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The power of introverts | Susan Cain
นอกจากการดูบทความนี้แล้ว คุณยังสามารถดูข้อมูลที่เป็นประโยชน์อื่นๆ อีกมากมายที่เราให้ไว้ที่นี่: ดูความรู้เพิ่มเติมที่นี่
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In a culture where being social and outgoing are prized above all else, it can be difficult, even shameful, to be an introvert. But, as Susan Cain argues in this passionate talk, introverts bring extraordinary talents and abilities to the world, and should be encouraged and celebrated.
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The Credit Decision (FRM Part 2 – Book 2 – Chapter 1)
For FRM (Part I \u0026 Part II) video lessons, study notes, question banks, mock exams, and formula sheets covering all chapters of the FRM syllabus, click on the following link: https://analystprep.com/shop/unlimitedpackageforfrmpartipartii/
AnalystPrep is a GARPApproved Exam Preparation Provider for FRM Exams
After completing this reading you should be able to:
Define credit risk and explain how it arises using examples.
Explain the components of credit risk evaluation.
Describe, compare and contrast various credit risk mitigants and their role in credit analysis.
Compare and contrast quantitative and qualitative techniques of credit risk evaluation.
Compare the credit analysis of consumers, corporations, financial institutions, and sovereigns.
Describe quantitative measurements and factors of credit risk, including probability of default, loss given default, exposure at default, expected loss, and time horizon.
Compare bank failure and bank insolvency.
Logistic Regression Using Excel
Predict who survives the Titanic disaster using Excel.
Logistic regression allows us to predict a categorical outcome using categorical and numeric data. For example, we might want to decide which college alumni will agree to make a donation based on their age, gender, graduation date, and prior history of donating. Or we might want to predict whether or not a loan will default based on credit score, purpose of the loan, geographic location, marital status, and income. Logistic regression will allow us to use the information we have to predict the likelihood of the event we’re interested in. Linear Regression helps us answer the question, \”What value should we expect?\” while logistic regression tells us \”How likely is it?\”
Given a set of inputs, a logistic regression equation will return a value between 0 and 1, representing the probability that the event will occur. Based on that probability, we might then choose to either take or not take a particular action. For example, we might decide that if the likelihood that an alumni will donate is below 5%, then we’re not going to ask them for a donation. Or if the probability of default on a loan is above 20%, then we might refuse to issue a loan or offer it at a higher interest rate.
How we choose the cutoff depends on a costbenefit analysis. For example, even if there is only a 10% chance of an alumni donating, but the call only takes two minutes and the average donation is 100 dollars, it is probably worthwhile to call.
Credit Risk Introduction
these videos go through the syllabus objectives for the Financial Exams of ST5/F105/SA5/F205. They are raw, unedited and contain a large amount of opinion. I’ve taken a skeptical approach to the subject and my views may not be correct. Feel free to correct me in the comment section below. I’ll be releasing a new video every day
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Credit Grades Estimating Probability of Default
Training on Credit Grades Estimating Probability of Default by Vamsidhar Ambatipudi
นอกจากการดูหัวข้อนี้แล้ว คุณยังสามารถเข้าถึงบทวิจารณ์ดีๆ อื่นๆ อีกมากมายได้ที่นี่: ดูวิธีอื่นๆINVESTMENT
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