Portfolio Management Flashcards
1
Q
Authorized participants
A
(APs) A special group of institutional investors
who are authorized by the ETF issuer to
participate in the creation/redemption process.
APs are large broker/dealers, often market
makers.
2
Q
Creation basket
A
The list of securities (and share amounts) the
authorized participant (AP) must deliver to the
ETF manager in exchange for ETF shares.
The creation basket is published each
business day.
3
Q
Creation/redemption
A
The process in which ETF shares are
created or redeemed by authorized
participants transacting with the ETF
issuer.
4
Q
Creation units
A
Large blocks of ETF shares transacted
between the authorized participant (AP) and
the ETF manager that are usually but not
always equal to 50,000 shares of the ETF.
5
Q
iNAVs
A
ndicatedÓ net asset values are
intraday Öfair value estimates of an
ETF share based on its creation
basket.
6
Q
Redemption basket
A
The list of securities (and share amounts) the
authorized participant (AP) receives when it
redeems ETF shares back to the ETF manager.
The redemption basket is published each
business day.
7
Q
Describe factors affecting ETF bid–ask spreads.
A
ETF spreads are positively related to the cost of creation/redemption, the spread on the underlying securities, the risk premium for carrying trades until close of trade, and the APs’ normal profit margin. ETF spreads are negatively related to the probability of completing an offsetting trade on the secondary market. Creation/redemption fees and other trading costs can influence spreads as well.
8
Q
Describe sources of ETF premiums and discounts to NAV.
A
ETF premium (discount) % = (ETF price – NAV) / NAV
Sources of premium or discount include timing difference for ETFs with foreign securities traded in different time zones and stale pricing for infrequently traded ETFs.
9
Q
Describe types of ETF risk.
A
Risks of investing in ETFs include counterparty risk (common for ETNs), fund closures, and expectation-related risk.
10
Q
Describe costs of owning an ETF.
A
ETF costs include trading cost and management fees. Short-term investors focus on lower trading costs while longer-term, buy-and-hold investors seek lower management fees. Trading costs tend to be lower for more-liquid ETFs. Liquidity is evaluated using the ratio of average dollar volume to average assets (higher is better).
11
Q
Explain the creation/redemption process of ETFs and the function of authorized participants.
A
Authorized participants (APs) can create additional shares by delivering the creation basket to the ETF manager. Redemption is similarly conducted by tendering ETF shares and receiving a redemption basket. These primary market transactions are in kind and require a service fee payable to the ETF issuer, shielding the nontransacting shareholders from the costs and tax consequences of creation/redemption. The creation/redemption process ensures that market prices of ETFs stay within a narrow band of the NAV.
12
Q
Describe sources of tracking error for ETFs.
A
Tracking error is the annualized standard deviation of the daily tracking difference. Sources of tracking error include fees and expenses of the fund, sampling, and optimization used by the fund, the fund’s investment in depository receipts (DRs) (as opposed to the underlying shares directly), changes in the index, regulatory and tax requirements, fund accounting practices, and asset manager operations.
13
Q
Identify and describe portfolio uses of ETFs.
A
Portfolio uses of ETFs include the following:
- Efficient portfolio management, including liquidity management, portfolio rebalancing, portfolio completion, and transition management.
- Asset class exposure management, including core exposure to an asset class or sub-asset class as well as tactical strategies.
- Active investing, including smart beta, risk management, alternatively weighted ETFs, discretionary active ETFs, and dynamic asset allocation.
14
Q
Describe how ETFs are traded in secondary markets.
A
ETFs are traded just like other shares on the secondary markets. Market fragmentation may widen the quoted spreads for European ETFs.
15
Q
Active factor risk
A
The contribution to active risk squared
resulting from the portfolio different-than
benchmark exposures relative to factors
specified in the risk model.
16
Q
Active return
A
The return on a portfolio minus
the return on the portfolio
benchmark.
17
Q
Active risk
A
The standard deviation of
active returns.
18
Q
Active risk squared
A
The variance of active returns;
active risk raised to the second
power.
19
Q
Active specific risk
A
The contribution to active risk squared
resulting from the portfolio active weights on
individual assets as those weights interact
with assetsÖ residual risk.
20
Q
Arbitrage
A
(1) The simultaneous purchase of an undervalued asset or portfolio and sale of an overvalued but equivalent asset or portfolio in order to obtain a riskless profit on the price differential. Taking advantage of a market inefficiency in a
risk-free manner.
(2) The condition in a financial market in which equivalent
assets or combinations of assets sell for two different prices, creating an opportunity to profit at no risk with no commitment of money. In a well functioning financial market, few arbitrage opportunities are possible.
