Price Prediction Markets primary purpose is to monitor and report problems. While they excel at detecting risks, their reporting mechanism relies solely on price signals rather than providing full disclosures of the issues they uncover, as whistleblowers do.

In the short term, bettors may choose to release reports—similar to short sellers—to inform the market and align prices with their bets. However, for medium- and long-term predictions, detailed reporting is less likely. Revealing too much information too soon could cause immediate price shifts, potentially invalidating a bettor’s future position.

To address this limitation and provide additional benefits, an add-on feature could be introduced: an Information Market—a platform where users can buy and sell reports containing information such as raw data, models, analysis, graphs, scenarios and recommendations.

In an Information Market, high-skill bettors and analysts conduct in-depth research on assets and sell their insights to other traders who may lack the time or expertise to analyze data themselves.

While independent market analysis services already exist, integrating an Information Marketplace directly into Price Prediction Markets offers unique and powerful synergies.

To foster trust and encourage high-quality research, the platform could implement key reputation features for report sellers, helping buyers assess credibility before making a purchase. These features could include:

• Verified Identity – Sellers can choose to verify their identity, increasing trust and transparency.
• Biography – Sellers may provide background details such as expertise, experience, and areas of focus.
• Buyer Ratings – Traditional star ratings and reviews from previous buyers help signal reliability and quality.
• Betting Performance – An aggregate accuracy score based on the seller’s past bets demonstrates their track record and competence in market predictions.
• Current Bet Disclosures – Sellers could disclose select details of their active bets, proving they personally believe in their own reports and reducing conflicts of interest.
• Collateral – Sellers can lock up a portion of their own funds before publishing a report. If their reports are flagged as misleading or low quality, they forfeit the locked funds, discouraging bad actors.
• Gamification – Introduce rankings, badges, and rewards to incentivize high-quality reports and recognize top analysts.

The platform could offer multiple ways for users to purchase reports, allowing for flexibility in pricing, access, and incentive alignment. Potential models include:

• Direct Purchase – Users buy the report at a fixed price, gaining immediate access to its contents.
• Dynamic Pricing – Report prices automatically adjust based on different factors:
  • Over time – Buying earlier is more expensive, incentivizing early access.
  • In relation to an asset or its prediction price – If the report appears to influence the market, or if markets begin moving in the predicted direction due to external factors, the report price increases dynamically. This ensures that early buyers gain an advantage while later buyers pay a premium for valuable insights that are proving accurate.
  • In response to demand – Prices fluctuate based on the number of users interested in the report.
• Exclusive Early Access – A limited number of reports are sold early at a higher price, allowing early buyers to allocate their bets. Later, the report is made available to everyone at a lower price.
• Auction – A limited number of reports are sold via auction, raising prices and restricting access, preserving a competitive edge for buyers.
• Profit-Sharing Model – Reports are free to access, but the seller takes a percentage of the buyer’s winnings if the bet proves successful. This aligns incentives, ensuring analysts profit only if their insights are accurate.
• Consultation – Buyers can pay for additional time with the seller to ask questions, request clarifications, or gain deeper insights beyond the written report.
• Subscription Model – Instead of one-time purchases, users can subscribe to trusted analysts for regular updates. Subscription tiers could offer different levels of access, including more detailed reports or early releases.
• Commission-Based Reports – Traders can commission reports from analysts they trust, ensuring tailored research specific to their needs.
• Crowd-Funded Reports – Groups of traders pool funds to commission a report, which is later made publicly available.
• Sponsored Reports – Organizations can commission reports, potentially contributing their own data to the analysis. They may later resell or publicly release the report to influence market perception.
  • A company could sponsor a report to demonstrate confidence in its stock.
  • A competitor might commission research to highlight weaknesses in a rival’s business model.

