The Cutting Edge of Investing: Quadratic’s AI-Powered Analytics Platform

Part 1: Interview with Quadratic

AlphaFund: Hi there, thanks for taking the time to speak with us today. To start, can you give us a quick overview of what Quadratic does?

Quadratic: Absolutely, thanks for having me. At Quadratic, we are building next-generation portfolio management and risk analytics tools for institutional investors. Our cloud-based platform leverages data science and machine learning to provide asset managers with powerful insights and optimization capabilities to enhance their investment strategies.

AF: Interesting. What are some of the key challenges facing institutional investors today that Quadratic aims to address?

Q: A major issue is the ever-increasing complexity of financial markets. Investors are dealing with more data, more asset classes, more complex instruments than ever before. Legacy systems and approaches are struggling to keep up. Our goal is to arm investors with cutting-edge technology to navigate this landscape effectively – to gather and analyze huge amounts of data, to model risk and opportunity in sophisticated ways, and to construct optimal portfolios aligned with their objectives.

AF: Can you share some specifics on how Quadratic’s platform actually works and the benefits it provides?

Q: Sure. At the core is our data aggregation engine, which pulls in market data, alternative data, and the client’s own portfolio data, and structures it for analysis. We then apply AI and machine learning models to identify patterns, correlations and insights that may not be apparent to the human eye.

This feeds into our risk analytics module, which provides a granular, multi-dimensional view of portfolio risk, spanning everything from traditional metrics like volatility and drawdown to more nuanced factor risks and scenario analyses. Managers can stress test their portfolios against a range of market conditions.

Finally, our optimization engine suggests the most efficient portfolio allocations to achieve the desired risk/return profile, taking into account the investor’s specific constraints and objectives. The whole platform is interactive and visual, allowing for real-time collaboration between teams.

AF: Who are some of your typical clients and what have been the results they’ve seen?

Q: We work primarily with institutional asset managers – hedge funds, family offices, pension funds, etc. The common thread is that they are all grappling with increasing market complexity and looking for an edge.

In terms of results, it varies, but we typically see clients able to achieve better risk-adjusted returns, often with lower volatility and drawdowns. They are able to identify risks that may have gone unnoticed before, and optimize their portfolios more scientifically. Operationally, the platform tends to make teams more efficient and frees up time for higher-value work. We have clients who have seen 100-200 basis point improvements in returns, and 20-30% reductions in risk, which can be quite meaningful at scale.

AF: Impressive. Any other thoughts you’d like to share about Quadratic or how you see the investment landscape evolving?

Q: I think we’re just scratching the surface in terms of what’s possible with AI and machine learning in finance. As alternative data proliferates and computing power continues to grow, the potential to uncover new insights and alpha sources is immense. At the same time, markets are not static – I expect we’ll see continued innovation in asset classes and instruments, which will present both challenges and opportunities for investors. Firms that can marry deep market expertise with advanced technologies will be well-positioned for the future.

More broadly, I think the asset management industry is at an inflection point. Fees are under pressure, and there is growing emphasis on not just returns but risk management, ESG considerations, and operational efficiency. Technology will be a key differentiator on all these fronts.

At Quadratic, we’re excited to be at the forefront of this wave, helping investors harness the power of data and AI to navigate the complexities of modern markets. We have a talented, multidisciplinary team and we’re just getting started. Look for more innovation from us in the months and years ahead.

AF: You mentioned Quadratic’s use of alternative data. Can you give some examples of the types of alternative data you work with and how it’s used?

Q: Alternative data is a broad category, but essentially it refers to any non-traditional data sources that can provide insights into company or asset performance. Some examples we work with include:

Satellite imagery: We use computer vision algorithms to analyze satellite images of retail parking lots, factory activity, agricultural land, etc. This can provide leading indicators of company revenues, production levels, crop yields, and more.

Web scraping: We collect data from company websites, such as job postings, product listings, pricing data, etc. Changes in this data can signal shifts in company strategy or demand trends.

Social media: We analyze social media chatter about companies and their products, looking at sentiment, engagement, and other metrics. This can provide insights into brand perception and potential reputational risks.

Geolocation: We look at geolocation data from mobile devices to track foot traffic to stores, restaurants, and other venues. This can help gauge consumer demand and market share shifts.

The key is not just collecting this data, but cleaning it, structuring it, and integrating it with traditional financial data in a way that provides a more comprehensive, real-time view of asset performance drivers.

AF: How does Quadratic approach the challenge of integrating all these disparate data sources?

