InstitutionalProduct-StrategyPrediction-Markets

How Goldman’s Interest in Prediction Markets Could Spawn New Fee-Based Products

UUnknown

2026-02-17

11 min read

Goldman's exploration of prediction markets could spawn structured products, index licensing and advisory services—here's how fees and revenue models may form.

Hook: Why investors and product teams should care now

Traders, allocators and product heads are drowning in data but starving for accurate, tradeable signals. If you manage institutional capital or build sell-side products, the promise of prediction markets is simple: probability-driven prices that encode collective expectations about events — from Fed moves to election outcomes — in real time. That’s why Goldman Sachs’ public exploration of prediction markets in January 2026 matters. It signals a potential shift from experimental crypto-native platforms to bank-grade, fee-bearing products that institutional clients can buy, license and hedge.

David Solomon described prediction markets as “super interesting” on Goldman’s Jan. 15, 2026 earnings call, saying the firm has met with leaders of established platforms to evaluate opportunity.

Executive summary — the inverted pyramid

Goldman can productize prediction markets across multiple business lines, with each product carrying distinct revenue models and fee structures. The most commercially viable paths are:

Structured products (binary/bounded payoffs embedded in notes and swaps)
Index licensing (probability indices and indices of event baskets)
Advisory and research (subscription intelligence, bespoke hedges, scenario pricing)
Market-making and liquidity services (spread capture and principal risk-taking)
Data, custody, and tokenization (enterprise feeds, custody fees, issuance)
Exchange/venue services (transaction fees, listing, clearing)

Below we examine each model, sample fee structures, institutional use cases, go-to-market considerations in 2026, and practical steps for investors and product teams.

Why prediction markets are commercially attractive in 2026

Market and regulatory context — what changed in late 2025 and early 2026

By the end of 2025, prediction markets moved from niche crypto playgrounds toward platforms with institutional plumbing. Several trends matter:

Growing institutional curiosity: hedge funds and macro desks increasingly back-tested prediction-market prices as high-frequency leading indicators for macro and event risk.
Regulatory attention and incremental clarity: regulators in key jurisdictions have increased scrutiny while some frameworks for derivatives-like event contracts and data licensing emerged, prompting banks to consider compliant on- and off-chain implementations.
Data quality and aggregation: aggregator services now normalise cross-platform probabilities into coherent indices, making them easier to productize.
Client demand for hedges that map to discrete outcomes (e.g., policy decisions, litigation outcomes, commodity disruptions) has grown amid volatile macro conditions in 2025–26.

That mix gives a leading investment bank like Goldman an opening to offer institutional-grade, fee-bearing products without relying solely on crypto-native distribution.

Business model 1: Structured products built on prediction market curves

What this looks like

Goldman can create structured notes and swaps with payoffs tied to prediction market probabilities. Examples include:

Binary-linked notes: pay $1 if an event (e.g., “rate hike by X date”) occurs; otherwise $0 — priced using live prediction market odds.
Range or digital-capped notes: pay scaled returns if probability stays within a band at maturity.
Volatility-linked products: monetize the implied dispersion across event markets.

Distribution and clients

Primary buyers: macro hedge funds, corporate treasuries seeking explicit event hedges, wealth clients wanting thematic exposure (political risk, ESG event outcomes), and proprietary desks seeking arbitrage. Distribution channels include private placement, structured product platforms, and ETFs/ETNs that embed event exposure.

Fee design & revenue levers

Structured products generate revenues from several sources:

Upfront structuring fees: typically 0.25%–1.5% of notional, depending on complexity and distribution scale.
Embedded hedging spreads: banks price in hedging costs and capture expected carry through spreads versus fair-market probabilities (10–50 bps effective margin annually, variable by liquidity).
Ongoing servicing/administrative fees: 5–50 bps annually on marketed wrappers (notes, ETFs).
Performance fees: for outcome-based wrappers with active management, 10%–20% carry above a hurdle can be structured.

Example model (simplified): a $500m issuance of binary-linked notes with a 0.75% upfront structuring fee + 20 bps annual servicing yields ~$3.75m upfront + $1m/year — scalable across multiple issuances.

Business model 2: Index licensing — standardising probabilities into sellable indices

Product concept

Goldman can construct and maintain prediction-market indices — single-event indices (probability of X) and multi-event baskets (weighted expectations across events). These indices would be licensed to asset managers, exchanges, and structured-product desks as benchmark references.

