Beyond the Echo Chamber: AI Agents Tackle Financial Forecasting

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Author: Denis Avetisyan

A new multi-agent system, MASFIN, aims to improve short-term stock predictions by actively mitigating common biases and leveraging the power of generative AI.

Over an eight-week period of live evaluation, the MASFIN system demonstrated performance characteristics indicative of inevitable decay-a natural progression where initial robustness gradually yields to the pressures of sustained operation within a dynamic environment.
Over an eight-week period of live evaluation, the MASFIN system demonstrated performance characteristics indicative of inevitable decay-a natural progression where initial robustness gradually yields to the pressures of sustained operation within a dynamic environment.

MASFIN integrates a multi-agent framework with financial data to address survivorship and hindsight bias, achieving demonstrably improved performance during an eight-week evaluation.

Despite advances in quantitative finance, reliably integrating diverse signals and mitigating inherent biases remain significant challenges. This paper introduces MASFIN: A Multi-Agent System for Decomposed Financial Reasoning and Forecasting, a modular framework leveraging large language models alongside structured financial data to address these limitations. During an eight-week evaluation, MASFIN-designed with explicit bias-mitigation protocols-achieved a 7.33% cumulative return, outperforming major market benchmarks in most weeks, though with increased volatility. Can this multi-agent, bias-aware approach pave the way for more transparent, reproducible, and ultimately, more effective AI-driven financial forecasting?

The Illusion of Performance: Unveiling Hidden Biases

Stock forecasting traditionally centers on analyzing past performance, yet this reliance introduces significant distortions. A core issue is survivorship bias, where datasets predominantly feature companies that have survived to the present day, omitting those that failed. This creates an artificially inflated view of average returns, as unsuccessful ventures – and their associated losses – are systematically excluded from the calculations. Consequently, models built on this incomplete historical record overestimate potential gains and underestimate risks, leading to overly optimistic projections and potentially flawed investment strategies. The resulting skewed perspective fails to accurately represent the true landscape of market performance, as it doesn’t account for the full spectrum of outcomes experienced by all participating entities.

Traditional financial modeling frequently presents a distorted view of market reality by systematically omitting delisted firms from analysis. This exclusion creates an artificially inflated picture of average returns, as companies that performed poorly and ultimately ceased trading – representing genuine investment losses – are conveniently disregarded. Consequently, investors relying on these incomplete datasets may overestimate potential gains and underestimate risks, leading to suboptimal portfolio construction and ultimately, poorer investment decisions. The practice fosters a dangerous illusion of consistent profitability, masking the true extent of market failures and hindering accurate performance evaluation of investment strategies; a complete picture necessitates acknowledging and incorporating the outcomes of all firms, including those no longer actively traded.

Financial models frequently suffer from a critical flaw: hindsight bias. This occurs when model construction inadvertently incorporates data or insights that would not have been accessible at the time the forecast was originally made. For example, a model might accurately predict a stock’s performance after a major news event, but this predictive power is illusory if the model was built using knowledge of that event. This practice creates an artificially inflated sense of accuracy and reliability, leading investors to overestimate the model’s ability to predict future outcomes in real-time. Consequently, decisions based on these models can be fundamentally flawed, as they rely on information that simply wasn’t available during the period being forecasted, rendering the model impractical and potentially detrimental to investment strategies.

MASFIN: A Framework for Navigating Complexities

The MASFIN framework addresses stock forecasting complexity by employing a multi-agent system structured as a five-stage pipeline. This decomposition allows for the segregation of forecasting tasks into specialized components, each managed by a distinct agent ‘crew’. The pipeline begins with data acquisition and bias mitigation, proceeds through screening and in-depth analysis, incorporates timing optimization, and culminates in portfolio construction. This modular approach facilitates independent development, testing, and refinement of each stage, ultimately aiming to improve the accuracy and robustness of the overall forecasting process compared to monolithic systems.

The ‘Postmortem Crew’ stage of MASFIN directly addresses survivorship bias in stock forecasting by incorporating data from delisted firms. Traditional datasets often exclude companies that have failed or been removed from exchanges, creating an artificially inflated view of market performance. This stage systematically analyzes historical data from these delisted entities – including financial reports, trading volumes, and delisting reasons – to provide a more comprehensive and realistic assessment of investment risk and potential returns. By including this previously excluded data, the ‘Postmortem Crew’ generates a more accurate baseline for evaluating the performance of currently listed companies and mitigating the overestimation of success rates inherent in survivorship-biased datasets.

