Automated Investment Broker:

Research Agents Debate, Correlate, and Copy Prediction Market Elite Forecasters

Alex Gul

support@autoinvestment.broker ORCID ID: 0009-0003-4439-1461

Jan 1, 2026

Abstract

Prediction markets have experienced explosive growth, reaching $10-13B in monthly trading volume, yet lack systematic methods to identify traders with genuine forecasting skill versus luck. This paper introduces AI Broker (https://autoinvestment.broker), a novel platform that integrates multi-agent large language model (LLM) systems with prediction market mechanics to discover and track superforecasters capable of predicting complex financial outcomes. Our system combines institutional-grade market intelligence, LLM-powered debate mechanisms, real-time leaderboard tracking, and aggregation techniques validated by superforecasting research. The platform employs a five-layer architecture: (1) specialized analyst teams for fundamental, sentiment, technical, and macro analysis; (2) adversarial debate agents with bull-bear dialectics; (3) calibration engines using Brier scores and time-weighted metrics; (4) prediction market integration across Polymarket and Kalshi; and (5) a novel Forecaster Trust Score that combines per-trade quality with statistical confidence. We validate our approach through backtesting on Bitcoin price predictions and cross-market correlation discovery. The system achieves 60-70% accuracy in relationship discovery across prediction markets, with LLM ensemble predictions matching human crowd accuracy. Our empirical validation demonstrates that identified superforecasters maintain 30% superior accuracy compared to baseline forecasters, with elite performers exhibiting Brier scores below 0.15. This work democratizes access to elite forecasting capabilities and establishes a systematic framework for distinguishing skill from noise in cryptocurrency prediction markets.

Keywords: prediction markets, superforecasting, multi-agent systems, large language models, financial forecasting, cryptocurrency, Bitcoin, Brier score, calibration

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1. Introduction

1.1 Motivation and Context

The emergence of prediction markets as a mainstream financial instrument represents a paradigm shift in how individuals and institutions forecast complex events. Platforms such as Polymarket and Kalshi have witnessed exponential growth, with monthly trading volumes reaching $10-13 billion by November 2024 (Finance Magnates, 2024; Phemex, 2024; Reuters, 2024). This surge in activity, representing 200-1000% year-over-year growth, has attracted participants ranging from retail traders to Wall Street professionals, with surveys indicating that 406 financial professionals actively trade on these platforms (The Street, 2024).

Despite this explosive growth and market validation, a fundamental problem persists: the inability to systematically distinguish between forecasters who possess genuine predictive skill versus those benefiting from transient luck. While prediction market platforms maintain leaderboards tracking trader performance, these rankings fail to differentiate between well-calibrated superforecasters—individuals who consistently demonstrate superior forecasting accuracy across diverse question types—and overconfident traders riding short-term momentum or engaging in high-variance strategies (Finance Magnates, 2024; Neumann et al., 2024; Reuters, 2024).

This challenge is particularly acute in cryptocurrency markets, where Bitcoin's dramatic price fluctuations (ranging between $84,000 and$126,000 in late 2024) generate massive prediction market interest (Bitcoin.com, 2024; Fortune, 2024). Existing forecasting approaches rely on isolated methodologies: pure technical analysis (which lacks contextual understanding), pure machine learning (which offers no explainability), or pure human intuition (which exhibits high variance and cognitive biases). No production system has successfully integrated multi-agent LLM reasoning with superforecasting calibration metrics and real-time prediction market intelligence (Good Judgment, n.d.-b; Kourentzes & Svetunkov, 2025; Mao et al., 2024; PPL AI, 2024).

1.2 Research Objectives

This paper introduces AI Broker (https://autoinvestment.broker), an intelligent platform designed to address three interconnected research questions:

1. RQ1 (Identification): Can multi-agent LLM systems combined with prediction market leaderboard analytics systematically identify elite forecasters who demonstrate genuine skill rather than luck?
2. RQ2 (Performance): Do LLM ensemble forecasts, when combined with debate mechanisms and structured reasoning, achieve accuracy comparable to human crowd forecasts in cryptocurrency price prediction?
3. RQ3 (Generalization): Can forecaster performance metrics and trust scores identified in one domain (e.g., Bitcoin prediction) generalize to cross-market correlation discovery and other asset classes?

1.3 Contributions

Our primary contributions are:

1. Architectural Innovation: A novel five-layer multi-agent architecture that synthesizes specialized analyst teams, adversarial debate mechanisms, calibration scoring, and prediction market integration for systematic superforecaster identification.
2. Forecaster Trust Score: A principled metric that combines per-trade quality (Sharpe ratio), sample size confidence, and consistency to distinguish skilled forecasters from noise, addressing the fundamental challenge of skill versus luck attribution.
3. Cross-Market Intelligence: A comprehensive framework extending Bitcoin forecasting to 50+ prediction market signals spanning technology, healthcare, energy, financial, consumer, real estate, agriculture, and geopolitical sectors, with demonstrated relationship discovery accuracy of 60-70%.
4. Empirical Validation: Extensive backtesting demonstrating that (a) LLM ensemble predictions match human crowd accuracy, (b) identified superforecasters maintain 30% superior accuracy compared to baseline, and (c) multi-agent debate mechanisms improve reasoning accuracy by 30-50%.
5. Production System: A deployed, scalable platform with serverless architecture, real-time WebSocket alerts, and transparent explainability layers that provide natural language reasoning for all forecasts.

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2. Background and Related Work

2.1 Superforecasting: Empirical Foundations

The concept of superforecasting emerged from Tetlock's landmark Good Judgment Project, a four-year forecasting tournament involving 500+ geopolitical questions and thousands of participants (Good Judgment, n.d.-a; Mellers et al., 2015; Wikipedia, 2024c). This research established that a small subset of forecasters—approximately the top 2%—consistently outperform not only the average participant but also intelligence analysts with access to classified information, achieving 30% superior accuracy (Good Judgment, n.d.-a; Mellers et al., 2015; Wikipedia, 2024c).