Pros of Information Markets

• Income for Analysts – Provides independent analysts, traders, data scientists, and researchers with a new way to monetize their expertise, potentially creating a decentralized alternative to traditional financial analysts.
• Analysis Over Speculation – Encourages deeper research and data-driven insights, shifting the focus away from purely speculative trading.
• Market Efficiency – Better-informed traders contribute to more accurate market signals, enhancing overall price discovery.
• Incentivizes Long-Term Predictions – With in-depth reports and better incentives for analysis, long-term bets become more viable, improving market signals and unlocking the full benefits of Price Prediction Markets.
• Levels the Playing Field – Reduces the advantage of institutional players over retail traders by making high-quality research more accessible to the broader market.
• Attracts Institutional Investors – Professional research firms (such as Bloomberg, Morningstar, or independent hedge funds) could sell premium research within prediction markets, drawing high-net-worth bettors and institutional participants into the ecosystem.
• Legal and Regulatory Access – Information is stored in a centralized and accessible manner, making it available to regulators or law enforcement when investigating illegal activities.

Cons of Information Markets

• Manipulation – Sellers could manipulate the market by selling reports that falsely claim an asset's price will rise while secretly placing larger bets against it using another account or a different market. The increasing difficulty of verifying investor identities in the age of computers, the internet, and cryptocurrencies makes it challenging to prevent such deceptive practices.
• Leaks – A buyer may pay for a report, but if it gets leaked or freely shared, its value decreases, as more people can place similar bets based on the insights. However, this issue already exists with financial reports produced by institutions, and it has not deterred them from continuing this line of business. Additionally, buyers have a personal incentive to keep purchased reports private, as leaking them would reduce their own competitive advantage in the market.
• Legal & Regulatory Backlash – Governments might classify paid research as investment advice, subjecting it to strict financial regulations. If insider trading is involved, legal risks could escalate.

Currently, startups and small-to-medium enterprises (SMEs) are excluded from public markets due to the risks they pose to investors and the broader financial system. These risks include low trading volumes leading to price volatility, rapid shifts in financial and market conditions, and limited transparency in operational and financial reporting.

However, these restrictions unintentionally create additional barriers for startups. By being unable to access public markets, they struggle to establish credibility with suppliers, distributors, banks, and other key stakeholders. This makes it unnecessarily difficult for promising businesses to secure funding, form partnerships, and scale effectively.

If Price Prediction Markets and other financial innovations can mitigate these risks—for instance, by improving price discovery, enhancing transparency, and reducing volatility—then it may become viable to introduce alternative public markets tailored for startups and SMEs.

Such a shift could be transformative, unlocking greater innovation, job creation, and economic dynamism by allowing smaller businesses to access liquidity and market-based credibility without the full regulatory burden of traditional stock exchanges.

Other innovations can provide further assistance:

Narrow Trading Windows

Education as a Prerequisite

Indexes and ETFs

One way to decrease investment risk in startups and SMEs is by creating index funds and Exchange Traded Funds (ETFs) that hold diversified baskets of stocks in these companies. This would allow investors to gain exposure to high-growth businesses while mitigating the risk associated with investing in a single company.

Price Prediction Markets could further support this model by predicting the prices of these indexes and ETFs, further decreasing risk.

This approach could synergize with the Investor Maturity Score. For example:

• Investors with a low maturity score might not be allowed to invest in individual startups but could still invest in a diversified index or ETF.
• As their maturity score improves, they could gain access to higher-risk individual investments.

Traditional ETFs follow passive management strategies, which keep fees relatively low. However, startup-focused ETFs may require more active oversight of invested companies, creating risks of mismanagement or conflicts of interest.

One solution is to promote performance-based ETF fee structures, where fund managers are compensated only if they outperform a benchmark. Potential models include:

• Benchmark-linked fees – Management fees apply only if the fund outperforms a specific index.
• Profit-sharing structures – Fund managers receive a percentage of profits exceeding a set benchmark.

This pay-for-performance model better aligns the interests of fund managers with those of less experienced investors, ensuring that investors pay fees only when they see real returns. This model has already shown some success in improving fund performance and fairness.

