Group 6 [Robo-Advisor for Asset Allocation in the Indian Context]

[Simran Jhawar, Devisha Mehrotra, Lakshya Sangtani , Saity Banerjee, Sayan Talapatra, Ashish Shetti]

What are Robo-advisors?

Robo-advisors are digital platforms that provide automated, algorithm-driven financial planning services to investors with little or no human intervention. The market for robo-advisors has seen tremendous growth in recent years, with many investors seeking out low-cost, easily accessible financial advice. In this report, we will analyze the current state of the robo-advisor market, the factors driving its growth, and the future outlook for this industry.

Market Overview: -

The robo-advisor market has been growing rapidly over the last few years, with many firms entering the space to capture a share of the market. According to a report by Grand View Research, the global robo-advisor market was valued at USD 19.82 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 32.8% from 2021 to 2028. The Asia-Pacific region is expected to witness the highest growth rate during the forecast period, owing to the increasing adoption of robo-advisors in countries like China and India.

The growth of the robo-advisor market can be attributed to several factors, including:

  1. Low Fees: Robo-advisors typically charge lower fees than traditional financial advisors, making them a more attractive option for cost-conscious investors.
  2. Convenience: Robo-advisors offer 24/7 access to investment advice and can be accessed from anywhere with an internet connection.
  3. Customization: Many robo-advisors use algorithms to provide personalized investment advice based on an investor's risk tolerance and financial goals.
  4. Increased Adoption: The COVID-19 pandemic has accelerated the adoption of digital financial services, including robo-advisors.

The use cases of Robo-advisors and current examples in India are as follows :-

1) Portfolio management/Asset Allocation - Robo-advisors use algorithms to create and manage diversified client portfolios based on risk tolerance and financial goals. Use case - One example of a portfolio management robo-advisor in India is Zerodha Coin. It offers a low-cost, automated investment solution that helps individuals create and manage their investment portfolios.

2) Retirement planning - Robo-advisors can help individuals plan for their retirement by creating customized investment portfolios that align with their retirement goals and risk tolerance. Use case - One example of a retirement planning robo-advisor in India is Scripbox. It provides a user-friendly platform that helps individuals plan and save for their retirement by creating customized investment portfolios based on their financial goals and risk tolerance.

3) Tax-saving investments - Robo-advisors can help individuals invest in tax-saving instruments like Equity-Linked Savings Schemes (ELSS) and help them save on taxes. Use Case - One example of a tax-saving investment robo-advisor in India is ClearTax Invest. It provides a platform that makes it easy for individuals to invest in tax-saving instruments like Equity-Linked Savings Schemes (ELSS) and save on taxes.

4) Systematic Investment Plans (SIPs) - Robo-advisors can automate the process of investing in mutual funds through SIPs, making it easier and more convenient for individuals to save for their financial goals. Use Case - One example of a Systematic Investment Plan (SIP) robo-advisor in India is Groww. It provides a platform that makes it easy for individuals to invest in mutual funds through SIPs and achieve their financial goals.

Gaps in the market

1) Automation – All the services need to be fully automated. The existing platforms in India require the user to at least initiate the transaction & are not on a fully auto-pilot mode like the way they are in US. 

2) Exchange Traded Funds Vs. Mutual Funds – In US, investor’s money is directly invested in Exchange Traded Funds (ETFs) which is actually passive investments. In India, ETFs are still in very early stages & money under robo-advisory firms is invested in mutual funds that are very actively managed

3) Choice of assets – many platforms are offering mostly equity-focused Robo-advisory and focusing on stock allocation rather than asset allocation.


1) Create a low-cost Robo-advisor which does asset allocation

2) Create a mechanism for automated rebalancing in case of deviations

3) Provide an allocation which at least provides non-negative real returns to customers


Step 1 – Research for selecting the asset classes to be included

Step 2 – Select which theories will be applied. Modern portfolio theory with concepts of Monte Carlo simulation and mean-variance optimisation will be used.

Step 3 – Data fetching – creating the data file and adjusting the returns for inflation

Step 4 – Simulating returns for the future using Monte Carlo Simulation

Step 5 – Applying mean-variance optimisation using constraints from customer’s inputs such as duration etc

Step 6 – Automating. This may be limited to excel, however future scope lies in coding

Conclusion and learnings from the project

This project aimed to give us model for a robo advisor that provides asset allocation for non-negative real returns. After optimisation using Sharpe ratio, the model was tested and deviations were found. For that, another optimisation was done to get a portfolio which could give lesser risk and good returns at the same time.

Some key learnings were:-

· Reading research papers helped us a lot to learn more about asset allocation, robo advisors etc. Even though such things may not be incorporated in the model. It still gave a head start to do something different in future.

· We learned a new way of extrapolating inflation to find daily returns under the guidance of our professor.

· Some myths about asset classes like gold was also cleared. Investing in gold + equity does give better returns over the years.

· We learned how to do back testing. This was not taught in any other course but we were able to learn this under the guidance of our professor.