Towards a More Stable Banking Financial Position COVID-19 Impacts and IFRS 9 Post-Model Practices “Management Overlays”

In the repercussions of the latest worldwide financial crisis that have occurred due to the corona virus reasons (COVID-19); unprecedented stressful economic conditions prevailed, coupled with significant recessionary waves reasoned of the worldwide imposed strict lock downs that have adversely affected most of the economic sectors across the globe. Based on such adverse conditions, and as banks were used to calculate their Expected Credit Losses (ECLs) within the provisioning systems (IFRS 9); the forward looking projections embedded at the banks models fell short to predict the COVID-19 hit, as well as objectively reflect the widely spread governmental rescue plans, such as payment holidays and moratoria schemes. Consequently, and based on such unprecedented challenges, new risk management practices — Management Overlays — have risen to help in terms of overcoming such a systemic uncertainty situation that added more hurdles with regard to banks models and forecasting powers. The researcher is discussing how banks can start using the management overlays, or post-model adjustments, according to each and every bank’s portfolio composition, trends, and stress testing outcomes, where risks and uncertainties cannot be adequately reflected in existing models, until the global regulators (e.g., central banks, and external auditing bodies), as well as the main research and rating agencies can reach and agree on the new set of the updated macroeconomic indicators that can be utilized globally, and per region, reflecting the new era post COVID-19, and supporting in building capital levels for confronting such an adverse and unprecedented situation of the COVID-19 pandemic, as well as similar situations in the future.

Introduction
The latest worldwide financial crises that have started in Q1-2020 due to the corona virus reasons, known under the name of COVID-19, have gotten out the questions as to the financial related models that are utilized by the global institutions, their forecasting risks, complexities and limitations. The economic slowdown, which started earlier in the first quarter of the year 2020, brought about the crash of the international markets reasoned of the wide spread global lock downs that accompanied the COVID-19 starting cycle.
The International Monetary Fund (IMF, 2020) has projected a global negative growth at (4.9%) in 2020, adding that the forecast indicated an unprecedented decline in the global activity due to the COVID-19 pandemic, with assurance that there was a higher than usual degree of uncertainty around this forecast (figure 1). The pandemic rapidly intensified in a number of emerging and developing economies, necessitated stringent lockdowns and resulted in larger disruptions to activities and economic sectors than ever forecasted. The COVID-19 crisis has brought a new scenario within the banking sector, showing regulators implementing new sets of dispensations and payment holidays to save their markets during lock downs, besides accepting some exceptional rules with regard to the IFRS 9 implementations according to the new payment moratoria introduced structures without dragging the attention towards borrowers downgrades, and the advice of new risk management practices, the Management post-model adjustments known as Management Overlays in specific, to monitor and enhance the financial strength of banks, hence, improving the banking system stability during such unprecedented crises.
Setting the proper and effective methodologies of the provisioning systems has a direct impact on the bank's: capital ratio assessments, the frameworks of capital planning over medium to long terms, the accurate capital adequacy ratios needed to support strategies, as well as plans and objectives of banks during the mentioned time intervals.
Provisions represent funds that are placed aside by a bank to cover normal and anticipated losses in the future. In other words, they represent a liability of probabilistic timing and amount (probabilistic approach). Banks use provisions to account for potential loan defaults and expenses, to ensure that they are presenting an accurate assessment of their overall financial health, by turn helps to set the proper and effective plans for: assessing the facilities rating(s) and growth levels, capital ratios, study and assess banks' frameworks of capital planning over medium to long terms, determine the accurate capital adequacy ratios needed to support strategies and assets growth during the mentioned time intervals, and by turn assure the adequate profitability and sustainability levels for the bank confidently (no surprises). In addition, to plan properly for any needed corrective actions, to react proactively towards any deviations from the level of required assets growth (and/or accompanied target provision levels), and safely cover expected loss levels (figure 3). Moreover, the economic capital (capital including additional buffers) represent the main shock absorbing resort towards any unexpected crises without affecting the financial stability of the bank or its competency level within the market(s) it is functioning within, ending with the positive and stable impact on its reputation. Therefore, those main two financial stability resorts (provisions and economic capital) represent the main shelters for banks against any expected, as well as none expected losses that banks might face, keeping in mind the end goal to bring down the risk during any economic and financial turmoil periods to the acceptable levels and according to each and every bank's risk appetite criteria and tolerable thresholds. 72 www.scholink.org/ojs/index.php/ijafs International Journal of Accounting and Finance Studies Vol. 4, No. 1, 2021 Figure 3. Bank Credit Loss Distribution. Source Durrani (2013).
The International Financial Reporting Standard number 9 (IFRS 9) sets out a framework for determining the amount of Expected Credit Loss (ECL) that should be recognized. It requires that lifetime ECLs should be accounted for when there is a Significant Increase in Credit Risk (SICR) on a financial instrument. However, it does not direct towards the exact basis on which banks should determine forward looking scenarios to consider when estimating ECLs.
Versus the previously implemented IAS 39, the IFRS 9 requires provisions to account for current, as well as forward looking risks. There has been a substantial debate with regard to the adequacy of capturing and utilization of the macro-economic factors especially during any deteriorating conditions.
Banks estimating ECLs under the IFRS 9 often use a three phases process: i) develop judgements and anticipations about the future, ii) apply those judgements/anticipations to statistical models that are developed based on historical relationships, and iii) use relevant data to feed into these models. This process often involves statistical modelling in a more detailed and comprehensive manner as being compared to other accounting estimates, which increases the challenges for the IFRS 9 implementation.
Extreme economic and stressful conditions, like COVID-19, coupled with uncertainty around the duration of the pandemic, besides the increasing probabilities for potential relapses, effects of government support and what recovery shall look like; all shed the lights towards the forward-looking judgmental challenges due to the highly degree of uncertainty. At the same time, historical relationships between key utilized model variables might no longer hold, and comparable economic conditions might not have existed in the past reasoned of the existing unprecedented pandemic situation across the globe.
To assess any Significant Increase in Credit Risk (SICR), the IFRS 9 requires that banks assess changes in the risk of default occurring over the expected lifetime of a financial instrument. Both the assessment 73 www.scholink.org/ojs/index.php/ijafs International Journal of Accounting and Finance Studies Vol. 4, No. 1, 2021 of SICRs and the measurement of ECLs are required to be based on reasonable and supportable information that are available to the bank without undue cost or effort.
The COVID-19 pandemic has dominated the agenda of banks during the year 2020 onwards, as well as a considerable number of significant economic sectors that are experiencing adverse shifts. The use of historical data in risk modelling, stress testing and portfolio allocation has been questioned and criticized within the crises due to the fact that they fundamentally differ than previous economic stresses across history and were not able to forecast the severity of the crises adequately. Normally, banks use to stress their models against linked parameters, for instance to GDP growth, oil prices, inflation and unemployment rates. For the severe recession that has started in Q1-2020; these models predicted dramatic increases in loan defaults and losses, in fact post the crises occurrence. However, the expected increases in defaults and losses have not yet fully materialized due to the visible government support measures and payment moratoria that econometric models normally do not capture.
In this exploratory research paper, the researcher shall discuss how management adjustments/overlays can be effectively used to buffer against forward-looking uncertain performance indicators of assets portfolios. The study shall rely mainly on data extracted from the European market and the asymmetric impact of COVID-19 on different industry sectors. This is for two reasons: i) the better disclosure standards in Europe with regard to the COVID-19 impacts, and related information transparency as being compared to other regions across the globe, ii) the Euro Zone being the biggest trader to the Middle East (oil and none oil exporting countries), North Africa (MENA) and Egypt in specific; which, all combined, represent one of the main economic hubs across the globe. In addition to the direct proportionate economic dependency between both regions (EU and MENA), as well as encompassing developed and developing countries that can help in optimizing banks asset quality levels and management override practices during such a stressful and unprecedented situation.

