CFA_Level_1_Portfolio Management_组合管理_2023最新

CFA，Level 1，Portfolio Management，组合管理，2023最新，欢迎大家学习。

Portfolio Management

Portfolio Management: An Overview

Steps in portfolio management process

Planning

Execution

Feedback

Type of investors

Individual investors

Individuals

Defined contribution (DC) pension plan

Institutional investors

Defined benefit (DB) pension plan

Endowments and foundations

Banks

Insurance companies

Sovereign wealth funds

Characteristics and needs

Pooled Investments

Mutual funds

Open-end funds

buy and redeem at net asset value (NAV)

Closed-end funds

traded at a premium or discount to net asset value

Exchanged traded funds (ETF)

Portfolio Risk and Return: Part I

Measurements of return

Gross return

Net return

After-tax return

Real return

Leveraged return

Holding period return

Average return (Arithmetic return)

Time-weighted return (TWR)

Money-weighted return (MWR)

Measurements of risk

Population variance and standard deviation

Sample variance and standard deviation

Risk aversion

Risk averse

Risk neutral

Risk seeking

Utility function

Efficient frontier

Return

Risk

Portfolio Risk and Return: Part II

Capital allocation line (CAL)

Return

Capital market line (CML)

Return

Left: ledning portfolio

RIght: borrowing portfolio

Systematic risk

Beta

how sensitive return to market

Unsystematic risk

well-diversified portfolios

Return generating model

Multi-factor model

Single-index model

Market model

Capital Asset Pricing Model (CAPM)

beta = market risk

Security market line (SML)

properly priced on SML

overpriced below SML

underpriced above SML

CML vs SML

Performance Measures

Sharpe ratio

M-squared

Treynor ratio

Jensen's alpha

Basics of Portfolio Planning and Construction

Risk objectives

Willingness to bear risk

willingness > ability

go with ability

ability > willingness

educate investor about risk, but not change

Investment constraints (TTLLU)

Liquidity

Legal and regulatory

Time horizon

Tax concerns

Unique needs and perferences

Strategic asset allocation

<- optimization and/or simulation

<- Long-term capital market expectations

<- Investment objectives and constraints (IPS)

