Balancing quantitative models with common sense 2008

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  • There is so much data available – but what is actually relevant? Data-mining attractive, easy, simple… and useless. Key questions are rarely asked – assumptions behind underlying data, sources of data, etc Information/Noise – in the eye of the beholder: our STM detrends series (short term focus),. Whereas LTMs incorporate trend. A long-term investor wants to know about real information incorporated in price movements, whereas a trader can profit from making a market in securities where there is a lot of noise trading
  • There are a great many disadvantages that come with high frequency data that tend to outweigh the benefits, except in some rather unusual situations.
  • 5% of assets, but 30% of volume
  • Note our work on direct property risk modelling – consider an office / hotel / etc as a portfolio of junk bonds. Yields a risk number much higher than NACREIF but lower than S&P 500… intuitive result.
  • Non-linear payoff patterns – can’t capture that with tracking error.
  • Mahalanobis distance Implied correlation – limitation: only one statistic, says nothing about skew or kurtosis of cross-sectional distribution “ Statman & Scheid” – correlation is not a good measure of the benefits of diversification Causal Induction and Confirmation – Bradley and Fitelson – confirmation “ Interpreting the First Eigenvalue of a correlation matrix” Togetherness Average correlation Review other cutting-edge or novel techniques
  • AHP: questions e.g. what is your income, what is your age, how old are your kids? For each answer to each question there is a ranking of most to least suitable asset classes. The questions are also ranked in importance We can do some matrix math using the answers to each question and the ranking of the questions to give us a “most suitable” asset allocation. IMPORTANT: based on more than one criteria. Existing systems try to convert answers into one parameter (risk tolerance) and implicitly therefore assume all investor utility same.
  • Balancing quantitative models with common sense 2008

    1. 1. Balancing Technological Change with Intuition Nick Wade Director, Asia Marketing Northfield Information Services Asia Ltd. [email_address] +81 (0)3 5403 4655 +61 (0)2 9238 4284
    2. 2. “ 30,000 foot view” or “999 things to find later on Google” <ul><li>Northfield </li></ul><ul><li>What is changing in our world? </li></ul><ul><li>What are the implications? </li></ul><ul><li>Degustation Menu of Recent Quant Tools </li></ul><ul><li>A few applications of technology </li></ul>
    3. 3. Northfield Overview <ul><li>Northfield – established 1985, open philosophy, over 300 clients </li></ul><ul><li>Northfield products and services fit each part of t he investment process </li></ul><ul><li>Innovative risk modelling principles & broad coverage </li></ul><ul><li>Appropriate Risk models – and hence our motivation </li></ul>
    4. 4. What is Changing <ul><li>Data Availability, timeliness, accuracy(?) </li></ul><ul><li>Market Dynamics </li></ul><ul><li>Models & Techniques </li></ul><ul><li>Business Processes - Applications </li></ul><ul><li>Client, Regulatory and Competitive Environment </li></ul>
    5. 5. DATA: Distinguishing Signal from Noise <ul><li>Old Problem: no data </li></ul><ul><li>Current Problem: no filter </li></ul><ul><li>Still need to know: </li></ul><ul><ul><li>how is it calculated? </li></ul></ul><ul><ul><li>is it reliable? </li></ul></ul><ul><ul><li>is it comparable? </li></ul></ul><ul><li>What is information, and what is noise (subjective)? </li></ul>- Data is a commodity, not a competitive edge - Whoever has the best filter wins?
    6. 6. TECHNOLOGY: Just because we can doesn’t mean we should <ul><li>Northfield MARS: Fully customized tax-aware optimization of 200,000 HNW client accounts in a couple of hours ( good idea ) </li></ul><ul><li>Intra-day performance attribution ( bad idea ) </li></ul><ul><ul><li>Statistical inference problems </li></ul></ul><ul><ul><li>Cross-term problems </li></ul></ul><ul><li>Technology allows us to do great things… …and a lot of things we shouldn’t… </li></ul>
    7. 7. MARKET: market evolution example 1 <ul><li>effect of hedge funds’ increasing share of the volume – highly correlated “bad days” across “uncorrelated” asset classes as hedge funds have “fire sale” and close out liquid positions to meet margin calls. Business as usual on up days. </li></ul><ul><ul><li>Implications: </li></ul></ul><ul><ul><ul><li>Asymmetric beta </li></ul></ul></ul><ul><ul><ul><li>Different up/down market correlations </li></ul></ul></ul><ul><ul><ul><li>different small/large move correlations </li></ul></ul></ul>Time for Non-linear asymmetric models?
