Latest news with #WorldQuant
Forbes
3 days ago
- Business
- Forbes
How Anthropic Just Validated The End Of Wall Street's $500K Quant Jobs
Close-up of a person's hand holding a smartphone and using the Opus 4 model within the Claude app ... More from AI company Anthropic, Lafayette, California, May 22, 2025. (Photo by Smith Collection/Gado/Getty Images) Just weeks after reporting on how AI startups are democratizing quantitative analysis across financial markets, Anthropic has launched Claude for Financial Services, a comprehensive platform that transforms how finance professionals analyze markets and make investment decisions. The timing couldn't be more telling. While billionaire fund managers like Igor Tulchinsky at WorldQuant have been quietly deploying large language models to "convert and discover alphas in different domains" with teams of 150+ PhDs, the broader financial industry has been watching from the sidelines. Anthropic's new offering changes that dynamic entirely. How Anthropic's AI Makes Quant Jobs Obsolete Claude for Financial Services represents exactly the kind of revolution in financial services that AI is expected to deliver. The platform unifies financial data from market feeds to internal databases stored in platforms like Databricks and Snowflake into a single interface. More importantly, it provides direct hyperlinks to source materials for instant verification, addressing the hallucination concerns that have kept institutional investors cautious about AI adoption. The technical capabilities mirror what specialized startups have been building. According to Anthropic, the Claude 4 models outperform other frontier models as research agents across financial tasks, and when deployed by FundamentalLabs to build an Excel agent, Claude Opus 4 passed 5 out of 7 levels of the Financial Modeling World Cup competition with 83% accuracy on complex tasks. But the real validation comes from the client roster. Norway's sovereign wealth fund NBIM reports achieving 20% productivity gains equivalent to 213,000 hours using Claude. Their portfolio managers and risk departments can now seamlessly query their Snowflake data warehouse and analyze earnings calls "with unprecedented efficiency." Why AI Quant Startups Still Matter Despite Anthropic Anthropic's entry validates the market thesis of smaller AI startups that have been entering financial services space. Companies like Y Combinator-backed Findly continue pushing boundaries in commodity trading, where founder Ignacio Hidalgo's background as a former LPG trader gives him insider knowledge of industry pain points. FINTool's focus on public equity research and Metal AI's private equity data unification represent targeted solutions that major platforms may struggle to match in depth. The commodity trading world where Findly operates particularly benefits from specialized knowledge, understanding that bunker traders often conduct business through WhatsApp channels while sophisticated operations deploy complex algorithms. As Hidalgo explains, "Charts don't give you the context. It's impossible for a human to ingest all the parameters: overnight price changes, ship loading information, weather data and forecasts, news. With AI, you can ask 'What happened to the price of crude this week? Is it a good time to buy?' and get a much clearer picture with market context." Anthropic's AI Platform Strategy For Quant Jobs What's particularly interesting about Anthropic's approach is the ecosystem play. Rather than trying to replace specialized providers, Claude for Financial Services integrates with leading financial data companies including FactSet, S&P Global, PitchBook, Morningstar, and Databricks. This suggests a future where large language models serve as the intelligent layer connecting existing data infrastructure. The platform's pre-built connectors access financial data providers and enterprise platforms for comprehensive market intelligence, exactly what smaller startups have been building custom integrations to achieve. But Anthropic's scale allows them to negotiate these partnerships at the platform level. Commonwealth Bank of Australia's CTO Rodrigo Castillo captures the transformation: "Our strategic partnership with Anthropic is foundational to our success and our strategy to become a global leader in AI innovation in banking." AIG reports even more dramatic results, compressing their underwriting review timeline by more than 5x while improving data accuracy from 75% to over 90%. What AI Means For The Future Of Quant Jobs The convergence of enterprise platforms and specialized startups suggests the AI transformation of finance is accelerating rather than consolidating. While Anthropic provides the infrastructure layer, specialized companies maintain advantages in domain expertise and custom workflows. For commodity traders working with Findly's Darling Analytics, the ability to ask natural language questions about propane stocks and East Coast weather patterns represents knowledge that took years to accumulate. For private equity teams using Metal AI, the understanding of how deal data flows between CRMs, data rooms, and market research platforms reflects deep industry experience. The question is how quickly the technology will reshape the entire financial analysis workflow. Anthropic's enterprise-grade platform provides the foundation, while specialized startups continue pushing the boundaries of what's possible in specific domains. Whether AI can truly replace the intuition and market feel that experienced traders bring remains to be seen. But with major platforms and specialized startups both seeing significant traction, the financial industry appears ready to find out. The democratization of quantitative analysis to everyone in the financial services industry is no longer a prediction. It's happening across every corner of finance, from sovereign wealth funds to commodity trading desks. Anthropic's platform launch is simply another step in the same direction.