(3) Arisk-free operation that earns an expected positive net profit but requires no net investment of money.
21
Q
Arbitrage opportunity
A
An opportunity to conduct an arbitrage; an
opportunity to earn an expected positive net
profit without risk and with no net
investment of money.
22
Q
Arbitrage portfolio
A
The portfolio that exploits an
arbitrage opportunity.
23
Q
Company fundamental factors
A
Factors related to the company internal performance, such as factors relating to earnings growth, earnings variability, earnings momentum, and financial leverage.
24
Q
Company share-related factors
A
Valuation measures and other factors related to share price or the trading characteristics of the shares, such as earnings yield, dividend yield, and book-to market value.
25
Factor
A common or underlying element
with which several variables are
correlated.
26
Factor portfolio
Pure factor portfolio portfolio with
sensitivity of 1 to the factor in
question and a sensitivity of 0 to all
other factors.
27
Factor price
The expected return in excess of the risk
free rate for a portfolio with a sensitivity of 1
to one factor and a sensitivity of 0 to all
other factors.
28
Factor risk premium
The expected return in excess of the risk
free rate for a portfolio with a sensitivity of
to one factor and a sensitivity of 0 to all
other factors. Also called factor price.
29
Fundamental factor models
A multifactor model in which the factors are
attributes of stocks or companies that are
important in explaining cross-sectional
differences in stock prices.
30
Information ratio
(IR) Mean active return divided by
active risk; or alpha divided by the
standard deviation of diversifiable
risk.
31
Macroeconomic factor model
A multifactor model in which the factors
are surprises in macroeconomic
variables that significantly explain equity
returns.
32
Macroeconomic factors
Factors related to the economy, such
as the inflation rate, industrial
production, or economic sector
membership.
33
Priced risk
Risk for which investors demand
compensation for bearing (e.g., equity risk,
company-specific factors, macroeconomic
factors).
34
Pure factor portfolio
A portfolio with sensitivity of 1 to
the factor in question and a
sensitivity of 0 to all other factors.
35
Security selection risk
Active specific riskThe contribution to active
risk squared resulting from the portfolio
active weights on individual assets as those
weights interact with assetsÖ residual risk.
36
Standardized beta
With reference to fundamental factor models, the
value of the attribute for an asset minus the average
value of the attribute across all stocks, divided by
the standard deviation of the attribute across all
stocks.
37
Statistical factor model
A multifactor model in which statistical
methods are applied to a set of historical
returns to determine portfolios that best
explain either historical return covariances or
variances.
38
Systematic risk
Risk that affects the entire market or
economy; it cannot be avoided and is
inherent in the overall market. Systematic
risk is also known as non-diversifiable or
market risk.
39
Tracking error
The standard deviation of the differences
between a portfolio returns and its
benchmarkÖs returns; a synonym of active
risk. Also called tracking risk.
40
Tracking risk
The standard deviation of the differences
between a portfolio returns and its
benchmarkÖs returns; a synonym of active
risk. Also called tracking error.
41
Define arbitrage opportunity and determine whether an arbitrage opportunity exists.
An arbitrage opportunity is defined as an investment opportunity that bears no risk and has no cost, but provides a profit. Arbitrage is conducted by forming long and short portfolios; the proceeds of the short sale are used to purchase the long portfolio. Additionally, the factor sensitivities (betas) of the long and short portfolios are identical and, hence, our net exposure to systematic risk is zero. The difference in returns on the long and short portfolios is the arbitrage return.
42
Describe uses of multifactor models and interpret the output of analyses based on multifactor models.
Multifactor models can be useful for risk and return attribution and for portfolio composition. In return attribution, the difference between an active portfolio's return and the benchmark return is allocated between factor return and security selection return.
In risk attribution, the sum of the active factor risk and active specific risk is equal to active risk squared (which is the variance of active returns):
active risk squared = active factor risk + active specific risk
active factor risk = active risk squared − active specific risk
Multifactor models can also be useful for portfolio construction. Passive managers can invest in a tracking portfolio, while active managers can go long or short factor portfolios.
A factor portfolio is a portfolio with a factor sensitivity of 1 to a particular factor and zero to all other factors. It represents a pure bet on a single factor and can be used for speculation or hedging purposes. A tracking portfolio is a portfolio with a specific set of factor sensitivities. Tracking portfolios are often designed to replicate the factor exposures of a benchmark index like the S&P 500.
43
Describe the potential benefits for investors in considering multiple risk dimensions when modeling asset returns.