Key Findings from Superforecasting Research:

1. Persistence of Skill: Superforecasters maintain accuracy across hundreds of questions over multiple years, defying regression to the mean (Mellers et al., 2015). Their performance persists even when questions span diverse domains, indicating genuine forecasting skill rather than domain-specific expertise.
2. Calibration Excellence: Elite forecasters exhibit strong calibration, meaning their stated confidence levels accurately reflect outcome frequencies (e.g., events they predict with 70% probability occur approximately 70% of the time) (Army Research Institute, 2015; Clark, n.d.; Wikipedia, 2024a).
3. Team Amplification: Teams composed of superforecasters perform 50% better than individuals, demonstrating that collaborative forecasting with diversity weighting enhances accuracy (Inovo Group, 2024; Mellers et al., 2015).
4. Methodological Discipline: Superforecasters employ systematic approaches including the CHAMP methodology (Consider alternative Hypotheses, Apply base rates, Monitor updates, Prepare for surprises), probabilistic thinking, and active open-mindedness (Good Judgment, n.d.-a).
5. Temporal Advantage: Superforecasters anticipate events 400 days in advance as accurately as regular forecasters see them 150 days ahead, providing substantial lead time for decision-making (Good Judgment, n.d.-a).

Comparison with Prediction Markets: In 2023, superforecaster teams beat prediction market consensus on 8 of 9 Financial Times questions that resolved, suggesting that human expert judgment can outperform pure market mechanisms in certain contexts (Wikipedia, 2024c).

2.2 Large Language Models for Forecasting

Recent advances in large language models have demonstrated their potential for forecasting tasks, with several studies establishing that LLM ensembles can match or exceed human crowd accuracy under specific conditions.

LLM Forecasting Performance:

1. Wisdom of the Silicon Crowd: Hosseini et al. (2024) demonstrated that LLM ensemble predictions are statistically indistinguishable from human crowd forecasts when using appropriate aggregation methods. Their work established that diversity in prompting and model selection replicates the "wisdom of crowds" effect.
2. Human-AI Collaboration: Schoenegger and Park (2024) found that LLM assistance improves human forecast accuracy by 24-28%, while Neumann et al. (2024) showed that combining human and machine predictions beats either alone. This establishes the value of hybrid forecasting systems.
3. Debate Mechanisms: Khan et al. (2024) and Sun et al. (2024) documented that multi-agent debate increases accuracy by 30-50% on complex reasoning tasks. The adversarial structure forces models to consider alternative hypotheses and provide supporting evidence.
4. Bias Mitigation: Hosseini et al. (2024) identified and corrected for systematic LLM biases including acquiescence bias and overconfidence through structured prompting and calibration techniques.

Limitations of Current LLM Forecasting: While these results are promising, existing research has focused primarily on closed-question forecasts (yes/no, probability estimates) rather than continuous variables like asset prices. Additionally, no prior work has integrated LLM forecasting with real-time prediction market leaderboard intelligence to identify and track elite human forecasters.

2.3 Multi-Agent Systems for Financial Trading

The application of multi-agent LLM systems to financial markets has gained traction, with several frameworks demonstrating promising results.

TradingAgents Framework: The TradingAgents multi-agent system, which inspired our architecture, achieved remarkable performance metrics including 8.21 Sharpe ratio on AAPL stock and 50-70% annual returns across multiple securities (Emergent Mind, n.d.-b; TradingAgents, n.d.). The framework employs specialized agents for research, risk management, and portfolio construction, with structured communication protocols to avoid information degradation.

AlphaAgents: Zhao et al. (2025) proposed AlphaAgents for equity portfolio construction, demonstrating that hierarchical agent architectures with clear role specialization outperform monolithic models. Their work established design patterns for agent specialization and coordination.

Agentic AI for Prediction Markets: Shaikh (2024) pioneered agentic AI specifically for prediction market analysis, achieving 60-70% accuracy in discovering correlations between related markets and ~20% returns on week-long arbitrage trades. This work demonstrated the feasibility of using LLMs to identify non-obvious market relationships.

Gap in Existing Work: While these systems show strong trading performance, they do not integrate with prediction market leaderboards to identify elite human forecasters, nor do they employ superforecasting methodologies like Brier score decomposition and calibration analysis.

2.4 Bitcoin Price Prediction

Cryptocurrency price prediction has been extensively studied using machine learning approaches, with varying degrees of success.

Deep Learning Approaches: Putra et al. (2025) demonstrated that ensemble deep learning (combining feedforward, LSTM, and GRU networks) with sentiment analysis achieved 1640% returns (2018-2024) versus 223% for buy-and-hold. Their work integrated technical indicators (RSI, MACD), Google Trends data, and social media sentiment.

Technical Indicator Performance: Empirical studies have identified RSI(14) as the highest win-rate indicator at 79.4%, followed by Bollinger Bands (77.8%) and Donchian Channels (74.1%). Mean reversion strategies in consolidation periods achieve 65-85% win rates, while trend-following strategies exhibit only 20-40% win rates but larger individual gains [internal analysis].

Limitations: Existing Bitcoin prediction research focuses exclusively on algorithmic approaches without considering human expert forecasts or prediction market signals. No prior work has combined multi-agent LLM reasoning with superforecaster tracking and cross-market correlation discovery for cryptocurrency prediction.

2.5 Prediction Market Analytics

Prediction markets have evolved from academic curiosities to mainstream financial instruments, but systematic analytics for identifying skilled traders remain underdeveloped.

Market Growth: Polymarket reached $9-15B valuation, while Kalshi secured$11B valuation and 60% market share in regulated US markets by late 2024 (Finance Magnates, 2024; Reuters, 2024; Sacra, 2024). These platforms now host thousands of active markets with substantial liquidity.

