Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I’m a Computer Science graduate from IIT Kanpur whose journey into the investment management industry has been unconventional and driven by curiosity, experimentation, and adaptability.

My first exposure to the financial industry came through an internship at Goldman Sachs (Investment Management Division) in Bangalore. That experience sparked my interest in this space. Soon after, I joined WorldQuant through campus placements—a formative experience where I discovered that my background in mathematics and CS gave me a unique edge in quantitative investing. Over four years at WorldQuant, I worked across multiple asset classes and strategies and helped build one of the firm’s top-performing products at the time.

After that, I ventured into entrepreneurship—raising capital and developing a high-performance strategy. However, regulatory constraints around leverage (especially in the Indian AIF framework) led me to explore setting up the product offshore via an FPI structure. Unfortunately, the COVID-19 pandemic disrupted those plans.

I later joined Tower Research Capital, a leading high-frequency trading firm, where I contributed to the development of their hedge fund initiative—drawing upon my prior experience in metrics-driven strategy design and risk.

Around the same time, I explored the emerging crypto ecosystem, aligning with the excitement in India’s startup space. While the experience was valuable, the volatility of the ecosystem and key disruptions like FTX's collapse shaped my decision to return to more institutional domains.

Today, I’m the co-founder of ArthAlpha, an investment management and financial technology company. We operate as a hedge fund and license our proprietary tech stack to mutual funds and investment houses. Our PMS, launched in October last year, has grown steadily crossing ₹100 crore in AUM. Prior to that, our strategies were managing ~₹500 crore under a shared licensing structure. We expect to cross that mark again under our own PMS in the coming months.

While I don’t come from a traditional finance background, I’ve always approached this field through the lens of mathematics, computing, and systems design—using that foundation to build differentiated products that perform in real markets.

Q2. How are macroeconomic variables like persistent inflation, interest rate normalization, or geopolitical risks influencing strategy design today?

Within the investment management landscape, I’ve worked with both discretionary and quantitative strategies, and each offers distinct advantages and challenges—especially when viewed through the lens of adaptability.

Discretionary strategies provide a natural advantage in dynamic environments. Since human decision-makers are in control, they can interpret shifting market narratives, macroeconomic developments, and regime changes—like transitioning from a low-volatility to a high-inflation environment—and respond immediately. This agility makes discretionary approaches powerful, particularly in markets driven by narrative, sentiment, or unexpected shocks.
On the other hand, quantitative strategies, particularly in a prop shop or fully automated setup, require systems to not just detect change but adapt autonomously. This is a non-trivial challenge. Financial markets are inherently non-stationary, and models that perform well in one regime may underperform—or outright fail—in another. For instance, a strategy optimized for a low-volatility environment often breaks down under high-volatility conditions, rising interest rates, or liquidity shocks.

To address this, many practitioners attempt to build separate strategies for separate regimes, toggled based on discretionary or rule-based signals. But in my experience, some of the most successful global players do not compartmentalize in this way. Instead, they build unified frameworks—systems that learn underlying state transitions using deep learning, hidden Markov models, or other forms of latent variable modeling. These strategies dynamically switch modes internally, informed by patterns in macro indicators, productivity shifts, interest rate cycles, or microstructure signals like retail vs institutional flows.

The key is not in hardcoding the regime switch, but in enabling the system to learn latent states and adjust accordingly—much like an experienced discretionary trader would, but at scale and with speed.

Q3. What trends are you observing in investor preferences—e.g., demand for risk-managed returns, ESG overlays, or multi-strategy exposures?

Investor preference is highly cyclical and tied closely to market regimes. After the COVID-19 shock in 2020, markets rebounded sharply, and there was significant interest in long only strategies, which promised higher returns in a rising market. Today, as volatility returns, we’re seeing rise in interest in long-short and absolute return strategies—particularly from investors seeking protection and asymmetric returns.

However, investor behavior varies by profile.

• Institutional investors tend to maintain consistent allocations across index, absolute return, and long-short strategies.
• Smaller investors, on the other hand, shift more aggressively based on performance trends, creating pronounced skews in demand.

Over the past few years, we’ve seen strong inflows into multi-strategy hedge funds, especially giants like Millennium and ExodusPoint. Their ability to diversify risk and deliver steady returns has been attractive. However, much of the industry’s net inflow has been concentrated in these large platforms.