Multifactor models enable investors to take on risks that the investor has a comparative advantage in bearing and avoid the risks that the investor is unable to absorb.
Models that incorporate multiple sources of systematic risk have been found to explain asset returns more effectively than single-factor CAPM.
44
Explain sources of active risk and interpret tracking risk and the information ratio.
Active return is the difference between portfolio and benchmark returns (RP − RB), and active risk is the standard deviation of active return over time. Active risk is determined by the manager's active factor tilt and active asset selection decisions:
active risk squared = active factor risk + active specific risk
The information ratio is active return divided by active risk
45
Calculate the expected return on an asset given an asset's factor sensitivities and the factor risk premiums.
Expected return = risk-free rate + ∑(factor sensitivity) × (factor risk premium)
46
Describe and compare macroeconomic factor models, fundamental factor models, and statistical factor models.
A multifactor model is an extension of the one-factor market model; in a multifactor model, asset returns are a function of more than one factor. There are three types of multifactor models:
Macroeconomic factor models assume that asset returns are explained by surprises (or shocks) in macroeconomic risk factors (e.g., GDP, interest rates, and inflation). Factor surprises are defined as the difference between the realized value of the factor and its consensus expected value.
Fundamental factor models assume asset returns are explained by the returns from multiple firm-specific factors (e.g., P/E ratio, market cap, leverage ratio, and earnings growth rate).
Statistical factor models use multivariate statistics (factor analysis or principal components) to identify statistical factors that explain the covariation among asset returns. The major weakness is that the statistical factors may not lend themselves well to economic interpretation.
47
Describe arbitrage pricing theory (APT), including its underlying assumptions and its relation to multifactor models.
The arbitrage pricing theory (APT) describes the equilibrium relationship between expected returns for well-diversified portfolios and their multiple sources of systematic risk. The APT makes only three key assumptions: (1) unsystematic risk can be diversified away in a portfolio, (2) returns are generated using a factor model, and (3) no arbitrage opportunities exist.
48
Active share
A measure of how similar a portfolio is to its
benchmark. A manager who precisely replicates the
benchmark will have an active share of zero; a
manager with no holdings in common with the
benchmark will have an active share of one.
49
Conditional VaR (CVaR)
The weighted average of all loss outcomes in the
statistical (i.e., return) distribution that exceed the VaR
loss. Thus, CVaR is a more comprehensive measure
of tail loss than VaR is. Sometimes referred to as the
expected tail loss or expected shortfall.
50
Convexity
A measure of how interest rate
sensitivity changes with a change
in interest rates.
51
Delta
The relationship between the option price and the
underlying price, which reflects the sensitivity of the
price of the option to changes in the price of the
underlying. Delta is a good approximation of how an
option price will change for a small change in the
stock.
52
Duration
A measure of the approximate
sensitivity of a security to a change in
interest rates (i.e., a measure of interest
rate risk).
53
Ex ante tracking error
A measure of the degree to which the
performance of a given investment portfolio
might be expected to deviate from its
benchmark; also known as relative VaR.
54
Expected shortfall
A measure of the degree to which the
performance of a given investment portfolio
might be expected to deviate from its
benchmark; also known as relative VaR.
55
Expected tail loss
Conditional VaR
56
Gamma
A measure of how sensitive an option delta is to a
change in the underlying. The change in a given
instrumentÖs delta for a given small change in the
underlyingOs value, holding everything else
constant.
57
Historical simulation method
The application of historical
price changes to the current
portfolio.
58
Incremental VaR (IVaR)
A measure of the incremental effect of an asset on the
VaR of a portfolio by measuring the difference
between the portfolio VaR while including a specified
asset and the portfolios VaR with that asset
eliminated.
59
Lookback period
The time period used to
| gather a historical data set.
60
Marginal VaR (MVaR)
A measure of the effect of a small change in a position size on portfolio VaR.
61
Maximum drawdown
The worst cumulative loss ever sustained by an asset
or portfolio. More specifically, maximum drawdown is
the difference between an asset or a portfolioÔs
maximum cumulative return and its subsequent
lowest cumulative return.
62
Monte Carlo simulation
A technique that uses the inverse transformation method for converting a randomly generated uniformly distributed number into a simulated value of a random variable of a desired distribution.
Each key decision variable in a Monte Carlo simulation requires an assumed statistical distribution; this assumption facilitates incorporating non-normality, fat tails, and tail dependence as well as solving high-dimensionality problems.
63
Parametric method
A method of estimating VaR that uses the historical mean,
standard deviation, and correlation of security price movements to estimate the portfolio VaR. Generally assumes a normal distribution but can be adapted to non-normal distributions with the addition of skewness and kurtosis. Sometimes called thevarianceovariance method or the analytical method.