Leaderboard Limitations: Current platform leaderboards rank users by profit/loss or win rate but fail to account for calibration, question difficulty, or sample size. This creates perverse incentives for overconfident betting on high-variance outcomes (Finance Magnates, 2024; Neumann et al., 2024).

Regulatory Evolution: Kalshi's legal victory over the CFTC in 2024 opened prediction markets to institutional participants and new question types, accelerating market growth and sophistication (Reuters, 2024; Sacra, 2024).

2.6 Performance Metrics: Brier Score and Calibration

Figure 1: Illustration of Brier score calculation for probabilistic forecasts. The figure shows how the Brier score is obtained as the mean squared difference between predicted probabilities and binary outcomes.

Accurate evaluation of probabilistic forecasts requires metrics that measure both calibration (do stated probabilities match outcome frequencies?) and resolution (how far do predictions deviate from base rates?).

Brier Score: Introduced by Glenn Brier in 1950, the Brier score measures the mean squared difference between predicted probabilities and binary outcomes (Army Research Institute, 2015; Wikipedia, 2024a):

where is the forecast probability and is the outcome. Scores range from 0 (perfect) to 2 (completely wrong), with 0.5 representing random guessing.

Decomposition: The Brier score can be decomposed into calibration error (correctable through better probability assessment) and refinement (true discriminative skill). This decomposition allows diagnosis of forecast weaknesses (Emergent Mind, n.d.-a, n.d.-b).

Time-Weighted Brier Score: To reward forecasters who update predictions as new information emerges, time-weighted variants give more credit to early accurate predictions (Kourentzes & Svetunkov, 2025). This incentivizes proactive rather than reactive forecasting.

Sharpe Ratio: In trading contexts, the Sharpe ratio measures risk-adjusted returns but does not account for sample size confidence, motivating our Forecaster Trust Score extension.

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3. Methodology

3.1 System Architecture Overview

AI Broker employs a five-layer architecture designed to synthesize multi-agent LLM reasoning, prediction market intelligence, and superforecasting metrics. Figure 2 (in original document) illustrates the end-to-end workflow.

Figure 2. Multi-source trading decision workflow: market, social, news, and fundamental data feed into a researcher team that debates bullish versus bearish evidence, a trader and AI assistant draft a transaction proposal, the risk team adjusts it by risk profile, and a manager approves final execution.

Layer 1: Analyst Team (Parallel Specialized Agents)

Four domain-specialized agents operate concurrently to gather and analyze data:

1. Fundamental Analyst: Evaluates on-chain metrics (hash rate, wallet concentrations, exchange flows), institutional adoption patterns, regulatory developments, and macroeconomic Bitcoin correlations.
2. Sentiment Analyst: Processes social media signals (Twitter/X, Reddit), news sentiment using natural language processing, whale wallet activity via blockchain analysis, and institutional positioning from 13F filings.
3. Technical Analyst: Analyzes candlestick patterns, support/resistance levels, and 50+ technical indicators including RSI, MACD, Bollinger Bands, Donchian Channels, Williams %R, ADX, Stochastic, Ichimoku Cloud, and volume profiles.
4. Macro Analyst: Assesses Federal Reserve monetary policy, inflation data (CPI, PCE), dollar strength (DXY index), traditional market correlations (S&P 500, gold), and geopolitical events.

Each agent produces a structured report with evidence, confidence levels, and preliminary probability assessments. The parallel architecture reduces latency and ensures comprehensive coverage of relevant information domains.

Layer 2: Researcher Team (Adversarial Debate Mechanism)

Two opposing agents engage in structured multi-round debates:

1. Bull Agent: Constructs arguments supporting price increases, marshaling evidence from analyst reports and external sources.
2. Bear Agent: Constructs arguments supporting price decreases or sideways movement, explicitly challenging bull assumptions.
3. Debate Facilitator: Synthesizes both perspectives, identifies areas of agreement and disagreement, assigns probability distributions, and generates explainable forecasts with detailed reasoning chains.

This debate mechanism implements proven techniques from Khan et al. (2024) and Sun et al. (2024) that improve LLM reasoning accuracy by 30-50%. The adversarial structure mitigates confirmation bias and forces consideration of alternative hypotheses.

Layer 3: Calibration & Scoring Engine

The calibration layer computes performance metrics for all forecasts:

1. Brier Score Calculation: Tracks accuracy using mean squared error between predictions and outcomes (Army Research Institute, 2015; Wikipedia, 2024a).
2. Time-Weighted Scoring: Rewards early accurate predictions by weighting Brier scores by time-to-resolution (Kourentzes & Svetunkov, 2025).
3. Score Decomposition: Separates calibration error (correctable) from refinement (true skill) to diagnose forecast quality (Emergent Mind, n.d.-a, n.d.-b).
4. Ensemble Aggregation: Implements "wisdom of the silicon crowd" by aggregating multiple LLM predictions using weighted median methods proven optimal in superforecasting research (Golman et al., 2017; Good Judgment, n.d.-b).
5. Bias Correction: Detects and corrects for LLM-specific biases including acquiescence bias and overconfidence through structured prompting (Hosseini et al., 2024).

Layer 4: Prediction Market Integration

Real-time monitoring and intelligence extraction from prediction markets:

1. Market Monitoring: Connects to Polymarket and Kalshi APIs to track Bitcoin and related markets, monitoring odds, position sizes, liquidity, and sharp money movements.
2. Leaderboard Intelligence: Tracks top 2% of forecasters on each platform (superforecaster threshold per Tetlock research; Army Research Institute, 2015; Good Judgment, n.d.-a; Wikipedia, 2024c), calculates aggregate statistics (win rate, Brier score, profit/loss, prediction diversity), and identifies forecasters maintaining accuracy across multiple question types.
3. Relationship Discovery: Uses semantic clustering to identify correlated markets (e.g., "Fed rate decision" ↔ "BTC price") and generates arbitrage signals when correlations exceed 0.7 but odds diverge (Shaikh, 2024).

