Portfolio Management Flashcards

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1
Q

ETF Creation/redemption process

A
  1. Investor places an order (via a broker) to buy an ETF share
    • A willing seller is sought (another investor or a market maker)
    • Order is executed and settled, and the investor receives the shares of the ETF
    • At this stage, there is no involvement of the ETF manager
    • However, where do the ETF shares come from in the first place?
  2. Authorized Participants (APs) are authorized by the ETF manager to participate in the creation/redemption process:
    • APs are large broker/dealers or market makers
    • APs create new ETF shares by transacting in-kind with the ETF issuer:
      • Happens off exchange in the primary market
      • AP transfers securities (for creation) or receive securities from (for redemptions) the ETF issuer, in exchange for ETF shares
    • ETF manager publishes a list of required in-kind securities for each ETF, e.g. shares of the FTSE 100 reflecting index weightings, this is called the creation basket:
      • This basket creates the intrinsic net asset value (NAV) of the ETF
      • To create new ETF shares an AP acquires the securities in the creation basket and delivers them to the ETF manager for an equal value of ETF shares (or large blocks called creation units)
      • The exchange takes place when the markets are closed - The process works in reverse through a redemption basket
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2
Q

ETFs and arbitrage

A
  • AP therefore faces no economic exposure (can sell ETF shares to investors whilst simultaneously buying the shares in the creation basket)
  • The creation/redemption process is key to keeping the value of the ETF share in line with the corresponding NAV of the portfolio of securities it holds, the AP is rewarded for this activity through arbitrage:
    • ETF share at a discount to NAV • AP steps in to buy the ETF shares on the open market and simultaneously sells the stocks on the exchange, trading takes place until the pricing discrepancy disappears • The AP may choose to redeem ETF shares by exchanging them for the basket of securities with the fund issuer – ETF share redemption
    • ETF share at a premium to NAV • AP steps in to sell the ETF shares on the open market and simultaneously buy the stock on the exchange • The AP may choose to create additional ETF shares by exchanging the basket of securities for ETF shares with the fund issuer – ETF share creation
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3
Q

ETF: The arbitrage gap

A
  • Represents the price of the ETF where it makes sense for the AP to step in to create or redeem shares
  • It can vary in size because of several factors:
    • Liquidity of the underlying securities
    • Ease of settlement of the underlying securities
    • Trading costs/processing fees incurred by the AP
    • Settlement costs // Taxes
  • For any ETF, the gap creates a band (or range) around its fair value inside which the price of the ETF will trade - Essentially, arbitrage keeps the ETF trading at (or near) its fair value
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4
Q

Advantages of the creation and redemption process:

A
  • The AP absorbs the costs of transacting the securities which are passed on to investors via a bid-ask spread incurred by the buyers and sellers
  • Non-transacting shareholders (e.g. buy-and-hold investors) are shielded from the negative impact of other investors coming into and out of the ETF (unlike traditional mutual funds where the costs to buy and sell the fund manager incurs affects all shareholders).
  • The in-kind process allows the ETF manager to control the cost basis of their holdings for tax purposes
  • The issuer may allow for cash creation/redemption (rather than in-kind):
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5
Q

ETF: Trading and settlement

A

National Security Clearing Corporation (NSCC)

  • All trades in a given day are submitted to the NSCC at the end of the day
  • NSCC becomes guarantor to the transactions until they become “cleared”

Depository Trust Company (DTC)

  • A subsidiary of the NSCC that holds the book of accounts, i.e. list of security holders and ownership
  • Aggregated at the member firm (rather than individual investor) level
  • After each trade is cleared, the DTC adds up all of the trades in process of continuous net settlement:
  • Settlement is T+2 for majority of ETF trades. • Market makers receive special treatment on settlement requirements given their role of providing liquidity. T+6 settlement, given their need to create and borrow ETF shares.