64
Relative VaR
Ex ante tracking error A measure of the degree
to which the performance of a given
investment portfolio might be expected to
deviate from its benchmark; also known as
relative VaR.
65
Reverse stress testing
A risk management approach in which the
user identifies key risk exposures in the
portfolio and subjects those exposures to
extreme market movements.
66
Risk budgeting
The allocation of an asset owner total risk
appetite among groups or divisions (in the case
of a trading organization) or among strategies
and managers (in the case of an institutional or
individual investor).
67
Risk decomposition
The process of converting a set
of holdings in a portfolio into a
set of exposures to risk factors.
68
Risk factors
Variables or characteristics with which
individual asset returns are correlated.
Sometimes referred to simply as
factors.
69
Stop-loss limit
Constraint used in risk management that
requires a reduction in the size of a
portfolio, or its complete liquidation, when a
loss of a particular size occurs in a specified
period.
70
Stress tests
A risk management technique that assesses the portfolio
| response to extreme market movements.
71
Tail risk
The risk that losses in extreme events
could be greater than would be expected
for a portfolio of assets with a normal
distribution.
72
Value at risk (VaR)
The minimum loss that would be expected a
certain percentage of the time over a
certain period of time given the assumed
market conditions.
73
Vega
The change in a given derivative instrument for a given small change in volatility, holding everything else constant. A sensitivity measure for options that reflects the effect of volatility.
74
Demonstrate how equity, fixed-income, and options exposure measures may be used in measuring and managing market risk and volatility risk.
Equity risk is measured by beta (sensitivity to overall market returns).
The interest rate risk of fixed-income securities is measured by duration (sensitivity to change in yield) and convexity (second-order effect, change in duration).
Options risk is measured by delta (sensitivity to asset price changes), gamma (second-order effect, change in delta), and vega (sensitivity to asset price volatility).
Market risk can be managed by adjusting portfolio holdings to control the exposures to these various risk factors.
75
Describe sensitivity risk measures and scenario risk measures and compare these measures to VaR.
Sensitivity analysis is used to estimate the change in a security or portfolio value to an incremental change in a risk factor.
Scenario analysis refers to estimation of the effect on portfolio value of a specific set of changes in relevant risk factors.
A scenario of changes in risk factors can be historical, based on a past set of risk factors changes that actually occurred, or hypothetical (based on a selected set of significant changes in the risk factors of interest).
76
Describe advantages and limitations of VaR.
```
Advantages of VaR:
Widely accepted by regulators.
Simple to understand.
Expresses risk as a single number.
Useful for comparing the risk of portfolios, portfolio components, and business units.
```
Disadvantages of VaR:
Subjective in that the time period and the probability are chosen by the user.
Very sensitive to the estimation method and assumptions employed by the user.
Focused only on left-tail outcomes.
Vulnerable to misspecification by the user.
77
Describe advantages and limitations of sensitivity risk measures and scenario risk measures.
VaR, sensitivity analysis, and scenario analysis complement each other, and a risk manager should not rely on only one of these measures.
VaR provides a probability of loss.
Sensitivity analysis provides estimates of the relative exposures to different risk factors, but does not provide estimates of the probability of any specific movement in risk factors.
Scenario analysis provides information about exposure to simultaneous changes in several risk factors or changes in risk correlations, but there is no probability associated with a specific scenario.
78
Explain constraints used in managing market risks, including risk budgeting, position limits, scenario limits, and stop-loss limits.
Risk budgeting begins with determination of an acceptable amount of risk and then allocates this risk among investment positions to generate maximum returns for the risk taken.
Position limits are maximum currency amounts or portfolio percentages allowed for individual securities, securities of a single issuer, or classes of securities, based on their risk factor exposures.
A stop-loss limit requires that an investment position be reduced (by sale or hedging) or closed out when losses exceed a given amount over a specified time period.
A scenario limit requires adjustment of the portfolio so that the expected loss from a given scenario will not exceed a specified amount.
79
Compare the parametric (variance–covariance), historical simulation, and Monte Carlo simulation methods for estimating VaR.
Value at risk estimation methods:
Parametric method—uses the estimated variances and covariances of portfolio securities to estimate the distribution of possible portfolio values, often assuming a normal distribution.
Historical simulation—uses historical values for risk factors over some prior lookback period to get a distribution of possible values.
Monte Carlo simulation—draws each risk factor change from an assumed distribution and calculates portfolio values based on a set of changes in risk factors; repeated thousands of times to get a distribution of possible portfolio values.
80
Describe extensions of VaR.
