Students record their reasoning, commit to positions and revise those positions as information and market prices change. Trades, prices, position changes and notes create a time-stamped record for later discussion and reflection.
Bring real-world analysis into a market with price discovery
FTSPM brings real-world events and their effects on financial and economic variables into the classroom. Students research the event, express their views across possible outcomes and trade with one another in a market where their decisions help form the price.
Paper-trading exercises use prices formed elsewhere, so students usually have no market impact and performance is often evaluated mainly through returns or risk measures. In FTSPM, the class forms the prices: every order can reveal information, affect other participants and create a strategic decision.
This makes analysis, uncertainty, interaction and decision-making part of the learning experience—not simply inputs to a final report.
More than a simple yes-or-no
A simple question can often be answered by finding an existing market price or asking an AI tool. FTSPM's pre-specified cases instead use ranges, multiple outcomes and related markets. Students must decide not only what is most likely, but how probability should be distributed across plausible results.
Research the evidenceUse filings, economic releases, market data and other sources to develop an estimate rather than copy an answer.
Express uncertaintyTrade across ranges and outcomes. The resulting prices show the class forecast and how confident the group is.
Evaluate AI criticallyAI can assist with research, but students still have to check its sources, definitions and calculations. They then go beyond AI by applying their own insights through real-time trading decisions. FTS can collect this reasoning before the market event is realized.
Why the Research tab is part of the application
Trading should begin with evidence, not with an order ticket. Research sits beside Market and Notes so students can move deliberately from source material, to a recorded judgment, to a trade. Today it brings curated sources and case guides into that workflow. It also provides a natural place for optional AI-assisted research in the future—without changing the student's responsibility to verify the analysis and make the forecast.
Use AI to analyze the results
FTSPM lets the moderator create a compact or full anonymized, AI-ready session history, review the exact text, and then copy it or save it as a text file for upload to an AI tool selected by the instructor. This makes it practical to analyze the class's reasoning, trading decisions, price discovery and results quickly enough to bring the synthesis back into the classroom discussion.
Review the analysisCompare the evidence and reasoning participants recorded, including how their views changed over time.
Connect decisions to performanceRelate notes, trades, position changes and outcomes without reducing the exercise to a single score.
Return insights to the classUse the synthesis to discuss price discovery, disagreement, information, strategy, overconfidence or herding.
FTSPM does not send session data to an AI service. The moderator reviews the exact text before copying it or saving a file for upload.
What price discovery adds
A forecast made alone is one person's answer. A classroom market turns many independently researched judgments into a visible, evolving assessment—and makes every participant compare a personal estimate with the price created by the group.
1Form an independent viewStudents research the question and estimate the likelihood of each outcome.
2Reveal convictionBids, asks and trades require students to act on differences between their estimate and the current price.
3Update with the groupPrices change as information and judgment enter the market, exposing consensus, disagreement and uncertainty.
4Settle and reflectThe realized outcome and performance provide feedback on both the class forecast and individual decisions.
An AI tool can propose an answer, but it cannot replace this classroom process. Students must evaluate the analysis, decide what they believe, observe what others believe and determine when the market price gives them a reason to trade.
Designed to fit into a course
The initial case library focuses on Accounting, Finance and Economics—areas where forecasts can be researched, traded and later compared with an authoritative result.
Analyze financial statements, earnings releases and consensus estimates, then forecast earnings outcomes and the market's reaction.
Explore the Earnings Reaction case →Study event returns, interest rates and monetary-policy decisions while observing how orders and changing beliefs produce market prices.
Explore the Fed Decision case →Combine labor-market and macroeconomic indicators to estimate releases, express uncertainty across ranges and evaluate the forecast after publication.
Explore the Employment Situation case →Use a case as a focused class exercise, a research assignment or a recurring market activity. Prepared cases provide a starting point; instructors can adapt them or create entirely new questions for other subjects.
Watch how it works
Silent, captioned walkthroughs show an instructor creating a case, two fictitious traders discovering prices through their orders, and the final settlement and performance. Play either here or open it in a larger viewer.
Moderator overview
Participant overview
See FTS Prediction Markets in action
Contact FTS to arrange a live demonstration, discuss your course, or set up access for an evaluation. Email ftsweb@gmail.com.
Before you begin
The moderator starts the classroom session; participants then join that session. Both browser applications connect securely through this website, so no server address, port or IP address is required.
Step 1Moderator signs in with FTS credentialsUse the moderator email and password registered with FTS, then create or load the case.Launch Moderator →
Step 2Participants joinEach person enters the moderator's FTS email, a trading name and any password the moderator assigned.Launch Participant →