TradeEQ Science Of Success


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

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TradeEQ Science Of Success

  1. 1. THE SCIENCE OF SUCCESS INCREASING ALPHA WITH PRACTICAL BEHAVIOURAL FINANCE TradeEQ in conjunction with Bloomberg Tradebook December 2009
  2. 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. 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. 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. 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. 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. 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. 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. 9. Speakers Peter Harnett ( 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 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 Liverpool.