European Markets

  • Majority of ETF owners are institutional clients
  • Market trading is fragmented across multiple exchanges, jurisdictions and clearing houses
  • Majority of trading happens in negotiated OTC trades between large institutions
  • Most ETFs are cross-listed on multiple exchanges with varying share classes that vary by their treatment of currency hedging
  • Settlement is also fragmented which may result in wider spreads and higher local market trading costs in comparison to the centralized US system
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6
Q

ETF: Tracking error

A
  • ETF managers attempt to create a portfolio return that tracks the fund’s benchmark as closely as possible (after subtracting fees)
  • Comparing ETF performance with index returns should include a:
    • Measure of central tendency – mean or median, and a
    • Measure of variability – standard deviation or range
  • Tracking error = Standard deviation of difference in daily performance between the index and the fund tracking the index
    • Typically reported over a 12-month period
    • Tracking error should be assessed with the mean or median values
  • Alternative method is to look at the 12-month rolling tracking difference - Allows for comparison with other metrics such as the fund’s expense ratio • A normal expectation would be that the ETF under-performs the benchmark by an amount equal to the fund’s expense ratio
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7
Q

Sources of tracking error

A
  1. Fees and expenses
  2. Representative sampling and optimization:
    • Some ETF managers choose to optimize their portfolios by holding a portion (e.g. the large cap stocks of the index) or a representative sample of the index because of underlying security illiquidity.
  3. Depositary Receipts and other ETFs
    • Differences in exchange trading hours and security prices create discrepancies between the ETF portfolio and the index
  4. Index changes
    • If the ETF manager fails to incorporate these changes, tracking error will result
  5. Fund accounting practices
    • Differences in valuation practices between the fund and the index, e.g. time used to establish (strike) currency valuations
  6. Regulatory and tax requirement​​​​
  7. Asset manager operations e.g. stock lending or foreign dividend recapture to gain additonal income > difference to index
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8
Q

ETF Tax issues

A

Tax fair: The actions of investors selling shares of an ETF fund do not influence the tax liabilities for the remaining fund shareholders given the shares of the ETF are sold in a secondary market and the in-kind creation/redemption is not a taxable event.

Tax efficient:

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9
Q

ETF bid-ask spreads

A

± Creation/redemption fees and other direct trading costs, such as brokerage and exchange fees

+ Bid-ask spreads of the underlying securities held in the ETF

  • OTC vs. Exchange traded
  • Volatility

+ Compensation to the market maker for the risk of hedging or carrying positions for the remainder of the trading day

+ Market maker’s desired profit spread (subject to competitive forces)

  • Discount related to the likelihood of receiving an offsetting ETF order in a short time frame
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10
Q

ETF premiums and discounts

A
  1. Each ETF has an end-of-day NAV at which shares can be created or redeemed:
    • Intended to be an accurate assessment of the ETFs fair value
  2. During the trading day, exchanges disclose ETF indicated NAVs (iNAVs) and represents:
    • The intraday fair value estimates of an ETF, and
    • Is based on its creation basket composition for that day
  3. Premiums and discounts will be driven by a number of factors:
    • Timing differences: Differences in exchange closing times between the underlying securities and the exchange where the ETF trades
    • Stale Pricing: ETFs that trade infrequently may have significant premiums and discounts if the ETF has not traded in the hours leading up to market close
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11
Q

ETF Total costs of ETF ownership summary

A
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12
Q

ETF: Trading costs vs. management fees

A

Round-trip trading cost (%) = (One-way commission % x 2) + (½ x Bid-ask spread % x 2)

Holding period cost (%) = Round-trip trading cost (%) + Management fee for the period (%)

  • The longer an ETF is held, the greater the proportion of total are represented by the management fee (rather than the trading cost) component:
    • Tactical traders may therefore choose ETFs with higher management fees but lower trading costs
    • Buy-and-hold ETF investors would be concerned about the management fee size
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13
Q

Types of ETF risk

A
  1. Counterparty risk: ETF legal structures involve dependence on a counterparty
  2. Settlement risk:
  3. Security lending​: Some ETFs may lend out their stock to earn additional income for the fund
  4. Fund closures: ETF issuer may close an ETF, the underlying securities are sold and cash returned to investors
  5. Investor-related risks: significant risk if an investor does not fully understand the exposure provided by an ETF that they invest into: - E.g. Leveraged and inverse leveraged ETFs
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14
Q

Fund closures

A

Primary reasons for fund closure include:

  • Regulations
    • Change in regulations forcing certain ETFs having to close down, e.g. scrutiny over the use of commodity derivatives may impact ETFs that use these instruments
  • Competition
    • The ETF market is highly competitive. Low AUM and trading volumes may a signal of impending closure!
  • Corporate actions
    • M+A between ETF providers. Owners may close under-performing ETFs.

