Using data science to grow clients’ wealth: Interview with Sonam Srivastava founder and CEO at Wright Research

By Puja Sharma

Sonam Srivastava, founder, and CEO at Wright Research is a globally recognised researcher in the field of machine learning in finance & quantitative investing. She has expertise in Factor Investing, Momentum Investing, Algo trading, technical analysis, and fundamental analysis. Srivastava has worked with HSBC Edelweiss & Qplum where she has built and handled large-scale portfolios & trading algorithms in US & India.

Srivastava explained to IBS Intelligence how Wright Research’s AI-driven quantitative algorithms help Indian investors in a way that is simple to understand and easy to use.

How would you describe Wright’s Research in brief?

Wright Research is a wealth creator in the digital space that uses scientific data-driven methods to tactically extract opportunities across assets in the public markets to grow clients’ wealth. Globally recognised quant researchers started Wright Research to create scalable quant-driven products for Indian investors.

Using award-winning data science and machine learning-driven algorithms, Wright Research creates a dynamic investment approach that looks at multiple factors to derive outperformance and uses risk management best suited for Indian markets. Wright has been a favorite advisor for Indian investors due to their exemplary performance, which stands out in any market regime.

We are affordable, transparent, and accessible, aiming to democratise everyone’s access to AI-driven investment products.

How is the firm transforming the stock market industry with its unique high-technology model?

The Indian market has myriad opportunities, which come and go dynamically, which is why well-researched and risk-managed quantitative investment strategies can flourish here.

At Wright Research, we have a proven and unbeatable track record of consistent performance in good and bad times. We are a stand-out investment product in the Indian market, which is scalable and tailor-made for the unique risks in India. We deliver exceptional performance with our expertise in quantitative factor-driven strategies and regime-based risk management.

Through artificial intelligence and data science-based research in investing, we are helping investors find the best investments. We work across market factors, time horizons & asset classes to maximise returns while minimising the risks.

We strive to be as transparent and explainable as possible for everyone, thus empowering every person to make suitable investments at the right time.

With emerging technological ways of investing, why should retail investors seek interest in Wright Research for investing?

For the Indian investor looking for expert-backed portfolio solutions that can deliver exceptional returns, Wright Research provides a research and tech-powered platform supported by expert AI-driven quantitative algorithms that are simple to understand and easy to use. Thus we are making wealth creation accessible to all by minimising their risks and maximising their returns.

Our ultimate aim is to cultivate a culture of financial success by empowering Indian investors to make the most out of their savings within their risk constraints through transparent, accessible, and consistently outperforming products.

What is the growth potential of Algo Investing in the near future?

Algo-driven investing can be of many types – fast-paced or low-frequency investment algorithms. We see the tremendous potential for growth in both of these areas. However, India has a severe under-representation of quant and algos in the market. Therefore, we must grow many folds to reach comparable figures to the global markets.

With the Indian investor now trusting technology and digital transactions, we see the industry take off and grow many folds soon.

How can using machine learning help investors reduce risk investing?

The modeling and reduction of risk of a portfolio is a complex problem. A multitude of stocks and factors influence the risk of a portfolio, and the interactions are complex and non-linear. Machine Learning is an excellent solution to optimising the risk in a portfolio due to its ability to bring out actionable insights from large data sets. Furthermore, machine learning models can account for the complexity and the non-linearity. Using these models, a researcher can evaluate multiple complex scenarios, incorporate a gamut of data and create an investing strategy that can be dynamic to reduce investor risk.

How is your AI different from Algorithmic trading? How important is it for investment?

AI and algorithmic trading are both very general terms that capture a large set of functions. For example, algorithms can be used in the stock market for the short term or long term, and any automated strategy is called an “algorithmic” strategy. On the other hand, when a researcher uses machine learning methods to extract extra insights from the data and uses prediction, classification, or any different learning paradigm to make decisions, it is called using AI in investing.

Our investing philosophy is a combination of algos and AI. We do everything using an automated process, and at various points in the process, we use machine learning to add an edge. This makes our philosophy unique. This has tremendous value as it reduces biases, gives us risk-return expectations to work with, and enhances the performance of the portfolios while also reducing the risk.