Example Of Probabilistic Approach

Valuation is the estimation of an asset’s value, whether real or financial, based on variables perceived to be related to future investment returns, on comparison with similar assets, or, when relevant, on estimates of immediate liquidation proceeds (Pinto, Henry, Robinson, Stowe; 2010). Correct valuation of real assets can present challenges to financial analysts. Different models can be used to arrive at the closest estimate of value and yet certain issues will always arise. This case attempts to tackle two approaches in real asset valuation: Discounted Cash Flow (DCF) analysis and the issues surrounding such, as well as the Black-Scholes Model for Real Options. Questions to be addressed in the study are:

Ross, S. A., Westerfield, R. W., & Jordan, B. D. (Eds.). (2011). Essentials of corporate finance (7th ed., Rev.). New York, NY: McGraw-Hill Irwin.

Problem 2.6: In fitting a model to classify prospects as purchasers or non-purchasers, a certain company drew the training data from internal data that include demographic and purchase information. Future data to be classified will be lists purchased from other sources, with demographic (but not purchase) data included. It was found that “refund issued” was a useful

In an attempt to improve the model, we attempt to do a multiple regression model predicting income based on credit balance, years, and size.

However, two known authors in this field of study believe that companies with low business risk obtains factors of production at a lower cost which may also pave to the ability of the firm to operate more efficiently (Amit & Wernerfet, 1990). Therefore, many stockholders faced a high of uncertainty; this is because some companies do not have the financial strengths to cover its debts that even may result to bankruptcy.

In 2006, Merrill Lynch became the lead book runner for a $5 billion convertible bond issue for MoGen, Inc. This was the single, largest convertible bond issuance in history and required a considerable amount of effort on the part of Merrill Lynch’s Equity Derivatives Group to convince MoGen’s management to choose Merrill Lynch over its competitors. The case is focused on Merrill Lynch’s choice of the conversion premium and coupon rate to propose to MoGen management. This pricing decision requires students understand the concept of valuing a convertible as the sum of a straight bond plus the conversion option. Valuing the conversion option as a call option requires the

So it is also attractive for Jules Kroll to take this opportunity to enter the credit rating industry. In addition, the “issuer pays” model, which used by the big three rating agencies, lets companies shop around for the best ratings, putting pressure on the agencies to inflate their grades. As a result, it is very difficult to argue that they can adequately represent the users’ side. Therefore, Jules Kroll wants to use another model that can assign unbiased and reliable ratings.

The effect of financial leverage on the cost of equity is prevalent in the Modigliani-Miller capital structure theory. Since the financial leverage increases the cost of equity, it can be considered one of the disadvantages of borrowing. As shown in Appendix A, the cost of equity, at each debt to capital ratio, increases by 0.1% as the financial leverage increases by 10%. With a higher

The Black Scholes Merton (BSM) model is the best-known model for valuing options as it is the original of many option pricing models today (Haug and Taleb, 2009; Le, 2015). Developed in 1973 by Fisher Black, Myron Scholes and Robert Merton, the BSM model is still widely used today as the benchmark for many models and techniques that financial analysts use to analyse and determine the fair prices of given options (Jumarie, 2010). It is widely used due to its simplicity however there are many criticisms regarding the assumptions made by the model (Bharath and Sumway, 2008; Haug and Taleb, 2009; Le, 2015)

A combination of business risk and financial risk shows the risk of an organization’s future return on equity. Business risk is related to make a firm’s operation without any debt whereas financial risk requires that the firm’s common stockholders make a decision to finance it with debt. Business risk can be evaluated volatility in earnings and profits (coefficient of variation of returns on assets and of operating profits). A measure of business risk is also asset beta or unlevered beta. In case of AHP, it is 1.2 (βa) which is very low signifying low business risk for the firm.

On the other hand, my interest in the incorporation of artificial intelligence and machine learning by financial institutions stems from my work as a Credit Risk Analyst for BECU. I recently joined the institution, and am working on a year-long project to integrate the Synergy Component System which will enable increased automation, improved attribution management, and reduced decision times for credit card and auto loans. Beyond testing plans and cases, I am most excited about designing decision matrices by analyzing member data such as credit quality, debt-to-income ratio, membership years, and trade lines to reach a credit decision. While I have had already experienced academic research, this will be my first taste of applied industry research.

How data mining can assist bankers in enhancing their businesses is illustrated in this example. Records include information such as age, sex, marital status, occupation, number of children, and etc. of the bank?s customers over the years are used in the mining process. First, an algorithm is used to identify characteristics that distinguish customers who took out a particular kind of loan from those who did not. Eventually, it develops ?rules? by which it can identify customers who are likely to be good candidates for such a loan. These rules are then used to identify such customers on the remainder of the database. Next, another algorithm is used to sort the database into cluster or groups of people with many similar attributes, with the hope that these might reveal interesting and unusual patterns. Finally, the patterns revealed by these clusters are then interpreted by the data miners, in collaboration with bank personnel.4

Stress testing is a pre-emptive risk management process designed to help determine the impact of charge-offs against your current capital levels and the amount of capital you’ll need to fill the holes caused by lost earnings. This scenario planning enables you to project peak potential losses by asset type within a specified geography over a defined time horizon. Typically, analytics are applied against a range of potential situations:

Regression analysis seems to be extremely useful in determining financial distress and the bankruptcy of firms. In fact, in a particular study conducted by Bredart in 2014, it was presented that regression analysis was able to show an 84% prediction accuracy rate in determining bankruptcy of 870 firms between the years of 2000 and 2012 (Bredart, 2014). The independent variables used in Bredart study included profitability (net income/total assets), liquidity (current assets/current liabilities), and solvency (equity/total assets) while the dependent variable included financial distress.

Knowing what other outlying debts customers have could be helpful in determining high-risk customers. Along with past credit history this could be helpful in determining customers to reject.