Forbes
05-07-2025
- Business
- Forbes
The End Of The Quant? How AI Is Democratizing Financial Analysis
NEW YORK, NEW YORK - MARCH 28: Traders work on the floor of the New York Stock Exchange (NYSE) on ... More March 28, 2025, in New York City. As President Trump's escalating trade war and signs of inflation concern investors, the Dow Jones Industrial Average (DJI) dropped more than 700 points or nearly 1.7%. (Photo by) Getty Images A new wave of artificial intelligence startups is setting its sights on one of Wall Street's most specialized roles: the quantitative analyst. From hedge funds to commodity trading floors, AI platforms are promising to democratize the complex mathematical models and data analysis that have long been the exclusive domain of highly-paid quants. Until recently, large language models for trading have been the domain of billionaire fund managers like Igor Tulchinsky, whose WorldQuant hedge fund manages over $23 billion and employs more than 150 PhDs to build custom AI systems. As Tulchinsky recently told Forbes , his firm is using LLMs to "convert and discover alphas in different domains," creating proprietary tools that can answer "very sophisticated questions" by combining standard models with internal data that "really nobody can replicate." But a new generation of startups is working to change that exclusivity, offering sophisticated AI-powered analytics to firms that previously couldn't afford such capabilities. The trend represents a fundamental shift in how financial institutions approach data-driven decision making. Rather than hiring teams of PhD-level analysts to crunch numbers and identify market patterns, firms are increasingly turning to AI systems that can process vast amounts of information in seconds and deliver insights in plain English. Three companies highlighted in recent case studies (FINTool, Metal AI, and Findly) are targeting different corners of the financial world with AI-powered research and analytics platforms. Each promises to transform hours of manual analysis into automated insights, potentially reshaping how investment decisions get made. The key trend has to do with the ability of AI to take disparate data sources to analyze them according to the wishes of risk takers. The promise is that AI systems can search for, aggregate and synthesise data-sources without human intervention. For instance, FINTool focuses on public equity research for hedge funds and banks, analyzing millions of documents from earnings reports to SEC filings. The platform claims to reduce analyst workloads from hours to seconds while maintaining "zero hallucinations" through a three-tier peer-evaluation system. On the other hand, Metal AI targets private equity firms, where deal teams struggle with fragmented data across multiple systems, be it market research platforms to confidential data rooms. The company's intelligence platform claims to unify internal and external data sources, allowing investment professionals to ask complex questions in natural language rather than spending time manually aggregating information. But perhaps the most developed attempt to replace traditional quant work comes from YC backed Findly , whose Darling Analytics platform is making waves in the notoriously complex world of commodity trading. Ignacio Hidalgo knows commodity trading from the inside. As a former lead book trader at some of the most prominent LPG trading desks, he experienced firsthand the daily struggle of synthesizing massive amounts of market data, weather patterns, shipping & flows information plus geopolitical developments into profitable trading decisions. "The problem was the same, just different," Hidalgo explains of his transition from trader to tech entrepreneur. "Most advanced tools for structured and advanced data analytics were still leaving traders without the context they needed. A very hard problem to solve" Now, alongside co-founder Pedro Nascimento, Hidalgo is building what he calls "brand new in the world" technology through their Y Combinator-backed startup Findly. Their Darling Analytics platform aims to give average commodity trading desks the "super analytical powers" traditionally restricted to specialized quant desks. Commodity trading operates in a world of extremes. Sophisticated mathematical models coexist with surprisingly basic tools. While some operations deploy complex algorithms and real-time analytics, others rely on WhatsApp group chats for deal-making. Traders often conduct business through messaging apps with minimal technological sophistication. "Charts don't give you the context," Hidalgo notes. "It's impossible for a human to ingest all the parameters: overnight price changes, ship loading information, weather data & forecasts, news. With AI, you can ask 'What happened to the price of crude this week? Is it a good time to buy?' and get a much clearer picture with market context. AI Quants: Real-World Implementation Darling Analytics is already being piloted at several large commodity firms. The system automates the kind of morning and event driven reports that junior traders typically compile manually, freeing up human analysts to focus on higher-value