How licensing fees typically work

Index licensors use two primary fee levers:

Fixed annual licensing: flat fees for enterprise clients or white-label access, commonly $50k–$500k for bespoke enterprise agreements.
BPS-based usage fees: basis-point fees on AUM tied to the index or on notional of products referencing it — typically 1–10 bps for well-established, value-adding indices.

Why institutions would pay

Indices solve two problems: standardisation and brand trust. Institutional buy-side clients prefer an index run by a trusted provider instead of stitching together raw platform feeds. For asset managers, even a few basis points on large AUMs can justify licensing. For example, an index licensed at 3 bps to a $5bn overlay would generate $1.5m/year for Goldman.

Business model 3: Advisory, research and bespoke hedging

Service offerings

Subscription research: daily/weekly probability-adjusted intelligence feeds and scenario desks for macro desks (retainer + per-seat pricing).
Bespoke hedging solutions: tailored derivatives that map to client-specific event exposures (retainers + transaction fees + performance-linked fees).
Scenario pricing and stress testing: integrate prediction-market probabilities into risk models used by pensions and insurers.

Monetisation

Advisory is margin-rich: retainers ($100k–$1m+ annually for enterprise advisory), per-project fees, and performance fees for successful hedges (10–20% of gains relative to agreed benchmarks). Research subscriptions (per-seat) can be charged $5k–$50k/year depending on depth and client type.

Business model 4: Liquidity provision and market-making

Role and revenue

Goldman can act as a principal liquidity provider to new institutional venues or its own platform. Revenues come from:

Spread capture: quoted bid-ask spreads on event contracts; effective yield depends on turnover and inventory risk.
Rebates or fees negotiated with operators in exchange for committed liquidity.
Prop trading profits from exploiting temporary mispricings or arbitrage with related instruments.

Operational considerations

Market-making in event markets requires sophisticated risk systems (to model tail risk across correlated events) and capital allocation. Pricing the risk of rare events is non-linear — market-makers will charge higher spreads for low-liquidity, binary outcomes, which materially contributes to revenue if volume is adequate.

Business model 5: Custody, tokenization and clearing services

Why custody matters

Institutional clients demand custody and operational controls. If prediction markets adopt on-chain settlement or tokenized contracts, banks can offer:

Custody and operational controls including qualified custody for tokens and on-chain claim rights (charged as % AUM or fixed fee).
Token issuance and white-labeling services (issuance fees + ongoing support fees).
Clearing and settlement guarantees for off-chain or hybrid implements (clearing fees per trade/notional).

Fee benchmarks

Custody fees typically range from 5–25 bps annually depending on assets under custody and service scope; one-off issuance fees often run from $50k to $500k depending on complexity.

Business model 6: Data & analytics platform

Products

Sell enterprise-grade real-time feeds, historical probability datasets, and analytics APIs. Clients include quant funds, lenders pricing event-driven credit risk, and policy researchers.

Monetisation

Per-API call pricing and enterprise licensing ($100k–$2m+/year).
Tiered feeds (real-time premium vs delayed free tiers).
Bundled analytics and consulting for higher fees.

Business model 7: Exchanges, platforms and clearinghouses

If Goldman builds or partners with an exchange/venue, standard revenue sources apply: transaction fees (10–50 bps per trade or maker-taker models), listing fees for new event contracts, and clearing/settlement fees. Platforms can also sell premium market access and smart order routing as fee lines.

Institutional adoption — who pays and why

Institutional clients will pay for prediction-market products when they solve measurable problems:

Hedge funds and macro desks: pay for alpha-generating signals and hedges (willing to pay higher margins for shorter payback cycles).
Asset managers and insurers: pay for indices and custody that integrate into risk management and product shelf.
Corporate treasuries and corporates: pay for bespoke hedges against policy, litigation or supply shock risks.
Wealth channels: mass-market structured notes built on event outcomes for clients seeking thematic exposure.

Practical revenue modeling — illustrative scenarios

Below are simplified illustrative scenarios for a large bank entering prediction-market productization. These are directional, not prescriptive.

