Hiring at $200K Annual Salary: Wall Street Advances into Prediction Markets

marsbitPublicado a 2026-01-15Actualizado a 2026-01-15

Resumen

Wall Street firms are aggressively entering the prediction markets, with trading giants like DRW, Susquehanna, and Tyr Capital building specialized teams. DRW is offering up to $200,000 in base salary to hire traders who can monitor and trade on platforms like Polymarket and Kalshi. Trading volume in these markets surged from under $100 million in early 2024 to over $8 billion by December 2025, attracting institutional interest. Unlike retail traders who often bet on single events, institutions focus on cross-platform arbitrage and structural opportunities. For example, hedge funds can use prediction markets to hedge investments with greater precision by pairing positions—such as buying "no recession" contracts on Polymarket while shorting overvalued bonds in credit markets. Market makers like Susquehanna, which has privileged access to lower fees and higher limits on platforms like Kalshi, are set to reduce arbitrage opportunities and improve liquidity. This professionalization may lead to more complex products, such as multi-event combos and conditional probability contracts. The entry of well-capitalized, technologically advanced institutions signals a maturation of prediction markets, mirroring the historical pattern of散户-driven innovation eventually dominated by professional players. While retail traders may find niches in long-tail events, the era of easy profits from informational edges is likely over.

Author: Niusike, Deep Tide TechFlow

It has finally arrived. The prediction markets, once built by political supporters, speculative retail investors, and airdrop hunters, are now welcoming a group of silent yet deadly new players.

According to a Thursday report by the Financial Times, several well-known trading firms, including DRW, Susquehanna, and Tyr Capital, are forming specialized prediction market trading teams.

DRW posted a job advertisement last week, offering a base annual salary of up to $200,000 for traders capable of "monitoring and trading active markets in real-time" on platforms like Polymarket and Kalshi.

Options trading giant Susquehanna is recruiting prediction market traders who can "detect incorrect fair value," identify "anomalous behavior" and "inefficiencies" in prediction markets, and is also building a dedicated sports trading team.

Crypto hedge fund Tyr Capital is continuously hiring prediction market traders who are "already running complex strategies."

Data supports this ambitious expansion.

Monthly trading volume surged from less than $100 million at the beginning of 2024 to over $8 billion by December 2025, with a record single-day trading volume of $701.7 million on January 12.

When the pool of funds becomes deep enough to accommodate the size of giants, Wall Street's entry becomes inevitable.

Arbitrage First

In prediction markets, institutions and retail investors are not playing the same game.

Retail investors often rely on fragmented information to predict single events, which is essentially gambling, while institutional players focus on cross-platform arbitrage and structural market opportunities.

In October 2025, Boaz Weinstein, founder of hedge fund Saba Capital Management, stated at a closed-door meeting that prediction markets allow portfolio managers to hedge investments with greater precision, particularly regarding the probability of specific events occurring.

Standing next to Polymarket CEO Shayne Coplan at the time, he said, "A few months ago, Polymarket showed a 50% probability of recession, while the credit market indicated a risk of about 2%. You can think of countless paired trades that were previously impossible."

According to Weinstein's view, a fund manager could buy the "no recession" contract on Polymarket. Because the market believed there was a 50% chance of recession, this contract was relatively cheap.

At the same time, in the credit market, one could short some bonds or credit products that would fall sharply in a recession. Because the credit market only assigned a 2% probability to a recession, these products were still priced high.

If a recession did occur, you would lose a small amount on Polymarket, but make a large profit in the credit market as those overvalued bonds plummet.

If no recession occurred, you would make money on Polymarket and might incur a small loss in the credit market, but overall still profit.

The emergence of prediction markets has provided traditional financial markets with a new "price discovery tool."

The Arrival of the Privileged Class

What tilts the scales even further is privilege at the regulatory level.

Susquehanna is the first market maker on Kalshi and has reached an event contract agreement with Robinhood.

Kalshi offers market makers numerous benefits: lower fees, special trading limits, and more convenient trading channels. The specific terms are not public.

The entry of market makers will quickly change this market.

Previously, prediction markets often suffered from insufficient liquidity, especially for niche events. When you wanted to buy or sell a large number of contracts, you might face wide spreads or simply no counterparty.

Professional institutions will quickly eliminate obvious pricing errors. For example, price differences for the same event on different platforms, or clearly unreasonable probability pricing, will be rapidly smoothed out.

This is not good news for retail investors. Previously, you might find that "Trump wins" was at a 60% probability on Polymarket and 55% on Kalshi, allowing for simple arbitrage. In the future, such opportunities will基本ally not exist.

With Wall Street's PhDs earning hundreds of thousands of dollars, future prediction contracts may also enter an era of specialization and diversification, not just单一 event prediction, such as:

1. Multi-event combination contracts, similar to parlays in sports betting

2. Time series contracts, predicting the probability of an event occurring within a specific time period

3. Conditional probability products, e.g., if A happens, what is the probability of B happening

......

Looking back at financial history, from foreign exchange to futures, to cryptocurrencies, the development of every emerging market follows a similar trajectory: ignited by retail investors, eventually taken over by institutions.

Prediction markets are repeating this process. Technological advantages, capital scale, and privileged access will ultimately determine who stays in this game of probability until the end.

For retail investors, although there may still be a glimmer of hope in long-cycle predictions or niche areas, they must face reality. When Wall Street's精密 machines start running at full speed, the狂欢 period of easy profits from information asymmetry may be gone forever.

Preguntas relacionadas

QWhat is the reported base salary that DRW is offering to traders for monitoring and trading on prediction market platforms?

