These 5 wealthtech startups are using quantitative investing to help people with investments
Here are some startups providing cost-effective end-to-end investing solutions to retail investors.
Three out of every four Indian Demat accounts today are inactive, according to a report by stock market tracking firm MarketMojo.
New-to-market investors need a system that can not only tell them where to invest, but also automate trading and rebalance their portfolios without having to constantly monitor their stock movements.
Such investors are now increasingly looking at quantitative investing, which uses quantitative analysis to make investment decisions. It is based on detailed statistical analysis and often involves developing complex models and algorithms that assess markets, asset valuations, volatility, and other investing factors.
Some examples of quantitative investing are high-frequency trading, algorithmic trading, and statistical arbitrage.
Quantitative trading is a method of gathering and analysing vast amounts of current and historical data to produce results that traders can use to make informed investment decisions. Traders select a quantitative analysis algorithm that includes pre-defined rules for buying and selling signals.
The algorithm monitors the market for data at predetermined intervals and analyses it with current market characteristics to forecast possible future outcomes. This system is permitted, whereby the model is constructed to perform efficiently under the given time period or market conditions it is tested against, but underperforms when it is made live. It is in high demand because of an effective buy-sell rule, as it can be implemented easily.
This type of investment is a cost-effective and technology-based strategy, which helps in reducing the possibility of human errors.
Here are some wealth management startups that use the quantitative investing methodology and provide numerous returns on their AI-Driven portfolios.
Founded in 2020,, a subsidiary of Estee Advisors Pvt. Ltd., is an investment platform focused on empowering and educating retail investors.
The startup offers model portfolios that are purely driven by factor investing strategies drawn from focussed quant research, and has become a one-stop solution for retail investors.
Its model portfolios are purely algorithm-based, back-tested investment strategies, with a strong focus on quantitative research. Gulaq has been in the space for more than 12 years, managing close to $100 million in funds and holding a Portfolio Management Service (PMS), Global Funds, and a Hedge Fund.
It offers different portfolios ranging from Gear 1 to Gear 6, with different combinations of debt and equity.
The startup has a directional strategy that aims to consistently outperform the benchmark equity index while maintaining low volatility. It is quantitatively managed and implemented using a systematic rule-based trading model to remove human subjectivity.
Mumbai-based, founded by IIT Kanpur alumnus Sonam Srivastava in 2019, is a SEBI Registered Investment Advisory firm and an AI-powered robo advisor, creating multi-factor tactical model portfolios.
The startup uses deeptech, AI, and ML modules to invest, and a smart beta-based investment strategy backed by quantitative research. It is a complete computer-based strategy that reduces the possibility of human errors and manages the risk efficiently.
The startup has two types of portfolios - the core and thematic portfolios. Core portfolios are tactical portfolios that allocate to the best-performing themes or factors, and asset class and thematic portfolios are based on a particular theme like momentum, innovation, etc.
These portfolios use investment philosophies like equity factor research and market regime modeling. For equity factors, they use data science techniques to research various themes in the market like momentum, value, growth, quality, etc. and build robust portfolios with these themes.
Wright Research uses advanced machine learning models for market regime modelling to forecast market risk in the next month and quarter. It changes the allocations to factors and asset classes based on this.
The startup has raised an undisclosed amount of pre-seed funds from (BRTSIF), Govt. of India, and Nidhi SSS. NASSCOM has also awarded it as one of the top 50 AI game changer startups.
Founded in 2016 by IIM Bangalore alumni Ram Medury and Manoj Trivedi, Hyderabad-basedaims to make investing truly transparent and safer by removing emotion, using technology, from the investing process.
The startup is a SEBI registered equity investment advisory, leveraging quantitative algorithms to build long-term portfolios that help create wealth.
It analyses extensive data sets on all companies, tracking thousands of data points going back more than twenty years in time. This quantitative analysis is done using its Roots & Wings investment philosophy, which focuses on companies with strong balance sheets, aligned promoters, and growing sales and earnings.
Its machine learning algorithms continuously learn and optimise the portfolios by leveraging powerful neural networks that map business parameters and stock patterns.
The result is an optimally diversified portfolio across sectors that is tested for low drawdowns and faster recovery. According to Jama Wealth, it combines the best of human intelligence, rule-based heuristics, and autonomous networks.
Gurugram-based, a fintech startup founded in 2017 by IIT Kharagpur graduates Gangwar and Mohit Bansal, provides algorithmic trading tools.
It is an online stock simulator for Indian investors that takes the pain out of algorithmic trading by eliminating the need for programming.
The startup claims to be an expert in high-frequency data management and web-based stock market simulation.
Kuants is a web-based platform that can be used by anyone and does not require computer programming experience. It was accelerated by the India Accelerator.
The startup has SEBI-approved algorithms to trade on their platform.
It provides services like back-testing facilities to those who can code by themselves, and users can apply their quantitative skills into developing trading algorithms and back-testing them on the platform. It also allows users to submit their strategies on its marketplace, in which users will be able to select a pre-developed strategy and live trade on it.
Mumbai-based, founded by Kanika Agarrwal, Nikhil Hooda, and Atanuu Agarrwal in 2017, is a SEBI-registered portfolio management startup that invests for domestic and foreign investors in Indian equities.
The startup uses ML (machine learning) to create better long-term investment decisions. It teaches machines how to recognise and buy companies that are not only fundamentally good businesses, but also in-demand stocks.
Upside AI manages assets for both domestic and foreign investors such as institutions, HNIs, and family offices.
In 2021, the startup raised $1.2 million in a seed round led by Endiya Partners to build a robust pipeline of differentiated tech products and a network of wealth managers, IFAa, large distributors, and brokers.
Edited by Megha Reddy