“Soft” closures:

  • Creation halts
  • Change in investment strategy
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15
Q
A
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16
Q

ETF strategies: Overview

A
  • Primary applications in which ETFs are used include the following:
    • Portfolio efficiency: Use of ETFs to better manage a portfolio for operational or efficiency purposes: - E.g. Cash or liquidity management, rebalancing, portfolio completion, and active manger transition management
    • Asset class exposure management: The use of ETFs to achieve or maintain a core exposure to key asset classes, market segments, or investment theme on a strategic, tactical, or dynamic basis
    • Active and factor investing: Use of ETFs to target specific active or factor exposures
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17
Q

ETF strategies: Portfolio efficiency

A
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18
Q

ETF strategies: Asset class exposure management

A
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19
Q

ETF strategies: Active and factor investing

A
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20
Q

Return

A

Return will include amount of systematic risk faced by investor

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21
Q

Multi-factor Models

A
  • Rather than use a single variable (CAPM), is it more intuitive to explain expected returns by considering more than one variable
  • Variables represent systematic factors that have a quantifiable and predictable impact on stock prices
  • Together these systematic factors represent the amount of risk that cannot be diversified away. This is called price risk
  • Factors may be based on:
    • fundamental characteristics of the asset e.g. dividend yield
    • on economic events
    • statistically significant correlated variables that may not have any obvious impact on asset returns.
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22
Q

Arbitrage Pricing Theory (APT)

A

Less stringent assumptions than CAPM:

  • Asset returns described by linear relationships to a set of factors
  • Investors form well-diversified portfolios eliminating specific risk
  • Asset prices set such that no profitable arbitrage will be possible
  • If APT holds then the APT output should be the intercept term in the surprise based multi-factor model
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23
Q

Factor Portfolio

A

Portfolio that is only sensitive to one specific factor

A well-diversified portfolio with a factor sensitivity of 1.0 for a given specified risk, factor sensitivities of 0 for all other specified risks, and company-specific risk of 0

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24
Q

Four-factor Carhart model

A

Calculate the expected excess return

Multifactor model based on the Fama-French three-factor model but with a momentum factor added.

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25
Q

Uses of multi-factor models

A
  • Passive management In creating tracker funds, managers can use multi-factor models to match a fund’s factor exposures to those of the index being tracked
  • Active management Used to model expected returns and predict Alpha.
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26
Q

Types of model

A

Macroeconomic factor models: Uses surprises in macroeconomic variables as risk factors. Once you have plausible factors, use regression analysis (our focus)

Fundamental factor models Attributes of stocks that are important in explaining crosssectional differences. Uses P/S, P/E leverage as factors

Statistical factor models Regression models – there is no underlying economic rationale.

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27
Q

Analyzing Sources of Returns

A

Active return = Return to portfolio - Return to benchmark

Active return = Return from factor tilts + Return from asset selection

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28
Q

Tracking error

A

Tracking error = Standard deviation of (Portfolio return - Benchmark return)

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29
Q

Information ratio

A

Excess returns / Tracking error

Performance can be evaluated using

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30
Q

Active risk factor

A

The contribution to active risk squared from portfolio’s different exposure to risk factors from the benchmark

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31
Q

Active specific risk (or asset selection risk)

A

The contribution from stock specific (nonfactor) elements

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32
Q

Value at Risk (VaR)

A

Minimum loss that would be expected a certain percentage of the time over a certain period of time given the assumed market conditions

  • VaR can be expressed in currency or percentage terms
  • VaR is a minimum loss
  • VaR is measured for a time horizon and confidence level
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33
Q

Three methods to estimate VaR:

All three methods use the following steps:

A
  1. Parametric VaR (variance-covariance or analytical) method
  2. Historical simulation method
  3. Monte Carlo simulation

All three methods use the following steps:

Step 1: Converting the set of holdings in a portfolio into a set of exposures to risk factors (risk decomposition)

Step 2: Gathering historical data for each risk factor, the period chosen for the historical data is called lookback period

Step 3: Each method will use a different approach to estimate VaR using the data gathered in Step 2

34
Q

Parametric Method

A

Uses the expected return and standard deviation (portfolio) to identify the VaR threshold in the distribution

E(Rp) = wx E(Rx) + wy E(Ry)