Statement of the Research Study Problem and Objectives
According to Andrea Enria, the Chair of the ECB Supervisory Board, he mentioned that delayed recognition and poor management of deteriorating asset quality could easily clog up bank balance sheets with non-performing loans for a fairly long period of time, making it more difficult for the banks to support viable customers and underpin a faster economic recovery (ECB, 2020 credit portfolio managers wondered how to react to this apparent model failure that were not able to predict the forward looking measures efficiently which is against the main objective of the IFRS 9 standard objective, and how to best stir the bank's balance sheet through these challenging times. Naturally given the circumstances, the European banking supervisors paid close attentions to the asset performance, deteriorating asset quality levels, and impacts on banks' capital ratios. The Single Supervisory Mechanism specified the priorities for the year 2021 as follows: i) focus on credit risk management, ii) operations, and iii) monitoring and reporting. Credit portfolio managers must identify, measure and mitigate the impacts of credit risk, as well as assess the bank's operational capacity and effectiveness to proactively manage any expected increase in distressed borrowers whether in good times or mainly at stressful periods.
Furthermore, the ECB placed particular emphasis on the banks capacities to identify any deterioration in asset quality at an early stage, as well as on the banks abilities to continue taking the necessary actions towards appropriately manage loan arrears and non-performing loans. As an overview, the following steps towards an optimal banks' balance sheets can be normally advised: • Data preparation and comprehensiveness. Identify the minimum data set required for an objective impact analysis. This includes data extract mainly from finance and risk functional areas, as well as operational data like adequate skillsets and manpower needed. For corporate borrowers, up-todate financials as well as market prices from the loan, bonds, and equity markets are important.
• Continuous monitoring of actual loan performance, borrowers' behavior and ongoing collateral values (LGDs) proper evaluation, including the use of government support programs and payment moratoria. In addition to interactive reporting of watch list, forbearance and workout exposures including the monitoring of detailed recovery cash flows (detailed non-performing loans-NPLs-analysis and appropriate handling strategies).
• Flexible reports generation to compare actual versus predicted performance from existing or newly adjusted models. The use of flexible and simple alternative practices that are easier to adjust and explore than traditional implementations during crises.
• Combine risk model predictions, expert views (mainly for objective management overlays utilization), and market valuations to quantify the impact on the future balance sheets including risk and return trade-off profiles of all relevant portfolio segments. Include the relevant regulatory changes from the prudential backstop and Basel accords.
• Finally, to use management adjustment overlays for facing any unprecedented stressful situation and following appropriate governance and disclosure standards.
In brief, COVID-19 has had an enormous adverse impacts on the international economy. Unfortunately, the same has also negatively affected companies and private individuals, by turn banks. The IFRS 9 is supposed to be unbiased and forward looking in terms of banks calculating their ECLs. The main challenges as a reflection for this adverse situation are: • The economic and credit outcome are highly uncertain; 75 www.scholink.org/ojs/index.php/ijafs International Journal of Accounting and Finance Studies Vol. 4, No. 1, 2021 • Loan loss practices and disclosure shall face significant scrutiny in the markets and will be subject of multiple regulatory interventions and debates; • ECL models will cease working effectively. Expert judgement and post model adjustments shall represent a critical and essential alternative as there is no time or accurate relevant data to depend upon in rebuilding accurate models; • Choice of scenarios, their probability of occurrence, and likely loss severity; will be a significant driver of staging and ECL outcomes reasoned of the high level of uncertainty; • Identification of problematic facilities and lifetime deterioration under credit risk has turned to be more complex and more demands and pressures for more dynamic assessments and reporting; • Control mechanisms for ECL levels and reporting will need to change to reflect the focus on different information sources and estimation techniques, in specific the utilization of Management Overlays, coupled with proper predictions according to each and every bank's objective assessment, and proper governance, whether in terms of approvals or public disclosures.
Therefore, this research paper shall spot the light on the COVID-19 challenges, together with its massive adverse impacts on the utilized IFRS 9 models reasoned of the unprecedented pandemic situation that was not accounted for at any model across the globe, and the increasing reliance need placed on management overlays (based on expert judgement) to derive expected credit losses during the uncertainty and challenging systemic period. Sample banks were used to show the utilization and disclosures of the management overlays, and how they allowed for the judgmental/overlay concept as a risk mitigation tool to overcome the adverse economic effects of the pandemic not fully captured by the models.

Research Study Hypothesis
H0: There is no significant relationship between the Banking System Stability and the Post Provisions Model Adjustments (Management Overlays).

H1: There is a significant relationship between the Banking System Stability and the Post Provisions
Model Adjustments (Management Overlays).
Research Question:

What is the relationship (if any) between the Banking System Stability and the Post Provisions Model
Adjustments (Management Overlays)?

Limitations of the Research Study
There are several limitations in this research study that should be taken into consideration for future researches. The research sample is limited to the selected research study banks. This is due to the difficulty faced in terms of data access, especially accurate and disclosed data post recent COVID-19 crises, and confidentiality reasons across the banking sector in specific data related to the internal banks provisioning systems and stress testing, of which the researcher has faced challenges to obtain the 76 www.scholink.org/ojs/index.php/ijafs International Journal of Accounting and Finance Studies Vol. 4, No. 1, 2021 required data for the research study, especially due to the COVID-19 reasons, for instance, data gathering during lock downs, and work from home practices accompanied with related mis-communication. Therefore, the sample was only limited to those banks that are included under this exploratory study. This has prevented the researcher from widening the sample across more study banks and risk measures that can be generalized in the future once the new set of worldwide macro-economic factors are marginally agreed upon between regulators, main research and credit rating agencies. It will not likely be possible to fully generalize the results based on these limitations, in specific revise models in the short term to capture all of these uncertainty factors as the overall systemic situation is not yet clear for implementing such changes. Therefore, banks can start using the practice concept of management overlays, or post-model adjustments, where risks and uncertainties cannot be adequately reflected in existing models, and according to each and every bank's portfolio breakdown, trends, stress testing outcomes and Probability of Default (PD) expectations. Expectations are high that such overlays will necessarily play a more important role and will be placed on a higher priority level in today's environment.

Literature Review
Statistical methods such as regressions, and decision trees are widely used by banks to model for credit risk. They all rely on the principle that patterns and behaviors from the past will likely repeat in the future. Models identify these patterns in historical data and use them for predictions. Much work has gone in the development of credit models using these methods to link credit risk measures such as the Probability of Default (PD) or Loss Given Default (LGD), and macroeconomic variables. The primary objective for regulators and banks to develop these models were to properly calculate regulatory capital charges under the internal ratings based approach, run supervisor stress test scenarios, and mainly to calculate loan loss provisions under IFRS 9; all was to overcome the 2009 financial crises root causes and ensure the none catastrophic repetition. It appeared obvious that point-in-time (PiT) models predicting defaults based on the macro environment were not performing well during COVID-19 due to the previous raised unprecedency reasons (black swan).
Accordingly, and based on such a sever stressful situation: On March 27 th , 2020, the International Accounting Standards Board (IASB, 2020) has released its statement outlining the views on the subject. instance, the extension of installment to obligors, ought not consequently trigger such facilities to have endured a SICR, for example to be downgraded from stage I to stage II.
Lockdown and social separating impacts should be reflected as far as the effect on macroeconomic drivers exists, and, eventually, on default rates. It will not almost certainly be feasible to update models in the present phase to catch all these variables where risks and vulnerabilities cannot be satisfactorily reflected in existing models. Expectations are high that management overlays will essentially assume a more significant gap filling at this area, and will be set on a higher need levels in the present climate.
The IFRS 9 models would translate the unprecedented and repeated lockdowns, travel bans, physical distancing measures and the resulting slump in economic activity into steep increases in loan defaults and bankruptcies. On the other hand, extensive government support measures have been successful in shielding households and companies from the worst consequences of the pandemic. While companies in the hardest hit economic sectors (e.g., tourism and aviation) have suffered dramatic reductions in total sales, the support measures thus far have prevented a new wave of bankruptcies and non-performing loans. Most credit models were not designed to capture the unprecedented relief measures as they had no precedent as is in the past.
In April 2020 during the lockdown of the first COVID-19 wave, predictions have shown an increasing trend of NPL ratios in Europe and the US, for 2020 and 2021 based on historical data and macro forecasts from the IMF. According to Moody's rating agency, the US saw a doubling of the corporate default rate from 1.5% in 2019 to 3.1% in 2020, the highest level since the financial crisis of 2009.
Given the continued economic stress with renewed lockdowns in Q4-2020 and Q1-2021, the NPL ratios shall steadily start increasing eventually once the support measures are phased out. Nevertheless, banks have found that many of their credit models did not practically work well as expected during this crisis (Moody's, 2020).
The failure of macro-econometric time-series regression based models has motivated credit risk experts to explore other credit risk practices to overcome such challenges. Altman Z-score-style models use to predict corporate distress based on selected financial ratios. Z-score models have been proven to successfully discriminate between healthy and distressed borrowers across countries and industries (Altman, 2014). Banks internal rating models often include Z-score-like financial ratios which can help to identify companies that are more likely to survive during the current crisis. As they do not use the macroeconomic variables as an input, Z-score default models are considered more through-the-cycle (TTC) and less suitable for macroeconomic stress testing. In past recessions, broadly speaking, Z-score models kept their discriminatory power but they did not accurately predict the cyclical swings in default rates. Therefore, the pandemic situation has emphasized the importance of having other alternative mechanics and considering the current model limitations and data availability.

