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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
“ 30,000 foot view”  or “999 things to find later on Google” ,[object Object],[object Object],[object Object],[object Object],[object Object]
Northfield Overview ,[object Object],[object Object],[object Object],[object Object]
What is Changing ,[object Object],[object Object],[object Object],[object Object],[object Object]
DATA: Distinguishing Signal from Noise ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],- Data is a commodity, not a competitive edge - Whoever has the best filter wins?
TECHNOLOGY: Just because we can doesn’t mean we should ,[object Object],[object Object],[object Object],[object Object],[object Object]
MARKET: market evolution example 1 ,[object Object],[object Object],[object Object],[object Object],[object Object],Time for Non-linear asymmetric models?
MARKET: market evolution example 2 ,[object Object],[object Object],[object Object],[object Object],Northfield currently do this for  all  our equity risk models…
MARKET: market evolution example 3 ,[object Object],[object Object],Expect to see a mini-industry in third-party “objective” pricing?
MARKET: market evolution example 4 ,[object Object],[object Object],[object Object],[object Object],[object Object],Implications: more sophisticated risk measures needed to capture short-vol strategies, bets moving opposite to rules
Pause for thought: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MODELS: it’s not just linear regression ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Models: decision systems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Models: Time-Series Techniques ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Models: Machine-Learning ,[object Object],[object Object],[object Object],[object Object]
Machine Learning Example: Complex Event Processing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Machine Learning Example: Temporal Difference Learning ,[object Object],[object Object],[object Object]
Machine Learning Example: “boosting” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Machine learning Example: SVM, RVM, and sparse Bayesian models ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],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
Risk Model Design: Hybrid Risk Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Pause for thought ,[object Object],[object Object],[object Object],[object Object]
BUSINESS PROCESSES: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
REGULATORY/COMPETITIVE PRESSURES: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Balancing quantitative models with common sense 2008

  • 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
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Notes de l'éditeur

  1. 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
  2. There are a great many disadvantages that come with high frequency data that tend to outweigh the benefits, except in some rather unusual situations.
  3. 5% of assets, but 30% of volume
  4. 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.
  5. Non-linear payoff patterns – can’t capture that with tracking error.
  6. 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
  7. 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.