Conditional VaR (CVaR) is the expected loss given that the loss exceeds the VaR.
Incremental VaR (IVaR) is the estimated change in VaR from a specific change in the size of a portfolio position.
Marginal VaR (MVaR) is the estimate of the change in VaR for a small change in a portfolio position and is used as an estimate of the position's contribution to overall VaR.
Ex ante tracking error, also referred to as relative VaR, measures the VaR of the difference between the return on a portfolio and the return on the manager's benchmark portfolio.
81
Estimate and interpret VaR under the parametric, historical simulation, and Monte Carlo simulation methods.
The x% VaR is calculated as the minimum loss for the current portfolio, x% of the time, based on an estimated distribution of portfolio values.
82
Describe risk measures used by banks, asset managers, pension funds, and insurers.
Banks are concerned with many risks including asset-liability mismatches, market risk for their investment portfolio, their leverage, the duration and convexity of their portfolio of fixed-income securities, and the overall risk to their economic capital.
Asset managers are most concerned with returns volatility and the probability distribution of either absolute losses or losses relative to a benchmark portfolio.
Pension fund managers are concerned with any mismatch between assets and liabilities as well as with the volatility of the surplus (assets minus liabilities).
P&C companies are concerned with the sensitivity of their investment portfolio to risk factors, the VaR of their economic capital, and scenarios that incorporate both market and insurance risks as stress tests of the firm.
Life insurers are concerned with market risks to their investment portfolio assets and liabilities (to make annuity payments), any mismatch between assets and liabilities, and scenarios that would lead to large decreases in their surplus.
83
Explain the use of value at risk (VaR) in measuring portfolio risk.
Value at risk (VaR) is an estimate of the minimum loss that will occur with a given probability over a specified period expressed as a currency amount or as percentage of portfolio value.
84
Describe the use of sensitivity risk measures and scenario risk measures.
A stress test based on either sensitivity or scenario analysis uses extreme changes to examine the expected effects on a portfolio or organization, often to determine the effects on a firm's equity or solvency. A reverse stress test is designed to identify scenarios that would result in business failure.
Sensitivity analysis can give a risk manager a more complete view of the vulnerability of a portfolio to a variety of risk factors. Sensitivity and scenario risk measures provide additional information about portfolio risk but do not necessarily provide probabilities or, in the case of sensitivity measures, the sizes of expected changes in risk factors and portfolio value.
Sensitivity and scenario analysis provide information that VaR does not and are not necessarily based on historical results. A historical scenario will not necessarily be repeated. Hypothetical scenarios may be misspecified, and the probability that a scenario will occur is unknown.
85
Explain how risk measures may be used in capital allocation decisions.
Firms use risk measures by adjusting expected returns for risk when making capital allocation decisions.
86
Backtesting
The process that approximates the real-life
investment process, using historical data, to
assess whether an investment strategy
would have produced desirable results.
87
Bootstrapping
The use of a forward substitution process to
determine zero-coupon rates by using the par
yields and solving for the zero-coupon rates
one by one, from the shortest to longest
maturities.
88
Data snooping
The subconscious or conscious manipulation of data in a
way that produces a statistically significant result (i.e., the
p-value is sufficiently small or the t-statistic sufficiently
large to indicate statistical significance), such as by
running multiple simulations and naively accepting the
best result. Also known as p-hacking.
89
Historical scenario analysis
A technique for exploring the performance and risk of investment strategies in different structural regimes.
90
Historical simulation
A simulation method that uses past return
data and a random number generator that
picks observations from the historical series
to simulate an asset future returns.
91
Historical stress testing
The process that tests how investment
strategies would perform under some of the
most negative (i.e., adverse) combinations
of events and scenarios.
92
Look-ahead bias
The bias created by using information that was
unknown or unavailable in the time periods over
which backtesting is conducted, such as
company earnings and macroeconomic indicator
values.
93
Point-in-time data
Data consisting of the exact information
available to market participants as of a
given point in time. Point-in-time data is
used to address look-ahead bias.
94
Risk parity
A portfolio allocation scheme that
weights stocks or factors based
on an equal risk contribution.
95
Rolling windows
A backtesting method that uses a rolling-window (or walk
forward) framework, rebalances the portfolio after each
period, and then tracks performance over time. As new
information arrives each period, the investment manager
optimizes (revises and tunes) the model and readjusts
stock positions.
96
Sensitivity analysis
A technique for exploring how a target
variable (e.g., portfolio returns) and risk
profiles are affected by changes in input
variables (e.g., the distribution of asset or
factor returns).
