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
Long Only Portfolios Roadmap for future work
Long Only Portfolios
By Sanskar Jain
Long Only Portfolios
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