Conservative scenario (soft market adoption)

Structured issuance: $1bn/year, 0.5% upfront fee => $5m
Index licensing: $10bn AUM referencing indices at 2 bps => $2m/year
Advisory & research: enterprise contracts => $3m/year
Data & custody: combined => $2m/year
Total recurring + one-off revenue ~ $12m/year

Moderate scenario (institutional traction)

Structured issuance: $5bn/year, 0.75% upfront => $37.5m
Index licensing: $50bn AUM at 3 bps => $15m/year
Advisory & research: $10m/year
Liquidity provisioning/trading profits: $10–30m/year
Data/custody/platform fees: $10m/year
Total ~ $82.5m–$102.5m/year

Aggressive scenario (market leadership)

Structured issuance: $20bn/year, 0.75% upfront => $150m
Index licensing: $200bn AUM at 3 bps => $60m/year
Advisory, liquidity, data, custody & platform => $100–200m/year
Total potential > $300m/year

These scenarios show how different fee levers scale once institutional adoption and distribution are in place.

Key risks and mitigants

Regulatory risk

Prediction markets can resemble derivatives or gambling in some jurisdictions. Mitigants include: structured off-chain bilaterals, robust KYC/AML, working with regulators to create compliant benchmarks, and limiting retail distribution where necessary.

Liquidity and market integrity

Low liquidity can make prices unreliable. Mitigants: Goldman can seed liquidity, aggregate prices across venues, and create index smoothing methodologies to produce robust reference prices.

Model and basis risk

Prediction market probabilities are sometimes disconnected from tradable exposures. Product design must explicitly account for basis risk with clear disclosures and hedging strategies.

Go-to-market playbook for Goldman (or any major bank)

Start with institutional pilot products — bespoke hedges and indices for top clients to fine-tune pricing and risk controls.
Partner with regulated venues and on-chain aggregators to source liquidity and create hybrid settlement models.
Build a clear legal and compliance wrapper with engagement from relevant regulators early on.
Productise data and indices before full-blown trading platforms — indices are low operationally but high trust value.
Leverage existing distribution channels (wealth, FICC, prime brokerage) to scale issuance and licensing quickly.

Actionable advice for institutional clients and product teams

Evaluate use-case fit: Map potential event exposures in your book (policy, earnings, litigation) and test small hedges using prediction-market-linked structured notes.
Demand transparency: Require index methodology, data sources and execution rules from providers before licensing.
Stress-test basis risk: Back-test how prediction-market prices correlate with realized outcomes and related tradable instruments across different regimes (2022–2026 volatility episodes).
Negotiate hybrid fee structures: Seek lower fixed licensing fees in exchange for revenue share or tiered bps as AUM or volumes grow.
Start with data feeds: Buy historical and real-time feeds to integrate into quant models before taking on counterparty exposure via structured products.

Future predictions to watch (2026–2028)

Expect a multi-year evolution rather than an overnight disruption. Key signals that productization is accelerating:

Major banks launching index families and licensing to ETF sponsors.
Regulators publishing clearer guidance on event contracts and data licensing.
Significant issuances of bank-backed structured notes tied to event probabilities.
Consolidation of liquidity into a few regulated venues with institutional custody options.

If these occur, fee pools could shift from exchange commissions to higher-margin advisory and index licensing revenues.

Conclusion — why this matters for strategy and revenue

Goldman Sachs’ interest in prediction markets is not just a technology story — it’s a potential productization play that spans structured products, index licensing, advisory, market-making, custody, and data. Each line offers distinct fee mechanics and scalability pathways. For clients, prediction-market products promise more direct hedges and informative signals; for banks, they open mid- to high-margin revenue lines if distribution, compliance and liquidity are executed well.

Key takeaways

Prediction-market pricing can be embedded into multiple fee-bearing products — particularly structured notes and licensed indices.
Fee structures vary: upfront structuring fees, bps licensing, performance fees, spreads for market-making, and custody charges are all viable levers.
Institutional adoption hinges on liquidity, regulatory clarity and trusted indices/custody.
Products should be piloted with chief clients to validate pricing, hedging mechanics and demand before scaling.

Call to action

If you manage event risk or build institutional products, now is the time to pilot prediction-market exposure. Contact your product team to request a whitepaper, modelled scenarios for your book, or a bespoke pilot structured note. For product teams: start building index prototypes and regulatory wrappers — and engage top clients for anchor demand. The next two years will separate data providers from true, fee-generating financial products.

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