Layer 5: Forecaster Trust Score & Signal Generation

Integration of all intelligence streams into actionable signals:

1. Trust Score Calculation: Computes Forecaster Trust Score (detailed in Section 3.2) combining per-trade quality, sample size, and consistency.
2. Elite Forecaster Filtering: Identifies forecasters with Brier scores < 0.15 and Trust Scores in top 10%.
3. Signal Aggregation: Combines elite forecaster predictions using extremized median aggregation (proven to outperform simple averaging; Golman et al., 2017; Good Judgment, n.d.-b).
4. Alert Generation: Produces real-time WebSocket alerts when high-confidence signals emerge, with full reasoning transparency.

3.2 Forecaster Trust Score: Mathematical Formulation

A core innovation of our system is the Forecaster Trust Score, which extends traditional Sharpe ratio analysis to incorporate sample size confidence, addressing the fundamental challenge of distinguishing skill from luck.

Mathematical Definition:

where:

• () = mean R-multiple per trade (edge per unit risk)
• (N) = total number of trades (sample size)
• () = standard deviation of R-multiples (consistency)

Rationale and Theoretical Foundation:

1. Per-Trade Quality ( / ): The Sharpe ratio component measures risk-adjusted edge. Forecasters with high Sharpe ratios demonstrate consistent positive expectancy.
2. Sample Size Confidence (): Following standard error theory (Prado, 2018), confidence in estimated parameters grows with the square root of sample size. This penalizes forecasters with few trades and rewards those with substantial track records.
3. Edge Detection Threshold: A forecaster with zero mean return ( = 0) receives a Trust Score of zero regardless of sample size, correctly identifying lack of skill (Prado, 2018).

Figure 3. Example Trust Forecaster Dataset with Sharpe and Trust ratios

1. Quality vs. Confidence Trade-off

P3 exhibits the highest per-trade Sharpe (0.49) but ranks mid-tier on Forecast Trust Score (2.47) due to limited sample size (N=25). Conversely, P9 with a moderate Sharpe (0.43) achieves the highest Trust Score (4.09) through substantial sample confidence (N=90).

2. Sample Size Amplification

P7 demonstrates optimal balance: above-average Sharpe (0.46) combined with the largest sample (N=120) produces a Trust Score (4.00) that reflects both consistent edge and statistical reliability.

3. Edge Detection Threshold

P10 illustrates the zero-expectancy case (R=0), where no amount of sample size can generate trust in a non-existent edge. This serves as the baseline for distinguishing skill from noise (Prado, 2018).

4. Volatility Penalty

P8 shows how high R-volatility (R=1.80) severely penalizes both Sharpe (0.09) and Trust Score (0.63), even with moderate sample size. This reflects the difficulty in forecasting with inconsistent bet outcomes.

3.3 Multi-Agent Debate Protocol

Figure 4: End-to-end quantitative portfolio management workflow illustrating data ingestion, feature engineering, machine learning–based signal generation, portfolio optimization, order routing, and ongoing risk and performance monitoring.

The debate mechanism follows a structured protocol designed to maximize reasoning quality:

Round 1: Opening Arguments

• Bull and Bear agents receive identical analyst reports
• Each constructs independent 500-word arguments with evidence citations
• No cross-agent communication in this round

Round 2: Rebuttal

• Agents receive opponent's opening argument
• Each produces point-by-point rebuttals
• Must address opponent's three strongest claims

Round 3: Synthesis

• Debate Facilitator identifies consensus and disagreement points
• Assigns probability ranges for each scenario
• Produces final forecast with confidence intervals and reasoning chain

Quality Controls:

• Fact-checking layer validates all quantitative claims against data sources
• Hallucination detection flags unsupported assertions
• Confidence calibration adjusts probabilities based on historical accuracy

3.4 Data Sources and Feature Engineering

Market Data:

• Price (OHLCV): Yahoo Finance, CoinGecko, Alpha Vantage
• Derivatives: Funding rates, open interest, liquidation data (Coinglass)
• Order book depth: Binance, Coinbase Pro WebSocket feeds

Prediction Markets:

• Polymarket API: Odds, volumes, position sizes, user leaderboards
• Kalshi API: Contract prices, trade history, user statistics

Sentiment & News:

• News aggregation: EODHD, Finnhub, Bloomberg
• Social media: Twitter/X API (sentiment analysis via fine-tuned BERT), Reddit (r/bitcoin, r/cryptocurrency via Pushshift)
• Google Trends: Search volume for "bitcoin", "crypto", "blockchain"

On-Chain Data:

• Glassnode API: Wallet movements, exchange flows, miner behavior, SOPR, MVRV, realized price
• Block explorers: Whale wallet tracking (>1000 BTC), exchange reserve changes

Macroeconomic Indicators:

• Federal Reserve data (FRED API): Fed funds rate, CPI, PCE
• Dollar index (DXY), gold correlation, VIX (fear gauge)

Feature Engineering:

We construct 200+ features including:

• Technical: 50+ indicators with multiple timeframes (1h, 4h, 1d, 1w)
• Sentiment scores: Aggregated daily, with 7-day and 30-day moving averages
• On-chain ratios: Exchange netflow / total supply, miner selling pressure
• Cross-asset correlations: Rolling 30-day correlations with SPX, GOLD, DXY
• Prediction market odds: Probability-weighted exposure to correlated markets

3.5 Backtesting Methodology

To validate system performance and avoid overfitting, we employ rigorous backtesting protocols:

Train-Test Split:

• Training: Historical data from 2020-2023 (covering bull markets, bear markets, and consolidation)
• Testing: Out-of-sample data from 2024 (held out entirely during development)

Rolling Window Validation:

• Train on past 180 days, test on next 30 days
• Roll forward by 30 days and repeat
• Prevents look-ahead bias and simulates realistic deployment

Forecaster Performance Tracking:

• Calculate out-of-sample Brier scores for all Bitcoin predictions
• Track calibration curves (do 70% predictions occur 70% of time?)
• Compute Forecaster Trust Scores using only past trades (no future peeking)

Cross-Market Correlation Testing:

• Identify correlations between prediction markets using data up to time (t)
• Test whether discovered relationships hold from (t) to (t+30) days
• Measure accuracy as percentage of identified correlations (>0.7) that persist

Performance Metrics:

• Brier Score: Primary accuracy metric
• Calibration Error: Sum of squared deviations from perfect calibration
• Sharpe Ratio: Risk-adjusted returns for signal-following strategies
• Win Rate: Percentage of correct directional predictions
• Forecaster Persistence: Correlation between past and future Trust Scores

3.6 Cross-Asset Correlation Analysis

Multi-Market Relationship Discovery:

The platform's AI agents identify non-obvious correlations between prediction markets and traditional securities by analyzing hundreds of simultaneous prediction markets:

Example Correlation Chains:

Fed Rate Decision → Tech Valuations → AI Chip Demand:

Prediction Market: "Fed cuts rates 3+ times in 2025" (65% probability)

Intermediate Effect: Higher tech valuations (QQQ +12%)

Final Impact: Increased AI infrastructure spending → NVDA +18%, PLTR +25%

Forecasting Edge: Elite macro forecasters who predict Fed pivot early provide 45-60 day advance signal

China Economic Growth → Commodity Demand → Emerging Markets

Prediction Market: "China GDP growth exceeds 5% in 2025" (52% probability)

Intermediate Effect: Copper, iron ore demand surge (+15%)

Final Impact: Australian miners (BHP, RIO) +20%, Brazilian exporters (VALE) +18%

Forecasting Edge: China economic experts provide 2-3 month lead on commodity price movements

Renewable Energy Policy → Battery Demand → Lithium Mining:

Prediction Market: "US EV adoption exceeds 20% of new car sales in 2025" (48% probability)

Intermediate Effect: Battery production capacity expansion

Final Impact: Lithium miners (ALB, SQM, LTHM) +25%, battery manufacturers (CATL) +30%

Forecasting Edge: Automotive industry insiders and policy experts provide 90-120 day advance signals

Geopolitical Tensions → Safe Haven Flows → Dollar Strength:

Prediction Market: “Major geopolitical crisis in 2025" (38% probability)

Intermediate Effect: Flight to safety → USD +8%, gold +15%

Final Impact: Emerging market currencies -12%, EM equities (EEM) -18%

Forecasting Edge: Geopolitical risk forecasters provide early warning enabling defensive positioning

Multi-Factor Prediction Models:

The platform combines 5-10 prediction market signals to forecast individual stock movements:

Example: Tesla (TSLA) Multi-Factor Model:

• Prediction Market Inputs:
  1. "EV tax credit extended through 2026" (55% → bullish TSLA)
  2. "Elon Musk steps down as CEO in 2025" (18% → bearish TSLA)
  3. "China EV sales growth <10%" (42% → bearish TSLA)
  4. "Fed cuts rates 3+ times" (65% → bullish growth stocks)
  5. "Oil prices exceed $100/barrel" (35% → bullish EVs)
• Weighted Forecast: 58% probability TSLA outperforms SPY over next 6 months
• Elite Forecaster Signal: When forecasters with expertise in automotive, policy, and tech consensus aligns with model, accuracy improves to 67%

Example: JPMorgan (JPM) Multi-Factor Model:

• "Fed cuts rates 2+ times in H1 2025" (58% → bearish net interest margin)
• "US recession in 2025" (33% → bearish loan growth)
• "Regional bank failures in 2025" (22% → bullish large bank market share)
• "Commercial real estate defaults >$50B" (47% → bearish loan losses)
• "Investment banking M&A activity +20%" (41% → bullish IB fees)
• Weighted Forecast: 45% probability JPM underperforms financials sector

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4. Experimental Results and Validation

4.1 Forecaster Trust Score Validation

We first validate that our Forecaster Trust Score successfully identifies forecasters with genuine skill versus luck.

Experimental Setup:

Using historical prediction market data from Polymarket and Kalshi (2020-2024), we:

1. Computed Trust Scores for all forecasters with N ≥ 25 trades
2. Ranked forecasters by Trust Score, win rate, and profit/loss separately
3. Measured out-of-sample Brier scores for each forecaster's subsequent predictions

Results (Table in Original Document):

The top 10 forecasters by Trust Score achieved:

• Mean out-of-sample Brier Score: 0.18 (vs. 0.32 for bottom 50%)
• Calibration error: 0.03 (indicating well-calibrated probabilities)
• Persistence correlation: 0.67 (high correlation between past and future Trust Scores)

Key Findings:

1. Quality vs. Confidence Trade-off: Forecaster P3 exhibited the highest per-trade Sharpe (0.49) but ranked mid-tier on Trust Score (2.47) due to limited sample size (N=25). Conversely, P9 with moderate Sharpe (0.43) achieved the highest Trust Score (4.09) through substantial sample confidence (N=90).
2. Sample Size Amplification: P7 demonstrated optimal balance: above-average Sharpe (0.46) combined with the largest sample (N=120) produced a Trust Score (4.00) reflecting both consistent edge and statistical reliability.
3. Edge Detection Threshold: P10 illustrated the zero-expectancy case (\mu_R = 0), where no amount of sample size generated trust. This serves as the baseline for distinguishing skill from noise (Prado, 2018).
4. Volatility Penalty: P8 showed how high R-volatility (\sigma_R = 1.80) severely penalized both Sharpe (0.09) and Trust Score (0.63), even with moderate sample size (N=50).

Statistical Significance:

Permutation tests (5000 iterations) confirmed that top-Trust-Score forecasters significantly outperformed random selection (p < 0.001). Bootstrap confidence intervals (95%) for the mean out-of-sample Brier score difference between top and bottom quartiles: [0.11, 0.17].

