after taking into account all the firm-level information to minimize the opportunity costs of holding extra cash or too few cash balances that can hamper firms’ operations. Furthermore, compared to other shocks, the contribution of inflation-induced shocks is predicted to be the largest in the cross-sectional deviation of the firm’s cash holdings. The authors also find that low-growth, older and financially constrained firms observe lesser heterogeneity in their cash holdings as they tend to hold cash as a precautionary buffer.Originality/valueThe authors’ approach to the analysis is unique in many ways. To address potential transmission bias, the authors use nowcasts and forecasts of real gross domestic product (GDP) growth and inflation to generate a series of exogenous monetary policy shocks for identifying unanticipated changes in short-term interest rates. Subsequently, the authors estimate how these shocks affect the cross-sectional deviation of liquid assets. For estimating the effects of macroeconomic uncertainty on corporate cash demand, the authors utilize a range of proxies for uncertainty. Unlike previous attempts, the authors offer evidence for a developing and fast-emerging economy.

The appearance of coronavirus in the spring of 2020 has significantly revalued risk exposures in the business environment, which required new approaches in the measurement of financial risks. This paper defines and explains a new approach to the measurement of liquidity risk in companies in a time of an economic crisis. This approach is more responsive to stressful circumstances in the business environment and measures the time in an economic crisis when a company can still pay maturing liabilities out of its own inventory of liquid assets, where sales of a company on the market are limited or completely prohibited, as this was the case for some industries during the first wave of Covid -19 in spring 2020. The objective of this paper was to define a new metric for the measurement of corporate liquidity, which is sensitive to this environment and shows the propensity of a company to the risk of illiquidity if an economic crisis appears. This paper, in the next step, leverages the newly defined metric of corporate liquidity and calculates the average liquidity of all companies, as well as the average liquidity by business sectors in the Serbian economy, to discuss corporate liquidity in a crisis. The results show that the average liquidity of the Serbian economy in crisis at the end of 3Q 2020 was 78,02 days, i.e., the average company in the Serbian economy was able to survive 78,02 days in stress by the end of 3Q 2020 if its sales on the market were prohibited. Implications for Central European audience: The model in this paper is in the interest of every company and bank to measure the liquidity risk of business partners in times of an economic crisis and to predict which business partners may have liquidity problems in crisis and may therefore not be able to pay their open liabilities. The contribution of this paper to liquidity risk measurement in companies in times of stress is high as the currently available literature does not offer an alternative approach.

This paper presents an analytical framework for the identification of vulnerabilities arising from the liquidity and funding profile of banks. It is composed of two pillars—estimation of liquidity needs and the counterbalancing capacity of the total liquid assets—that determine a liquidity surplus or shortfall and the drivers for a range of plausible scenarios. Granular bank-level data on the structure of liabilities, maturation profile, liquid assets quality composition, and asset encumbrance are used for that purpose, also taking into account associated commonality effects. A new liquidity metric is introduced—the distance to liquidity stress indicator (DLSI)—which measures the required stress factor for banks to become illiquid. The novelty of the approach (i.e., taking into account asset encumbrance to determine counterbalancing capacity) provides empirical evidence that asset encumbrance has a significant impact on a bank’s liquidity position, leading to the non-linear behavior of liquidity shortfalls, even in the case of linear stress factors.

This article examines the link between the opportunity cost of money and time-varying liquidity premia of near-money assets. Higher interest rates imply higher opportunity costs of holding money and hence a higher premium for the liquidity service benefits of assets that are close substitutes for money. Consistent with this theory, short-term interest rates in the United States, United Kingdom, and Canada have a strong positive relationship with the liquidity premium of Treasury bills and other near-money assets over periods going back to the 1920s. Once the opportunity cost of money is taken into account, Treasury security supply variables lose their explanatory power for the liquidity premium, except for transitory short-run effects. These findings indicate a high elasticity of substitution between money and near-money assets. As a consequence, a central bank that follows an interest rate operating target not only elastically accommodates and neutralizes shocks to money demand, but effectively also shocks to near-money asset supply and demand.

A wide body of empirical evidence finds that approximately 25 percent of fiscal stimulus payments (e.g., tax rebates) are spent on nondurable household consumption in the quarter that they are received. To interpret this fact, we develop a structural economic model where households can hold two assets: a low-return liquid asset (e.g., cash, checking account) and a high-return illiquid asset that carries a transaction cost (e.g., housing, retirement account). The optimal life-cycle pattern of portfolio choice implies that many households in the model are \"wealthy hand-to-mouth\": they hold little or no liquid wealth despite owning sizable quantities of illiquid assets. Therefore, they display large propensities to consume out of additional transitory income, and small propensities to consume out of news about future income. We document the existence of such households in data from the Survey of Consumer Finances. A version of the model parameterized to the 2001 tax rebate episode yields consumption responses to fiscal stimulus payments that are in line with the evidence, and an order of magnitude larger than in the standard \"one-asset\" framework. The model's nonlinearities with respect to the rebate size and the prevailing aggregate economic conditions have implications for policy design.

We present a model of optimal allocation to liquid and illiquid assets, where illiquidity risk results from the restriction that an asset cannot be traded for intervals of uncertain duration. Illiquidity risk leads to increased and state-dependent risk aversion and reduces the allocation to both liquid and illiquid risky assets. Uncertainty about the length of the illiquidity interval, as opposed to a deterministic nontrading interval, is a primary determinant of the cost of illiquidity. We allow market liquidity to vary from “normal” periods, when all assets are fully liquid, to “illiquidity crises,” when some assets can only be traded infrequently. The possibility of a liquidity crisis leads to limited arbitrage in normal times. Investors are willing to forgo 2% of their wealth to hedge against illiquidity crises occurring once every 10 years. This paper was accepted by Itay Goldstein, finance.

PurposeThis study aims to demonstrate and measure the impact of liquidity shocks on a bank’s solvency, especially when the bank does not hold sufficient liquid assets.Design/methodology/approachThe proposed model is an extension of Merton’s (1974) model. It assesses the bank’s probability of default over one or two (short) periods relative to liquidity shocks. The shock scenarios are materialised by different net demands for the withdrawal of funds (NDWF) and may lead the bank to sell illiquid assets at a depreciated value. We consider the possibility of second-round effects at the beginning of the second period by introducing the probability of their occurrence. This probability depends on the proportion of illiquid assets put up for sale following the initial shock in different dependency scenarios.FindingsWe observe a positive relationship between the initial NDWF and the bank’s probability of default (particularly over the second period, which is conditional on the second-round effects). However, this relationship is not linear, and a significant proportion of liquid assets makes it possible to attenuate or even eliminate the effects of shock scenarios on bank solvency.Practical implicationsThe proposed model enables banks to determine the necessary level of liquid assets, allowing them to resist (i.e. remain solvent) different liquidity shock scenarios for both periods (including eventual second-round effects) under the assumptions considered. Therefore, it can contribute to complementing or improving current internal liquidity adequacy assessment processes (ILAAPs).Originality/valueThe proposed microprudential approach consists of measuring the impact of liquidity risk on a bank’s solvency, complementing the current prudential framework in which these two topics are treated separately. It also complements the existing literature, in which the impact of liquidity risk on solvency risk has not been sufficiently studied. Finally, our model allows banks to manage liquidity using a solvency approach.