Venture Capitalists (VCs) are well-positioned to create ETFs focused on startups and SMEs, leveraging their expertise in evaluating, funding, and monitoring early-stage companies. Transforming VC funds into publicly available ETFs is the natural next step in their evolution.

Conclusion

Another application of Price Prediction Markets is their potential to increase transparency and accountability among individuals with potential conflicts of interest. Politicians, regulators, corporate executives, financial analysts, media pundits, and other influential figures could be required—or given the option—to publicly and automatically disclose their betting positions in these markets.

This level of transparency would help expose biases, reveal hidden incentives, and build trust in both public figures and financial markets.

Similar mechanisms already exist in traditional finance:

• U.S. STOCK Act – Requires members of Congress and senior government officials to disclose stock trades within 45 days. However, many miss deadlines or file incomplete reports.
• SEC Form 4 Filings – Corporate executives must disclose relevant stock derivative trades to the U.S. Securities and Exchange Commission (SEC) by the end of the second business day following the trade.

To prevent immediate copycat behavior while still ensuring accountability, Price Prediction Market disclosures could be delayed for a predetermined period. This delay would balance transparency with fairness towards these individuals.

Furthermore, an AI-powered tracking system could detect contradictions between public statements and private market positions. This system would flag cases where a public figure advocates for one outcome while secretly betting against it, highlighting potential conflicts of interest, misinformation, or unethical behavior in real-time.

One potential disincentive for placing long-term bets in Price Prediction Markets is the unpredictability of major external shocks. Military conflicts, economic crises, pandemics, and other unforeseen events can drastically alter asset prices, turning a well-reasoned winning bet into a losing one. This uncertainty discourages long-term participation, potentially depriving society of essential long-term market signals.

However, this challenge presents not only an opportunity for a solution, but this solution can also be a powerful tool to unlock new possibilities: Event and Price Hybrid Prediction Markets.

These markets function like standard Price Prediction Markets but include an additional safeguard: if certain pre-defined major events occurs, all bets are voided, and participants receive their money back. For example:

• A long-term prediction market on a stock’s price could be canceled if a major global pandemic (similar to COVID-19) severely disrupts economic activity.
• A market on the future value of a currency might be voided if a country experiences a sudden military or trade war.

Benefits of Event and Price Hybrid Prediction Markets:

• Encourages Long-Term Bets – By mitigating the risk of unpredictable shocks, these markets make long-term participation more appealing.
• Provides Crucial Market Signals – Investors, governments, businesses, and even individuals like students can use these predictions to make informed long-term decisions about where to allocate time and resources.
• Maintains Market Fairness – Bettors are protected from extreme losses caused by unforeseeable crises rather than poor predictions.

While this model offers many benefits, it also introduces certain challenges. The primary issue is the inherent subjectivity of event prediction—determining whether an event has actually occurred can be complex. For example:

• What specific metrics should be used to declare a pandemic as “major” or "global"?
• How do we define an trade war, distinguishing it from routine geopolitical tensions?

One potential partial solution is for Price Prediction Markets to link their event conditions to existing Event Prediction Markets, leveraging their evolving definitions and resolution rules. These markets are continuously refined and adapted, improving accuracy over time.

For example, a Price Prediction Market forecasting a stock’s future price could be linked to:

• A separate Event Prediction Market estimating the likelihood of a major global pandemic.
• Another Event Prediction Market predicting the chances of a large-scale global war.

Benefits of This Approach:

• Clearer Definitions – Event Prediction Markets would serve as independent references, improving objectivity in determining whether an event has occurred, which can be leveraged by any number of Price Prediction Markets.
• Public Visibility – The public could observe how price predictions fluctuate in response to shifting probabilities of major events.
• Enhanced Market Insights – Event Prediction Markets would effectively act as real-time factor analysis tools for Price Prediction Markets, providing greater transparency into the forces influencing asset prices.

