Where we are right now

We have managed to incorporate in our retail portfolios, all features that one expects from a roboadvisor, and more.

 

 

 

 

We have also been able to:

      outperform the benchmarks

      demonstrate the capability of managing sizeable assets

      find our niche as a data driven and AI based asset manager

How we got here

• Aim was to follow best practices and bring institutional investing to retail clients
• Started with hedge fund like strategies like risk parity, trend following, value etc
• Incorporated features like risk management, HFT like execution
   
• Roll-out of retail offering
• Initially targeted data driven individuals for early adopters
• Recommended portfolios as a custom mix from a huge pool of strategies
   
• Incorporated deep learning based strategies
• Moved to a fixed set of featured portfolios
   
• Systematic CIO for allocating to strategies in featured portfolios
• Long-short and long only portfolios for institutional clients

2015

Early 2016

Late 2016

 2017

We need our long only offerings to appeal to institutional investors too

There are a few aspects about our portfolios that institutional investors might not be comfortable with:
 

• High correlation with market (or high beta)
• Capacity constraining aspects ​
  • Our strategy and execution alphas should not be limited to smaller investments.
  • Our execution should be scalable, with little chances of a mistake.
• Black box-like approach (AI models, no anxiety management, insufficient demystification)
• No easy customisation (turnover, product set, and risk management)

This is how we are planning to tackle these problems

​

High Correlation:

• Incorporate all strategies we have worked on so far in our long only portfolio.
  • ​Adapt the L/S deep learning strategies to long only portfolios
  • Payroll
  • US investor index strategy
     
• Explore alternate data sources, to generate alternative risk premia
   
• Make our portfolios resilient to market regime shifts  by improving the systematic CIO so that it can:
  • Detect regime and allocate accordingly
  • Diversify among existing alphas

​Customization and scalability

• It shouldn't take long to run studies and subsequently move to production.
  • Parallelize and support caching in the backtesting framework.
  • Bring the prototyping and production frameworks in line with each other.
• Tool to make a custom portfolios.
   

​Moving away from black-box approach

• Make it easier for investors to understand what it would really feel like investing in this portfolio​
  • ​Scenario analysis

This is how we are planning to tackle these problems