    8. 8. MARKET: market evolution example 2 <ul><li>Speculative Trading – example China. See Derman paper on “temperature”. This plays hell with many of our usual assumptions, but can be intelligently modelled fairly easily. </li></ul><ul><ul><li>Implications: </li></ul></ul><ul><ul><ul><li>Include trend (bubble) risk </li></ul></ul></ul><ul><ul><ul><li>Adjust for skew, kurtosis </li></ul></ul></ul>Northfield currently do this for all our equity risk models…
    9. 9. MARKET: market evolution example 3 <ul><ul><li>Illiquid assets / funds / appraisal pricing / mark-to-market issues – adjusting for lack of real trade information </li></ul></ul><ul><ul><li>Political risk of pricing being controlled by portfolio management who bought it / broker they bought it from. </li></ul></ul>Expect to see a mini-industry in third-party “objective” pricing?
    10. 10. MARKET: market evolution example 4 <ul><ul><li>HF strategies: </li></ul></ul><ul><ul><ul><li>various ways to disguise short-vol strategies, but if it quacks like a duck… </li></ul></ul></ul><ul><ul><ul><li>selling insurance e.g. OTM puts to ramp up return adds value little by little every day most days, very occasional catastrophic losses </li></ul></ul></ul><ul><ul><ul><li>Increasing bets after losses </li></ul></ul></ul><ul><ul><ul><li>Can happen by accident – shorts go bad </li></ul></ul></ul>Implications: more sophisticated risk measures needed to capture short-vol strategies, bets moving opposite to rules
    11. 11. Pause for thought: <ul><li>As markets evolve we must question our assumptions: </li></ul><ul><li>Arbitrage Pricing Model: </li></ul><ul><ul><li>perfect competition </li></ul></ul><ul><ul><li>linear relationship between factors & return </li></ul></ul><ul><li>Modern Portfolio Theory </li></ul><ul><ul><li>Quadratic utility function – not true for levered HF or trading desk </li></ul></ul>
    12. 12. MODELS: it’s not just linear regression <ul><li>Technique </li></ul><ul><li>Decision Systems: AHP, ANP, MCDM </li></ul><ul><li>Time-Series Models: GMM, Markov Chains, HME, etc </li></ul><ul><li>Machine Learning: </li></ul><ul><ul><li>support vector machines, radial basis functions, “boosting” </li></ul></ul><ul><ul><li>Complex Event Processing, Event Stream Processing </li></ul></ul><ul><ul><li>Temporal Difference learning </li></ul></ul><ul><li>Application </li></ul><ul><li>Dynamic Alpha Models: Sorensen, Macquarie </li></ul><ul><li>Hybrid Risk Models (Northfield) </li></ul><ul><li>Simultaneous Estimation of risk models (UBS, MacQueen, Heston & Rouwenhorst) </li></ul>
    13. 13. Models: decision systems <ul><li>Objective: robust result based on expert opinion and auditable process adds consistency to typically subjective rules of thumb… </li></ul><ul><li>Applications: </li></ul><ul><ul><li>Suitability in asset allocation </li></ul></ul><ul><ul><li>Credit risk scoring </li></ul></ul><ul><li>Examples: </li></ul><ul><ul><li>Analytic Hierarchy Process, Analytic Network Process </li></ul></ul><ul><ul><li>Multiple Criteria Decision Modelling </li></ul></ul>
    14. 14. Models: Time-Series Techniques <ul><li>Objective: Predicting the next observation from a series of observations where the distribution varies over time </li></ul><ul><ul><li>Arima, Garch, Kalman </li></ul></ul><ul><ul><li>Multiple generators e.g. Hidden Markov Experts (GMO implemented this) </li></ul></ul><ul><li>Issues: </li></ul><ul><li>over-fitting </li></ul><ul><li>reliance on history / sample </li></ul><ul><li>tells us nothing about why the distributions change… </li></ul><ul><li>linear regression analogy: 1000 factors, great R 2 , lousy out-of-sample performance </li></ul>
    15. 15. Models: Machine-Learning <ul><li>Objective: Pattern recognition – humans are brilliant at it, and most of machine learning attempts to replicate how we learn. </li></ul><ul><li>Applications: </li></ul><ul><ul><li>Recognizing patterns in financial time series </li></ul></ul><ul><ul><li>Predicting next observation based on learning </li></ul></ul>
    16. 16. Machine Learning Example: Complex Event Processing <ul><li>Objective: Pattern Recognition – imply the “complex” event from an event “cloud” </li></ul><ul><ul><li>White gown, tuxedo, bells, rice in air => wedding </li></ul></ul><ul><ul><li>White gown, tuxedo, band playing, rice in air => rowdy dinner party </li></ul></ul><ul><ul><ul><li>Obvious applications to algorithmic trading </li></ul></ul></ul><ul><ul><ul><li>Avoid specifying distribution of underlying factors </li></ul></ul></ul><ul><ul><li>Problems: </li></ul></ul><ul><ul><li>Assumption that history will persist – but 1998 Russian debt crisis, whilst very similar to debt aspects of sub-prime crisis, did not spill over into equities… </li></ul></ul>
