Page 62 - FINAL CFA II SLIDES JUNE 2019 DAY 3

Risk-Adjusted Value –Do not double count risk! READING 10: PROBABILISTIC APPROACHES: SCENARIO ANALYSIS, DECISION TREES, AND SIMULATIONS

Investor should be indifferent between X and Y as they are equally underpriced relative to their risk-
adjusted values of $50.
Do not choose stock X over stock Y based on its lower SD since this would be penalizing stock Y twice; we
have already accounted for stock Y’s greater risk by discounting its cash flows using a higher discount rate.

LOS 10.e: Describe some common constraints introduced into simulations.

A condition that, if violated, would pose dire consequences for the firm!

Types of constraints – 3 Types:
1. Book value constraints: Imposed on a firm’s book value of equity – 2 types:
• Regulatory capital requirements: Banks and insurance companies cannot violate minimum capital level ratios.
• Negative equity: Some European countries require firms to raise additional capital in the event that book value of equity becomes negative.

2. Earnings and cash flow constraints: Imposed internally to meet analyst expectations or to achieve bonus targets. Failure to meet analyst
expectations could result in executive job losses, so they may pursue expensive risk hedging (NOT related to value of the firm, but rather to managerial
employment contract or compensation levels). Earnings constraints can also be imposed externally, such as a loan covenant!

3. Market value constraints: In a simulation, we can explicitly model the entire distribution of key input variables to identify situations where financial
distress would be likely (e.g. explicitly incorporate the costs of financial distress in a valuation model for the firm).

LOS 10.f: Describe issues in using simulations in risk assessment – 3 limitations!

1. Input quality: If underlying inputs are poorly specified, the output will be low quality (i.e., garbage in, garbage out).

2. Inappropriate statistical distributions: If the underlying distribution of an input is improperly specified, the quality of that input will be poor. Real
world data often does not fit the stringent requirements of statistical distributions.

3. Non-stationary distributions: Input variable distributions may change over time, so the distribution and parameters specified may not be valid
anymore.

4. Dynamic correlations: If we model the correlation between variables based on past data and such relationships amongst variables change, the
output of simulation will be flawed.