Presentation notes from the seminar on applied behaviora finance that took place on 9 Dec 2009 in London

1. THE SCIENCE OF SUCCESS
INCREASING ALPHA WITH PRACTICAL BEHAVIOURAL
FINANCE
TradeEQ in conjunction with Bloomberg Tradebook
December 2009
tradeEQ.com

2. Introducing TradeEQ
TradeEQ is a specialist performance consultancy and
member of the European Association of Independent
Research Providers. We work with active investment
managers and traders, helping them to understand their
decision making behaviour patterns. Our analysis
enables them to identify differences in behaviour and the
presence of behavioural biases in order to improve their
performance.
Our service consists of research reports containing an
extensive set of proprietary, quantitative metrics that
break down the drivers of investment performance.
Our metrics extract behaviour patterns from all of an
Our proprietary Efficiency analysis produces a unique, investor’s decisions and builds profiles of typical behaviours
rankable measure of manager ability that allows fund around certain events. We also search for and quantify
selectors, fiduciaries and CIOs see right to the heart of differences in the values of the key return drivers according
the question of skill. by security and manager behaviour type.

3. The Trading Equation
Expected Return Winning Positions Positive Flow Preference
= All Positions
Success Rate %
Success Rate is the percentage of
× positions that are closed with a
80% Success Rate
Payoff Ratio Negative Return
positive return (in absolute or excess
× return terms).
Frequency Certain types of strategy have People tend to prefer frequent positive
× naturally high intrinsic success rates: re-enforcement of their actions. This
Sizing we refer to these as Convergent can lead to biased behaviour .
Strategies and include Mean
- Reversion, Stat Arb and short volatility. Overvaluing Success Rates can lead to
Costs cutting profitable positions too early
In contrast, Divergent Strategies such relative to their true potential. Likewise
Return is a function of how often we as Trend Following, Long Volatility and Biases such as Anchoring and Loss
win when we take positions, how much Global Macro styles often have lower Aversion can lead to running losing
we win and lose on average, how often intrinsic Success Rates. However, as positions longer than justified by reality.
we have the opportunity to trade and long as the other variables in their
take positions, the size of the positions Trading Equation – particularly the Whenever biases are present, action is
we choose to take and our trading Payoff Ratio – are good they still have not fully Congruent with what we
costs positive expected returns. actually seek and opportunity is being
lost.

4. Congruence
All investors have an underlying decision making process. Success Variation by Congruence
Processes differ in their sophistication and the extent to
which they are articulated.
Information Edge
Variant Perception
Momentum
Value Rank
Implied RoC
Catalyst
A Process has a set of criteria which are required to be When the criteria of an Investment Process can be
present in order to establish positions, increase their size, articulated and quantified – which may involve subjective
determine their maximum size and define when the position scoring by the decision maker – we can measure the
should be closed. Congruence of individual positions and compare the
Success Rate of those are highly Congruent and those that
When the actual positions and decisions we take match are less so.
these criteria we have Process Congruence. When we allow
biases to effect us our actual decisions and therefore
positions will become Incongruent with our Process.

5. Success Variation by Security Type
Company Size
Differences in Success Rate can be examined in securities
with different characteristics.
Positions are group into buckets of interest to the manager
and Success Rates are calculated for each bucker. For
example a manager might be interested in seeing how his
Success Rate differs when he takes positions in Small, Mid,
Smallest Largest Large and Mega Cap stocks.
Whenever significant differences are identified we look at
Security Volatility how stable these differences have been through time and in
different market conditions. When persistent differences are
found we can begin the process of drilling down further to
understand the source and implication of the differences and
the potential performance improvement of acting on the
differences and doing more of what is consistently more
successful and less and that which succeeds less often.
Lowest Highest
Success differences can be examined across any
measurable security characteristic. Often these
Relative P / E characteristics are measurable with publicly observable data
such as company size, share price volatility or Price to
Earnings ratios.
We can also work with characteristics assigned by managers
themselves, for example “Management Quality”.
Lowest Highest