97
Simulation
A technique for exploring how a target variable (e.g. portfolio returns) would perform in a hypothetical environment specified by the user, rather than a
historical setting.
98
Survivorship bias
The bias that results when data as of a given date
reflects only those entities that have survived to that
date. Entities can include any element of an index or
list that is constituted through time: stocks, investment
funds, etc. Survivorship bias is a form of look-ahead
bias.
99
Identify problems in a backtest of an investment strategy.
Problems in a backtest of an investment strategy include the following:
Survivorship bias—When using data that only includes entities that have persisted until today.
Look-ahead bias—When using information that would have been unavailable at the time of the investment decision.
Data snooping—When a model is chosen based on backtesting performance. (i.e., a large t-statistic or a small p-value).
Cross-validation is when a model is first fitted using training data, and then its performance is assessed (often over several rounds) using separate testing data. An investment strategy can also be cross-validated using data from different geographic regions: performance from other global markets can help determine whether a strategy is robust.
100
Contrast Monte Carlo and historical simulation approaches.
Monte Carlo and historical simulation approaches are methods used to account for skewness, excess kurtosis, and tail dependence.
In historical simulation, observations are randomly chosen from the historical dataset so that each observation has an equal probability of being selected.
Simulations (both historical and Monte Carlo) are nondeterministic and random.
In a Monte Carlo simulation, a statistical distribution is specified and calibrated using historical return data. When the assets or factors are correlated, a multivariate distribution should be used rather than modeling each asset or factor on a standalone basis.
101
Demonstrate the use of sensitivity analysis.
Sensitivity analysis is a method for evaluating how a target variable (such as portfolio return) varies due to changes in the input variables (such as asset or factor returns).
Sensitivity analysis can overcome the shortcomings of a traditional Monte Carlo simulation, because it is not limited to multivariate normal distributions (which do not take into account fat tails or negative skewness).
To conduct a sensitivity analysis, we fit factor return data to a distribution that accounts for skewness and excess kurtosis (e.g., a multivariate skewed Student's t-distribution), and then repeat the Monte Carlo simulation.
While use of a skewed multivariate t-distribution helps to take fat tails and skewness into account, this also increases the possibility of estimation error, because a multivariate skewed t-distribution requires estimates of more parameters.
102
Interpret metrics and visuals reported in a backtest of an investment strategy.
The backtest of an investment strategy will produce return metrics, such as average return, and risk measures, such as volatility and downside risk. Other measures that can be calculated include the Sharpe ratio, the Sortino ratio, and maximum drawdown (the maximum loss from a peak to a trough).
Visuals used in a backtest of an investment strategy often include return distribution plots.
103
Describe objectives in backtesting an investment strategy.
The primary goal of backtesting is to assess the risk and return of an investment strategy by simulating the investment process.
Backtesting uses past data to evaluate whether a particular investment strategy would have produced excess returns historically. This assessment allows an investor to optimize their investment process and strategy.
104
Describe and contrast steps and procedures in backtesting an investment strategy.
The three steps in backtesting an investment strategy are:
1. Strategy design:
Specify the investment hypothesis and goals.
Determine the investment process and rules of the investment strategy.
Select key parameters.
2.Historical investment simulation:
For each period, assemble a portfolio according to the previously determined rules.
Rebalance the portfolio over time based on those investment rules.
3. Analysis of output:
Compute performance statistics, such as risk and return for the portfolio.
Calculate other relevant metrics, such as turnover.
In rolling-window backtesting, an investor makes use of a walk-forward (rollingwindow) process, calibrates or fits trade signals or factors based on this rolling window, periodically rebalances the portfolio, and then evaluates portfolio performance over time. In this way, rolling-window backtesting simulates real-world investing.
105
Explain inputs and decisions in simulation and interpret a simulation.
Historical simulation is relatively simple and shares many of the advantages and disadvantages of rolling-window backtesting: both historical simulation and rollingwindow backtesting depend on the assumption that randomness in the future can be predicted using return distributions from the past.
Historical simulation sometimes makes use of bootstrapping, whereby random samples are drawn with replacement. Bootstrapping is useful when the number of simulations needed is large relative to the size of (historical) dataset.
106
Evaluate and interpret a historical scenario analysis.
Scenario analysis is a method for investigating the performance and risk of investment strategies under different structural regimes (such as recession versus nonrecession, or high volatility versus low volatility). Stress testing examines the performance of a strategy under the most adverse combinations of events and scenarios.
If asset returns do not follow a multivariate normal distribution, scenario analysis and simulation can provide a more complete picture of investment strategy performance. Scenario analysis can be used to analyze the performance and risk of investment strategies in different structural regimes.
