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24-1 active equity investing: strategies (2 approaches to active …
24-1 active equity investing: strategies
1 introduction
fundamental -
discretionary
stress the use of human judegment in processing infor and making investment decisions
1 fundamental research - often begins with analysis of financial statements
2 estimates - intrinsic value and/or relative value
3 decision - overweighting / market weighting / underweighting relative to benchmark
top-down strategies
bottom-up strategies
quantitative -
systematic
tend to rely on more heavily on rules-based quantitative models
variables: valuatio metrics / size / profitability mereics / financial strength metrics / market sentiment / industry membership / price-related attributes
hybrids
combine elements of both
2 approaches
to active
management
differences in the nature
of the information used
bottom-up
more recent financial statements
notes, assumptions and management discussion and analysis disclocures
corporate governance, ESG characteristics
top-down
first on region, country or sector infor
quantitative
large amonts of historical data
not normally consider infor or characteristics that cannot be quantified
differences in the focus
of the analysis
fundamental
relatively small group of stocks, in-depth analysis
quantitative
focus on factors across potentially very large group of stocks
difference in orientaton to the data:
forecasting the future vs analyzing the past
fundamental
forecasting future prospects
quantitative
uses history to arrive at investment decisions
difference in portfolio
construction:
judgment vs optimization
fundamental
see risk at the company level
construction depend on judgment
quantitative
the risk - factor returns will not perfom as expected
lie at the portfolio level
construction done using portfolio optimizer
difference in portfolio changes
or rebalancings performed
fundamental
continuously adjust at any time
quantitative
rebalanced at regular intervals
3 types of active management strategies
4 creating a
fundamental
active
investment
strategy
the fundamental
active investment
strategy
the broad goal:
outperform selected benchmark on a risk-adjusted basis,
net of fees and transaction costs
processes
1 define investment unverse and market opportunity
2 prescreen to obtain a manageable set of securities
3 understand the industry and business -
industry and competitive ananlysis and financial reports
4 forecast caompany performance - cash flows or earnigns
5 convert forecasts to valuations and
identify ex ante profitable investments
6 construct portfolio with the desired risk profile
7 rebalance with disciplines
target price assigned to each stock
need not be constant but can be updated with new infor
the stop-loss point - set max loss under any conditions and limit such bahavioral biases
pitfalls in
fundamental investing
behavioral
biases
confirmation
stock love bias / selection bias
poor diversified, excessive risk exposure
and holding poor performing
actively seeking out opinions and infor to challenge existing beliefs
illusion of
control
excessive trading and/or heavy weighting on a few stocks
seek contrary viewpoints / proper diversification rules
availability
reduce investment opportunity set / insufficient diversification
appropriate strategy, disciplined analysis with long-term focus
loss
aversion
hold unbalanced poorly position and sold successful
disciplined strategy wiht stop-loss rules
overcon-
fidence
underestimates risks and overestimates returns
regularly reviewing records adn seeking feedback
regret
aversion
instead hold on too long, lose out profitable opportunities
review process
the value
trap
appears to attractively valued but still overpriced given worsening future prospects
should conduct thorough research
the growth trap
the share price may not move any higher due to already high starting level
5 creating a
quantitative
active
investment
strategy
creating a quantitative
investment process
1 defining the market opportunity / investment thesis
2 acquiring and processing data
company mapping
company fundamentals
survey data
unconventional / unstructured data
3 back-
testing
infor
coefficient
aggregates infor about factors from all securities
the pearson IC - simple correlation coefficient for the current and next period - the higher the predictive power - sensitive to outliers
the spearman rank IC - the pearson correlation coefficient between ranked factor scores and forward
multifactor
model
after studying the efficacy of single factors
factors may effective individually but not add material value
because they correlated with other factors
*the strategy a simulation of real-life investing
4 evaluating
the strategy
out-of-sample back-test to confirm model robustness
various statistics: t-statistic, sharpe, sortino, var, cvar, and drawdown characteristics
construction issues
risk models
estimate the variance
trading costs
explicit
implicit
pitfalls in quantitative
investment processes
survivorship bias - overly optimistic results
look-ahead bias - using infor unknown or unavailable at decision made
data mining - model overfitting
turnover, tranasction costs and short availability
face numerous constraints - easily erode returns significantly
6 equity investment
style calssification
different approaches
to style calssification
defined by pairs of common attributes reveal the sources of added value in the portfolio
holdings-based
approaches
done bottom-up
a portfolio's active exposure to a certain style = sum of the style attributes from all the individual stocks, weighted by active positions
large-cap / mid-cap / small-cap classifications
size classification determined by the market capitalization
top 70% / next 20% / balance
growth / value / core characteristics
returns-
based
approach
the objective - to find the style concentration of underlying holdings by identifying the style indexes that provide significant contributions to fund returns
a constrained multivariate regression:
r_t = α + ∑((β_s)*(R_t)_s) + ε_t
constant as the value added by the fund manager: α
the fund exposure to style s: β_s; ∑(β_s)=1
manager
self-identification
more efficiently identified using combination of manager self-identification and holdings- or return-based analysis
non-standard and not fit into
the investment objective laid out in the prospectus
strengths and
limitations of
style analysis
holdings-based style analysis generally more accurate
holdings-based style analysis requires the availability of all the portfolio constituents and style attributes of each stock
inaccurate results due to limitations of tha data or flaws
the limited availability of data on derivatives
ideally, should use both approaches
important
allow with similar styles compared
provide more infor about manager's
active strategy and approach