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Investment Philosophy

Through multi-cycle experience, the team has developed a disciplined approach to Asian Quant investing

High Breadth

Breadth is extremely important for any quant factor fund investment process - the ability of generate strong risk adjusted returns improves with breadth.


Strong Data Mining Safeguards

Data mining is the biggest pitfall for any factor driven quant investment methodology, so it is important to incorporate safeguards – empirically derived screens based on intuition and theory that have been tested in-sample and out-of -sample.


Non-linear Screens to Identify Mispricing Opportunities

Quant factors can be combined using a non-linear stock screening methodology to target market anomalies more precisely.


Non-systematic Stock Review to Enhance Returns

A non-systematic overlay, if correctly implemented, can generate significant alpha and mitigate risk.


No Portfolio Optimiser - Understand Positioning & Risk Exposures

An intuitive and transparent portfolio construction methodology which focusses on maximizing alpha and reducing risk exposures generates superior results to optimizers based on historically calibrated risk models, especially when dealing with exogenous shocks.


Minimise Transaction Costs

Portfolio rebalancing should focus on maintaining appropriate alpha and risk exposures while minimizing transaction costs, particularly market impact costs.

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