Long Only Portfolios

Roadmap for future work

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

Risk Management Frequent Rebalancing
Tax Optimisation Algorithmic execution

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

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