Portfolio Construction

1. Construct an efficient frontier

2. Strategic asset allocation

3. Tactical asset allocation

Temporarily away from strategic asset allocation

ESG considerations

ESG

Environmental issues

Social issues

Governance issues

Implementation approaches

Negative screening

Best-in-class

Thematic investing

ESG integration

The Behavioral Basies of Individuals

Behavioral biases and categorizations

Cognitive errors

can be corrected

Emotional biases

spontaneously

be adapted

Belief perseverance biases

Conservatism bias

Consequences

slow to update

Maintain a prior belief

Confirmation bias

Keywords

prior beliefs

ignore negative information

Consequences

hold investments too long

consider only positive information on existing investment

ignore some good investment

under-diversified portfolios

Representativeness bias

keywords

classify new information based on past experiences and classifications

Types

Base-rate neglect

Sample-size neglect

Illusion of control bias

keywords

control or influence

consequences

Inadequately diversify

Trade too often

Hindsight bias

keywords

past events predictable and reasonable to expect

Consequences

overestimate

Information processing errors

Framing bias

Consequences

misidentify risk tolerances

trade too often

Avaiability bias

keywords

retrievability, categorization, narrow range of experience, resonance

Consequences

choose based on advertising

fail to diversify

Mental accounting bias

keywords

mentally into "accounts" layered pyramid format

consequences

fail to reduce risk

overemphasis on income generating assets

Anchoring and adjustment bias

keywords

reply on initial piece of information

consequences

stick to original estimates

Emotional biases

Loss-aversion bias

Consequences

Disposition effect

Hold investment in a loss position loner than justified

Sell investments earlier than justified

Overconfidence bias

self-attribution bias

Consequences

poorly diversified

Self-control bias

lack of self-discipline

Consequences

save insufficiently for future

Borrow excessively

Status quo bias

Endowment bias

Consequences

failing to sell

Regret-aversion bias

Consequences

too conservative

engage in herding behabior

Under-diversified portfolios

confirmation bias

Illusion of control bias

Mental accounting bias

Availability bias

Overconfidence bias

Maintain existing positions

conservatism bias

Status quo bias

Endowment bias

Regret-aversion bias

Introduction to Risk Management

Risk management framework

Risk governance

top-down process to overall enterprise

Risk identification and measurement

Risk infrastructure

people and systems

Policies and processes

Risk monitoring, mitigation and management

Communication

Strategic analysis and integration

Elements of effective risk governance

Enterprise risk management

Risk tolerance (risk appetite)

Risk budgeting

Risk Management Process

Identification

Financial risk

Market risk

Credit risk

Liquidity risk

Non-financial risk

Settlement risk

occur just before a defaul

Legal risk

being sued

Model risk

improperly using a model

Tail risk

Operational risk

Solvency risk

run out of cash

Compliance risk

Measurement

Value at Risk (VaR) and Conditional VaR (CVaR)

minimum loss for a given period at a given level of probability

Modification

Risk prevention

Risk acceptance

Risk transfer

Risk shifting

Technical Analysis

Technical analysis

using price and volume data

Assumptions

price already reflects all known factors

price moves in trends

price action is repetitive

Charts

Line

Closing prices

Bar

high, low, opening and closing prices

Candlestick

high, low , opening and closing prices

Box

Clear/white: closing > opening

filled/dark/shaded: closing<opening

Relative strength analysis

compare asset with benchmark using line chart

relative strength ratio = price of asset / benchmark

trend

uptrend

higher highs and higher lows

uptrend line: connect lows

downtrend

lower highs and lower lows

downtrend line: a line connecting highs

consolidations

pauses in trends

Common chart patterns

reversal patterns

head-and-shoulders

uptrend -> downtrend

price target = neckline - (head - neckline)

downtrend -> uptrend

price target = neckline + (neckline - head)

double tops and bottoms

uptread -> downtrend

price target = neckline - (head -neckline)

downtrend -> uptrend

price target = neckline + (neckline - head)

triple tops and bottoms

triple top

three peaks (same)

triple bottom

three troughs (same)

continuation patterns

triangle patterns

sysmmetrical triangle

price target = up trendline + difference in price from two trendlines at the start

ascending triangle

price target = horizon price + difference in price at start

descending triangle

low prices from trendline and high prices from lower and lower highs

rectangles

bullish

price objective = breakout level + width of rectangle

bearish

price objective = breakout level - width of rectangle

flags and pennants

short-term price charts

trendlines slope of flags in direction opposite to the trend

Technical indicators

price-based

Moving average

bullish crossover (golden cross)

short-term cross long-term from underneath

bearish crossover (dead cross)

short-term cross long-term from above

strategy

buy bullish and selling bearish

Bollinger bands

moving average +/- a set of number * o

moving-average line with upper and lower lines

Ballinger band width = ((upper band - lower band)/middle band)*100

squeeze: volatility falls to a very low level and narrowing bands

momentum oscillators

Rate of Change Oscilator (ROC)

Relative strength index (RSI)

Application to portfolio management

Top-down approach

Global benchmarks

Bottom-up approach

Individual stocks

Fintech in Investment Management

Big Data

Traditional data

Non-traditional data (alternative data)

Characteristics

Volume

Velocity

Variety

Artifical Intelligence (AI)

Comparable or superior

Neural networks

Machine Learning (ML)

training dataset, validation dataset and test dataset

require human judgement

Overfitting may lead to prediction errors

Types

Supervised

Unsupervised

Application

Text analytics

identify future performance

Natural language processing (NLP): analyze and interpret human language

Robo-advisory services

invesor questionnaire

passive investment approach

Risk analysis

real-time

Algorithmic trading

Distributed Ledger Technology (DLT)

Pros

create, exchange and track ownership on a peer-to-peer basis

greater accuracy, transparency and security

faster transfer

Cons

not fully secure

massive amouts of energy