Now, a new trend is emerging- large platforms allocating capital to smaller, independent managers, essentially becoming allocators themselves. While this model has worked so far, it’s showing signs of stress. For instance, Millennium, known for 8–10% annual returns, was barely positive by May this year—highlighting the difficulty of sustaining performance at scale.

This sets the stage for a likely reversion toward specialist, agile managers—those with differentiated strategies and faster response times.
At ArthAlpha, we’re positioning ourselves at this intersection: combining institutional-grade strategy design with boutique flexibility, and a proprietary tech stack that supports both alpha generation and third-party licensing.

Q4. To what extent do you think technology—especially data analytics, automation, and AI—is reshaping how asset managers operate and scale today?

The shift from discretionary to quantitative strategies has been unmistakable over the past decade. Most leading firms have either fully transitioned to quant or adopted a hybrid model. The demand for quant talent is soaring—evidenced by $10M signing bonuses and aggressive lateral hiring from rival firms.

Why? Because technology and data-driven models scale better, adapt faster, and stick longer. Firms are investing heavily in infrastructure, talent, and alternative data to stay ahead.

AI is still largely a buzzword. Machine learning has been used in quantitative finance for years. What’s changed recently is the public spotlight on LLMs—but even that is just one part of a broader AI toolkit. Techniques like LLPs, deep learning, and signal processing have quietly powered some of the most successful hedge funds for a long time.

At ArthAlpha, we understand that building today means leapfrogging legacy methods. We can’t rely on what worked a decade ago. We’re building a tech-first platform—hiring top-tier quant and engineering talent, curating and engineering data in-house, and constantly refining our infrastructure.

The arms race in hedge funds is now as much about data and compute as it is about capital. As data volumes explode, the edge will go to those who can extract signal at scale—and do it faster than others.

 

Q5. What are the key investor segments driving capital into the industry today—HNIs, family offices, institutions, or sovereign wealth funds?

While I don’t track global capital allocation patterns closely, I can speak with conviction about India’s evolution over the last few years.

The Indian landscape has changed dramatically. With the startup boom and liquidity surge over the past 5–6 years, we've seen a new wave of HNIs and ultra-HNIs emerge. Traditionally focused on real estate and private investments, these investors are now shifting toward public markets, especially after regulatory and tax changes.

A major structural shift is the rise of family offices. Just six years ago, this concept barely existed in India. Today, it's becoming mainstream. Many successful entrepreneurs and legacy families are choosing to professionalize wealth management—either by allocating capital to external managers or building in-house investment teams.

This shift is driven by a generational transition: newer members are less involved in the family business and more focused on capital preservation and compounding. As a result, the demand for differentiated strategies, especially in public markets, has grown significantly.

That said, India remains a distribution-led market. Institutional capital pools—like sovereign wealth funds—are limited or absent. So, unlike global markets, India’s asset management ecosystem is still heavily reliant on individual investors and third-party distributors for scale.

Q6. What do you see as the core strengths of the Indian investment management ecosystem today compared to global peers?

I'll be blunt—the quant landscape in India, on average, is at least a decade behind global peers. This lag stems largely from a reluctance to invest in technology and talent. Many firms have historically hesitated to commit serious capital to quant infrastructure because they haven’t yet seen consistent results. And without results, it’s hard to build conviction—a classic chicken-and-egg problem.

At ArthAlpha, we’re trying to break that cycle. We've consistently outperformed by building at the intersection of deep domain expertise and cutting-edge technology. That’s our edge—and it’s real.

But there’s another layer to this conversation. While global quant firms may be ahead on infrastructure and modeling, Indian firms have a strong localization advantage—especially in discretionary strategies. Decades of accumulated market insight, sector-specific nuances, and relationships can’t be replicated overnight. A global player, no matter how sophisticated, will find it hard to navigate India’s fragmented and idiosyncratic markets without that local base.

So while India has catching up to do in the quant game, it also holds distinct structural advantages. The future belongs to those who can combine both—deep local understanding and world-class quant infrastructure.

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

To be candid, I may not be the best fit for this question—and here’s why: I’m a quant investor. While we do incorporate fundamental data, it’s always structured, quantitative, and systematically applied.

We don’t engage in direct conversations with company management or conduct traditional bottom-up research. That’s just not how our process is designed. I could list a few generic factors, but their practical value would be minimal, as they don’t drive our strategy in a meaningful way.

Our edge lies in data-driven decision-making, not qualitative assessments.