    17. 17. Machine Learning Example: Temporal Difference Learning <ul><li>Objective: adapt/revise consecutive forecasts based on the accuracy of recent forecasts </li></ul><ul><li>Example: 5 day weather forecast around a hurricane event </li></ul><ul><li>Used as a training technique in some machine-learning situations </li></ul>
    18. 18. Machine Learning Example: “boosting” <ul><li>Objective: make a good prediction out of a set of poor predictions </li></ul><ul><li>Gambler 1: apportion money across a team of friends based on how they are doing, aim to get close to what we would have won if we’d bet everything with luckiest friend </li></ul><ul><li>Gambler 2: take a number of “rules of thumb” that work sometimes, or better than random, and use those together to make a good prediction </li></ul><ul><li>Application: </li></ul><ul><ul><li>obvious application to dynamic weighting of alpha signals </li></ul></ul><ul><ul><li>Asset allocation? </li></ul></ul>
    19. 19. Machine learning Example: SVM, RVM, and sparse Bayesian models <ul><li>Objective: pattern recognition in data, for example handwriting </li></ul><ul><li>Support Vector Machines </li></ul><ul><li>Relevance Vector Machines </li></ul><ul><li>Informative Vector Machines </li></ul><ul><li>Linear / Non-linear Kernel Methods </li></ul><ul><li>Sparse Bayesian Models </li></ul><ul><li>Online Gaming? (Microsoft) </li></ul>RVM advantage – avoid a lot of cross-validation with SVM, but EM based so can get stuck in local minima… Problem: all predicated on the idea that the future will be the same as the past, and subjective in the sense that they are sample dependent
    20. 20. Risk Model Design: Hybrid Risk Model <ul><li>Objective: improve risk forecast accuracy by better inclusion of real-world dynamics </li></ul><ul><li>Hybrid Model: temporary factors </li></ul><ul><li>x 2 variance not (x- µ ) 2 </li></ul><ul><li>Parkinson volatility </li></ul><ul><li>Exponential weighting </li></ul><ul><li>Our existing linear framework can accommodate a variety of real-world violations of Arbitrage Pricing Theory </li></ul>
    21. 21. Pause for thought <ul><li>Advances in machine learning and time-series techniques allow us to (over-) fit a much wider variety of data series </li></ul><ul><li>New techniques like boosting and temporal difference learning may help us improve e.g. switching between alpha strategies or asset allocation </li></ul><ul><li>Innovations in decision systems can help us make subjective processes more robust </li></ul><ul><li>Caveat: As these techniques become more accessible and more widely known, the goalposts move… </li></ul>
    22. 22. BUSINESS PROCESSES: <ul><li>AHP: suitable asset allocation </li></ul><ul><ul><li>Define a questionnaire for suitability issues </li></ul></ul><ul><ul><li>Expert Involvement: hierarchy of asset classes </li></ul></ul><ul><ul><li>Expert Involvement: hierarchy of questions </li></ul></ul><ul><li>MARS: portfolio manufacturing </li></ul><ul><ul><li>Link accounting, custodian, client, and trading systems </li></ul></ul><ul><ul><li>Allow full customization of investment choices </li></ul></ul><ul><ul><li>Automated to demand minimal intervention </li></ul></ul>
    23. 23. REGULATORY/COMPETITIVE PRESSURES: <ul><li>Increasing regulatory pressure on risk </li></ul><ul><li>Client pressure – risk is value added? </li></ul><ul><li>Time-to-market, barriers to entry, IP </li></ul><ul><li>Soft-dollar disappearing </li></ul><ul><li>Best execution pressure </li></ul><ul><li>“Centralized Portfolio Management” - Vanguard </li></ul>
    24. 24. Conclusions <ul><li>Market Dynamics changing as participants change </li></ul><ul><li>Technology offers compelling advantages </li></ul><ul><ul><li>Resist temptation to overfit! </li></ul></ul><ul><ul><li>High Frequency Data – more trouble than it’s worth? </li></ul></ul><ul><li>Major Modelling Advances in the last few years </li></ul><ul><li>Stay current on modelling, but balance technological advances with your own intuition </li></ul>

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