Q: It’s certainly a challenge given the volume, variety, and velocity of data we’re dealing with. We’ve invested heavily in our data infrastructure to ensure we can handle this scale and complexity. This includes things like:

A robust ETL (extract, transform, load) pipeline to pull in data from various sources and formats and normalize it into a consistent schema

Distributed computing frameworks like Apache Spark to process and analyze data at scale

A flexible, cloud-native architecture that can scale up and down as data volumes fluctuate

Advanced data governance and lineage tracking to ensure data quality and provenance

Equally important is our team of data scientists and engineers who have deep expertise in wrangling alternative data sets and applying machine learning in a financial context. It’s a unique skill set that requires both technical chops and domain knowledge.

AF: Fascinating. One final question – as AI and machine learning become more prevalent in investing, do you see a risk of these strategies becoming crowded or commoditized?

Q: It’s a great question and one we think about a lot. I think there are a few factors that mitigate this risk for Quadratic:

First, while the broad concepts of AI and ML may become more commonplace, the specifics of how they’re applied still require significant expertise and customization. It’s not a trivial undertaking to build and train these models effectively.

Second, we believe that the real differentiator is not just the models themselves, but the unique combination of data, technology, and domain expertise that firms bring to bear. Our edge comes from our proprietary data sets, our cutting-edge ML techniques, and our team’s deep understanding of markets and investing.

Finally, we are continuously innovating and evolving our platform. We’re not standing still. As markets change and new data becomes available, we’re always looking for new ways to generate insights and alpha for our clients.

So while we certainly expect more firms to adopt AI and ML over time, we believe Quadratic is well-positioned to stay ahead of the curve.

AF: Great, thanks so much for your time and insights today.

Q: My pleasure, thanks for the thought-provoking questions.

Part 2: FAQ

Q: What types of data does Quadratic use in its platform?

A: Quadratic’s data aggregation engine pulls in a wide variety of data, including:

Traditional market data (prices, volumes, financial statements, etc.)

Alternative data (satellite imagery, social media sentiment, credit card transactions, etc.)

The client’s own portfolio holdings and transaction data This data is then cleaned, structured, and normalized for analysis.

Q: What machine learning techniques does Quadratic employ?

A: Quadratic uses a range of supervised and unsupervised learning algorithms, depending on the use case. This includes things like:

Clustering and anomaly detection to identify unique risk factors

Natural language processing to analyze unstructured data like news and earnings call transcripts

Predictive models to forecast asset returns and volatility

Reinforcement learning for trading strategy optimization The specifics vary, but the unifying theme is leveraging ML to uncover insights and patterns that may not be captured by traditional statistical techniques.

Q: How does Quadratic ensure the security and privacy of client data?

A: Data security is paramount for Quadratic. All data is encrypted in transit and at rest, and access is strictly controlled and audited. Quadratic employs multi-factor authentication, role-based access controls, and other security best practices. They are SOC 2 compliant and undergo regular third-party security audits. Client data is siloed and never commingled.

Q: What is the onboarding process like for new clients?

A: Onboarding typically involves a few key steps:

Data integration: Quadratic’s engineers work with the client to set up secure data feeds and map data fields to Quadratic’s data model. This usually takes 2-4 weeks.

Configuration: The platform is configured to the client’s specific needs – risk parameters, optimization objectives, reporting requirements, etc.

Training: Quadratic provides training to the client’s team on how to use the platform effectively.

Ongoing support: Each client has a dedicated customer success manager who provides ongoing support and helps them get the most value from the platform.

The whole process is designed to be as smooth and efficient as possible, so clients can start deriving insights quickly.

Part 3: About AlphaFund

AlphaFund is a leading provider of market intelligence and research for the institutional investment community. Our mission is to help asset managers stay on the cutting edge by offering deep insights into the innovators and disruptors shaping the future of finance.

Through our research reports, webinars, and events, we shine a light on emerging trends and technologies that are transforming how investors generate alpha, manage risk, and operate their businesses. From AI and machine learning to blockchain and alternative data, we cover the full spectrum of innovation in financial markets.

Our team combines decades of experience in the asset management industry with a passion for uncovering the next big ideas. We work with some of the most sophisticated investors in the world, including hedge funds, pension funds, sovereign wealth funds, and family offices.

In our popular interview series, we go in-depth with the founders and executives driving change at cutting-edge fintechs and investment firms. Through these conversations, we unpack the strategies and technologies that are shaping the future of investing.

Some of our recent interviewees have included:

A hedge fund using satellite imagery and computer vision to predict crop yields and trade agricultural commodities

A firm applying natural language processing to analyze central bank communications and anticipate monetary policy shifts

A company building a decentralized marketplace for data, allowing investors to buy and sell alternative data sets directly

An asset manager using reinforcement learning to optimize portfolio construction and automate trade execution

By bringing these stories to our audience, we aim to give investors the knowledge and tools they need to thrive in a rapidly changing market environment. If you’re interested in staying ahead of the curve, be sure to check out our latest research and subscribe to our newsletter at

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