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- 1. DAT 520 Milestone Three Guidelines and Rubric In this milestone, you will perform an evaluation of your decision model and revise your decision model as needed. Evaluation examples are if you are performing a bottom-up style recursive partitioning analysis, and you should report on the error rate and variable selection. You might also consider alternative variable categorizations to improve your model. If you are performing a top -down decision tree modeling exercise, what are the threshold values that cause the tree to flip? You should perform sensitivi ty analysis on the critical variables in your tree and report what those sensitivity analyses are telling you. For either sty le of modeling, what makes your tree stronger? What breaks the model? For more information on completing this milestone, please ref er to the Final Project Notes in the Assignment Guidelines and Rubrics folder. Specifically, the following critical elements must be addressed in your final submission: clear description. that is specific to your revised model. In your evaluation, reflect on the appropriateness and adjustments of the revised model, as well as the accuracy of the results you obtained.
- 2. Guidelines for Submission: This milestone should be 2 to 3 double-spaced pages of text, with tree model images and any other supporting material appended. Review your work to ensure that there are no major errors in writing mechanics. If you have citations, include the sources at the end and cite them APA format. Critical Elements Proficient (100%) Needs Improvement (70%) Not Evident (0%) Value Structure Deci s i on tree and des cri ption are cl early s tructured Deci s i on tree and des cri ption are s omewhat cl early s tructured Deci s i on tree and des cri pti on are not adequatel y s tructured 30 Evaluation of Results Eval uati on cons i ders reas onablenes s , accuracy, mi s s ing/extraneous el ements , and error i n the model Eval uati on does not ful l y cons i der reas onabl enes s , accuracy, mi s s i ng/extraneous el ements , and error i n the model
- 3. Eval uati on does not cons i der reas onabl enes s , accuracy, mi s s i ng/extraneous el ements , and error i n the model 30 Model Diagnostics Model i ncl udes cl ear us e of di agnos ti cs Model bui l ds i n parti al us e of di agnos ti cs Model does not i ncl ude di agnos ti cs 30 Articulation of Response Submi s s i on has no major errors rel ated to grammar, s pel l i ng, s yntax, or organi zati on Submi s s i on has major errors rel ated to grammar, s pel l i ng, s yntax, or organi zati on that negati vel y i mpact readabi l ity and arti culation of mai n i deas Submi s s i on has criti cal errors rel ated to grammar, s pel l i ng, s yntax, or organi zati on that prevent unders tandi ng of i deas
- 4. 10 Earned Total 100% Decision TreeAsset-Deposit/ Loss below 0.1Grossly UnstableAsset/ Deposit Ratio below 1.11Asset -Deposit/Loss above 1.0Relatively stable0Asset/ Deposite Ratio above 1.1 StableIDNameValueProbPredKindNSS1S2S3S4S5RowColMark0 TreePlan000D212000251TRUE100E234000185TRUE200T0000 00325TRUE31D356700109TRUE41T000000279TRUE503T0000 00213TRUE603E289000913TRUE703E210110001913TRUE86T 000000717TRUE96T0000001217TRUE107T0000001717TRUE1 17T0000002217TRUE x AnalysisFDIC: HSOB Bank & Thrift FailuresTable BF03Federal Deposit Insurance CorporationUS and Other Areas(Dollar amounts in thousands)Effective Date(s): 2010 - 2017Insurance Fund: ALLCharter Type: ALLTransaction Type: All FailuresStateNumber of InstitutionsNumber of FailuresNumber of Assistance TransactionsAssetsDepositsEstimated Loss (12/31/2016)Ratio (Assets/Deposits)Assets- Deposits/LossAlaska000000Alabama4403,923,5923,524,148527 ,1161.1133448420.76Arkansas220258,100237,22731,9801.0879 874550.65Arizona101001,724,9111,521,634367,1011.13359125 80.55California1818010,369,8638,527,9431,180,1361.21598643 41.56Colorado7705,907,9345,033,322997,0681.1737643650.88 Connecticut11026,36825,7159,2111.0253937390.07District of Columbia000000ERROR:#DIV/0!ERROR:#DIV/0!Delaware000 000ERROR:#DIV/0!ERROR:#DIV/0!Florida5656017,814,85515 ,603,0392,734,2351.1417554620.81Georgia6161017,615,64216, 457,1705,499,6121.0703931480.21Guam000000ERROR:#DIV/0 !ERROR:#DIV/0!Hawaii000000ERROR:#DIV/0!ERROR:#DIV/ 0!Iowa11091,58081,96716,0531.1172789050.6Idaho110153,361 145,8133,4871.0517649322.16Illinois4444018,464,88416,970,6
- 6. Virginia000000ERROR:#DIV/0!ERROR:#DIV/0!Wyoming0000 00ERROR:#DIV/0!ERROR:#DIV/0!Totals:36336301596478431 3927570428178689.591.1462720230.72 Running head: DAT 520 FINAL PROJECT MILESTONE TWO DAT 520 FINAL PROJECT MILESTONE TWO 7DAT 520 Final Project Milestone TwoStudent NameDecisions Methods and ModelingSouthern New Hampshire University Bank Failures Structure The model employed was a top-down structure. The focus of the decision tree is to find states where bank failures are most probable. It, therefore, defines a model where; the liquidity of the bank defines their stability. The more stable a bank, the less likely it is to fail. Using Asset to deposit ratios and further non - current assets to loss ratio will give the best estimate of the stability of different banks in a multitude of states. These three variables will be the major determinants through which the model will be used in determining the nature of the Bank Failures. Documentation Different banks have in the past failed. This is often characterized by their inability to meet depositor money. When