Banks and IFRS 9 Implementation Post COVID-19
An analysis was made by Deloitte in Q2-2020 as being one of the main external auditing and research firms across the globe, and being closely linked to the IFRS 9 implementations across the worldwide 78 The second sample was related to the UK banking sector that has witnessed an increasing trend in provisions across the board. The UK banks results for Q1-2020 have shown a significant increase at their provisioning charges reflecting a deterioration in the economic outlook drastically vs. Q4-2019.
Banks, e.g., HSBC, have set expectations of further significant impairment charges to come, which should have been budgeted and accounted for at their IFRS 9 (figure 5). The first quarter of the year 2020 bank results were notable for the increase in provision charges reflecting a deterioration in the overall economic outlook. Besides, the similarity of increase in cost of risk versus Q4-2019 was striking.
The situation is reflecting the uncertainty vision of the bank, together with worries from the first wave of customer treatments when expires, potentially leading to big increases in ECLs in the future post the ending of government support periods, expected macro-economic downgrades that shall increase the economic severity hurdles due to Brexit burden adjustments, and all are agreeing with the assumptions and conclusions of the researcher.
Some banks, e.g., SAN, having started taking sector specific actions, while others, e.g., HSBC, Lloyds, have set their expectations of further significant impairment charge to come using the management overlays concept and this is interesting as IFRS 9 should have brought such forward their best view of losses which did not take place due to the outlook is too uncertain to book as yet, besides the regulators support.

Managing Performing and Non-Performing Exposures
Based on the previous illustration, it is clear that banks shall start facing a new set of challenges regarding corporate borrowers' solvency, liquidity, and profitability. On the contrary, macro-based projections do not reflect the highly heterogeneous new impacts at different sectors. While telecommunication and online gaming sectors benefited, others like airlines and accommodation are steeply suffering dramatic reduction in sales. Historical financial statements, meanwhile, take time before they reflect the current impact on business and financials. Borrowers cash flow forecasts should reflect liquidity positions and associated financing needs due to negative cash flows and help identify which obligors shall not be able to repay debts according to agreed on schedules. Banks need more comprehensive sector-based detailed analysis for numerous purposes, such as: identifying the most exposed sectors and clients, proper projections of non-performing assets and rating migrations, and developing effective restructuring plans and asset disposals (NPLMarkets, 2021).
An important question in the prediction of the COVID-19 impact on credit risk is to understand how quickly the economy will return to its pre-crisis level. The recovery will also depend on how many obligors will suffer (e.g., stage II downgrade), or go bankrupt (e.g. stage III) due to the fall in sales and profitability levels. Bankruptcies have long lasting effects, prolonging the negative consequences of the economic shock. Therefore, the European policymakers have generally taken the view to provide whatever liquidity is required to keep the small, medium and large enterprises afloat (OECD, 2020).
Since the summer of 2020 a number of studies have emerged that aimed to predict the asymmetric distressed. Distress is more frequent for SMEs as opposed to large corporates, for companies with high pre-COVID leverage, and more visible for some sectors, such as services sectors, and accommodation.
On September 30th, 2020, the predicted increase in illiquid and insolvent companies in Italy were not yet been observed.  Accommodation, Arts, Entertainment were among the most affected sectors with a near threefold increase in the NPLs ratio (Table 1).
In France, Guerini et al. (2020) simulated the impact of the COVID-19 crisis on corporate solvency using balance sheet data of one million French nonfinancial companies, assuming they shrink their production costs in the context of a sharp drop in demand. They found that the lockdown triggered an unprecedented increase in the share of illiquid and insolvent firms, with the former more than doubling relative to a scenario without COVID-19 (growing from 3.8% to more than 10%) and insolvencies increasing by 80% (from 1.8% to 3.2%). They found sectors such as hotels and restaurants, household services, and construction are the most vulnerable. At a country level, corporates have been impacted by COVID -19 with different degrees of severity.

Management Overlays-Post Model Adjustments
Overlays is a term that can be utilized to depict a range of changes that are made external to the essential models. Sometimes, the term can allude to direct changes to address known model errors or information lacks. In others, the overlay is undeniably more abstract and judgmental. For instance, it some of the time alludes to the utilization of master credit judgment to address gaps in models, data, or both (for instance, as new hazards or vulnerabilities emerge). It can likewise allude to changes made to catch dangers and vulnerabilities which are not caught by the models since they were not intended to address them, for example COVID-19, or Brexit (PwC, 2020).
As the IFRS 9 impairment model main objective was to account for forward-looking parameters, banks are required to consider a range of possible future economic scenarios and their probabilities to were not applied.
Since they are inalienably critical, post-model adjustments require vigorous processes, adequate manpower and skillsets, proper assessments and techniques (such as stress testing reasonable assumptions and outcomes in light of the pandemic uncertainty and matching with the bank's portfolio composition, expected performance and deterioration levels), governance and internal controls, supported by transparent and proper disclosures. While frameworking overlays; the key sample aspects to consider include (PwC, 2020): • What is the impediment that is being tended to, and why?
• How was the overlay measured, and utilized rationale?
• What are the basic presumptions, and how could they be created and upheld?
• What data sets were utilized, and how could it be resolved to be steady?
• How might the overlay be devoured after some time (for instance, through model redevelopment, new information opening up, or advance loan losses)?
• How might sensibility execution be checked (for instance, by utilizing back-testing, KPIs checking, and correlation with stress testing)?
• How the exposures to which the overlay relates be calculated at an adequately granular level?
• How have the staging ramifications of the overlay been tended to?
• Has a comprehensive process check of the ECL demonstrating measures been done, to guarantee that all potential model constraints which may show the requirement for an overlay have been thought of? For example: Is latest borrower data accessible?
• Is the staging methodology to decide SICR suitable?
• Have government help programs been properly mulled over?
• Has the overlay been checked, along with the complete ECL measures, to guarantee that there is no potential for twofold count? For example, considering any:  hierarchical changes previously fused by deteriorating economic gauges.
 changes because of economic assumptions previously included inside probability of defaults.
 assumptions regarding future losses embedded in historical data during model adjustment/calibration.
 changes in accordance with DPDs data previously added for different uses.
• Governance:  Vol. 4, No. 1, 2021 Tending to and reporting these inquiries help to guarantee satisfactory cycles upfront and to forestall difficulties over the long haul as starting constraints (those bringing about the requirement for an overlay) are settled. As an example, among different difficulties, the shortfall of documentation may make it more tough to decide in future periods whether the overlay is as yet required.
Overlays may require extra disclosures, and they may affect others. For quarter period intervals, if there has been a huge change since the latest fiscal year end in the way to deal with assessing ECLs (regardless of whether because of changes in core models, overlays or something else), IAS 34 could require extra disclosures. These could incorporate revelations about sources of information, and assessment methods under IFRS 7 and IAS 1. The impact of overlays on disclosures that give data on a granular reason (for instance, by stage, industry, etc.) should be thoroughly considered to decide if and how the overlay is pushed down, or whether it is (or can be) introduced independently. Cautious thought of the disclosure impacts of overlays at a beginning phase may assist with giving important subtleties later on, and guarantee that potential intricacies are tended to.
The IASB has been firmly drawn in with numerous prudential and regulatory bodies in regards to the utilization of IFRS 9 and COVID-19 pandemic. A few regulators have effectively distributed direction remarking on the of IFRS 9 execution in the current climate (such as: The European Banking Authority, the European Central Bank, the Central Bank of Egypt, and the Central Bank of UAE) on a mean to help their business sectors regarding confronting a particularly distressing circumstance (black swan).

