Factor Based Thematic Investing


Clients expect lower fees and investments that outperform. But, managers can be overwhelmed by real information and fake noise.

Clients expect lower fees and investments that outperform
Build optimal portfolios by combining alpha signals from alternative and core data with transaction costs and risks management


Institutions need scalable, data-driven systems that strike the right balance of idea generation, decision making, optimisation and workflow automation.

Our Solution

We build optimal portfolios by combining alpha signals from alternative and core data, with transaction costs and risks management. So, managers can achieve the same investment performance at lower production costs.

How Our Superior Approach Helps You


Follow the recommended investment principle for the USD 1 trillion Norwegian Government Pension Fund Global.

“… much of the behavior of the Fund’s small active return can be explained in terms of systematic factors.”
“We recommend a more top-down, intentional approach to strategic and dynamic factor exposures.”
“In fact, approximately 70% of all active returns on the overall Fund can be explained by exposures to systematic factors over the sample.”

Recommendations by Professors Andrew Ang (Columbia Business School) , William Goetzmann (Yale School of Management) and Stephen Schaefer (London Business School) in 2009.

So, lower your production costs and capture the same investment alphas.


Idea generation, personalisation, backtesting and production run on single platform, so streamline your operations and increase efficiency.


We adopt direct indexing on long term themes - offering outperformance and personalisation at a fraction of cost to other approaches.

Factor based thematic investing
Learn how an optimal factor based portfolio can be constructed to capitalise on long term themes

Want To Do It Yourself ?

We allow users to screen based on core (e.g. past stock performance, valuation metrics, financial performance and position) and alternative (e.g. sentiment and news) data for ideas to build their portfolios, if our algorithm is not used.