strategic work. It integrates (near) real-time structured data with unstructured information from market reports, X, Web, emails and news feeds to provide comprehensive market intelligence. "The AI can give full context on data on your metrics. This is not the same as just plotting a graph, it tells you what the graph is in the current context of the market," Hidalgo explains. The platform builds what he calls a "knowledge graph", allowing users to ask trader-specific questions in natural language and receive analysis that would previously require hours of manual research. For instance, a trader can ask the tool to plot the relationship between the weather and the propane stocks on the East Coast of the United States. While this previously would've taken a junior analyst hours to prepare, a trader can now delegate the task to the platform and see results in minutes. Query within DarlingAnalytics DarlingAnalytics What is next for AI quants? The success of these AI platforms raises important questions about the future of quantitative analysis in finance. If artificial intelligence can truly replicate the pattern recognition and analytical capabilities that make quants valuable, it could significantly alter the structure of trading and investment teams. For trading desks that rely on analysts or quants to provide studies for risk deployment, AI-powered analytics provide a competitive advantage by amplifying human capabilities rather than simply replacing them. The technology promises to democratize access to sophisticated analysis across entire organizations. However, the transition isn't without challenges. Commodity markets are notoriously unpredictable, influenced by everything from geopolitical tensions to weather patterns. The companies building these AI systems must ensure their platforms can handle the complexity and volatility that make human expertise so valuable in the first place. As Hidalgo puts it, the goal is to "empower the average user in commodity trading companies" with analytical capabilities that were previously the exclusive domain of specialists. Whether AI can truly replace the intuition and market feel that experienced traders bring to trading remains to be seen, but what it does do is provide an edge on data intelligence in minutes. But with major commodity traders already piloting these systems, the financial industry appears ready to find out.
Forbes
16-05-2025
- Business
- Forbes
How A Soviet Refugee Became A Hedge Fund Billionaire
It's a cloudy March morning in Midtown Manhattan and Igor Tulchinsky is explaining from behind his wooden desk, between sips of coffee and long pauses to think, his latest algorithmic vision—the introduction of large language models for his hedge fund WorldQuant. 'The first thing that the LLM can do is it can structure data and 80% of data that's out there is unstructured,' says Tulchinsky, dressed in all black, his piercing blue eyes gleaming with excitement. 'It's like a free lunch.' Tulchinsky has spun his decades-long fixation with data into a Wall Street goldmine. The 58-year-old, who immigrated to the U.S. as a child from Belarus, has made a fortune in the obscure, highly technical world of quantitative investing. WorldQuant, which he owns, now manages $10 billion for billionaire Izzy Englander's much larger hedge fund Millennium Capital Management, which it spun out of in 2007. A separate entity, WorldQuant Millennium Advisors, which Tulchinsky and Englander cofounded, manages another $13 billion for outside investors. Between his equity stakes in the two firms and cash he's made for himself from trading, Tulchinsky is worth an estimated $1.7 billion. Igor Tulchinsky by Alexander Karnyukhin for Forbes Quants like Tulchinsky write computer code that automatically execute stock trades based on price signals. WorldQuant's specialty has long been in statistical arbitrage—algorithms, or what he calls 'alphas,' that exploit price inefficiencies between individual securities or entire equity portfolios. Those alphas execute all sorts of trades, including buying and selling stocks (long positions), betting against stocks (short selling), and a slew of more byzantine hedge fund strategies that remain confidential. Whether it's Trump's see-sawing tariffs policy or a tech company's quarterly earnings report, there's likely a WorldQuant alpha calculating how to make money from it. 'We trade the ripples, not the waves,' says Tulchinsky, whose funds likely execute hundreds of thousands of trades on a typical day. 'We have a stockpile of millions and millions of alphas.' Now Tulchinsky wants to turbocharge his alpha factory by pairing it with large-language models (think OpenAI's ChatGPT), which he believes can help build new and better algorithms. 'We can be using AI and LLMs to convert and discover alphas in different domains,' he says. 'Possibilities are endless. The LLMS are getting stronger and stronger.' While funds like WorldQuant have been employing AI tools for years – in research, in predictive modeling and in writing code – the use of LLMs to devise trading strategies is fairly novel. In a 46-page report on hedge funds' use of AI prepared last June by the U.S. Senate Committee on Homeland Security and Governmental Affairs (for which WorldQuant and other major players like Citadel and Renaissance Technologies participated), LLMs were mentioned only once. 