By integrating these mechanisms, Price Prediction Markets could offer more reliable long-term forecasting while maintaining clear, publicly verifiable event conditions. This could significantly enhance decision-making across industries, governments, and individuals.

However, this solution would admittedly only partially reduce the uncertainty around the resolution of predictions related to events.

Another downside of Event and Price Hybrid Prediction Markets is that they could weaken the analytical depth of price predictions. Price Prediction Markets purposefully force analysts to consider multiple factors at once, providing a valuable signal to the public. For example: a prediction on the future price of an individual stock must account not only for the company’s performance but also for broader economic conditions, such as the likelihood of a major technological shift or a recession. This is crucial because it contains a valuable price sigsnal, for example if long-term predictions for most stocks across various sectors show pessimism, it shows a recession is likely and society can prepare accordingly.

If hybrid markets introduce too many event-based exceptions, bettors could effectively gain a “get out of jail free” card by listing multiple external factors that nullify their predictions. This could dilute the depth of analysis in price forecasts, making them less useful as long-term signals.

The challenge will be determining which events justify market cancellation while preserving the predictive integrity of Price Prediction Markets. Some events are foreseeable and should be factored into bets, while others are entirely unpredictable and should qualify for exclusion.

• Clearly Predictable Events – Events that bettors could and should have accounted for, such as economic crises or recessions, should not be grounds for market cancellation. These are fundamental uncertainties that long-term predictions must consider.
• Clearly Unpredictable Events – Extremely rare, unforeseen occurrences, such as first contact with extraterrestrial intelligent and hostile life or an undetected massive meteor impact, would render financial forecasting irrelevant and could justifiably nullify predictions.
• Gray Area Events – Major global pandemics or wars fall somewhere in between. Early warning signs are often present, with specialists sounding the alarm, yet these signals are frequently ignored. While their exact timing is uncertain, their likelihood can be estimated based on historical patterns, risk assessments, and geopolitical trends. Including these events in predictions would allow the public to prepare, mitigate negative consequences and potentially even avert disasters.

Striking the right balance will be essential. Overuse of event-based cancellations could weaken market incentives, while underuse might discourage long-term participation. The goal should be to exclude only events so extreme and unforeseeable that they would render the prediction market ineffective while still ensuring that crucial long-term signals remain intact.

One potential solution is to introduce a partial refund mechanism for gray area events. For example, a major global war would not completely void a Price Prediction Market, but it could trigger a rule where 50% of the bet values are refunded to participants, while the market continues as planned. This approach ensures that long-term predictions remain valid while providing some protection against highly disruptive events.

It is likely that finding this balance will be the subject of extensive studies, discussions, and experiments. My recommendation is to go beyond just the fairness aspect and also consider the desirability of including certain events in price signals. The key questions should be:

• Do we want price signals to reflect these types of events?
• Would including these events make the predictions more useful to society?
• Are these events realistically predictable by bettors?

By addressing these considerations, Price Prediction Markets can be designed in a way that maximizes both fairness and their role as valuable forecasting tools.

Predicting Consequences of Decisions

Futarchy

Price Prediction Markets could be sponsored, meaning an entity contributes funds to the market without placing a bet, effectively seeding and increasing the future winners' pool. Even small sponsorships could act as catalysts, generating initial interest and triggering greater participation over time.

Various entities could benefit from sponsoring these markets:

• Governments – Sponsoring prediction markets could enhance transparency, strengthen market accountability, and improve decision-making. For example, the SEC could sponsor Price Prediction Markets for all S&P 500 companies, forecasting the Mean Price for each over a five-year horizon. This would incentivize participants to monitor and report potential fraud or mismanagement, reinforcing market integrity.
• Policymakers – Governments could sponsor hybrid Event and Price Prediction Markets to evaluate the impact of proposed policies. These markets could forecast how different legislative initiatives might affect industries, employment, exchange rates, or economic growth, providing valuable guidance on the best path forward.
• Companies – Businesses could sponsor markets in multiple ways:
  • Sponsoring markets on their own stock to demonstrate confidence and signal transparency.
  • Sponsoring markets on sector-wide indexes or ETFs to encourage analysis of their industry’s long-term viability.
  • Sponsoring markets on competitor stocks to stimulate analysis that could reveal vulnerabilities, ultimately benefiting themselves.

