Quantitative Investing

Since the position of quantitative investing (as opposed to trading) within the capital market framework has become increasingly difficult to distinguish, I would just like to use this page to outline my research interests.

Quantitative investment strategies are a 'buy-side' medium-term to long-term perspective of the asset allocation problem.  As such, it is typically undertaken by portfolio managers, analysts and investors looking for answers to the following problems:

1. The optimal proportions of assets in a portfolio to maximise (expected) returns.
2. The optimal proportions of assets in a portfolio to limit/minimise risk.
3. How to balance (1) and (2) whilst still managing the asset-liability relationship.

With this in mind, I have developed 2 key principles which dictate my use of computational methods:

1. Models should be based upon economic fundamentals.  Even when using statistical methods, there should be economic reasoning describing the strategy.  For example, mean-reversion techniques can be justified on grounds of investor over-reaction to declining prices.  Computational methods allow analysts to generate a large amount of statistics in a short amount of time, and this principle is here to limit the use of spurious statistics.
2. Models should limit complexity in favour of practical compromise.  A large amount of theoretical research into quantitative methods is still unused by the investment management profession, since at times academics neglect the practicalities of their research.  Thus whilst I believe that at times models are necessarily complex, there should still remain a distinction between rigorous mathematics and the quantitative investment industry.

I seek to use quantitative investment strategies to improve the allocation of assets in a portfolio, and combine this with my fascination for analysing individual equities.  Thus, combining both the quantitative and qualitative perspectives.