Following the initial postmortem analysis, the MASFIN framework employs two sequential agent groups for investment evaluation: the ‘Screening Crew’ and the ‘Analysis Crew’. The ‘Screening Crew’ applies predefined financial criteria – including metrics such as price-to-earnings ratio, debt-to-equity ratio, and revenue growth – to filter the universe of potential investments, reducing the set to a manageable size. The resulting candidates are then passed to the ‘Analysis Crew’, which performs a more in-depth fundamental analysis, incorporating qualitative factors and forecasting future performance. Concurrently, the ‘Timing Crew’ operates to identify optimal entry points for selected investments based on technical indicators and market trends, aiming to maximize returns while minimizing risk. These three stages work in concert to refine investment opportunities before portfolio construction.

The ‘Portfolio Crew’ stage of MASFIN integrates the outputs of the preceding stages to construct a diversified investment portfolio. This is achieved through an optimization process that balances predicted returns with risk assessments generated by the ‘Analysis Crew’ and ‘Timing Crew’. The crew employs a weighted allocation strategy, distributing capital across a range of assets to mitigate exposure to individual stock performance and market volatility. Diversification is prioritized to enhance portfolio robustness and to aim for consistent, balanced returns, independent of the success of any single investment. The resulting portfolio composition represents the final output of the MASFIN framework, ready for implementation and monitoring.

MASFIN employs a five-stage pipeline incorporating sequential human-in-the-loop (HITL) processing to refine its outputs.
MASFIN employs a five-stage pipeline incorporating sequential human-in-the-loop (HITL) processing to refine its outputs.

Data Integrity and the Pursuit of Accuracy

MASFIN’s data infrastructure is built upon integration with both Finnhub and Yahoo Finance APIs. Finnhub provides real-time stock prices, historical data, and global economic indicators, while Yahoo Finance supplements this with comprehensive company fundamentals, financial statements, and news sentiment analysis. This dual sourcing strategy ensures redundancy and a wider breadth of data points for analysis. Specifically, MASFIN accesses over 100 distinct financial metrics from each provider, encompassing price data, volume, earnings reports, dividend information, and key ratios. News headlines are also ingested from both platforms, allowing for event-driven analysis and sentiment scoring, contributing to a more robust and comprehensive data set for forecasting models.

The MASFIN framework integrates Generative AI to improve the accuracy and scope of its financial analyses. This implementation moves beyond traditional statistical methods by enabling the identification of non-linear relationships and subtle indicators within financial data. Generative AI algorithms are utilized to process both numerical data, such as stock prices and trading volumes, and qualitative data, including news sentiment and financial reports. This combined analysis allows MASFIN to uncover hidden patterns and enhance its predictive capabilities, ultimately contributing to more informed investment decisions.

The MASFIN framework incorporates a ‘Human-in-the-Loop Workflow’ as a critical component for maintaining data integrity and mitigating risks associated with automated analysis. This workflow involves qualified personnel reviewing outputs generated by the system, specifically focusing on identifying and correcting potential inaccuracies or biases that may arise from the Generative AI models. These reviews address issues such as data misinterpretations, illogical conclusions, or the generation of ‘hallucinations’ – fabricated information presented as factual. By integrating human oversight, the system ensures the reliability of its forecasts and minimizes the impact of algorithmic errors, thereby increasing confidence in the generated trading signals.

During an eight-week live evaluation period, the MASFIN framework generated a cumulative return of 7.33%. This performance represents a significant outperformance compared to key market indices: the NASDAQ Composite returned 5.36% over the same period, while the S&P 500 and Dow Jones Industrial Average achieved returns of 4.92% and 4.11%, respectively. These results were calculated based on actual trading data and demonstrate the potential for MASFIN to generate alpha relative to established benchmarks.