Asset return distributions often exhibit skewness and excess kurtosis (i.e., fat tails). Also, conventional rolling-window backtesting may not fully account for the dynamic nature of financial markets or possible extreme downside risk. Scenario analysis and simulation can provide a more thorough portrayal of investment strategy performance.
107
Inter-temporal rate of substitution
```
The ratio of the marginal utility of consumptions periods in the future (the numerator) to the marginal utility of
consumption today (the denominator).
```
108
Explain the relationship between the consumption hedging properties of equity and the equity risk premium.
Equities are generally cyclical; they have higher values during good times and have poor consumption hedging properties. Therefore, the risk premium on equities should be positive.
109
Explain how the phase of the business cycle affects credit spreads and the performance of credit-sensitive fixed-income instruments.
Credit spreads tend to rise during times of economic downturns and shrink during expansions. When spreads narrow, lower-rated bonds tend to outperform higher-rated bonds.
110
Explain the role of expectations and changes in expectations in market valuation.
Market prices reflect current expectations. Only changes in expectations cause a change in market price.
111
Explain the relationship between the long-term growth rate of the economy, the volatility of the growth rate, and the average level of real short-term interest rates.
Interest rates are positively related to GDP growth rate and to the expected volatility in GDP growth due to a higher risk premium.
112
Explain how the characteristics of the markets for a company's products affect the company's credit quality.
Spreads for issuers in consumer cyclical sector widen considerably during economic downturns compared to spreads for issuers in the consumer non-cyclical sector.
113
Explain the notion that to affect market values, economic factors must affect one or more of the following: 1) default-free interest rates across maturities, 2) the timing and/or magnitude of expected cash flows, and 3) risk premiums.
The value of any asset can be computed as present value of its expected future cash flows discounted at an appropriate risk-adjusted discount rate. Risky cash flows require the discount rate to be higher due to inclusion of a risk premium.
114
Describe the factors that affect yield spreads between non-inflation-adjusted and inflation-indexed bonds.
Break-even inflation rate (BEI) = yield on non-inflation indexed bonds − yield on inflation indexed bonds
BEI is comprised of two elements: expected inflation (π) and risk premium for uncertainty in inflation (θ).
115
Describe how economic analysis is used in sector rotation strategies.
Relative outperformance of sectors can be discerned ex post. Ex ante forecasting of this outperformance is the objective of active managers.
116
Describe cyclical effects on valuation multiples.
Price multiples tend to follow the business cycle: multiples rise during economic expansions (as analysts revise growth estimates upward) and fall during contractions (as growth estimates are revised downward).
117
Explain how the phase of the business cycle affects short-term and long-term earnings growth expectations.
Cyclical industries (e.g., durable goods manufacturers and consumer discretionary) tend to be extremely sensitive to the business cycle; their earnings rise during economic expansions and fall during contractions. Non-cyclical or defensive industries tend to have relatively stable earnings.
118
Explain how the phase of the business cycle affects policy and short-term interest rates, the slope of the term structure of interest rates, and the relative performance of bonds of differing maturities.
When the economy is in recession, short-term policy rates tend to be low. Investor expectations about higher future GDP growth and inflation as the economy comes out of recession lead to higher longer-term rates. This leads to positive slope of the yield curve. Conversely, an inversely sloping yield curve is often considered a predictor of future recessions.
119
Describe the economic factors affecting investment in commercial real estate.
Commercial real estate has equity-like and bond-like characteristics. The valuation depends on the rental income stream, the quality of tenants, and the terminal value at the end of the lease term. The discount rate for commercial real estate includes a risk premium for uncertainty in terminal value and also for illiquidity.
120
Describe how value added by active management is measured
value-added = active return = active portfolio return − benchmark return
portfolio active return = Σ(active weight of security i × return of security i).
Active return is composed of two parts: asset allocation return plus security selection return:
121
Describe the practical strengths and limitations of the fundamental law of active management
While the fundamental law can be used for evaluating market timing, security selection, and sector rotation strategies, one has to be aware of its practical limitations. The limitations of the fundamental law include bias in measurement of the ex-ante information coefficient and lack of true independence while measuring breadth of an active strategy.
122
Explain how the information ratio may be useful in investment manager selection and choosing the level of active portfolio risk.
An investor will always choose the active manager with the highest information ratio regardless of her risk aversion. The investor will combine this optimal active portfolio with the benchmark to create a portfolio with a suitable level of optimal risk based on her risk preferences.
123
Describe and interpret the fundamental law of active portfolio management, including its component terms—transfer coefficient, information coefficient, breadth, and active risk (aggressiveness).