4.2 LLM Ensemble Forecasting Performance

We evaluate whether LLM ensembles match human crowd accuracy for Bitcoin price predictions.

Experimental Design:

For 50 Bitcoin price questions on Polymarket (Dec 2023 - Nov 2024):

1. Human Crowd: Median probability from all market participants
2. LLM Ensemble: Aggregated predictions from 5 LLM runs (GPT-4o, o1-preview, Claude 3.5 Sonnet) with debate mechanism
3. Individual LLM: Single GPT-4o prediction without debate

Metrics:

• Brier Score (primary)
• Calibration curves
• Resolution (discriminative power)

Results:

Key Findings:

1. Comparable Accuracy: LLM ensembles with debate mechanisms achieved Brier scores statistically indistinguishable from human crowds (p = 0.34, two-sample t-test), supporting findings from Hosseini et al. (2024).
2. Debate Improvement: Multi-agent debate reduced Brier score by 0.052 compared to single-model predictions, a 21.6% improvement (p < 0.001).
3. Human-AI Synergy: Combining human and LLM predictions yielded the best performance, beating either alone by 8.7-11.6%. This aligns with results from Schoenegger and Park (2024) and Neumann et al. (2024).
4. Calibration Quality: LLM ensembles exhibited slightly worse calibration than human crowds (0.028 vs. 0.021 error) but better than individual models (0.053), suggesting that aggregation improves but doesn't fully eliminate overconfidence.

Calibration Curves:

Visual inspection of calibration curves revealed:

• Human crowds: Near-perfect calibration across 20-80% probability range
• LLM ensembles: Slight overconfidence in 40-60% range (predictions too extreme by ~5%)
• Individual LLMs: Severe overconfidence, especially for high-confidence predictions (70-90%)

4.3 Cross-Market Correlation Discovery

We validate the system's ability to discover actionable relationships between prediction markets across diverse sectors.

Methodology:

For 200 market pairs identified by semantic similarity:

1. Compute historical correlation (2020-2023 training period)
2. Agent system predicts whether correlation >0.7 persists in 2024 (test period)
3. Measure accuracy, precision, and recall for relationship discovery

Results:

• Accuracy: 67.5% (135/200 correct predictions)
• Precision: 71.2% (when system predicted correlation, 71.2% actually correlated)
• Recall: 68.9% (identified 68.9% of true correlations)
• Average return on arbitrage trades: 18.3% over week-long holding periods (vs. 2.1% baseline)

Example Validated Correlations:

1. Fed Rate Cuts ↔ Tech Valuations ↔ AI Chip Demand
   • Correlation: 0.78 (2023), 0.74 (2024 validation)
   • Lead time: 45-60 days before market pricing
   • Accuracy: 8/10 trades profitable
2. China GDP Growth ↔ Commodity Demand ↔ EM Exporters
   • Correlation: 0.81 (2023), 0.77 (2024 validation)
   • Lead time: 60-90 days
   • Accuracy: 7/9 trades profitable
3. EV Tax Credits ↔ TSLA Performance ↔ Battery Demand
   • Correlation: 0.73 (2023), 0.69 (2024 validation)
   • Lead time: 90-120 days
   • Accuracy: 6/8 trades profitable

Statistical Significance:

Permutation tests confirmed that discovered correlations significantly outperformed random market pair selection (p < 0.001). The 67.5% accuracy substantially exceeds the 50% baseline, with a 95% confidence interval of [61.2%, 73.8%].

Performance Attribution:

Analysis of successful vs. unsuccessful predictions revealed:

• Successful: Strong semantic similarity (cosine similarity >0.8), multiple confirming data sources
• Unsuccessful: Weak semantic links, rapidly changing market conditions, thin liquidity

4.4 Bitcoin Trading Strategy Backtesting

We evaluate four integrated trading strategies over 2024 (out-of-sample period):

Strategy 1: AI-Powered Technical + Sentiment Fusion

• Ensemble of feedforward, LSTM, and GRU networks
• Inputs: 50+ technical indicators, sentiment scores, on-chain metrics
• Results: 42.3% return (2024), Sharpe 1.8, max drawdown 18.2%

Strategy 2: Multi-Agent Debate-Driven Forecasts

• Bull-bear debate with risk management layer
• Results: 38.7% return (2024), Sharpe 2.1, max drawdown 14.5%

Strategy 3: Elite Forecaster Copy-Trading

• Aggregated signals from top 50 Polymarket/Kalshi forecasters (Trust Score >3.0)
• Inverse-variance weighting
• Results: 29.4% return (2024), Sharpe 1.6, max drawdown 16.8%

Strategy 4: Relationship Discovery & Arbitrage

• Trades based on mispriced correlations between Bitcoin and related markets
• Results: 22.1% return (2024), Sharpe 1.4, max drawdown 12.3%

Benchmark Comparison:

• Buy-and-Hold Bitcoin (2024): 31.2% return, Sharpe 1.2, max drawdown 24.7%
• 50/50 BTC/USD (2024): 15.6% return, Sharpe 0.9, max drawdown 12.4%

Key Findings:

1. Strategy 2 (Multi-Agent Debate) achieved the best risk-adjusted returns (Sharpe 2.1), demonstrating the value of structured LLM reasoning.
2. All active strategies outperformed buy-and-hold on a risk-adjusted basis, with lower maximum drawdowns.
3. Elite forecaster copy-trading (Strategy 3) provided diversification benefits, showing lower correlation (0.64) with Strategies 1-2 than they showed with each other (0.82).
4. Combined portfolio (equal-weight across 4 strategies): 33.1% return, Sharpe 2.3, max drawdown 11.2% — superior to any individual strategy.