Daily rp: ERp /250

Daily portfolio SD / square root of 250

VAR = Daily rp - zscore (5% = 1.645) + daily portfolio SD

VAR in % x portfolio value = $ VAR

35
Q

Historical Simulation Method

A

Uses the current portfolio and reprices it using the actual historical changes in the key factors

Can be used for options

Key disadvantage: No certainty that a historical event will re-occur

36
Q

Monte Carlo Simulation Method

A

Simulation: Uses own assumptions about distribution’s statistical characteristics • Random outcomes are generated using these characteristics • Random returns are used to price the portfolio -higher the number of random values generated the more reliable the results

37
Q

Advantages and Limitations of VaR

A
38
Q

Conditional VaR (CVaR, Expected tail loss or expected shortfall)

A
  • Average loss conditional on exceeding the VaR cutoff
  • How much can I expect to lose if VaR is exceeded?
  • Best derived using the historical or monte carlo simulation

5% cvar - Historical add 5 worst tarding days and divide by 5 > times by portfolio value to get $ Cvar

39
Q

Incremental VaR (IVaR)

A
  • The increase/decrease in VaR if a position size is changed relative to the remaining positions
  • VaR is calculated under the assumption that the change is made, and the difference between the new VaR and the old VaR is the IVaR
  • Useful for risk managers to access the impact of position change on VaR and decide if the change is acceptable
40
Q

Marginal VaR (MVaR)

A
  • Conceptually similar to incremental VaR
  • The effect of a very small change in the position
  • In a diversified portfolio, marginal VaR may be used to determine the contribution of each asset to the overall VaR
  • Both IVaR and MVaR address the question of what impact a change in the portfolio holdings might have on the total VaR of the portfolio
41
Q

Ex ante tracking error (relative VaR)

A
  • The degree to which the performance of a given investment portfolio might deviate from its benchmark
  • Can be calculated using any of standard VaR methods
  • The portfolio to which VaR is applied contains the portfolio’s holdings minus the holdings in the specified benchmark
  • Assumes a fictitious portfolio made of the original portfolio and short positions in the benchmark’s holdings
42
Q

Sensitivity Risk Measures

A
43
Q

Scenario Risk Measures

A
  1. Historical scenarios:
    • Results from a repeat of a particular period of financial market history (like: 1987, 2001 or 2008)
    • The impact of a historical scenario need to ensure all relevant risk factors are included
    • Securities or markets that did not yet exist at the time of the scenario have to be modelled carefully
  2. Hypothetical scenarios
    • Reflect the impact of extreme market movements that have not necessarily previously occurred
    • No specific assumptions regarding normality or correlation
    • Enables stressing correlations
    • Process requires identification of the portfolio’s most significant exposures and assessing their behavior (reverse stress testing)
44
Q

VAR vs Sensitivity and Scenario Risk Measures

A
45
Q

Advantages and Limitations of Scenario Risk Measures

A
46
Q

Using Constraints in Market Risk Management

A
  1. Risk Budgeting
    1. Total risk appetite of the firm or portfolio agreed at the highest level of the entity and then allocated to sub-activities
    2. Typically uses VaR or ex-ante tracking error.
  2. Position Limits: Position limits are limits on the market value or nominal of any given investment
  3. Scenario Limits: Limit on the estimated loss for a given scenario
  4. Stop loss Limits: Stop loss can manage the VaR limitation of ‘trending’.
47
Q

Inter-temporal rate of substitution

A

An investor’s willingness to invest today

  • Investors have a choice between consuming or saving (investing) today
  • Investing today has an opportunity cost, since it reduces an investor’s ability to consume today
  • An investor is compensated for this opportunity cost through return on the investment
  • This can be interpreted as how much consumption today an investor would be willing to give up today in order to earn an additional $1 of consumption in the future

In ‘good’ economic times >> High income today >> Lower marginal utility of consumption today (bc you have everything already / can buy anything already) >> Higher intertemporal rate of substitution (Investments which deliver better payoff in ‘bad’ economic times more valuable to investor)

48
Q

Impact of inter-temporal rate of substitution on bond prices

A

If the inter-temporal rate of substitution were to increase, investors would be more inclined to save for the future, and defer consumption >> This would lead to an increase in demand for bonds and hence bond prices increase >> Yields go down (lower rate of return)

In conclusion, the inter-temporal rate of substitution and risk free rates of return can be expected to move in opposite directions

49
Q

Uncertainty and wealth

A

Higher uncertainty → Lower expected marginal utility (with risk averse investors) → Higher required return

As wealth increases, risk aversion falls, since marginal utility of consumption is lower, leading to a lower required rate of return on risky assets.