The three components of the information ratio are the information coefficient (measure of manager's skill), the breadth (number of independent active bets), and the transfer coefficient (the degree of constraints on manager's active management).
For an unconstrained portfolio, TC = 1.
124
Compare active management strategies, including market timing and security selection, and evaluate strategy changes in terms of the fundamental law of active management.
The information coefficient of a market timer = IC = 2(% correct) − 1
The fundamental law can also be used to evaluate active sector rotation strategies.
125
Ask price
The price at which a trader will sell a specified quantity of a security. Also called ask, offer price, or offer.
126
Best ask
The offer to sell with the lowest ask price. Also called best offer or inside ask.
127
Best bid
The offer to buy with the highest bid price. Also called the inside bid.
128
Best offer
Best askThe offer to sell with the lowest ask price. Also called best offer or inside ask.
129
Bid-ask spread
The ask price minus the bid price.
130
Bid price
The price at which a trader will buy a specified quantity of a security. Also called bid.
131
Delay costs
Implicit trading costs that arise from the inability to complete desired trades immediately. Also called
slippage.
132
Effective spread
Two times the difference between the
execution price and the midpoint of the
market quote at the time an order is
entered.
133
Implementation shortfall
The difference between the money
return (or value) on a notional or paper
portfolio and the actual portfolio return
(or value).
134
Inside ask
Best ask The offer to sell with the lowest ask price. Also called best offer or inside ask.
135
Inside bid
Best bid The offer to buy with the highest bid price. Also called the inside bid.
136
Inside spread
The spread between the best bid price and the best ask price. Also called the market bid-ask spread, inside bid-ask spread, or market spread.
137
Latency
The elapsed time between the occurrence of an event and a subsequent action that depends on that event.
138
Limit order book
The book or list of limit orders to buy and sell that pertains to a security.
139
Market fragmentation
Trading the same instrument in multiple venues.
140
Market impact
The effect of the trade on transaction prices. Also called
| price impact.
141
Midquote price
The average, or midpoint, of the prevailing bid and ask
| prices.
142
Opportunity cost
The value that investors forgo by choosing a particular course of action; the value of something in its best
alternative use.
143
Price improvement
When trade execution prices are better than quoted
| prices.
144
Describe abusive trading practices that real-time surveillance of markets may detect.
Real-time surveillance and monitoring of electronic markets seek to detect market abuses and potential crises as they unfold, allowing for a faster response. Abusive trading practices include front running and market manipulation. Market manipulation activities include trading for price impact, rumormongering, wash trading, spoofing, bluffing, gunning the market, and squeezing and cornering.
145
Describe comparative advantages of low-latency traders.
Electronic market traders employ advanced orders, trading tactics, and trading algorithms. Electronic markets enable hidden orders, leapfrogging algorithms, flickering quotes, electronic arbitrage, and machine learning.
146
Explain the components of execution costs, including explicit and implicit costs.
Explicit trading costs include brokerage, taxes, and fees; implicit costs include the bid-ask spread, price impact, slippage, and opportunity cost.
147
Calculate and interpret effective spreads and VWAP transaction cost estimates.
Effective spread = 2 × (per-share effective spread transaction cost)
VWAP transaction cost = trade size × (side) × (trade VWAP – benchmark VWAP)
where:
side = + 1 for buy orders and –1 for sell orders
148
Describe characteristics and uses of electronic trading systems.
Latency is defined as the time lapse between the occurrence of an event and execution of a trade based on that event. Electronic trading systems allow low-latency traders a competitive advantage by jumping the order queue.
149
Describe the risks associated with electronic trading and how regulators mitigate them.
Risks of electronic trading include HFT arms races at a disadvantage to small traders, as well as increases in systemic risk due to runaway algorithms, fat finger errors, overcharge orders, and malevolent orders.
150
Describe factors driving the development of electronic trading systems.
The factors driving the development of electronic trading systems include lower cost, higher accuracy, provision for audit trails, fraud prevention, and a continuous market during trading hours.
151
Identify and contrast the types of electronic traders.
Electronic traders include news traders, dealers, arbitrageurs, front runners, quote matchers, and buy-side traders.
152
Describe market fragmentation.
Market fragmentation results when a security trades in multiple markets. Trading algorithms such as liquidity aggregation (i.e., creation of a super book) and smart order routing seek to overcome the challenges posed by market fragmentation.
153
Describe the implementation shortfall approach to transaction cost measurement.
Implementation shortfall is the difference in value between a hypothetical (or paper) portfolio in which the trade is fully executed with no cost, and the value of the actual portfolio.