6. Success Variation by Behaviour
Holding Period
We also look at how Success varies with differences in the
behaviour of managers.
Behaviours of common interest include the holding period of
positions – either realized or expected at the outset of the
position, overweight / long positions compared with
underweight / short positions and Focus (how often a
Shortest Longest manager has taken positions in specific securities in the
past).
Overweight (Long) v Underweight (Short)
Other behaviour types might include time of day, size of
position at opening, stated conviction at opening and time
taken to reach maximum position size.
Of course behaviour types can be mixed with security type
based analysis, looking at, for example the difference in
Success for long and short decisions in Small, Mid, Large
Underweights Overweights and Mega cap stocks.
Small Large Small Large
We also include physiological measurements of stress as a
Focus manager behaviour type and can examine Success
difference as a function of stress load.
All of these partioning methods are also used for other
metrics in the Trading Equation, such as the Payoff Ratio.
Lowest Highest

7. Behaviour Change Case Study
Holding Period The manager was exhibiting a strong tendency to cut
winners earlier than losers and was not getting to full
position size in the Successful positions.
This was depressing his Payoff Ratio and total return.
Analysis of Winning and Losing position return traces
aligned around the Position Closing date revealed that the
Shortest Longest manager was being shaken out of winning positions by
statistically insignificant retracements. He was actually “too
good” at cutting on small loses in his winning positions.
This pattern confirmed the presence of Success and
At first glance this manager’s high Success Rate in short Positive Flow Preference.
holding period positions suggested a successful, trading
oriented skill. Back testing a simple volatility based indicator would have
helped lengthen holdings in the Manager’s winners.
However further investigation revealed the following
information: A session of coaching and the establishment of a daily
journal where exit scenarios where rehearsed and
Average Winning Position Holding Period 35 days recorded helped the manager change his behaviour
Average Losing Position Holding Period 48 days generating an estimated 0.4 increase in his Sharpe ratio
Average Winning Position Size 1.8% over the next 6 months.
Average Losing Position 2.2%
Payoff Ratio 0.81

8. Other Measures and Reports
PREDICTED AND ACHIEVED RETURNS
The report quantifies the value added or subtracted through position
size variation relative to the return on the pure forecast embedded in an
active position.
RETURN TRACE ANALYSIS
Examining the typical price movements of securities in the period prior
to and following position initiation and prior to and following position
closing highlights ways to adjust timing and sizing to improve returns.
POSITION SIZING ANALYIS
The reports show how managers build their positions through time and
how these size profiles interact with the average return traces of their
investment ideas.
EFFICIENCY ANALYSIS
A proprietary set of measurements reveals how well timed entries and
exits are relative to the best available entry and exit points. Our cross
sectional efficiency measure examines the degree of skill present in the
selection of specific securities from the available universe subject to
user defined constraints. Skill can be compared between managers for
selection and allocation purposes and within different security groups
and behaviour types to help individual managers improve performance.
SKEW ANALYSIS
Another set of proprietary metrics measure precisely where, within the
distribution of individual returns, total return is coming from. This gives
allocators a unique measure of style and individual managers the ability
to identify the characteristics of the positions that matter most to total
return.

9. Speakers
Peter Harnett (peter.harnett@tradeEQ.com)
Before founding TradeEQ Peter worked as a portfolio manager for
institutional and retail investment products at HSBC Asset
Management. During his time at HSBC Peter received a number of
industry awards for the performance of his investment funds.
Peter became involved in the Alternative side of the investment
management business when he designed and launched one of HSBC’s
first hedge funds and was instrumental in the establishment of the
company’s Alternative Investments subsidiary.
From HSBC Peter moved to GLG, one of Europe’s leading hedge fund
and absolute return strategy managers. There he further broadened his
investment and trading experience through managing a team of
quantitative futures traders and heading research on third party trading
performance analysis.
Taras Chaban (taras.chaban@tradeEQ.com)
Taras Chaban is a co-owner and a director of TradeEQ Ltd. Before
founding TradeEQ Taras was an asset manager for an alpha capture
fund at GLG Partners Inc.
Prior to joining GLG he worked for a number of years as a quantitative
analyst on proprietary trading desks at Dresdner Kleinwort and Credit
Suisse. Taras started his career as a consultant at a software company
The Mathworks Inc, advising a variety of firms in the City of London +44 (0)207 608 5759
and across Europe.
Mr Chaban holds Master of Philosophy degree from the University of tradeEQ.com
Liverpool.