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2021 CFA Level III > Trade Strategy Perf Evaluation > Flashcards

Trade Strategy Perf Evaluation Flashcards

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1
Q

Trade Motivation

A
  1. Profit seeking
    Active pm seek to capitalize on investment ahead of the market
    - rate of alpha decay: detection in alpha once an investment decision has been made
    is high when manager needs to trade in shorter time frames

Profit-seeking algorithms use real-time market data to determine which securities to buy and sell

trade agency: refers to how quickly or slowly the order is executed over the trading time horizon

  1. risk Management / hedge needs
    - Portfolio needs to be traded to maintain target risk exposures
    - Derivatives may be used to facilitate risk management
  2. Cash flow needs
    - trades are caused by investor subscription into, and redemption is out of the fund
    - collateral and margin calls could require close-to-immediate liquidation, whereas a fund redemption entails a notice period
    - to avoid cash drag, PM may engage in equalization strategy
  3. corp. action, margin calls and index reconstitution
    - M&A or spinoff may require portfolio trading or dividend reverting
    - margin calls on leveraged positions that suffering losses may require urgent sale of portfolio holdings
    - when the bank index is reconstituted, PM needed to executed trades to reflect the changes
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2
Q

trading strategy inputs

Side

A

if price increase, executing a buy order may take longer than executing a sell order

  • herding effect
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3
Q

Size

A

the total absolute amount of the security being transacted. Large order have increase market impact cost.

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4
Q

Relative size

A

order size as a % of the security’s average daily volume (ADV)

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5
Q

ST alpha
alpha decay high
high urgency

A

increase rates of alpha decay require faster trading and associated higher trading costs

high alpha decay, high urgency
higher market impact cost

Greater trade urgency results in lower execution risk because the order is executed over a shorter period of time, which decreases the time the trade is exposed to price volatility and changing market conditions.

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6
Q

price volatility

A

increase price volatility implies increase execution risk

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7
Q

security liquidity

A

increase liquidity reduces execution risk and trading costs such as market impact

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8
Q

market crisis

A

market volatility and liquidity are negatively correlated

Liquidity is a key concern

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9
Q

Stock Index

A

During the normal market environment, a firm being added or removed from a stock index may directly affect its liquidity

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10
Q

High risk aversion

A

high urgency to trade

  1. more concentrated about market risk
  2. tend to trade with greater trade urgency to avoid the greater market exposure
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11
Q

market impact (size)

A

the adverse prices impact caused true trading a large volume order at one time.

To minimize info leakage, pM may choose trading in the dark pool or using market and limit orders.

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12
Q

execution risk

trading too slowly

A

the adverse price impact resulting from a change in the fundamental value of the security

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13
Q

trader’s dilemma:

A

all equity market impact causes execution risk and vice versa

tradeoff: urgency rise, market impact increase, execution risk decrease

urgency decline, market impact decline, execution risk increase

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14
Q

Reference prices

A

used to determine expected trading costs which enables managers/ traders to select the optional strategy for a trade

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15
Q

Pretrade benchmarks (before the start of trading)

A pre-trade benchmark is often specified by portfolio managers who are buying or selling securities seeking short-term alpha by buying undervalued or selling overvalued securities in the market.

A

decision price

previous close
use as proxy for decision price when it’s quantitative manager

opening price
subjective manager

arrival price: time the order is sent to the market for execution

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16
Q

intraday benchmark

A

based on prices during trading period, used by managers who trade passively over a day, or funds that maybe rebalancing or mining risk

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17
Q

VWAP

A

low order volume relative to avg. daily volume traded
narrow bid-ask
low urgency to complete

help achieve the objective of using the cash received from sell order to fund buy orders of the rebalancing

-equal weighted average price of all trades executed

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18
Q

Time-series average price
TWAP

average price ignore volume of all trade during trading horizon

A

best when there are outliers or if there is highly fluctuating volume during the day

appropriate for managers who wish to remove the impact of outliers since they believe they are less able to participate in extreme trades. It’s also appropriate in market environments with highly fluctuating volume throughout the day.