In some cases, the same market could have multiple sponsors with differing motives. For example, a company might sponsor a market for its own stock to reinforce investor trust, while a competitor sponsors the same market knowing that deeper scrutiny will expose weaknesses. Meanwhile, a government agency could also contribute, encouraging public oversight of a major industry player.

Hybrid markets could also be sponsored to assess corporate decisions or analyze how government policies might impact foreign exchange rates. By facilitating sponsorship, Price Prediction Markets could increase their contribution to transparency, strategic forecasting, and market accountability.

A common problem worldwide is that homebuyers, particularly those purchasing or new houses, often end up with properties riddled with hidden defects. Construction companies frequently cut corners to save costs, leading to severe issues that only become apparent years later. Problems such as water infiltration, unstable foundations, and fire-prone cladding are just a few examples. When these defects arise, residents are left with unexpected costs, sometimes running into millions.

Some governments enforce warranty periods, requiring construction companies to fix defects for a set number of years. However, this has limited success, as major repair costs can drive companies into bankruptcy. In some cases, former owners create new construction companies under different names, escaping liability. Either way, homeowners bear the burden.

Price Prediction Markets could be part of the solution by allowing participants to bet on the future sale value of homes. These markets would help signal the likelihood of construction defects, as well as other important factors such as:

• Structural Integrity and Quality – Homes with a higher likelihood of hidden defects, such as poor construction or substandard materials, would likely have lower predicted future prices.
• Location Changes – Markets could indicate whether a neighborhood is improving or declining, signaling trends such as rising crime rates or upcoming infrastructure projects like new subway stations.
• Environmental Risks – Homes in flood-prone areas or regions at risk of extreme weather events may show lower long-term sale values.
• Child-Friendly Design – Features like gated staircases, soft flooring, and enclosed yards may increase demand from families with young children.
• Energy Efficiency – As energy costs rise, homes with solar panels, battery storage, and high-efficiency insulation could attract higher predicted prices.
• Safety – Modern fire escapes, sprinkler systems, and the shift away from gas stoves toward induction cooking may influence future home desirability.
• Remote Work Adaptability – Dedicated office spaces, soundproofing, and high-speed internet connectivity could increase long-term value as remote work remains popular.
• Smart Home Features – Automated security systems, smart thermostats, and app-controlled lighting may impact home prices.
• Walkability & Transport Access – Pedestrian-friendly locations, proximity to bike lanes, and access to new public transit developments may drive higher demand.

These prediction markets would encourage construction companies to allow independent third-party inspections if they are confident in the quality of their work and later bet on future property values, making it easier for potential buyers to assess risks.

While the benefits are clear, real estate-focused Price Prediction Markets face at least two major challenges:

• Low Liquidity – Unlike financial markets, real estate transactions happen infrequently, sometimes with months or years between sales. This could be mitigated by setting prediction markets to operate over longer time intervals, such as the Mean Price of all apartment sales in a given complex over an entire year.
• Lack of Fungibility – Unlike stocks or commodities, no two apartments are exactly the same. Differences in size, layout, sunlight exposure, noise levels, and views all impact valuation. Standardizing varied properties into a simplified and comparable metric would be the greatest challenge for real estate Price Prediction Markets.

While a complete solution has yet to be developed, these initial ideas could serve as a starting point for discussion and experimentation. If their challenges is solved, Real Estate Price Prediction Markets could provide homebuyers with a powerful tool for making informed decisions and holding developers accountable.