- 7. a bank receives money from a prospective client, often than not, they decide to use the money deposited in investment projects. Should they be in a position to meet the obligations to their depositors, then they are continuing operations, however, in the event, their investments do not return favorable profits, a bank may lose its stability and be declared to have failed (Bruce, 2017). A bank may also be unable to meet its obligations to its creditors. Such instance often leads to an unstable economic and financial environment and have in the past led to the need for banks to receive bailouts through which they can meet their obligations to their depositors (Bruce, 2017). For this analysis, a summary, of different states and the corresponding failures was used to determine the trends between bank failures and states. To determine the probability of a bank being capable of offsetting some of its debt, the first comparison that will be made will be the ratio between the assets the bank holds, and the total amount made in deposits. This should give a rough estimate of the capacity of the bank to meet its obligations to its main clientele. The higher the ratio, the more stable the bank. Secondly, the banks capacity to mitigates itself from loss is another measure that can be used to determine the stability of the bank. The difference between the Assets and amount Deposited can give a good picture of the overall liquidity of the company. With this figure, finding its ratio against the losses incurred in the last fiscal year (2016) can give a good picture of the stability of the bank and hence the overall probability of it incurring losses. Evaluation Data on Bank Failures between 2010 and 2017 was used as the primary information on the trends in bank failures. From the analysis, nine states appear to have experienced a lot of failures over the past seven years ("FDIC: HSOB Commercial Banks," 2017). The states of Arizona, California, Florida, Georgia, Illinois, Minnesota, Missouri, South Carolina, and Washington have noted the highest propensity of bank failures. Georgia
- 8. ranked the highest with a total of 61 bank failures in this period. Florida then followed with 56 bank failures and then Illinois with 44("FDIC: HSOB Commercial Banks," 2017). Looking at the ratio between assets and deposit for these three banks, all were above 1, which is a sign of stability. However, the banks in Georgia and Illinois recorded significantly lower ratios. The state of Georgia had the least with 1.07("FDIC: HSOB Commercial Banks," 2017). It is important to note that states like Connecticut, Idaho, and Minnesota also recorded low Asset/Deposit ratios. The second measure of overall failure was to determine the ratio between the difference between the banks capacity to liquidate its assets and the losses that it made. This would make for a clearer picture of the stability of the bank as a recent figure was used in this instance. From the analysis, Connecticut recorded the lowest figure at 0.07("FDIC: HSOB Commercial Banks," 2017). Still, comparatively, the number of deposits for the state was significantly lower compared to that of Georgia. It can be difficult to predict the geographical location of banks that will experience the most loss in the future. It, however, can be assumed that the states of Georgia, Florida, and Illinois present the largest risk for bank failures ("FDIC: HSOB Commercial Banks," 2017). Consequently, states like Connecticut and Minnesota present some of the tales of a dwindling trust and investment into local banks. These states, therefore, present with the highest risk. To summarize the steps are followed, first, the data is pulled from the site provided by Federal Deposit Insurance Corporation that includes the summary of assets, deposits and loses in banks across fifty different states in the United States. Following that, the ratio of assets/deposits per state, and assets- deposits to loss are used to determine the stability of the banks in different states. As the decision model presents in relation with the defined ratio for stability, banks that scored a ratio above 1.1 in the first instance (assets/deposit ratio) are considered relatively stable, and therefore, they are the ones
- 9. identified as not likely to fail (see Appendix A for decision tree model). The financial ratio above 1.1 indicates that the banks in the specified area can meet their obligations to depositors, since they have more assets than they do deposits. However, the banks that score below the figure were at a high risk of failing. In the excel attachment, ‘’I’’ and ‘’J’’ columns provide the clear picture of the rational state by state. (see Appendix B for excel analysis). The financial ratio, which is below 1.1, shows that the bank is barely capable of meetings its obligations to depositors, and following that this is considered as not a good sign and a clue of failure. References Bruce, L. (2017). What happens to your accounts if the bank fails?. Retrieved from https://www.bankrate.com/banking/what- happens-if-your-bank-fails/ FDIC: HSOB Commercial Banks. (n.d.). Retrieved from https://www5.fdic.gov/hsob/hsobRpt.asp
- 10. Appendix A Decision Tree Model