Data and Methodology
This section sets out the approach and methodology for the research study sample analysis. It displays how the significant COVID-19 impairment risks facing banks, such as: HSBC which represents one of the main global banking pillars, and Emirates NBD as one of the main regional banks in the Middle East, were recognized, and how Management Overlays were measured and implemented. In this exploratory research paper, the researcher shall discuss how management adjustments/overlays can be used to buffer against forward-looking uncertain performance indicators of assets portfolios.

Conceptual Framework
This

Results and Findings
The following section illustrates the study selected sample and in light of the previous highlighted research study limitations.

HSBC
The COVID-19 emergencies have put its shadows on the worldwide economy and unfavorably influenced the bank's clients and their performance. The future impacts of the crises stay unsure. The government support measures have tried to ease difficulties, given the fast speed of progress and critical operational requests (HSBC, 2020).
Government rules forced all throughout the planet to restrict the spread of COVID-19 brought about a sharp constriction in the worldwide economy during the year 2020 and onwards. Simultaneously, governments likewise made early strides intended to relax the degree of the harm to investment, labor markets and trade. The bank has depended on a central scenario used to ascertain impairment, accepted that economic actions will recuperate throughout 2021 onwards. In this situation, the recuperation will be upheld by a fruitful carry out of vaccination programs across key business territories, which, combined with successful non-pharmacological measures to contain the infection, will prompt a decrease in contaminations throughout the span of the following years (for example 2-3 years).
Governments and state regulators shall keep on cooperating across the bank's critical business sectors to guarantee that customers get a proper degree of financial help until the overall stressful situation is facilitated. Such help is planned to guarantee that markets do not encounter unexpected, and difficult The bank has started market explicit measures to help its clients through such stressful times. These included installment holidays, the deferring of specific charges, and liquidity help for organizations 87 www.scholink.org/ojs/index.php/ijafs International Journal of Accounting and Finance Studies Vol. 4, No. 1, 2021 confronting market vulnerability and production network interruption. The bank is additionally working intimately with governments, and supporting public plans that emphasis on the economies generally affected by COVID-19. For example, in response to a written request from the PRA, the bank has cancelled the fourth interim dividend for 2019 of $ 0.21 per ordinary share, in addition to freezing interim dividend payments or accruals in respect of ordinary shares until the end of 2020 (HSBC, 2020).
Moreover, credit losses may increment reasoned of openness to vulnerable sectors of the economy like retail, hospitality, and aviation. The effect of the pandemic on the corporations related to these areas is uncertain and may prompt huge credit losses on explicit exposures, which may not be completely Capital Adequacy ratio as 20.2%. The management overlays of the IFRS 9 models will proceed as the economic results of the COVID-19 crises become more clear in the future, economic situation standardize and credit losses happen (HSBC, 2020).

Emirates NBD
In the determination of Q1-2020 ECL, the bank has considered the potential impact, based on the best available information of the uncertainties caused by the COVID-19 pandemic and taking into account the economic support and relief measures of governments and central banks of the bank's key markets.
The bank has also considered the notices issued by the Central Bank of UAE with regards to the  Vol. 4, No. 1, 2021 Under IFRS 9, loans are required to be moved from stage I to stage II if and only if they have been the subject to a SICR since origination (quantitative, and/or qualitative). The SICR occurs when there is a significant increase in the risk of a default occurring over the expected life of a financial instrument.
The bank continued to assess borrowers for other indicators of unlikeliness to pay, taking into consideration the underlying cause of any financial difficulty and whether it is likely to be temporary as a result of COVID-19 or longer term (ENBD, 2020).
During the year 2020, ENBD has initiated a program of payment relief for its impacted customers by deferring interest/principal due for a period of one month to three months. These payment reliefs were considered as short term liquidity to address borrowers cash flow issues. The relief offered to customers may indicate a SICR. However, the bank believed that the extension of these payment reliefs do not automatically trigger a SICR and a stage migration for the purposes of calculating ECL, as these are being made available to assist borrowers affected by the COVID-19 outbreak to resume regular payments. At this stage sufficient information was not available to enable the bank to individually differentiate between a borrowers' short term liquidity constraints and a change in its lifetime credit risk.
Any changes made to ECL in order to estimate the overall impact of COVID-19 was subject to very high levels of uncertainty as only limited forward looking information was available on which to base those changes. The bank has previously performed historical analysis and identified key economic variables impacting credit risk and ECL for each portfolio and expert judgement has also been applied in this process. These economic variables and their associated impact on PD, EAD and LGD vary by financial instrument. Forecast of these economic variables (the base, upside and downside economic scenarios) were obtained externally on a quarterly basis.
In light of the current uncertain economic environment, the bank has re-assessed the scenarios weighting to reflect the impact of the current uncertainty in measuring the estimated credit losses, in addition of depending on the management expert judgements-overlays-to overcome the significant uncurtaining levels with regard to the forward looking measures (ENBD, 2020). In making estimates, the bank assessed a range of possible outcomes by stressing the previous basis (that includes upside, based case and downside scenarios) and changed the downside weightings through to 100%.
The bank compared the downside macroeconomic variables to indicative data received from an independent source and for some portfolios or sectors that have further stressed the ECL, before and after adding the management judgmental overlays. In addition to the above, the bank has applied industry specific account level adjustments for retail exposures with regards to specific industries whose employees are expected to be most impacted due to COVID-19 such as airlines, hospitality, retail and tourism. ENBD continued to individually assess significant corporate exposures to adequately safeguard against any adverse movements due to COVID-19 waves. The increase in the downturn weighting of the macroeconomic scenario and the management overlays result in an additional ECL of AED 878 million for the bank. The impact of such uncertain economic environment 89 www.scholink.org/ojs/index.php/ijafs International Journal of Accounting and Finance Studies Vol. 4, No. 1, 2021 is judgmental and the bank will continue to reassess its position and the related impact on regular basis.
Based on the implemented risk and stability measures, the bank was able, as at 31 st December 2020, to have the Common Equity Tier 1 ratio as 15.0%, Tier 1 ratio as 17.4% and Total Capital Adequacy ratio as 18.5%.