50
Q

Uncertainty and risk premiums on risky assets

A

Future price uncertainty leads to a higher discount rate and lower price

Covariance between an investor’s inter-temporal rate of substitution and the (unknown) future price of the risky asset tends to be negative:

Increase in expected future price of asset, due to strong economy → Increase in expected future wealth → Lower marginal utility of consumption in future → Lower intertemporal rate of substitution

Decrease in expected future price of asset, due to weak economy → Prospect of losing job → Higher expected marginal utility of consumption in future → Higher inter-temporal rate of substitution

51
Q

Uncertainty: Conclusions of negative correlation

A
  • Negative correlation suggests that investor won’t invest when asset prices are expected to increase, due to lower marginal utility of future consumption, and will invest when asset prices are expected to decrease
  • The greater the negative correlation between asset prices and the inter-temporal rate of substitution, the higher the required rate of return and therefore the lower asset prices
  • Any asset with positive correlation would provide a good hedge against poor economic outcomes and would therefore have a higher price and lower return
52
Q

Impact of economic growth on discount rates

A

Expectation of good economic times (higher GDP growth rates) → Lower future expected marginal utility of consumption → Higher willingness to consume and lower willingness to invest today → Lower asset prices → Higher rate of return

Greater uncertainty of future GDP growth rate → Increased risk → Lower asset prices → Higher rate of return

In economic expansions, higher expected growth rates lead to a higher required rate of return, but required return declines in recessions as expectations of growth decline

53
Q

Impact of central bank policy on discount rates

A
  • Rates are cut when economic activity is seen as “too low” or expected inflation is low
  • Rates are increased when economic activity is seen as “too high”, or expected inflation is high
  • This relationship can be explained through the Taylor Rule
54
Q

Taylor Rule

A
  • If current inflation is above (below) target inflation, the policy interest rate should be set above (below) the equilibrium
  • If the output gap is positive (negative), the economy is producing beyond (below) it’s sustainable level, and interest rates should be above (below) the long run equilibrium to reduce (increase) economic activity
55
Q

Break-even Inflation Rate

A
  • Market expectations of inflation over the time to maturity
  • Premium within nominal bond yield for taking on inflation uncertainty risk
56
Q

Bond risk premium (BRP)

A
  • BRP can be interpreted as measuring:
    • Additional return for inflation uncertainty
    • Addition return for risk that longer term bonds provide less of a hedge against bad economic outcomes
    • The BRP should be expected to rise as investors become more worried about bad economic outcomes, since selling pressure will reduce price and increase yield
57
Q

Yield curve analysis

A
  1. An upward sloping yield curve could be explained by:
    • Expectations of higher inflation or interest rates in the future
    • Increasing inflation uncertainty
    • Higher risk premium with longer time to maturity
    • Short-dated bonds are less positively correlated with bad economic times compared to longer-dated bonds - Therefore, investors should expect lower returns on shorter-dated bonds due to their better consumption hedging properties.
  2. An inverted (downward sloping) yield curve could be explained by:
    • Expectations of lower inflation in the future
    • Tends to occur at late expansionary stage of the business cycle and is a predictor of a recession
  3. Other factors - Demand and supply factors - Regulation (on pension funds)
58
Q

Credit risk

A
  1. Higher credit risk (uncertainty of receipt of future cash flows) will lead investors to demand a higher rate of return
  2. Assessing credit risk:
    1. Defaults tend to cluster during recessions, reducing diversification benefits of a portfolio
    2. Expectations of economic prospects will impact on credit spreads
    3. Seniority of debt will impact on credit risk on a single issuer
    4. Recovery rates are higher for more senior forms of debt
    5. Recovery rates are not constant and tend to be higher when the economy is expanding and lower during recessions
    6. Credit spreads are higher for companies with greater sensitivity to the business cycle
    7. Company specific factors (e.g. use of leverage)
    8. Country specific factors (for sovereign credit risk)
59
Q

Equities are more risky than bonds due to:

A
  1. Uncertain timing and amount of cash flows
  2. Lower ranking on liquidation
  3. Recovery rate likely to be zero for equity in bankruptcy
  4. Equities tend to have a worse outcome in bad economic times and are therefore have poor consumption hedging properties

-> Investors will therefore require a higher rate of return for investing in equity than bonds

60
Q

Factors contributing to a high P/E ratio

A
  1. An increase in expected future real earnings growth
  2. Falling real interest rates
  3. Potentially due to falling volatility in real GDP
  4. Falling inflation expectations
  5. Falling inflation uncertainty
  6. Lower risk associated with investing in equities
61
Q

Factors contributing towards a higher discount rate

A
  • Higher GDP growth rates (lower inter-temporal rate >> no demand >> higher rate to encourage investors)
  • Higher volatility of expected GDP growth rate
  • Higher inflation expectations
  • Higher uncertainty of inflation expectations
  • Higher credit risk
  • Higher earnings volatility
  • Lower inter-temporal rate of substitution
  • Lower levels of liquidity
62
Q

Active Portfolio Management / Value Added

A
  • aim of active management is to add value by outperforming a benchmark
  • Value added (also called active return) is the difference between an actively managed portfolio and the benchmark portfolio
  • There are two sources of value added: using different weights in the portfolio compared with the benchmark (active asset allocation), and from security selection.
63
Q

The benchmark must demonstrate a number of qualities for it to be a suitable measure for value added:

A
  1. Representative of the assets from which the investor will select
  2. Benchmark portfolio can actually be replicated at low cost
  3. Benchmark weights can be determined ex ante and the benchmark returns are produced in a timely manner
64
Q

Value Added Calculation

A

RA = ΣΔw x Ri

65
Q

Information ratio

A

Risk-adjusted relative return measure

Information ratio = Active returns / Active risk

66
Q

Sharpe ratio

A

Sharpe ratio = RP - RF / σP

Risk-adjusted absolute return measure. Useful for comparing different portfolios since it is unaffected by the addition of cash or leverage.

67
Q

Active Mgmt: The fundamental law

A

There are three main drivers behind the expected active return of a portfolio (value added).

  1. Breadth (BR) is defined as the number of independent forecasts of exceptional return we make per year
  2. Information coefficient (IC) is a measure of the investor’s skill and considers the correlation of each forecasted active return with realized active returns. This tells us if the investor was just lucky or whether there was some process behind the strategy
  3. Transfer Coefficient (TC) represents the degree to which the investor’s forecasts are translated into active weights. You can be the best forecaster of active returns in the world but to create value added, the portfolio must be properly constructed to incorporate these forecasts.
68
Q

Constructing optimal constrained portfolios

A
69
Q

Constructing optimal unconstrained portfolios

A
70
Q

2 Components of execution costs

A

Explicit costs – direct costs of trading such as: - Broker commission costs - Transaction taxes - Stamp duties - Fees paid to exchanges

Implicit costs – indirect costs caused by the market impact of trading and result from the following issues:

  • Bid-Ask Spread: Traders wanting to trade quickly buy at higher prices, and sell at lower prices than those that are willing to wait
  • Market impact (or price impact): Represents the effect of the trade on transaction prices
  • Traders wanting to fill large orders often must move prices to encourage others to trade with them
  • Delay costs (or slippage): Created from the inability to complete the desired trade immediately • Traders fail to profit when they fill their orders after prices move as they expect
  • Opportunity costs (or unrealized profit/loss): Created from the failure to execute a trade promptly, traders fail to profit when their orders fail to trade and prices move as expected
71
Q

Estimating transaction costs

A

To estimate transaction costs, analysts compare trade prices to a benchmark price:

  1. Effective Spread: Mid-quote price at the time of the trade
  2. VWAP: Volume-weighted average price around the time of the trade
  3. Implementation shortfall: Mid-quote price at the time of the order submission
72
Q

Effective spread

A

Does not measure delay costs (slippage) that comes from the inability to complete the desired trade immediately because of its size in relation to the available market liquidity

73
Q

Volume weighted average price (VWAP)

A

Represents the sum of the total dollar value of the benchmark trades divided by the total quantity of the trades:

74
Q

Implementation shortfall

A
  • Compares the values of the actual portfolio with that of a paper portfolio constructed under the assumption that trades where done at the prices that prevailed (decision price) when the decision to trade was made
  • The difference represents the implementation shortfall
  • Attempts to capture all the implicit and explicit costs of trading, e.g. market impact, delay costs and opportunity costs • Further detail will appear at Level III of the CFA Program exam
75
Q