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19
Q

Post trade benchmark

A

determine after trading has been completed.

close-price used to reduce tracking error

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20
Q

Price target benchmark

A

base on manager’s view of fair value

Price used to profit-seeking managers aiming to earn short term alpha, related to the manager’s view of the fair value of the security

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21
Q

Trading Strategy Selection

ST alpha

A

trade ST mispricing in a liquid market

high urgency
price target Benchmark

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22
Q

LT alpha

A

trade over the LT due to change in fundamental conditions

low urgency
difficult to use

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23
Q

Risk rebalance

A

rebalance or hedge risk exposure

low urgency
TWAP

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24
Q

client redemption

A

liquidate the holdings to meet client redemption

EOD trading close price

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25
Q

New mandate

A

invest new client fund

low urgency
close price

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26
Q

trade implementation choices
high-touch approach
human

A

require for large trades (block trade), involving greater human engagement

low order volume relative to avg. daily volume
narrow bid-ask
high urgency

dealer market include: principle trade
dealer/ market makers assume all or some of the risk relating to executing the order, priced into spread.

Quarter-driven, OTC or exchange markets are primacy principal trade markets which also includes request-for-quote (RFQ) markets where market makers do not provide continuous quotes, but only does on request.

  • broker market
    agency trades dealers try to arrange trades by acting as agents (brokers) on behalf of client.
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27
Q

electronic trading

A

used for more liquid trades/market

involve direct market access (DMA) and/or algorithmic trading.
DMA allow buy side portfolio managers/ trades to access the order book of the exchange directly through a broker’s tech infrastructure.

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28
Q

algorithm trading

A

profit seeking & trade execution

Profit-seeking algorithms use real-time market data to determine which securities to buy and sell

execution algorithms focus on how to trade manager orders.

scheduled algorithms % of volume (POV)
Vwap
twap

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29
Q

POV (participation algorithm)

A

send orders according to volume participation schedule

adv:
automatically exploit increased liquidity when available

disadvantage:
they continue to trade at any potentially adverse price, and may not fill the order in a specific time if there’s a lack of trading
trading cost increase

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30
Q

VWAP & TWAP

time-slicing algorithms

A

VWAP algorithms attempt to watch the vwap price for the period by carving up the trade and steady slice orders based in historical intraday volumes

adv:
ensure that a specific # of shares are executed in a specific time period
disadvantage:
they may force trades in times of low liquidity or trade too little in times of high liquidity

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31
Q

liquidity-seeking algorithms

A

opportunistic algorithms

if the trade is high ADV of the daily volume

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32
Q

arrival price

price when sent order to market

A

algorithms seek to trade close to market prices prevailing at the time the order is entered. Trade more aggressively

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33
Q

Dark strategies / liquidity aggregators

A

execute trades in dark pools, with aggregate algorithms attempting to optimize trading across multiple farm revenues

Dark aggregator algorithms appropriate for liquid or wide bid-ask spread, large order size. Good for avoid information leakage.

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34
Q

Smart order routers (SORs)

A

that determine the best destination to route an electronic order to get the best result.
Focus on getting the best price for market orders or the highest probability of execution for limited orders.

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35
Q

Clustering

A

ML technique whereby a computer learns to identify which algorithm is optimal for different types of traders based on the key features of trades.

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36
Q

High frequency makes forecast

A

attempt to model ST market direction.

