Nasdaq Ventures into Prediction Markets: Wall Street Bets on Tech Index with 'Yes or No'

Odaily星球日报Pubblicato 2026-03-03Pubblicato ultima volta 2026-03-03

Introduzione

Nasdaq has filed a proposal with the SEC to launch binary options, or "outcome-related options," on its flagship Nasdaq-100 and Nasdaq-100 Micro indexes. These contracts allow investors to make "yes or no" predictions on whether specific conditions will be met by the index at expiration. Priced between $0.01 and $1.00, the contract value reflects the perceived probability of the outcome—settling at $1 if true and $0 if false. This simplifies trading to a direct bet on event outcomes, rather than price movements. The Nasdaq-100, heavily weighted toward major tech stocks like Apple and Nvidia, is highly sensitive to market sentiment, making it an ideal underlying asset for such products. This move signals traditional exchanges' growing interest in prediction markets. Unlike the Intercontinental Exchange's recent strategic investment in Polymarket, Nasdaq is directly integrating predictive structures into its existing product lineup. If approved, this would mark a significant step in bringing prediction-based trading into the regulated mainstream financial system, blending traditional derivatives with event-driven speculation.

Original | Odaily Planet Daily (@OdailyChina)

Author | Asher (@Asher_ 0210)

Last night, Nasdaq Inc. submitted a rule change proposal to the U.S. Securities and Exchange Commission (SEC), planning to introduce an options contract that allows investors to make "yes or no" judgments on major stock indices.

According to the document, Nasdaq intends to list "binary options," also known as "outcome-related options," on its flagship products—the Nasdaq 100 Index and the Nasdaq 100 Micro Index. If approved, this would mark Nasdaq's first official foray into products with prediction market attributes.

This move signifies that traditional stock exchange giants are actively entering the rapidly growing prediction market sector.

What Are Binary Options?

The proposed contracts have a pricing range of 1 cent to 1 dollar, with the price itself directly reflecting the market's judgment of the probability of a specific outcome.

For example, if a contract is based on whether "the Nasdaq 100 Index meets a certain condition at a specific time point," then:

  • If the market believes the probability of this outcome is 80%, the price might be close to $0.80;
  • If the condition is met at expiration, the contract settles at $1;
  • If the condition is not met, the contract value becomes zero.

If traditional options are about betting on "how much it will rise or fall," binary options are more concerned with "whether it will happen." There are no complex parameters, no interval calculations—only the outcome itself. This all-or-nothing settlement method makes trading more like making a clear judgment about the future.

Because of this, such products are closer in form to the logic of prediction markets.

Why Choose the Nasdaq 100?

Nasdaq's choice is not an ordinary index but one of the most sentiment-sensitive assets. The Nasdaq 100 has long been regarded as a core indicator of the U.S. technology sector, with concentrated holdings in heavyweight companies like Apple, NVIDIA, Microsoft, Amazon, and Meta. These companies almost always become market focal points each quarter. An earnings report, a regulatory update, or even a policy statement can quickly reflect in the index's movement.

The high concentration of components means that the Nasdaq 100's trends often revolve around a single focus. The market might bet on AI expectations for a period, then shift to interest rate paths or policy changes. During earnings or policy-intensive periods, the index typically reflects market judgments in a relatively short time rather than prolonged back-and-forth fluctuations.

Additionally, the Nasdaq 100 itself has a mature derivatives trading foundation, ample liquidity, and a well-established pricing system. Introducing new structured products on this underlying asset is risk-controllable and more likely to gain market acceptance.

Two Ways Traditional Exchanges Are Entering the Market

Nasdaq is not the first traditional exchange to show interest in prediction markets. In October 2025, Intercontinental Exchange, the parent company of the New York Stock Exchange, announced a strategic investment of approximately $2 billion in Polymarket, acquiring about a 20% stake, with the transaction valuation once reaching around $8 billion.

The NYSE's choice was not to launch its own prediction products but to enter the field through capital participation and data cooperation. Its core intention is to obtain real-time probability data formed by prediction markets and incorporate it into institutional service systems. For the NYSE, prediction markets are more like supplementary sentiment indicators and data assets.

In contrast, Nasdaq's approach is more direct. It chooses to embed binary structures into its core index product line, extending within the existing trading framework. Compared to investing in external prediction market platforms, this method means predictive trading is incorporated into the standardized securities product system, rather than being just an external data source.

The difference in strategies reflects the varying judgments of traditional exchanges when facing new trading structures.

Prediction Markets Are Being Integrated into Traditional Exchange Product Systems

Regardless of whether the SEC ultimately approves this proposal, Nasdaq's submission of the rule change application itself sends a clear signal—predictive trading is no longer just an experiment on crypto platforms or niche markets but is beginning to be integrated into traditional exchange product systems.

For a long time, mainstream derivatives have revolved around price fluctuations, with investors judging the magnitude and timing of rises and falls through different structures. Binary options simplify the question to whether the outcome will occur, shifting the trading focus from magnitude to the conclusion itself.

When the Nasdaq 100 Index is incorporated into such contract structures, the trading logic becomes more direct. The market's focus is no longer on the magnitude of fluctuations but on whether a specific outcome will materialize. The price reflects not just volatility but the consensus on the probability of the outcome.

For Nasdaq, this is an extension of its product line. For prediction market, it is the beginning of its structure being formally accepted by the mainstream system. If this product eventually launches, it could become a bridging attempt between traditional derivatives and event-based trading.

Domande pertinenti

QWhat is the new type of option contract that Nasdaq has proposed to the SEC?

ANasdaq has proposed a 'binary option' or 'outcome-related option' contract that allows investors to make 'yes or no' judgments on major stock indices.

QWhich specific indices will the new binary options be based on if approved?

AThe binary options will be based on the Nasdaq 100 Index and the Nasdaq 100 Micro Index.

QHow does the pricing of these binary options reflect market expectations?

AThe price, ranging from 1 cent to 1 dollar, directly reflects the market's judgment of the probability of a specific outcome. For example, a price of $0.80 indicates an 80% probability that the condition will be met.

QWhat is the key difference between traditional options and these new binary options?

ATraditional options focus on 'how much' the price will move, while binary options focus on 'whether' a specific outcome will happen, with a simple all-or-nothing settlement.

QHow does Nasdaq's approach to entering the prediction market differ from that of the New York Stock Exchange's parent company?

ANasdaq is directly integrating binary structures into its core index products, while NYSE's parent, Intercontinental Exchange, made a strategic investment in an external prediction market platform, Polymarket, to gain access to real-time probability data.

Letture associate

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit46 min fa

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit46 min fa

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit50 min fa

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit50 min fa

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit50 min fa

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit50 min fa

Trading

Spot
活动图片