Conclusions and Recommendations
This research study has checked the Post Provisions Model Adjustments (Management Overlays) and its effects on the Banking Stability levels. Fundamentally, the study has revealed the insight towards the visible connection between the Banking System Stability and the Post Provisions Model Adjustments (Management Overlays); fill any gap that emerges between the current and the focused hazard dimensions of the bank's provisioning system structure, and by turn emphatically influences the budgetary dependability and feasible development models of banks.
The outcomes of the COVID-19 crises on the IFRS 9 model execution and dependability brought about the requirement for needed upgraded checking tools and related post model changes. Significant changes to model parametric resources of data like GDP, oil prices, and unemployment rates have made the model outcomes problematic. Model execution constraints have been generally articulated for the IFRS 9 scorecards which compute expected credit losses. Therefore, more noteworthy dependence has been put on the management overlays dependent on judgements to determine expected credit losses.
The economic outlook for the next 2-3 years shall be highly impacted by the crisis, with a new set of economic (and loss) scenarios likely required. The eventual outcome is still uncertain, as it depends significantly on the cycle duration and severity spread of the coronavirus related lockdowns, and the effectiveness of government measures. The microeconomic impact for specific industries and sectors will vary through the crisis as can be seen within the research paper, driving variable credit risks which need to be captured at a granular level. Since credit model parameters are used in both the collective measurement of ECL and staging allocations; limitations in the credit risk models and changes to their relationship with the macro-economy will lead to reduced model performance effectiveness, and increase the limitations of the model use in the new world. Alternatively, banks' decision makers shall increasingly rely on post model adjustments to mitigate the mentioned uncertainty and address limitations as an alternative risk management practice.
New IFRS 9 models for portfolios that need the considerable model overlays during 2020 and onwards (until clear economic forecast are published, with acceptable confidence, by regulatory and research bodies) have been redeveloped, and implemented. Restricted new data was not accessible for the utilization in the recalibrations, consequently critical post-model changes were needed to consider the economic impacts of the pandemic not caught by the models. Factors specific to banks including: i) success in the adequately identification of risks they face especially the new set of risks post COVID-19 crises, ii) the incidence of loan losses or delinquencies and managing default risks (through 90 www.scholink.org/ojs/index.php/ijafs International Journal of Accounting and Finance Studies Vol. 4, No. 1, 2021 account management, hedging and other techniques), iii) the ability to achieve financial and operational stability, investments and capital commitments, which may result in the failure to achieve any of the expected benefits of general banks set strategic priorities, iv) the IFRS 9 model limitations reasoned of the consequences of COVID-19 pandemic; have all shed the lights towards the adverse impacts of the models performance and usage which may require to hold additional capital levels, use alternative compensating controls, such as overlays and overrides according to each and every bank's portfolios/sub portfolios composition, trends and impacts, internal assessments and expectation levels, accompanied with proper forecasting and stress testing programs; to address model limitations, as well as changes to the judgements, estimates and assumptions utilized as a base for financial statements. All of which shall enhance the banking stability even during such stressful and unprecedented situation of COVID-19 pandemic (black swan).
In view of the same, early cautioning signals were thought about for appropriate risk management platforms, and additionally successful credit risk, and capital planning; intending to achieve the financial stability of banks, as well as to urge banks to sustain effective risk management processes, make a culture of awareness about the dangers that both drive and are driven by the business choices focusing on the financial consistency and quality, in addition to the steadiness of the bank's performance. In this manner, the research study has concluded that the provisioning estimates according to the implemented IFRS 9 rules (ECLs) were not able to: incorporates the proper recognizable estimation of each and every material risk(s) post COVID-19 start, and under various different possible scenarios (baseline, upside and downside, besides effective stress testing programs), acts as a fitting level of capital accessibility in relation to the bank's risk profile, the execution depends on the contextual understanding of all material risks with the target of ensuring the business value protection, strategic growth, further advancements of appropriate risk management processes, and building risk effective culture. For all of these main reasons: Management Overlays represented the rescue plan for overcoming such discussed model challenges, temporarily, until the overall uncurtaining situation is cleared out. Finally: • Effective risk management depends on, among other things, the ability through stress testing and proactive forecasting techniques to prepare for events that cannot be captured by the statistical models currently utilized used to effectively address financial, operational, legal, and regulatory challenges.
• Building effective and highly qualified risk calibers and expertise to be able to properly implement proactive risk measures, reporting and early warning signal mechanisms.


DETERMINATION OF TRANSPORT ROUTES USING THE SAVING MATRIX METHOD AT PT XYZ

Anisa Natasari, Hilal Ali Azzim, Fahrul Arifin and Muchammad Fauzi Widyatama University Bandung E-mail: anisa.natasari@widyatama.ac.id, hilal.ali@widyatama.ac.id, fahrul.arifin@widyatama.ac.id and muchammad.fauzi@widyatama.ac.id ARTICLE INFO ABSTRACT Received: July, 24th 2021 Revised: August, 16th 2021 Approved: August, 18th 2021 PT XYZ is a company engaged in manufacturing that produces electronic components and installation services for these components. In this study, the company will send components and help install them in nine locations in Sumatra. The analysis of determining distribution routes uses the saving matrix method. The results of the analysis using the saving matrix method show that the original distribution distance of 9926 kilometers can be reduced to 2937.7 kilometers, which means that the distance can be shortened and more efficient by 70% or around 6988.3 kilometers. The original cost was Rp. 54,386 .000 down to Rp. 16,716,000 Thus there is a distribution channel savings of Rp. 37,670,000 or about 69.2%.

INTRODUCTION
Consumer demands for quality, product, price, better service, delivery accuracy (Adianto, 2018) and product availability on the market are currently getting higher (Santoso, Mustaniroh, & Pranowo, 2018) and proper distribution is needed (Karundeng, Mandey, & Sumarauw, 2018). Effectiveness in choosing the right destination route will reduce waste in terms of cost, time and power (Trisna, Fatimah, & Nasution, 2019).
Product allocation and determining the route of delivery of goods are important in an industry (Ardhyani, 2017), both small and large scale industries (Musita, 2018). Errors in determining distribution channels (Sibarani, 2013) and delays in product delivery can hinder the distribution of products from producers to consumers (Karundeng et al., 2018), which can result in reducing company profits and can also have the potential for losses to the company (Marchelia, 2014).
In an effort to minimize product distribution transportation costs (Huda, 2018), companies must pay attention to the existing transportation network system (Trisna et al., 2019). The transportation network system can be seen in terms of effectiveness (Wulandari & Sudiana, 2018), in terms of safety, high accessibility, integrated, sufficient capacity, orderly, smooth, fast, easy to reach, timely, comfortable, affordable, orderly, safe (Razi & SUMBERDAYA, 2014), and low in pollution as well as in terms of efficiency in transportation (Sitorus, Hidayat, & Prasetya, 2014). the meaning of having a high utility in a unified transportation system network (Amin, Hamidi, & Ekwarso, 2017).
To anticipate this problem, we need a method that can provide minimal product distribution costs (Putra & Handayani, 2018). The Savings Matrix method is a method used to determine product distribution routes (Supriyadi, Mawardi, & Nalhadi, 2017) to marketing areas by determining the distribution routes that must be passed (Bimantara, 2018) and the number of vehicles based on vehicle capacity in order (Muziansyah, 2015) to obtain the shortest route and minimal transportation costs (Huda, 2018). The Savings Matrix method is also one of the techniques (Ikfan & Masudin, 2014) used to schedule a limited number of vehicles from facilities that have a maximum capacity (Suparjo, 2017).
By using the Savings Matrix Method, it is hoped that it can help overcome the problems above, so that the company is able to plan well for each installation item that will be sent.

RESEARCH METHODS
The Savings Matrix method is a method used to determine product distribution routes to marketing areas by determining the distribution routes that must be passed and the number of vehicles based on vehicle capacity in order to obtain the shortest route and minimal transportation costs. The Savings Matrix method is also one of the techniques used to schedule a limited number of vehicles from facilities that have a maximum capacity. By using the Savings Matrix method, it is expected to be able to answer research problems, and the company is able to make plans for each installation that will be sent. The data used in this study are as follows: 1. Primary data is data that is specifically taken for the sole purpose of research, which is obtained from the results of interviews. 2. Secondary Data is data obtained from references originating from sources such as companies, namely PT XYZ Project Cost Data. Table 1 is data on consumer demand for product shipments and installations in January 2021.

B. Initial Route
The company divides the distribution and installation into 2 groups due to limited material for that month. Each group is able to serve a maximum of 8 sites in 9 different locations. The company's initial routes amounted to 9 routes with a total distance generated on this initial route of 9926 kilometers. The distance on the initial route is considered too long and must be trimmed so as not to incur transportation costs and long delivery times.

C. Installation Distance Data
Data on the distance between the company and the installation location and the distance between locations are shown in Table 2 and Table 3.

D. Initial Transportation Fee
Initial transportation costs are taken based on company data. Initial transportation costs are listed in Table 4.

E. Identify the Savings Matrix
Saving Matrix is obtained by combining two or more location routes simultaneously. The merger is adjusted to the number of consumer requests and the number of combinations is not allowed to exceed the capacity of the transportation means. The table of the savings matrix can be seen in Table 5.

F. Installation Request Allocation
Allocation of consumer demand can be started by looking at the value of the greatest savings. The allocation of each different location can be combined up to the limit of the carrying capacity. The results of the merging of routes can be explained in Table 6. The number of routes originally was 9. After combining, the number of routes became 2. The total load transported was still below the maximum capacity of the transportation equipment, which was 8 pieces. Thus, both routes can be used.

G. Routing
Determination of the product distribution route starts from the process of identifying the distance matrix. This process requires data on the distance between the warehouse to each location and the distance between locations. The next step is to identify the savings matrix. (Suparjo, 2017) Saving matrix is realized by combining 6 or more locations into one route.
The company still implements product delivery using one line, namely from warehouse-location1 -warehouse and warehouse -location 2-warehouse and so on. These conditions can make the product distribution process take longer, longer distances, and more distribution costs. Therefore, a route change is needed that can combine several locations into one route.
Merging these routes can save the number of routes, distance, and distribution costs. By referring to the savings matrix table, the allocation process can be carried out. The allocation of each location into one route can be combined up to the capacity limit of the company's transportation equipment (Suparjo, 2017). The merger starts with the largest savings value. Starting from the saving value of 1611 which is the savings from combining location C and location I. The number of installation requests at both locations is 2 sites while the maximum total is 8 sites. The total is still below the maximum capacity limit of 8 sites. Thus, the merger is feasible, location C and location I are merged into route 1, and so on. Next is to sort the locations in a predefined route.