LASSO is a ML technique that helps to reduce the # of explanatory variables to manageable # of significant variable

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37
Q

Equity

A

Stock exchange
Dark pools
most tech advanced
electronically executed

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38
Q

FI

A

low liquidity
large order size - dealer based quote driven market
High frequency execution for urgent trades, less urgent trade use broker-agent approach

OTC treasury can be traded algorithm
RFP

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39
Q

Exchange trade derivative

A

most common electronic trading
using low touch approach

buy-side traders generally use DMA for small trade

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40
Q

CTC Derivatives

A

dealer quote driven market

high touch approaches

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41
Q

Spot fx currency

A

OTcmarket both electronic trading and high touch broker approaches

Small order use DMA

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42
Q

Explicit

A

commission and fess

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43
Q

implicit embedded

A

market impact/ execution risk

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44
Q

Implementation shortfall IS
total cost =

+ (arrival- decision price) * filled size
+ (Pclose - decision) * unfilled size
+ fixed cost

A

IP
= execution (delay +trading cost)
+ opportunity
+ fixed fees

=paper - actual
= total shares * (close - decision)
-actual filled (close - execution)
-fixed cost

iS bps = IS$/(decision price * total shares)

45
Q

execution cost
= delay cost + opportunity cost

= (order executed price - decision price) * order size filled

delay cost= (arrival- decision price) * filled size

opportunity cost = = unfilled shares * (closing price - decision price)

A

due to executing shares at a less favorable price than the decision price
- delay cost due to adverse price movement intake order submission and trade reduced

minimize the efficient trading practices to give trader pre trade and post trade analysis to make swift decision on best trading strategy.

bps execution cost
execution/decision - 1
* filled order / total shares * 10,000

46
Q

Opportunity cost

= unfilled shares * (closing price - decision price)

A

cost of unearned return by the unfilled order

47
Q

fixed fees

A

any explicit commissions, or fees, incurred in executing the trade

48
Q

improving evolution performance

A
  1. delay cost can be reduced by having a process in place that provides traders with broker per ethics
  2. opportunity cost can be minimized by investing in the sub-optimal assets with detailed analysis
49
Q

absolute cost ($)= GEODE trade slippage

A

side * (execution price - bmk price) * shares executed

50
Q

trade cost (bps) =

A

side * (execution price / bmk price -1) * 10k

51
Q

Market-adjusted cost (bps)=Arrival cost (bps)−β×Index cost (bps)

=side * (execution/arrival price ) * 10k
- beta *
Side×(Index VWAP/Index arrival -1)×10k

Index cost (bps)= Side×(Index VWAP/Index arrival -1)×10k

A

ensure a trade is not penalized or rewarded for general market movements over the trade horizon by subtracting the index cost adjustments for security’s beta

if >0
market-adjusted cost is thus significantly lower than the total arrival cost. This indicates that most of the expense associated with buying is due to the effect of buying it in a rising market as opposed to the buying pressure induced by the order itself.

52
Q

index cost (bps)

A

side * ( index vwap / index arrival price -1)*10k

53
Q

added value (bps)

A

= arrival cost (bps) - est. pre-trade cost(bps)

negative cost = benefit

54
Q

trade governance

trade policy:

A
  1. meaning of best execution
  2. factors determine the optional order execution approach
  3. handling trading errors (collation of pm, risk manager ,legal/ compliance)
  4. listing of eligible broker & execution venues
  5. process to monitor, execution arrangements
55
Q

factors determine optimal execution approach

A
  • urgency and size of order
  • liquidity of security (APV) and the nature of security
  • characteristics of available execution venues
  • investment strategy objectives (LT vs. ST)
  • Reason for the trade
56
Q

Performance measurement

A

provides an overall indication of the portfolio’s performance, typically related to a benchmark

57
Q

performance attribution

A

held to understand return, both relative and absolute returns

58
Q

performance appraisal

A

determine whether the perf has affected primarily by investment decision, by the overall market, or by chance/ luck.

draw conclusion the quality of pm’s investment decisions.

combine output from both performance measurement and performance attribution to refer a professional judgement on the quality of performance.

