MSMEs do not have the technical acumen or the human resource expertise to produce documents which are required by financing institutions.
The most difficulty comes to producing and presenting cash flow statements.
Some potential borrowers cannot compute cash projections
Especially Micro and Small Firms, which are managed informally by households, usually do not maintain proper book-keeping and accounting systems.
Banks have to use their own historical loss databases to compute credit metrics such as PD, LGD, EAD,etc.
And most of them have errors
Demographic and Geographic indicators can play a role in assessing and evaluating credit risk management, especially during the loan examination phase.
Sectoral Loan Risk Data can be aggregated to compute Risk Measures. #Saunders FI-Risk management has a chapter dedicated to it
How accurate are Average Risk Measures?
Using Averages as statistical measures of central locations have its own drawbacks.
Accept or reject decision based on Default or No Default Outcomes derived from a binary logistic regression fails in backtesting
Creditscoring is subjective
Even the prehistoric Basel II Capital Accord originally introduced the application of the Average Pds(probability of defaults) models to compute credit risk capital charges under #FIRB
BIS PDs were usually pro-cyclical and lacked risk transmission signals bis.org/bcbs/irbriskwe…
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Will data science overcome quantitative finance in terms of employment and salary? @CQFInstitute@datafitter
Well, I think you are asking a relevant question.
It can be explained using a Social Darwinian Perspective.
First, the Simple answer =>
YES, => Quantitative Finance, will get absorbed into Data Sciences and Machine Learning Areas as a sub-field.
WHY?
Because many subjects develop independently, they get assimilated into the much larger disciplines or subjects which offer more attractive career remuneration, research opportunities, social status, social mobility and scholarly following and readership.
This is not an exhaustive list.
I am just sipping coffee and writing this tweet.
There are many other institutions which might be damn good. @Harvard Business School is the best for Business econs / #DBA.
Other Ivy League universities not aforementioned have superb PhD programs.
@Columbia offers MSc Financial Maths and MBA.
Financial Maths Degree was a conjoint project undertaken by Industrial Engineering and Operations Research Departments (plz check the website now)
Great if you want to do a Quantitative Finance PhD sharing expertise of different depts
I remember starting my career as a junior derivatives dealer.
The first stupid question I asked was why currency dealers use par-curve yield curve rates to compute FX Swap points?
No "#boostrapping"?
The Chief Dealer/ Head of FX Linear and Cross Currency Rates was not pleased.
Actually asking stupid questions early on in our career helps.
Provided you have a tolerable boss who can entertain stupidity.
But, if you ask questions which threaten your reporting line, then you are cooked!
Done.
Find your next job asap.
My CRO - Chief Risk Officer, was a banker who didn't know maths and statistics.
The man was a nightmare proposition for the academically tuned young graduates
Poor chap begged me to not bring my work to him because he could not check it.
Should you pair #Finance with #Economics or #Accounting?
If you want to work in a general finance department, Accounting and Finance combination is beneficial, you can presumably learn a bit of financial auditing in this way. @LSEfinance
If you want to work in Financial Markets, then Economics and Finance blend well.
But do note: that Economics is more mathematical.
Students who are weak at Maths, generally opt for Accounting or some other subject.
But having said that, if you don't like Maths at all, it might even be more difficult for you to do Quantitative Finance Modules at a higher level. So your aptitude and interests in Mathematics shall determine the outcome.