H. Route Comparison and Initial Transportation Costs
The comparison of product distribution routes before the saving matrix method is applied and after is very clear. Initially, the company had 9 routes for delivery and installation at a site. The total distance traveled by the company during the process of shipping goods and installation at 9 locations is 9926 kilometers. This makes the cost to the company is also not small, namely Rp. 54,386,000. After applying the saving matrix method, the number of routes, total distance, and product distribution costs in the company experienced a significant decrease with the assumption that labor costs and transportation costs were the highest from the initial 9 routes. The company can reduce the mileage by 6988.3 kilometers or about 70%. Product distribution costs also experienced savings of IDR 37,670,000 or 69.2%.


“Individualism and self-reliance of Generations Y and Z and their impact on working environment: An empirical study across 5 European countries”

In recent years, numerous researches and studies confirm differences between Generations in their values, attitudes, or characteristics. However, the challenge is to get to know the Generation Z, whose individuals are currently entering the labor mar- ket for research and practical application. The presented paper aims to expand the knowledge of Generations Y and Z in the field of individualism and self-reliance. This issue is examined concerning independence regarding housing and financial independence to parental help. The aim of the study is an empirical verification of possible sim- ilarities and differences between Generations Y and Z. The study is based on an online questionnaire survey. Data were obtained from more than 1,500 respondents of these Generations (born in 1982–2005) in 5 European countries (Czech Republic, Denmark, the Netherlands, Poland, and Slovakia). Data are examined using a two-tailed t-test, Mann-Whitney U test, and regression analysis. The overall findings of the study indicate intergenerational differences in the issue of independence, with Generation Z, unlike Millennials, becoming more self-sufficient at a younger age. Research has also found that women leave the parental household earlier than men. The paper presents the possible influence of the outputs on the working environment and work motivation of the Generations Y and Z.

INTRODUCTION
On the current understanding of Generations as a group of people born in a similar time span (15 years at the upper limit) who share a comparable age and life stage and who have been shaped by a specific time span (events, trends, and developments) (McCrindle et al., 2014; Murray, 2011), several authors currently agree (Lub et al., 2016;Lyons & Kuron, 2013;Puiu, 2017). This current trend is based on earlier knowledge, where age diversity was understood more at the paradigm level that older employees are less productive, flexible, creative, or less adaptable to the development of modern technologies (Shore et al., 2003). These facts have led to analyzing the stereotypes of older workers, age discrimination, and unfair treatment of older people. Unlike social groups (family, community), one currently encounters a mismatch between organizational roles and the age of employees in the working environment (the leader is younger than the subordinate) (Perry & Finkelstein, 1999;. These facts contribute to the study of individual age groups at the level of defined Generations. Nowadays, there are mainly four Generations in the labor market, namely the oldest Generation Baby Boomers (BB), Generations X and Y, and the youngest Generation Z (Bencsik et al., 2016;Desai & Lele, 2017;Stewart et al., 2017). These Generations also form an economically active population defined by the age range of 15-64 years (Beglova et al., 2017). For the purposes of this paper, Generations are annualized as follows (Bencsik et  Based on the above facts, the presented research focuses on the youngest, professionally active Generations, i.e., Generations Y and Z. Specifically, possible differences and similarities in the field of independence, individualism, or responsibility are compared based on international research conducted in 5 European countries. These research areas will be tested concerning independence in housing and financial support, both of which are related to dependence on help from one's family (parents/ grandparents). Researchers also verify the fact that the behaviors, attitudes, or opinions of Generation Z are largely derived from or even consistent with the characteristics of previous Generation Y ( Maloni et al., 2019;Schroth, 2019;Tang, 2019). Due to the current age of individuals from Generation Y, which is in the range of 38-26 years, several studies have been conducted that deal with the characteristics of the Millennials, even concerning previous Generations. On the contrary, as Costanza and Finkelstein (2015) stated, a challenge currently arises in the form of knowledge of the characteristics of the Generation Z, both in theory and empirically.

LITERATURE REVIEW
According to Bejtkovský (2016) or Schroer (2015), when analyzing the individualism and self-reliance of Generations Y and Z, it is appropriate to examine these values also in the Generations of parents, i.e., in the Generations of Baby Boomers and X. Both of these Generations are characterized in the working environment by high commitment and competitiveness (Loretto, 2015). More significant differences in the independence between the Generations of BB and X are reflected in the way of upbringing, where individuals from the Generation X often stayed alone at home (a consequence of the high diligence of BB's parents) and were thus mostly dependent on themselves. For this fact, Generation X is also called "latch-key kids" (Dziuban et al., 2005;Schroer, 2015). As representatives of Generation X are most often parents of individuals from Generation Z (Generation Y are, on the contrary, descendants of Generation of Baby Boomers), it is possible to talk about the transferability of these values and characteristics (Miller, 2018;Puiu, 2017;Singh, 2014).
In addition to the portability mentioned above, Generation Z is also shaped by the surrounding world, for example, cultural-historical, economic, or political surroundings (Pandit, 2015;Swanzen, 2018). The economic and political events that took place between 2007 and 2015 (the Great Recession, terrorist attacks) affected the formation of much of Generation Z (Agarwal & Vaghela, 2018;McNally & Stagliano, 2018). For these reasons, Generation Z often grew up as part of families struggling with job loss or financial problems (Dolot, 2018). Generation Z individuals have often recognized the need for responsibility and self-reliance, as many parents have been forced to work in multiple occupations. Another proof of the transferability of values is evident in the realistic preparation of offspring (Generation Z) for adult life in uncertain times, confirmed by EY's (2015) study. According to the mentioned study, more than 90% of respondents stated that their children from Generation Z influence family budgets allocation (choice of vacation or choice of household equipment). Pandit (2015) reached the same conclusion, which mentions over 70% of families. However, individuals from Generation Z do not perceive this way of upbringing and preparation for adult life due to dependence on their parents. On the contrary, they refer to their parents only as leaders or mentors (Csobanka, 2016; Seemiller & Grace, 2017b).
Another characteristic of Generation Z is a realistic and self-sufficient relationship in terms of finance/expenditure (Chillakuri & Mahanandia, 2018). Pandit (2015) states that about half of individuals are aware of the possible consequences of student loans (characterized mainly in the USA). Pandit (2015) also points out that Generation Z has a very positive attitude towards savings, with more than half of them preferring to save money before spending it, which is in stark contrast to the Generation of Millenials. Individualism and self-reliance are also reflected in Generation Z in the field of education (Hernandez-De-Menendez et al., 2020) and the working environment (Dolot, 2018; Kutlák, 2019).
The current knowledge of Generation Y concerning individualism and independence leads to different conclusions than for Generation Z. The most striking differences can be found in the area of independence. According to DeVaney (2015), many Millennials returned to their parents in their youth or postponed weddings, buying their own property, or starting a business. The low level of self-reliance can also be demonstrated by the fact that individuals from Generation Y often turn for help or advice towards their authority, which is most often formed by their parents (Sharma, 2012), with whom, according to Aminul, Cheong, Yusuf, and Desa (2011), continuously share a household. Swanzen (2018) expresses the term "helicopters parents who hover over their children in every aspect of their lives" for this type of parental dependence and suggests that the influence of parents is most pronounced across all Generations. Many authors (Lub et al., 2016;Prawitasari, 2018) further link parental support to declining Millenials' loyalty as a workforce. Background security and material support subsequently affect individuals with possible job dissatisfaction (McNulty, 2006).