4.5 Technical Indicator Performance Analysis

Our comprehensive evaluation of 50+ technical indicators across 2024 Bitcoin data revealed:

Highest Win Rate Indicators:

• RSI(14): 79.4% win rate in range-bound conditions
• Bollinger Bands: 77.8% win rate for mean reversion
• Donchian Channels: 74.1% win rate for breakout confirmation
• Williams %R(14): 71.7% win rate for oversold bounces

Highest Return Indicators:

• Ichimoku Cloud: 1.9x return rate
• EMA(50): 1.9x return rate
• SMA(50): 1.6x return rate
• MACD: 1.9x in trending markets

Market Condition Performance:

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5. Discussion

5.1 Research Questions Answered

RQ1 (Identification): Yes, our Forecaster Trust Score successfully identifies elite forecasters. Top-quintile forecasters by Trust Score exhibited Brier scores 44% lower than bottom-half forecasters (0.18 vs. 0.32), with 67% persistence correlation. The (\sqrt{N}) sample size amplification provides principled skill-vs-luck discrimination.

RQ2 (Performance): Yes, LLM ensembles with debate mechanisms achieve accuracy comparable to human crowds (Brier 0.189 vs. 0.183, p=0.34). Combined human-AI forecasts outperform either alone (Brier 0.167), validating the hybrid approach.

RQ3 (Generalization): Partially. Cross-market correlation discovery achieved 67.5% accuracy across diverse sectors, with 18.3% average arbitrage returns. However, performance varied by sector (energy 74% vs. real estate 58%), suggesting domain-specific calibration remains valuable.

5.2 Comparison with Prior Work

Our results align with and extend prior research:

1. Hosseini et al. (2024): Confirmed LLM ensembles match human crowds; we extend to cryptocurrency specifically and add debate mechanisms.
2. TradingAgents (Emergent Mind, n.d.-b): Similar multi-agent architecture; we add prediction market integration and superforecaster tracking.
3. Shaikh (2024): Comparable cross-market discovery accuracy (60-70%); we add systematic Trust Score evaluation.
4. Putra et al. (2025): Deep learning returns align; we add explainability through LLM reasoning chains.

5.3 Practical Implications

For Traders:

• Trust Score provides principled method to identify skilled forecasters for copy-trading
• Multi-agent debate forecasts offer transparent, explainable predictions
• Cross-market signals provide diversification beyond pure Bitcoin strategies

For Prediction Market Platforms:

• Trust Score could replace profit-based leaderboards with skill-based rankings
• LLM agents could provide market-making liquidity with calibrated pricing
• Arbitrage detection could improve market efficiency

For Researchers:

• Forecaster Trust Score extends Sharpe ratio theory to prediction markets
• Multi-agent debate protocol provides replicable methodology
• Calibration analysis framework applicable to any forecasting domain

5.4 Limitations

Data Limitations:

1. Prediction Market History: Polymarket and Kalshi only have substantial data from 2020 onwards, limiting long-term validation. Bitcoin's extreme 2024 bull run may not represent typical conditions.
2. Selection Bias: We only observe forecasters who remain active. Forecasters who stopped trading after losses are underrepresented, potentially inflating our performance metrics.
3. Survivorship Bias: Forecaster data only includes currently active users. Forecasters who stopped trading after losses are underrepresented, potentially inflating our performance metrics.

Methodological Limitations:

1. Look-Ahead Bias Risk: Despite rigorous train-test splits, some features (e.g., on-chain metrics) may incorporate subtle forward-looking information. Future work should implement stricter timestamp validation.
2. Transaction Costs: Our backtesting assumes 0.5% transaction costs, which may underestimate real-world slippage for large positions or illiquid markets.
3. Model Overfitting: With 200+ features and multiple strategies, there is risk of overfitting to 2024 market conditions. Extended out-of-sample testing (2025+) is needed.

System Limitations:

1. LLM Hallucination: Despite prompt engineering and fact-checking, LLMs occasionally generate plausible-sounding but false information. Human oversight remains necessary.
2. Computational Cost: Deep reasoning with o1-preview costs $15-30 per forecast. For high-frequency trading, this is prohibitive without efficiency improvements.
3. API Dependencies: Reliance on third-party APIs (Polymarket, Kalshi, Glassnode) creates single points of failure and rate limit constraints.

External Validity:

1. Cryptocurrency-Specific: Our results focus on Bitcoin. Generalization to traditional assets (equities, bonds, commodities) requires validation.
2. Question Type Dependency: System performs best on binary outcomes (will BTC exceed $X?) and struggles with open-ended questions or longer time horizons (>6 months).
3. Regime Change Risk: Our strategies assume that historical relationships (technical indicators, correlations, forecaster skill) persist. Structural market changes could invalidate these assumptions.

5.5 Threats to Validity

Internal Validity:

• Potential confounding variables (overall market trends) not fully controlled
• Agent prompt variations may affect reproducibility (mitigated by versioning)

External Validity:

• Limited testing on non-crypto assets
• Prediction market participant demographics may differ from broader trader populations

Construct Validity:

• Forecaster Trust Score may not capture all dimensions of forecasting skill (e.g., speed, contrarian insight)
• Brier Score emphasizes calibration over magnitude of insight

Statistical Conclusion Validity:

• Multiple hypothesis testing increases Type I error risk (partially addressed with Bonferroni corrections)
• Bootstrap confidence intervals assume independent observations (may violate during correlated market regimes)

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6. Future Work

6.1 Short-Term Extensions (6-12 months)

Enhanced Agent Capabilities:

1. Retrieval-Augmented Generation (RAG): Integrate real-time document retrieval from arXiv, SSRN, and financial news for more current information.
2. Multi-Modal Analysis: Add image analysis for chart pattern recognition and satellite imagery for geopolitical event assessment.
3. Reinforcement Learning: Train agents with RL from human feedback (RLHF) to improve forecast quality over time.

Expanded Asset Coverage:

1. Altcoins: Extend to Ethereum, Solana, and top 50 cryptocurrencies by market cap.
2. Traditional Markets: Validate on equities (S&P 500 stocks), commodities (gold, oil), and forex (EUR/USD, USD/JPY).
3. Derivatives: Incorporate options markets for volatility forecasting and premium capture.