'These sort of internal, proprietary LLM systems, that's sort of an underlooked factor in how investment firms, especially quant firms, can use AI,' says Francesco Fabozzi, research director at Yale's International Center for Finance. WorldQuant is well positioned to capitalize on LLMs. Sources close to the company tell Forbes it employs over 150 PhDs in math, computer science and related STEM fields. Beyond Tulchinsky's comments, the firm declined to provide specifics, though Fabozzi says it's likely a hedge fund like WorldQuant would tap an open-source LLM model, such as Facebook's Llama, and feed it with its existing algorithms, thus training it to discover and even write new algorithms on its own. 'You can take standard LLMs and you can combine it with extra information. We have a lot of internal information, ' says Tulchinsky. 'We can create this tool, which can answer very sophisticated questions for us.' Beyond alpha discovery, Tulchinsky wants to harness LLMs to boost his firm's research efforts. 'You can ask questions of the LLM. You can give it a model like the Ray Dalio world model, and then something happens: Japan drops interest rates, and you can say, 'Okay, using the Ray Dalio model, make predictions for how it will trickle through the world,' and it'll do that,' he says as a hypothetical example. (He doesn't want to share an actual example for competitive reasons). 'That becomes our own data that really nobody can replicate.' Sources familiar with the firm say that WorldQuant's deployment of LLMs is still in its early stages, but is actively being developed and rolled out. For someone like Tulchinksy, AI is not just a way to make money, but a means of thinking about the world, says Stanley McChrystal, the retired four-star U.S. Army General, who has been working with Tulchinsky as a consultant since 2015 and counts him as a friend: 'The ability to use data to predict things is sort of what makes him tick.' Says a grinning Tulchinsky, 'With the proliferation of data and AI, we're getting to the point where you can kind of quant everything.' Tulchinsky's data obsession likely took root in his unpredictable upbringing. Born in Minsk, Belarus in 1966, he grew up under the Communist regime of Pyotr Masherov, a Belarusian revolutionary turned Soviet politician. His parents, professional musicians, decided to leave their home country in 1977 when their only child was 11. 'Those days when you decided to leave you immediately lost your job, they branded you as traitors publicly,' he recalls. The Tulchinskys spent three months in Italy before they received asylum status in the United States. In America, they moved around, living in four different states by the time Tulchinsky was 17. 'It's a very transformational experience, when you give up all the things you've taken for granted,' he recalls of his childhood. 'You become less afraid of change, and kind of even get used to it.' Fascinated with computers from a young age, Tulchsinky was programming video games at 17. He received his B.S. and his M.A. in computer science from the University of Texas, followed by his M.B.A. in finance and entrepreneurship from the Wharton School. He began his career at AT&T Bell Laboratories (the wireless carrier's now-defunct research and development arm), before nabbing his first finance gig at Timber Hill, the options trading firm founded by fellow Soviet refugee Thomas Peterffy. 'Of course I remember Igor,' recalls 80-year-old Peterffy, who came to the U.S. from Hungary and later transformed Timber Hill into the stock brokerage giant Interactive Brokers, the source of his $60 billion fortune. 'He is a deep thinker. He reminded me of myself in my younger years as he sometimes seemed to have gotten completely lost in his thoughts and looked like he was unaware of his surroundings.' In 1995, Tulchinsky left to team up with another future billionaire: Israel Englander, a young hedge fund trader who had founded Millennium Management six years earlier with $35 million in investor assets. As Millennium grew, Tulchinsky developed a reputation within the firm for engineering successful statistical arbitrage trades. By the early 2000s, Tulchinsky and Englander were discussing the former founding his own shop within the Millennium mothership. 'As one of the pioneers of quantitative investing, Igor helped define what is possible. He has always been a step ahead,' said Englander, who seldom speaks with the press, in a written statement shared with Forbes. 'It has been extraordinary to see what he has achieved.' Yet, WorldQuant got off to the worst possible start. In August 2007, seven months after launching, there was a widespread meltdown across quant-driven investment funds after one trader's liquidation triggered a cascade of other liquidations in what became an early tremor in the emerging financial earthquake. WorldQuant suffered but Tulchinsky says he took all of the firm's money out of the market before it bottomed out, evading the worst. 