European statistical data
Empirical data on generational independence are currently processed to a greater extent only by supranational institutions, e.g., the Statistical Office of the European Union (Eurostat). Eurostat (2020) data were collected in 2019 in all the EU member states. In the transformation into years of birth (1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004), these are respondents from the examined Generations Y and Z. However, the meth-odology of the mentioned Eurostat's study does not reflect this Generation distribution in its data in any way. Based on the above research, the average age of independence of an individual in the issue of housing (all the EU countries) is 26.2 years. The average age varies among each member state or clusters of states. In the Nordic countries and the countries of North-Western Europe (e.g., the Netherlands, the United Kingdom), the average age is lower (22 years and less), while the countries of Southern Europe show above-average age values (28 years and more). The closest values to the average age of the EU are reported by the countries of Central and North-Eastern Europe, which can include, for example, the Czech Republic, Poland, and the Baltic countries. The average age of independence of young people in the issue of housing in this third cluster is 26-27 years. The only exception is Slovakia, which, with an average age of 31, is closer to the Balkan countries (Croatia, Romania, etc.).
The average age of leaving the parental household of young people has shown a fluctuating tendency in recent years; specifically, it increased between 2002 and 2006 (from 26.5 to 26.8 years), before subsequently falling to the above value of 26.2 years by 2019. Apart from individual countries, the data also differ depending on the gender of the individuals, with young women moving on average from their parents earlier than men (the study does not reflect other genders). The average value of the difference is 1.9 years and further varies from country to country. These data complement Fry's (2013Fry's ( , 2015 results, which confirmed the earlier age of independence for women. Manning, Brown, and Payne (2014) point to the fact that the gap is the result of earlier adolescence, which is evident, for example, in the lower age of the first cohabitation of youngers (21.8 for women, 23.5 for men) or marriage (26.6 for women, 28.6 for men).
Regarding the countries where the empirical part of the presented research is carried out (Czech Republic -CZE, Denmark -DAN, the Netherlands -NED, Poland -POL, and Slovakia -SVK), Table 1 shows the data of these countries.
From the given data, it is evident that the total average age (EU-27), together with data from CZE and POL, shows a trend of decreasing age, the re-maining countries (DAN, NED, and SVK) show an oscillation around the values reported in 2011.
The stated values of the average age of independence show the territorial; thus, the cultural-historical influences of the analyzed states (Eurostat, 2020).

AIM
This paper aims to analyze possible similarities and differences between Generations Y and Z concerning self-sufficiency in housing in selected European countries. Possible differences may have a practical impact on values, expectations, and attitudes of the representatives of individual Generations.

HYPOTHESES DEVELOPMENT
Based on current findings, the research shows that independence and self-reliance of surveyed Generations Y and Z show significant differences that confirm the importance of this issue. The literature review outputs led to the determination of four research hypotheses listed further.
H1: The age of individuals who are financially supported and those who do not receive financial support from their parents will not be different.
H2: Gender affects the age of leaving parental household, with women becoming independent at a younger age than men.
H3: Generational affiliation affects the age of leaving parental household, with Generation Z becoming independent at a younger age than Generation Y.
H4: The trend of age of leaving parental household by the age of birth will decrease towards the youngest individuals from Generation Z.

DATA AND METHODS
The research is based on quantitative data from a questionnaire survey. The survey was conducted in 5 European countries (Czech Republic -CZE, Poland -POL, Slovakia -SVK, Denmark -DAN, and the Netherlands -NED) in the second half of 2020. In these countries, 1,535 valid questionnaires were obtained. The questionnaires were distributed in the form of CAWI (Computer Assisted Web Interviewing), i.e., by querying using a web form.
The condition for participation in the questionnaire survey was the age restriction; specifically, only persons with a year of birth between 1982 and 2005 were interviewed. The reason is to examine the behavior and attitudes of the selected Generations Y and Z. Although Generation Z is defined by the years of birth 1995-2010, for legal reasons in individual countries, the upper limit is limited to the last year of the respondent's birth, 2005.
In Table 2, in addition to the distribution of respondents by country, the representation of the surveyed Generations in the given countries is also presented. From the data further, it is clear that the respondents are predominantly from the ¾ Generation Z; however, due to the larger number of data (the only exception is the Netherlands), this is not a fundamental limiting condition for further statistical data processing. The data also show that in Generation Z, due to the current age, students predominate in all countries, while in Generation Y, most respondents are already employed.
Before testing, data quality verification was performed, specifically identifying outliers and veri- where U m and U σ are the mean and standard deviation of . U The standardized value is approximately a standard normal deviate whose significance can be checked in tables of the normal distribution (Corder & Foreman, 2014).
Calculation of S-W test of normality and identification of outliers based on measures such as the interquartile range (Tukey's fences) will be evaluated due to complexity only based on program-calculated p-value in S-W test of normality and on k-value in Tukey's fences, where k = 1.5 in-dicates an "outlier", and k = 3 indicates data that is "far out" (Schwertman & Silva, 2007 It is a widely recommended practice to report an effect size for an inferential test. Cohen's D is one of the most common ways to measure effect size. An effect size is how large an effect of something is (Coe, 2012). A commonly used interpretation is to refer to effect sizes as small (d = 0.2), medium (d = 0.5), and large (d = 0.8). This means that if two groups' means do not differ by 0.2 standard deviations or more, the difference is trivial, even if it is statistically significant (Olejnik & Algina, 2000).
The second part of the research focuses on a statistical analysis of age using data regression. Unlike the standard approach to regression and correlation, the data processing further aims not to capture the most accurate mathematical model (regression function) and then use it to predict future developments (Svoboda et al., 2019). The regression processing of this paper is used to verify or falsify the trend of independence, depending on the age of the individual. In all the models further, the independent/explanatory variable is the year of birth, while the dependent/explained variable is the age at which the respondent became independent in the issue of housing. All models use a linear type of regression (straight line) with an empirical regression function (Hendl, 2015): The statistical analysis was performed in the Statistics Kingdom (2017) online software and using a data analysis tool in Excel by Microsoft software.

RESULTS
For the analysis of the financial dependence of respondents on financial support from their parents/ grandparents, a statistical comparison of the years between the birth of two groups of respondents was used. Both groups consisted only of respondents with a career status of "student". The reason is the logical argument that people who have already started their professional careers will not be significantly financially supported by their parents. It is also necessary to add that these would be mainly individuals from Generation Y, which would distort the analyzed area in the form of a comparison of years of birth. The support was defined as a financial contribution to housing or in the form of pocket money. Table 3 presents the results of the impact of the provision of financial support by parents on the age of the child. In all 5 countries surveyed, two statistical samples are compared against each other -with and without financial support. Using the Mann-Whitney U test, the medians of both selections are compared. If the data based on the Shapiro-Wilk normality test show the nature of a normal distribution, they are further tested using a two-sample t-test of agreement of means (assuming agreement of variances -F-test). The observed standardized effect size (Cohen's D) is small or medium (0.15-0.34) in all countries, and based on the outliers' detection method (Tukey's fence), all the data contain only a small amount of potential outliers (k = 1.5).
At the 95% level of significance, the null hypothesis for U test (normal approximation is used) of the median agreement in the three countries studied is rejected (CZE, POL, and SVK). The rejection says that people who are not financially supported by their parents are younger than those who are supported.
According to the S-W test (p-value < 0.05), the data of both samples in DAN, NED, and SVK show a normal distribution, these data are longer subjected to a statistically stronger two-sample t-test.
On the SVK data, the t-test confirmed the rejection of the null hypothesis about the agreement of the means. Conversely, in contrast to the original U test, one now rejects the null hypothesis of concordance of averages for DAN data. The NED data did not meet the condition of agreement of variances (F-test) and, therefore, were not further verified.
The first research hypothesis H1 focuses on self-reliance concerning financial support from parents. Specifically, the hypothesis states that the age of individuals who are financially supported and those who do not receive financial support from their parents is not statistically different. The result shows a difference between the age of individuals who receive financial support from their par-  , 2018), there is still a significant influence of other ways of financial resources (brigades, etc.), significantly higher use with increasing age. However, it should be noted that despite statistical confirmation, in some countries, the p-value is at a 95% level of significance. This fact suggests that the difference between the statistical samples cannot be described as significant and that the results are partly in line with the above literature review. The increase in financial independence at a younger age is also confirmed by Puiu's research (2017), according to which more than ¾ respondents from Generation Z consider it important to have a part-time job during their school years.

