Hazard-Based Model, Hilscher, and Szilagy for Analysis - Data Analytics Assignment Help

Assignment Task

Introduction
Before beginning any task, risk associated is to be gauged, and when money is involved, where investment is involved and if certain data is available of the past results then using the same with the help of technical analysis is the most judicious way to make the decision. In the given scenario the company is planning to invest in the new demographic market and as since 2019 World is almost on the standstill as regards to Pandemic Affecting the world in the name of COVID-19 which has taken almost over 3 million of life’s across the world and in this time thinking of the expansion plan requires a very much of the detailed scrutiny of the data and numbers and viability of the project. As regular analysis is the pre-emptive requirements and while doing this analysis various financial models are taken into considering.
Objective of this analysis to be provide information to take a decision to the management who wants to open a Store in America, once the things are settled and market is back into normalcy.

Looking to the requirement u tried to find out about the O- Score method. I understood that it uses coefficient of 9 factors which are derived from the financial statement. It takes in to account more sample data compare to Altman – Z score. This is the beneficial point for doing any analysis for financial decision making. Along with that the analysis gives 90% accurate result which is actually a guaranteed guidance towards any decision making. (Source - Ohlson O-score, WikiVisually).

Methodology
Though there are so many methods available to predict the bankruptcy but the one which is popular and used widely is O –score. In Olson is about the result to predict bankruptcy is a multivariate financial formula postulated in 1980 by Dr. James Olson. CITATION ohl \l 16393 (ohlson_score, n.d.)The O Score breaks it down into 9 various approximate steps of a firm 's default risk, 2 of the 9 currently being dummy variables: these 9 are utilized to establish solid measurement, influence, working capital, liquidity, earnings, change in total cash flow, and debt funding. Together, these 9 variables build an O Score where probability of failure is EXP(O Score) divided by 1+EXP(O score). Results greater compared to >,5 suggest a firm with a significant chance of default.
It's been argued the Ohlson Score is a much better predictor of bankruptcy than some other related accounting models such as for instance the Altman Z Score, nonetheless, investors might find merits in making use of both Ohlson and Altman in helping anticipate a firm 's bankruptcy.
Because both Altman and Ohlson use an accounting based design to help you predict bankruptcy, its energy is its fairly simplicity. Nevertheless, you will find various other bankruptcy models like Merton's Distance to Default as well as CHS are also bankruptcy models which are utilized by academics and argued to be better in forecasting bankruptcy danger.

Analysis
Considering the excel data file and calculation methodology as per the word file, I calculated O – Score for all the companies and derived following state wise result of O – Score and risky or safe company based count for companies on total company from that state.
Count of Probability of Failure Column Labels  
Row Labels RISK COMPANY SAFE Grand Total % of safe company
Alabama 3 3 0
Arizona 4 1 5 20
Arkansas 6 6 0
California 8 1 9 11.11111
Colorado 4 1 5 20
Connecticut 5 2 7 28.57143
Delaware 1 1 2 50
Florida 4 1 5 20
Georgia 7 1 8 12.5
Idaho 3 5 8 62.5
Illinois 2 1 3 33.33333
Indiana 4 4 0
Iowa 4 2 6 33.33333
Kansas 2 2 4 50
Kentucky 4 1 5 20
Louisiana 9 1 10 10
Maine 1 2 3 66.66667
Maryland 3 3 0
Massachusetts 6 6 0
Michigan 6 1 7 14.28571
Minnesota 6 2 8 25
Mississippi 3 3 0
Missouri 3 3 0
Montana 5 1 6 16.66667
Nebraska 9 9 0
Nevada 5 2 7 28.57143
New Hampshire 2 1 3 33.33333
New Jersey 5 5 0
New Mexico 5 1 6 16.66667
New York 4 4 0
North Carolina 3 1 4 25
North Dakota 5 2 7 28.57143
Ohio 3 3 0
Oklahoma 2 2 0
Oregon 5 1 6 16.66667
Pennsylvania 4 1 5 20
Rhode Island 4 4 0
South Carolina 1 1 2 50
South Dakota 3 2 5 40
Tennessee 7 7 0
Texas 6 3 9 33.33333
Utah 4 4 0
Vermont 2 2 0
Virginia 3 1 4 25
Washington 3 3 0
West Virginia 6 2 8 25
Wisconsin 5 1 6 16.66667
Wyoming 5 1 6 16.66667
Grand Total 204 46 250

The analysis shows clear result of high risk of bankruptcy within most of the states of North America. Only few states are there that have positive probability against bankruptcy which are as follow,
Kansas Idaho Maine South Carolina
50 62.5 66.66667 50
Thus, based on the database These are the green states where company can plan its expansion program.
Limitations of O – Score model

As we know that no model is 100% accurate and same way, Dr. James Ohlson’s O-Score model for bankruptcy has its own limitations which can be as follow,
Financial statements represent the information only about the financial transactions, any activity which are not covered within the statement like difference in depreciation method, fraud at management level, any other window dressing will not be considered while deriving the results
There are many other analysis models available for the same analysis which actually provides higher accuracy compare to the O-Score model. Some examples can be Campbell’s hazard-based model, Hilscher, and Szilagyi are found highly accurate models.
The model says that any company having P>0.5 will default within short period of time and vice versa which is actually not a justifiable criterion. Many company with the P value less than 0.5 may also go for bankruptcy because of some unfound situations.
(Source - ACCURACY RATE OF BANKRUPTCY PREDICTION MODELS FOR THE DUTCH PROFESSIONAL FOOTBALL INDUSTRY, P.54)

Limitations of Data collected
If we look to the data collected, we will find that the data compiled by the graduate is not appropriate.
The selection of company vary for each state. Only two companies are selected from many states where as in case of Louisiana 10 companies are chosen for analysis purpose. Thus, there is a difference in sample size for state wise selection.
The analysis is done for the company having total assets worth $0.23 million to $375,319.00 which is comparatively very high then the lower limit. Thus, selection of balance sheet size is as well not accurate.
The companies are selected from different manufacturing industries including food industry, medical industry, technological industry and many more and the analysis is done with merging all of them to a common portal of manufacturing. Different manufacturing industry may have different bankruptcy rate.

Conclusion
To conclude it can be said that in every business works on data and information, and in current situation it has become more relevant when so much advance technology and data analysis tools are available. Decision what ever or wherever it may be if based on information are more correct and right , instead of those which are just on core assumptions and without facts , generally it leads to failure. Analysis of business failure is very much necessary before making any investment decision. Specially in the present situation, when the businesses are highly affected with because of the lockdown situation, it is a crucial step before entering in to the market. According to the Thomas- for Industry, 90% of the manufacturing businesses were affected by COVID -19 up to June,2020. Although 91% people are having faith that things goanna be okay by next year, We can see the impact from the below chart, (Source - COVID-19’s Impact on North American Manufacturing, 2020)

Considering the above impact and O-score analysis of the given data, company should plan for expansion only in the states with higher safety probability which is Maine here. This recommendation is for overall manufacturing requirement regardless of any industry or balance sheet size. To have more accurate analysis, following recommendations are there,
Equalize the sample size of the number of company selection taking some base like % of total manufacturing company, population of the state, industrial growth etc.
Choose the balance sheet size for sampling based on company’s planning for overall investment.
Use other available methods like hazard-based model, Hilscher, and Szilagy for analysis.
Go for industry wise O-scoring. It is not possible to go that much detailed. However following are the growing manufacturing industries where company can invest, incase, company has no intention of any specific industry, (Source - COVID-19’s Impact on North American Manufacturing, 2020)

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