A$200,000

QWhich specific prediction market platforms are mentioned in the article as being targeted by the new trading teams from firms like DRW?

APolymarket and Kalshi

QAccording to Boaz Weinstein, how can hedge fund managers use prediction markets to hedge their investments more precisely?

AThey can use prediction markets to hedge against the probability of specific events occurring, such as by creating trades that pair a position on a prediction market with an opposite position in a traditional market like credit.

QWhat advantage does the article state that market makers like Susquehanna have on platforms such as Kalshi?

AThey receive benefits such as lower fees, special trading limits, and more convenient trading channels, the specific terms of which are not publicly disclosed.

QWhat does the article suggest is the likely outcome for retail traders as large institutional players enter the prediction market space?

AThe era of easy profits from information asymmetry is likely over for retail traders, as institutions will quickly eliminate pricing errors and arbitrage opportunities, though some opportunity may remain in long-cycle predictions or niche areas.

Lecturas Relacionadas

Dan Koe: The Counterintuitive Truth—You Don't Need to Remember Everything You Read

Dan Koe: The Counterintuitive Truth — You Don't Need to Remember Everything You Read The central idea is that deliberately trying to remember information is often misguided. True learning isn't about memorizing facts but about having important knowledge surface naturally when needed through use. Most forgetting is normal, not a failure. The article reframes learning using a control theory framework—a four-step feedback loop: having a clear Goal, accurately Sensing your current state, Comparing the gap, and Acting to close it. Most learning stalls because people only do step 2 (blind input) without a goal to create the necessary "error signal" for focused action. The most effective method is to start with output, not input. Begin a meaningful personal project first, and learn only what's necessary to complete it. This project-driven, "just-in-time" learning ensures knowledge is contextual and retained. The concept of a "Second Brain" often fails because it becomes a digital graveyard—over-collected and under-utilized. The goal should be building a "Second Subconscious"—a dynamic system that proactively surfaces relevant ideas during creation, not a static storage vault. Tools like Obsidian+Claude or Eden can help by automating organization and enabling semantic search, but their value depends on linking knowledge to active projects. Ultimately, what matters is not what you store, but what you filter and internalize. Focus on ideas that shape your worldview, use projects as filters, and transform collected material through writing and sharing. AI should be used to reduce friction in research and editing, not to formulate your core views. In conclusion, remembering is a byproduct, not the goal. Knowledge that sticks comes from pursuing personal goals, applying it in real projects, and digesting it through creation. The tools are merely aids; the crucial step is to start doing meaningful work and let the necessary knowledge find you.

marsbitHace 48 min(s)

Dan Koe: The Counterintuitive Truth—You Don't Need to Remember Everything You Read

marsbitHace 48 min(s)

A New Era: The Fundamental Transformation of China's Entrepreneurs

A profound generational shift is underway among Chinese entrepreneurs. The wealth and influence once dominated by real estate and internet giants is now being claimed by a new wave of founders driving breakthroughs in AI, semiconductors, and robotics. This change is vividly reflected in 2026's wealth rankings. Figures like Zhang Yiming (ByteDance), Liang Wenfeng (DeepSeek), Chen Tianshi (Cambricon), and Wang Xingxing (Unitree Robotics) are ascending. Their wealth stems not from traditional business models but from market expectations for future technological competitiveness, with AI, chips, and smart hardware becoming the primary engines of wealth creation. Their common trait is a foundational focus on technology, often starting from the laboratory rather than a business plan. Examples include Chen Tianshi's decade-long push in AI chips, Liang Wenfeng's core algorithmic innovations at DeepSeek with a compact team, and Zhu Yiming's "no salary until profitable" 9-year journey to build Changxin Memory into a global DRAM player. This transition marks a fundamental shift in China's economic imperative: from commercial expansion and learning to indigenous innovation and deep industrial capability. While the previous generation built the foundational market and infrastructure, this new cohort is tasked with achieving global leadership in core technologies, moving China from "keeping pace" to pioneering original, breakthrough innovations that are industrialized at scale. The baton is being passed to those competing on the world stage through technological originality.

marsbitHace 48 min(s)

A New Era: The Fundamental Transformation of China's Entrepreneurs

marsbitHace 48 min(s)

Once-Popular Web3 Enters Wave of Layoffs

The once-hot Web3 industry is experiencing a severe wave of layoffs. While many companies attribute job cuts to AI-driven restructuring, the primary reason is often financial pressure. The Web3 sector, at the intersection of tech and finance, has been hit particularly hard. Employees at major cryptocurrency exchanges report sudden, impersonal layoffs—often with system access revoked overnight—and minimal or no severance. Common tactics include setting impossible performance targets or terminating employees for minor policy violations. The working atmosphere has become toxic, marked by intense monitoring, excessive meetings, and management obsessed with control and internal politics rather than product innovation. The industry's core business model is collapsing. Exchange revenue from trading fees and listing charges has plummeted due to a decline in quality projects and retail investor exodus. Events like the massive forced liquidation on October 10th further shattered confidence. Competition from on-chain derivatives platforms and prediction markets is intensifying the downturn. As layoffs continue, displaced workers struggle to find new opportunities. Many transition to the AI sector, but face significant bias from traditional finance and even some AI firms, which view crypto industry experience with suspicion. The current downturn appears more structural than cyclical, driven by unsustainable practices, internal strife, and a failure to innovate, raising questions about the industry's future trajectory.

marsbitHace 1 hora(s)

Once-Popular Web3 Enters Wave of Layoffs

marsbitHace 1 hora(s)

Trading

Spot
活动图片