'Cutting losses is a key principle that we follow, that's the essence of risk management' he says. 'In life people don't cut losses enough because it's emotional, it's unpleasant, but it's a very good strategy.' As assets rebounded and eventually grew, so did the embrace of global talent. WorldQuant opened its first international office in China shortly after launching when Tulchinsky tapped a Chinese friend of his to interview 1,000 candidates, and ultimately hired five of them. 'They were just spectacular alpha makers,' he recalls. The firm now has 1,000 employees working in outposts across 27 cities in 16 countries including other major financial centers, like London and Tokyo, but also in less typical locales, such as Armenia, Hungary and Vietnam. 'We are providing opportunity to the talent, and the talent is providing us with alphas,' explains Tulchinsky, who spends a significant amount of his time traveling to WorldQuant's various outposts. 'I'm so used to traveling, I get jet-lagged when I don't travel.' Outside of work, Tulchinsky also keeps busy. Between 2013 and 2023, he dropped $65 million into his charitable foundation, which primarily supports the firm's hallmark philanthropic initiative, WorldQuant University, an accredited and tuition-free online university that he founded in 2014, which offers free masters of science degrees in financial engineering and other STEM coursework. He has also invested in over 100 startups including robot maker Figure AI and financial startup EquityZen through his venture firm WorldQuant Ventures. In his spare time, he has published three books about investing and is working on a fourth about cutting losses. Tulchinsky has plenty more to say but Forbes' time with him is up. The billionaire has calls to take and then an afternoon flight to Miami to visit WorldQuant employees there. He clearly doesn't like sitting still: outside his office his firm's credo 'Change Is Progress' flashes from a 3-dimensional wall structure. 'The interesting thing about Belarus,' notes Tulchinsky with astonishment, 'I looked at it through Google Earth the other day—nothing's changed in 50 years.' And with that, he gets up and leaves.
Business Insider
07-05-2025
- Business
- Business Insider
How hedge funds Citadel, WorldQuant, and Freestone Grove are using AI
Executives from Citadel, WorldQuant, and Freestone Grove spoke at the Milken conference about AI. Umesh Subramanian, Citadel's chief technology officer, said AI helps them leverage humans. They warned that funds need to ensure that people are using their tools for their intended purposes. Hedge funds have always been quick to adapt to the latest technology. Given the industry's ultracompetitive nature and large budgets of the biggest managers, big-name hedge funds have built out machine learning and artificial intelligence capabilities for years. The jump in AI's applications in recent years, though, still has them excited. Umesh Subramanian, the chief technology officer of $65 billion Citadel, said that applying AI to discretionary investing is where it's getting interesting, as investors and analysts are bombarded with documents, news, filings, and data to digest. "The surface area of the amount of information that you really want to consume is very large," for the average investment professional at his firm, Subramanian said, and AI gives them leverage to do more. "We fine-tune our investment workflow," he said, and mentioned that Citadel's billionaire founder, . To speed up decision-making, Subramanian said that building intuitive tools like chatbots that can be talked to are important because people generally prefer asking questions naturally. The firm is also hiring data scientists and AI professionals to embed them in the fund's various groups to optimize various investment workflows. Andreas Kreuz, WorldQuant 's deputy CIO, said the firm was using AI to expand the data it can bring into its models since it can restructure data from images and audio. "What excites us is beyond the low-hanging fruit," Kreuz said. Still, Subramanian, Kreuz, and Freestone Grove Cofounder Daniel Morillo warned that the tech can be misused. "You need to teach your people to still pay attention," Morillo said, adding that his firm does more work on thinking about how people use a tool than on building out new AI capabilities. Freestone Grove, the fundamental equity firm he launched with former Citadel executive Todd Barker in 2024, also wants to make sure it retains its own views, Morillo said. This means the firm is being intentional about not "killing off" any edge its investors get from doing the grunt work themselves. "It's super important to be highly intentional about how you're using the tools," he said. Kreuz said "we don't think AI is replacing human judgment" and wants employees to question the "black box." "It can produce a tremendous amount of noise instead of signal," he said. Investors' judgment will still be the ultimate differentiator, Subramanian said. "While I think there's going to be a lot of leverage in the system with AI as a tool in the toolbox, I don't think it changes what is making the decision."