59
Q

effective profess of PA:

A
  • a reflection of 100% of the portfolio’s return or risk exposure
  • the portfolio manager’s current decision-making process
  • the active investment decisions taken by the pm
  • a full explanation of the portfolio’s excess return and risk

must account for 100% of the portfolio’s risk and return and adequately represent the pm’s current investment process

60
Q

return/ risk attribution

return- based

A

adv:
- easy to implement
- does not require holdings data useful for private fund (HF)

disadvantage:

  • least accurate because does not consider holdings
  • return data can be manupulated
61
Q

risk-based

A
  • parallel of return attribution but analyze the impact of pm’s active management decisions on portfolio risk
62
Q

holdings-based

A

adv:
- more accurate than return-based because considers underlying holding

disadvantage:
- needs data on fund holdings
- ignores transaction impact

63
Q

transaction-based

A

adv:
most accurate method

disadvantage:
- highest data requirement
- highest complexity
- time consuming to implement

  • use both the holding and transaction
  • most accurate method but most difficult and time consuming to implement
  • underlying data must be complete, accurate, and reconciled from period to period
64
Q

Micro

A

analyze the portfolio at the portfolio manager’s level and sees to verify that the PM did what they said they would and to understand the drives of the portfolio’s return

65
Q

Macro

A

analyze investment decision at the fund sponsor’s level. Common used with institutional investing quantifies fund sponsor’s decisions to deviate from heir strategic asset allocation and the timing when they made those decisions.

66
Q

Factor-based return attribution

carport model based on

A
  1. value growth index RMRF
  2. market cap SMB
  3. book value to price HML
  4. momentum VWL

allow practitioner to remove the effects of known market factors to quantify the excess return.

67
Q

arithmetic attribution

A

focus on active return of a portfolio in a single period

alpha = rp-rb

68
Q

geometric attribution

A

periodic excess return of the portfolio
(1+rb)(1+ag) =1+rp

alpha g = (1+rp)/(1+rb)-1

69
Q

Bronson model

BHB Bronson-hood-beebover

A

allocation: Ai = (wi-W)* Bi
selection = Si = W (ri-bi)
interaction i= (wi-W)*(Ri-Bi)

70
Q

BH Bronson-fachler method

A

allocation: Ai = (wi-W)(BI-BT)
selection = Si =W(ri-bi)

INTERACTION = (w-W)*(Ri-bi)

address draws back of BHB’s allocation effect for segment because the sign of the resulting allocation effect does not automatically indicate whether the decision to oer/underweight a particular segment of the portfolio is correct

in BF, allocation effect looks at the active weight in the segment multiplied by the passive benchmark return of the segment relative to the overall benchmark.

71
Q

FI attribution

3 common method

A
  • exposure decomposition duration based
  • yield curve decomposition -duration based
  • yield curve decomposition - full repricing
72
Q

exposure decomposition duration based

A

allow single presentation of the output of the attribution with relative low data requirements

  • duraion
  • curve sharpe
    sectorselection
  • bond selection
73
Q

yield curve decomposition duration based

A
  • require more data
  • more operational complexity
  • yield or income
  • roll
  • shift
  • shape
  • spread
  • residual
74
Q

yield curve decomposition - full repricing

A
  • most comprehensive
    break into individual spot rates for CF occurring at different maturities
  • most precise and can accommodate the broadest range of instruments and yield curve change
    -most complex, least likely to be easily understood
75
Q

bottom up

A

relative:
security marginal contribution to tracking risk TE

absolute:
security’s marginal contritbution to total risk

76
Q

top-down risk based method:

A

relative:
attribute TE to relative allocation and selection

absolute:
factor’s marginal contribution to total risk and specific risk

77
Q

factor based

A

relative:
factor’s marginal contribution to TE and active specific risk

absolute:
factor’s marginal contribution to total risk and specific risk

78
Q

liability-based benchmarks

A
  • likely to be used by fund sponsors and PMs when a firm has a specific liability to pay in the future
  • focus in CF necessary to satisfy the liability and frequently limits the investment choices available to PM
  • nominal bonds, inflation-adjusted bonds and high quality stocks
79
Q

plan factors that impact liabiltiy and PM aa to most CFs:

A
  • # years work on average
  • # workers retired
  • impact of inflation on the liabilities
  • correlation between EDIT & PA
  • if plan is frozen or has a terminal life
  • avg actuarial assumption, including life expectancy and required discount rate over the plan
80
Q

Asset-based benchmark 7

A
  1. absolute return benchmark
  2. broad market indexes (sp500)
  3. Style indexes (large cap value/growth)
  4. factor model based CAPM
  5. return-based benchmark
  6. manager universe (peer group)
  7. Custom security-based benchmark
81
Q
  1. absolute return bmk
A

a return objective that arises to exceed a minimum target return

adv: simple and straightforward
disadvantage: its’ not an investable benchmark

82
Q
  1. broad market indexes
A

adv:

  • well recognized, easy to understand by clients, and widely available
  • unambiguous, generally investable, measurable and may be specific in advance
  • appropriate to use if it reflects the current investment process to the manager

disadvantage:
- manager’s style may deviate from the style reflect in the index
- differing definition of inv. style can provide diff. bm returns

83
Q
  1. style indexes
A

adv:
- widely available, widely understood by clients, widely accepted
- if the index reflects the manager’s style and it’s investable, it’s an appropriate benchmark

disadvantage:
- some style indexes can contain weightings in certain securities and sectors that may be larger than considered prudent.
- Differing definitions of inv. style can produce quote different benchmark returns, making them appropriate benchmark

84
Q
  1. factor model based benchmark CAPM
A

ADV:

  • useful in perf evaluation
  • provides managers and sponsors with insight into manager’s style by capturing factor exposure that affect an account’s performance

disadvantage:
- focus on factor exposures is not intuitive to all managers or sponsors
- the data & modeling are not always available and maybe expensive to obtain
- it maybe ambiguous because diff. factor models can produce different outputs, leading to misspecification

85
Q
  1. return based benchmark
A
  1. managed account returns over specific periods and
  2. corresponding returns on several style indexes for the same periods

adv:
- general easy to use and intuitive
- meets the criteria of a valid benchmark
- useful where the only info available is account returns

disadvantage:
- the style indexes may not reflect what the manager owns or what the manager or client would be willing to own
- enough monthly returns would be needed to establish a statistically reliable pattern of style exposures
- will not work when applied to managers who change style

86
Q
  1. manager universe / peer group
A

the median pm/fund from a broad universe of manager of fund is used as the benchmark.

Median is the fund that falls at the middle when ranked from the highest to lowest by performace

adv: measurable
disadvantage:
- subject to survivor bias as underperforming managers’ performance results are revamped from the universe history
- rely on the university has been accurate complied
- can not be identified in advanced, so it’s not investable, thus not an acceptable benchmark

87
Q
  1. custom security based benchmark
A

designed to reflect the manager’s security allocation and inv. process

adv:
- meet all required benchmark properties and all the benchmark validity
- allow continual monitoring of inv. process
- allow fund sponsors to effectively allocate risk across inv. management teams

disadvantage:
- it can be expensive to construct and maintain
- a lack of transparency by the manager (HF)
can make it impossible to construct such a benchmark

88
Q

valid benchmark process:

A
  1. specified in advance
    know to both pm and fund sponsor at the start of an evaluation period
  2. appropriate benchmark is consistent with the manager investment style
  3. measurable value & return can be determined on a reasonable frequent basis
  4. unambiguous
    clearly defined identifies and weights of securities
  5. reflective of the manger’s current investment opinion
  6. accountable
  7. inevitable
    possible to replicate the benchmark and forgo active management
89
Q

benchmark quality evaluation P=

A

M+S+A
return on market index + excess return to style + active return

s= B-M style index - market index 
A= p-B= portfolio return - style benchmark return
90
Q

rp=

A

r(market)+r(Style)+r(active)

B Benchmark= r(market )+r(style)