Beyond Prediction: A System Designed to Endure

Investment strategies frequently suffer from biases embedded within data or analytical models, leading to skewed outcomes and unreliable predictions. The MASFIN framework addresses this challenge through proactive bias mitigation, systematically identifying and neutralizing these distortions before they impact investment decisions. This isn’t simply about correcting errors after the fact; instead, MASFIN builds a foundation of reliability and transparency by actively ensuring that the analytical process itself is impartial. The result is a more robust and trustworthy investment approach, minimizing the risk of results driven by artificial or unintended influences and fostering greater confidence in long-term performance. By prioritizing unbiased analysis, MASFIN aims to deliver a clearer and more accurate reflection of market realities, ultimately benefiting investors seeking consistent and dependable returns.

The MASFIN framework isn’t envisioned as a static solution, but rather as a dynamically evolving system. Its modular design allows for seamless incorporation of novel data streams – from alternative economic indicators to real-time social sentiment analysis – without requiring a complete overhaul of the existing architecture. This adaptability extends to analytical techniques; new machine learning models and financial algorithms can be plugged into the framework to refine predictions and optimize investment strategies. Crucially, this modularity ensures the system remains responsive to shifting market dynamics and emerging trends, offering a pathway to sustained performance even as conditions change and allowing researchers to readily test and implement advancements in financial modeling.

The MASFIN framework achieves a noteworthy 75% win rate in its investment strategies, positioning it competitively alongside established market benchmarks like the NASDAQ and S&P 500. This consistent success suggests a robust and reliable methodology capable of navigating complex financial landscapes. Such performance isn’t simply about isolated gains; it indicates a systemic ability to identify and capitalize on favorable market conditions with a frequency comparable to leading indices. The framework’s consistent ability to generate positive outcomes establishes it as a viable and potentially valuable tool for investors seeking dependable returns, and further research could explore how its strategies contribute to this level of sustained success.

Despite exhibiting a weekly volatility of 2.61%, exceeding that of established benchmarks, the MASFIN framework demonstrably achieves a cumulative return of 7.33%. This performance suggests a capacity for substantial gains, indicating that the system’s inherent fluctuations may be offset by its potential for significant positive outcomes. While increased volatility typically signals higher risk, MASFIN’s overall return profile positions it as a potentially rewarding investment strategy, particularly for those willing to accept a degree of short-term fluctuation in pursuit of long-term profitability. Further investigation into the factors driving this return, despite the volatility, could unlock valuable insights for refining algorithmic trading strategies and risk management protocols.

MASFIN’s architecture, a deliberate composition of interacting agents, acknowledges the inherent fragility of complex systems over time. The system isn’t envisioned as a static solution, but rather as an evolving framework capable of adapting to changing market dynamics and mitigating the insidious creep of cognitive biases. This echoes Linus Torvalds’ sentiment: “Most developers think that ‘good code’ is code that works. I think that’s a very limited view. Good code is code that’s readable and maintainable.” MASFIN, by explicitly addressing survivorship and hindsight bias, prioritizes a robust and understandable foundation – anticipating the inevitable decay of predictive accuracy and building in mechanisms for continuous refinement. The focus isn’t solely on immediate performance, but on establishing an enduring system capable of graceful aging.

What Lies Ahead?

MASFIN, as presented, is not a solution, but a localized deceleration of entropy. Every bug in its reasoning is a moment of truth in the timeline, a pinpointed failure illuminating the inherent instability of predictive systems. The eight-week outperformance against benchmark indices is not a victory, but an extension of viability – a temporary reprieve before the inevitable regression toward the mean. The system’s strength lies in its explicit confrontation with cognitive biases, yet bias is not an external contaminant, but a fundamental property of information processing itself.

Future work must acknowledge that bias mitigation is not eradication, but a shifting of the error surface. The integration of generative AI introduces new vectors of decay – hallucinations, emergent confabulations, and the subtle drift of semantic meaning. Technical debt is the past’s mortgage paid by the present; each added layer of complexity demands a corresponding increase in monitoring and recalibration. A fruitful avenue lies in exploring the system’s failure modes – not as anomalies to be corrected, but as valuable data points revealing the boundaries of its predictive capacity.

Ultimately, the pursuit of perfect forecasting is a fool’s errand. A more pragmatic approach centers on resilience – the ability to adapt, learn, and degrade gracefully. MASFIN’s true legacy may not be its predictive accuracy, but its contribution to a deeper understanding of the inherent limitations of all complex systems operating within the relentless current of time.

Original article: https://arxiv.org/pdf/2512.21878.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-29 06:09