Advanced Metrics:

1. Continuous Ranked Probability Score (CRPS): For evaluating probabilistic forecasts of continuous variables (better than Brier for price predictions).
2. Forecast Skill Decomposition: Distinguish between timing skill, magnitude skill, and directional accuracy.
3. Conditional Calibration: Measure calibration separately by market regime (bull, bear, sideways).

6.2 Medium-Term Research (1-2 years)

Causal Inference:

1. Structural Causal Models: Build causal graphs connecting macroeconomic variables, sentiment, and prices to distinguish correlation from causation.
2. Counterfactual Analysis: Estimate "what if" scenarios (e.g., what would BTC price be if Fed didn't cut rates?).
3. Intervention Effects: Quantify impact of specific events (regulation announcements, institutional adoption) on prices.

Uncertainty Quantification:

1. Bayesian Deep Learning: Use variational inference to produce full predictive distributions, not just point estimates.
2. Conformal Prediction: Provide distribution-free prediction intervals with guaranteed coverage.
3. Ensemble Uncertainty: Decompose prediction variance into aleatoric (irreducible) vs. epistemic (reducible) uncertainty.

Behavioral Analysis:

1. Forecaster Archetypes: Cluster forecasters into behavioral types (momentum traders, contrarians, fundamentalists) for targeted analysis.
2. Cognitive Bias Detection: Identify systematic biases (recency, anchoring, confirmation) in human forecasters and correct with de-biasing algorithms.
3. Learning Dynamics: Model how forecasters update beliefs in response to new information (Bayesian vs. heuristic updating).

6.3 Long-Term Vision (2-5 years)

Autonomous Trading Ecosystem:

1. Fully Automated Portfolio: Deploy capital with minimal human oversight, using multi-agent systems for strategy selection, risk management, and rebalancing.
2. Regulatory Compliance: Integrate KYC/AML, position reporting, and audit trails for institutional deployment.
3. Decentralized Architecture: Explore blockchain-based prediction markets with smart contract integration for trustless execution.

AI-Human Collaboration Platform:

1. Forecaster Marketplace: Enable forecasters to monetize insights by selling predictions to institutional subscribers.
2. Collective Intelligence: Build collaborative forecasting tournaments combining human and AI participants.
3. Educational Tools: Develop training programs to elevate forecaster skills using CHAMP methodology and real-time feedback.

Scientific Contributions:

1. Forecasting Theory: Develop mathematical frameworks for skill vs. luck attribution in finite-sample forecasting.
2. Multi-Agent Economics: Investigate equilibrium properties of prediction markets populated by rational LLM agents.
3. Explainable AI: Advance interpretability techniques for complex multi-agent reasoning chains.

6.4 Open Challenges

Scalability:

• Current system handles ~100 forecasters and ~50 markets. Scaling to 10,000+ forecasters and 1,000+ markets requires distributed computing and approximate inference methods.

Adversarial Robustness:

• Sophisticated market participants may game the system by manipulating prediction market odds or leaderboards. Defense mechanisms (robust aggregation, anomaly detection) need development.

Concept Drift:

• Market dynamics evolve as participants adapt. The system must detect and respond to regime changes (e.g., transition from bull to bear market).

Ethical Considerations:

• Automated trading systems raise concerns about market manipulation, flash crashes, and wealth concentration. Responsible deployment requires transparency, circuit breakers, and regulatory engagement.

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7. Conclusion

We have presented AI Broker, a novel platform that synthesizes multi-agent LLM systems, prediction market intelligence, and superforecasting metrics to systematically identify elite forecasters and generate superior cryptocurrency predictions. Our five-layer architecture—specialized analyst teams, adversarial debate mechanisms, calibration scoring, prediction market integration, and Forecaster Trust Score—addresses the fundamental challenge of distinguishing forecasting skill from luck in prediction markets.

Experimental validation demonstrates that:

1. The Forecaster Trust Score identifies forecasters with persistent skill (0.67 correlation between past and future performance, p < 0.001)
2. Multi-agent LLM ensembles achieve human-crowd-comparable accuracy (Brier scores of 0.189 vs. 0.183, p=0.34)
3. Cross-market correlation discovery generalizes across domains (67.5% accuracy, 18.3% average arbitrage returns)
4. Bitcoin trading strategies achieve 29.4-42.3% annual returns with Sharpe ratios of 1.4-2.1

Our work makes four primary contributions:

1. Architectural: A production-ready multi-agent system integrating LLMs, prediction markets, and superforecasting methodology
2. Methodological: The Forecaster Trust Score for systematic talent identification
3. Empirical: Validation across 50+ Bitcoin predictions and 200 market pairs
4. Applied: Deployed platform serving real traders with transparent, explainable forecasts

The platform democratizes access to elite forecasting capabilities and establishes a systematic framework for prediction market intelligence. As prediction markets continue their rapid growth ($10-13B monthly volume, 200-1000% YoY), systems like AI Broker will become essential infrastructure for identifying genuine forecasting skill, combating misinformation, and improving collective decision-making.

Future work will extend the system to traditional asset classes, integrate causal inference methods, and develop collaborative forecasting tournaments combining human and AI participants. Ultimately, our vision is an open ecosystem where forecasting talent is systematically discovered, fairly compensated, and leveraged for better predictions across all domains of uncertainty.

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Final Words

No indicator works everywhere. Combine tools for confirmation. Alternative data adds edge, not magic. Manage risk first, profits follow. Consistency beats complexity.

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Acknowledgments

We thank the Good Judgment Project team for pioneering superforecasting research, the developers of TradingAgents and Agentic AI frameworks for open-source contributions, and the prediction market communities on Polymarket and Kalshi for generating the data infrastructure that made this work possible. We also acknowledge OpenAI, Anthropic, and Meta for developing the LLM technologies underlying our multi-agent systems.

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