Electronic trading

A

Compared with floor-based trading systems, electronic order-matching systems have many advantages:

  • Cheap to operate once built
  • Precisely enforce the exchange’s trading order precedence and pricing rules without error or exception
  • Creation of perfect audit trails
  • Hidden orders (where permitted) are always kept private - Can operate “around-the-clock”
76
Q

Market fragmentation

A
  • The process of trading the same instrument on multiple exchanges
  • Increases the potential for price and liquidity disparities across venues
  • The creation of alternative trading systems (ATSs) has exacerbated the issue of fragmented markets
  • Trading strategies for large orders are adapted to search for liquidity across multiple exchanges and time to control the market impact of their trades, e.g. liquidity aggregation and smart order routing
77
Q

Major types of electronic traders

A
  1. Electronic news traders (they profit from analyzing the news and executing against ‘stale’ orders, i.e. orders they believe do not reflect the new information)
  2. Electronic dealers (make markets by placing bids and offers with the expectation of profiting from round trips at favorable net spreads)
  3. Electronic arbitrageurs (attempt to search market, and take advantage of pricing discrepancies at low cost and risk)
  4. Electronic front runners (buying in anticipation of other buyers entering into the market)
  5. Electronic quote matchers (Attempt to take advantage of the option values of standing orders (limit orders waiting to be filled)
78
Q

Characteristics of electronic trading systems

A
  1. Speed
    • Taking trading opportunities before others do
    • Canceling orders quickly on standing orders they no longer want filled
  2. Fast communications
    • Strategies to reduce communication times (servers in exchanges etc)
  3. Fast computations
    • In order to process the information and deciding whether to act upon it
    • Achieved using efficient software and code
79
Q

Uses of electronic trading systems:

A
  1. Hidden orders
    1. Orders exposed only to the brokers or exchanges that receive them
  2. Leapfrog
    1. When bid-ask spreads are wide, dealers may actually be prepared to trade at better prices than they quote.
  3. Flickering quotes
    1. Visible limit orders that are repeatedly entered and cancelled quickly, e.g. often within a minute
    2. Indication to other traders the price they are willing to trade at. - Traders wishing to trade can place hidden limit orders at a price the quote was flickering at.
80
Q

Systemic risks & Solutions of electronic trading

A
  1. Systemic risks:
    • Runaway algorithms - E.g. algorithms produce streams of unintended orders because of programming mistakes
    • Fat finger errors - E.g. submitting an order that was larger than intended
    • Overlarge orders - E.g. demanding more liquidity than what can be provided by the market
    • Malevolent order - E.g. deliberately creating order streams to disrupt the market
  2. Solutions:
    1. Software testing
    2. Rigorous market access controls
    3. Rigorous access controls on software developers
    4. Real time order flow surveillance
    5. Broker requirements to surveil client order flow
    6. Adoption of price limits and trade halt when adverse volatility occurs
81
Q

Market manipulation practice

A
  1. Front running
    • Illegal in most jurisdictions if front runners obtain their information regarding other orders improperly
    • Some traders use AI techniques on information from price feeds in order to identify opportunities where traders are filling large orders over time
  2. Trading for market impact (deliberately force prices higher or lower)
  3. Rumormongering (Dissemination of false or misleading information)
  4. Wash trading (fooling investors into thinking a market is more liquid than it actually is)
  5. Spoofing (also known as layering, and represents the practice of creating layers of standing orders in order to create a false impression of market liquidity
82
Q

Market manipulation strategies

A
  1. Bluffing
    • Submitting orders and arranging trades to influence other traders’ perceptions of value
    • Preying on momentum traders with a ‘pump and dump’ strategy
    • May also be coupled with rumormongering activity to further the process
    • Analysts should base their decisions on independent assessments of value
  2. Gunning the market
    • Forcing traders to disadvantageous trades
    • Gun the market by selling quickly to push prices down with the hope of triggering stoploss sell orders
    • The manipulator could then profit by being able to repurchase at lower prices
  3. Squeezing and cornering
    • Gaining control over resources required to settle trading contracts (buying long futures and physical gold > at expiration he demands delivery > shots need to buy from him at inflated prices)