E Expected return= r(style) + r(active)

91
Q

E=p-m=s+a

A

A good benchmark should reflect

correlation (S,A)=0 style active
correlation (S,E) OR (S,S+A) >0 style expected return

92
Q

hedge funds benchmark

A
  1. broad market index (Not appropriate)
  2. rf rate
    + spread unsuitable
  3. hedge fund peer universe
    unstable due to survivorship bias and backfire bias, smoothing effect decrease STD, increase sharpe ratio and allocation fo hF
93
Q

real estate

A

benchmark exists but not all suitable

  • use to appraisal data lead to smoothing effect and
  • benchmark return are self-reported so subjective and bias maybe presented
  • benchmark are derived from a sample of universe not representative of asset class
  • perf. of index bears a very high correction to largest investment
  • benchmark that are value-based could be biased toward the most expensive properties or geographical areas
    understated volatility or risk
  • lack of comparability with benchmark return give that same benchmark use leverage while others do not
  • indexes assume no transaction cost, full transparency, and normal liquidity which is rally not the case; those factors would impact actual real estate returns
94
Q

Private Equity

A

valuation varies by PM

IRR is influenced by target CF at the begging of investment

95
Q

commodity

A

basis risk

96
Q

managed derivatives/ futures fund

A

survivorship bias

97
Q

distressed securities

A

iliquidity and lack of marketbility

stable pricing

98
Q

appropriate choice of benchmark

A

garbage in garbage out

99
Q

perf appraisal

sharpe ratio

A

= ra-rf /std
measure of additional / excess retrun for bearing risk above the rf rate, stated for unit of return volatility

draw back:

STD does not differentiate b/w volatility that is upside vs. downside.
there’s a penalty for all volatility, even if it’s good volatility

100
Q

Treynor ratio

systematic risk

A

Ta= (ra-rf)/(beta)

only considers systematic risk rather than total risk
only appropriate for assume efficient market
only useful for well diversified /systematic risk and do not have unsystematic risk

101
Q

IR

A

active return /active risk

102
Q

appraisal ratio AR

A

=alpha return (factor based regression )
/ std of error

Alpha is excess return, which is calculated as the return earned by the portfolio minus the return suggested by CAPM

= return - capm return

std of error = sqr root of ( std^2- b^2*(bmk std^2))

103
Q

Sortino ratio

A

only target semi-std measure of downside risk
SRP = (E(rp)-rT) / std

rt= minimum accepted return
the larger the ratio, the better the risk adjusted performance
- more appropriate for non-normal (nonsytematical skewed) return distribution

  • position & negative skewed investment strategy
    result in lower sharpe ratio
104
Q

capture ratio CR

A

determine the manager’s relative performance when market are up or down

UC/DC = rp/rb
uc > 1 outperform the market
dc <1 outperform the market

CR= UC/DC >1 postive asymmetry convex
<1 negative asymmetry concave

105
Q

Maximum drawdown

A

is the cumulative peak-to-trough loss during a continuous period.

drawdown duration = total time from the start of the drawdown until the cumulative drawdown returns to zero

Drawdown duration is the total time from the start of the drawdown until the cumulative drawdown RECOVERS to zero, which can be segmented into the drawdown phase (start to trough) and the recovery phase (trough to zero cumulative return).

106
Q

Scheduled algorithms

A
  • do not have expectations for adverse price movement during the trade horizon.
  • greater risk tolerance for longer execution time periods and
  • more concerned with minimizing market impact.
  • often appropriate when the order size is relatively small (e.g., no more than 5%–10% of expected volume)
  • the security is relatively liquid, or the orders are part of a risk-balanced basket and trading all orders at a similar pace will maintain the risk balance.
107
Q

arrival cost(Bps)

A

= (avg. EXECUTION price / arrival price order sent for execution) -1 *10K

108
Q

turnover

A

lower of purchases and sales divided by average monthly net asset.

2021 CFA Level III (12 decks)

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