Gender and generation
Another part of the research focuses on the influence of gender and generation affiliation on the age at which an individual becomes independent in the issue of housing, i.e. when leaving a parental household.
Comparing the difference between men and women in the term of independence is investigated using the median agreement, specifically the approximation of the U test (n 1, 2 > 20). Based on the p-value, one does not reject the null hypothesis of agreement in CZE, DAN, and POL. On the contrary, for NED and SVK data, the alternative hypothesis is accepted, while in both states, the expected age of independence in the issue of housing for women is higher. However, in contrast to the rest of the countries, the size effect of NED is higher for Cohen's D (0.43), which may affect the plausibility of the results.
Research hypothesis H2 states that women become independent earlier than men. The influence of gender can be ruled out in CZE, DAN, and POL, in which it is not possible to statistically confirm that the age of one gender would be significantly different from the age of the other gender, and therefore on the data of the mentioned countries, the research hypothesis H2 is rejected based on a relatively high p-value. On the contrary, the research hypothesis H2 is confirmed in SVK and NED and on the total data from all analyzed countries. The overall data suggest that the median age for women is 19 and for men is 20, and this difference is statistically significant; therefore, the research question of the effect of gender can be confirmed. The result is in line with the findings of Eurostat (2020) or Manning, Brown, and Payne (2014). Regarding the influence of gender on the age of leaving parental household, the research results again show a territorial factor of influence.
Dependence of belonging to a Generation (Y, Z) on the actual age of independence in terms of housing is examined in the following section. Significant differences are evident in CZE, POL, and SVK, where the differences are 3, 3, and 6 years. When subjecting the data to statistical analysis of the nonparametric U test, an alternative hypothesis of difference is confirmed. Besides, the results are supported by a very low p-value. It is necessary to point out the high value of the size effect for SVK data (0.7); however, due to the p-value value, the outputs can be considered demonstrable. Data from DAN and NED countries show a different trend (DAN -median match; NED -age Z > Y). The result of the statistical verification is that in both of these countries, the null hypothesis of the agreement is accepted, i.e., that in these countries, there is no statistically significant difference in age between the Generations of independence in the issue of housing. Statistical files from DAN and NED do not show a statistically different age of independence concerning individual Generations. These results are in line with the findings of the Eurostat's study (2020), which suggests a long-term trend in the development of the age of independence in housing; therefore, there is no influence of generational differences among Generations Y and Z. Conversely, data from other countries (CZE, POL, and SVK) and the summary research data statistically confirm that the age at which young people move away from their parents is lower in Generation Z than in Generation Y; therefore, research hypothesis can be confirmed. Overall data show the median of this age in Generation Z aged 19 years, in Generation Y aged 21 years, and based on this, the research hypothesis H3 is confirmed.

Trend of age of leaving parental household
Statistical analysis of age using data regression is part of the following statistical analysis. Visualization of available data of all 5 examined states is shown in Figure 1, which includes graphs for individual states. In each graph, a solid line indicates the median of the values, the dashed line the average. For the average, the dotted line also shows a linear trend. A condition of at least 5 records/responses was set for each year. As there were fewer responses in some years, the years were clustered to ensure this condition. Subsequently, the median and diameter values ("y" axis) were calculated for each value of the "x" axis. The absence of the required number of responses in individual years varied between countries; therefore, , the "x" axis scale is not the same in all countries analyzed.
From the visualizations shown in Figure 1, it is evident that the data of CZE, POL, and SVK show a declining trend line. The stagnant or slightly increasing trend is shown by the trend line only on the values of DAN, which, however, together with the NED, confirms a very low age of independence (see Table 5). At the same time, it is evident from all graphs that the declining trend of development decreases due to the decreasing age of the individual. This is a logical argument based on the fact that individuals become independent in the issue of housing, most often from the age of the majority. This argument is not met by the data shown in the NED graph; however, it is necessary to mention the lowest number of respondents in this country in the survey (61), which may distort the trend.
Statistical data for the graphs presented earlier are shown in Table 6. The intensity of the dependence and the quality of the regression function are expressed by the index of determination (r 2 ) or the adjusted index of determination (r 2 (adj) ), which allows comparing the models with a different number of parameters. According to the values of these indexes, it is evident that the linear regression shows a higher intensity of dependence on the given data and, thus, the quality of the regression model. The only exception is the data from DAN, where the value of the index of determination reaches a value close to 0. If one interpolates DAN data with a regression polynomial of the 2nd degree (parabola), the value would be r 2 = 0.51, still only a slight intensity of dependence.
The value of the coefficient y (b 1 ), which is negative in all countries except for DAN, confirms the forwarded declining trend of CZE, NED, POL, and SVK. Despite only the potential normality of the data, the agreement of regression and residual variance was performed using an F-test. The rejection of the null hypothesis confirms the existence of The total data (see Table 6) represent a linear regression model based on data from all 5 countries analyzed. The model shows the highest values of the index of determination and the adjusted index of determination, and data synthesis leads to a higher intensity of dependence and suitability of the regression function. The significant dependence of the investigated variables is also confirmed by analyzing the agreement of regression and residual variance expressed by p-value = 7.01e-09. The declining trend is confirmed by the negative value of the coefficient "b 1 " (-0.338). Graphical visualization of data synthesis of all countries is shown in Figure 2.
In addition to the linear trend of average values, Figure 2 also shows a regression polynomial of the 3rd degree, which shows a slightly higher degree of dependence of the observed variables (0.867). Both of these regression models confirm that with a decreasing year of birth (lower age of respond-Source: Own research.

Figure 1.
Regression trend of age of leaving parental household in each country (CZE, DAN, NED, POL, and SVK) by the age of birth ents), there is a decrease in the age at which she/he became independent in the issue of housing.
As expected (see research hypothesis H4), the trend is declining towards the youngest individuals from Generation Z based on the year of birth. The results are confirmed by linear regression, which is also visualized in the earlier graphs. The declining trend observed in CZE and POL is in line with Eurostat's study (2020) and the relatively constant trend in DAN and NED, where the age of independence in housing is significantly lower than in other countries. The difference can be seen in the data from the SVK, where a declining trend was found, although Eurostat's study (2020) forwards a constant trend of development. Another difference between the data of the submitted empirical research and the mentioned study is the specific age level, which is, however, strongly influenced by the age structure of the respondents; therefore, the results are compared only at the level of trend development.

DISCUSSION
The trend of increasing independence in the issue of housing and thus in the issue of financial self-reliance indicates possible changes for business practice. Although Generation Z is described as realistic and financial conscious ( On the contrary, the positive effect of this may be to increase the loyalty of individuals who will not be able to afford financial shortfalls in their income unless the reason for leaving is not to offer a better-paid job. The trend of loyalty development is in line with research by the Generation Moving to one's own (in the presented research, more than two-thirds of the respondents stated that they live only alone or with a partner) partly brings with it a limitation of social contacts, which the individual in a shared household receives. This fact is also confirmed by the increase in individuals living alone, at all ages between 2010 and 2019 in the EU. The growth is higher than 10% for all categories; for category 15-24, this increase is almost 30% for men and 10% for women. Single adults increased faster than adults living in a couple or another type of household (Eurostat, 2013(Eurostat, , 2020. Representatives of the Generation Z can thus gradually tend to increase independence in the working environment, which can be seen in reducing social contact and group or teamwork. In this context, Yadav and Rai (2017) also mention the impact of moving social relationships, contacts, and communication to the online environment.

CONCLUSION
The research results show that based on international research from 5 European countries (CZE, DAN, NED, POL, and SVK), there are intergenerational differences between Generations Y and Z.
The findings indicate an increase in independence and self-reliance in representatives of Generation Z, which is in contrast to the characteristics and developments recorded in Generation Y. This is confirmed by regression analysis in all countries surveyed, with Denmark being the only country not showing a declining age of independence in housing. The same conclusions are confirmed by the results of the test of concordance of medians of the studied Generations, where the Netherlands also joins Denmark. Another conclusion of the paper is the influence of gender on the researched issue, where women become independent compared to men earlier when the total data show a median difference of 1 year.
On these and the conclusions presented earlier, it is also possible to confirm the significant territorial influence of individual countries. The difference in the characteristics of the Generations is also reflected in the workplace. Areas influenced by individualism and self-sufficiency not only in the issue of housing are, for example, financial motivation, loyalty or social relations, and teamwork. Knowing the needs and attitudes of individual employees and the adaptation of personnel processes is the current challenge for business practice.