The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting

arXiv:2602.03395 · cs.LG · Submitted 2026-08-19 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting".

Jane: The paper was written by Chen-Hui Song, Shuoling Liu and Liyuan Chen from E Fund Management Company, Limited, Guangzhou, Guangdong, China..

Tom: Stay tuned as we take you through the paper and discuss its implications.

Summary: Tom: We’ve established the paradox, so let's move to the summary of what this research actually revealed in The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting.

Jane: The authors used a bunch of real-world financial data across different market sizes like CSI three hundred and S andP five hundred. They found that if you force the training label to match the final target, it doesn't always work.

Lu: They discovered this phenomenon by looking at how signal realization—how much useful information is available—competes against noise accumulation over time.

Meng: This is critical because in high-frequency trading, noise accumulates so fast that waiting for the full target window can be a huge disadvantage for any predictive model.

Lalam: The implication here is that our current training methods are fundamentally missing the timing of when information actually becomes useful versus when it becomes useless.

Tom: So, what was the practical finding regarding these different scenarios?

Jane: They tested three types of predictions: standard day-to-day, short thirty-minute windows, and a longer ninety-minute window. The results showed that for the short day-to-day task, the optimal label was much shorter than the target.

Lu: It suggests that in those high-momentum market environments, you should only look at what happened right at market open to capture that initial burst of alpha before it disappears.

Meng: That's a practical shift—we might need models trained on fifteen-minute chunks for thirty-minute predictions, not the full half hour.

Lalam: We are moving away from static, fixed learning cycles toward recognizing that information has a shelf life in financial markets.

Tom: Let's transition to how this paper suggests we fix it with a massive technical improvement.

Improvements: Tom: Now for the third segment, discussing the improvements suggested by The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting.

Jane: The authors propose this bi-level optimization framework which is quite clever and elegant. Instead of choosing just one fixed label, they treat all possible intermediate horizons as candidates.

Lu: This allows the model to learn lambda, a weight vector that dynamically decides which specific horizon is actually the most informative for training.

Meng: I was particularly interested in how they manage this complexity without needing hundreds of manual retraining runs; their single-step inner loop update is surprisingly efficient.

Lalam: The cultural shift here is moving from a one-size-fits-all approach to an adaptive, dynamic learning process that mirrors real market conditions.

Tom: So, the mechanism uses a warm-up phase and then the bi-level updates. Jane, can you explain what that means in simple terms?

Jane: Think of it like this: first we let the model get a basic understanding of the data during a warm-up phase, and then we start fine-tuning its weights based on which horizon is performing best against the final target.

Lu: This is essentially giving the model self-awareness regarding its own learning limitations—it's not just blindly minimizing error.

Meng: And I liked that they also included an entropy regularization term in the outer loop, which prevents the system from collapsing onto one single, potentially noisy label.

Lalam: It’s about building a more robust and resilient AI pipeline that is designed to find the optimal signal rather than just forcing a correlation.

Tom: We've seen how it works conceptually; let's move into our final wrap-up segment.

Conclusion: Tom: Alright, we are wrapping up this deep dive into The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting and its implications for a new wave of AI applications.

Jane: It’s clear that the world is moving past the idea that training labels must always match inference goals.

Lu: I feel this is one of those papers where the theory perfectly meets the practice, proving that our assumptions about data labeling were outdated.

Meng: The practical impact seems huge; we are finally having a framework that autonomously decides when to look at short-term versus long-term data for maximum benefit.

Lalam: This technology has the potential to improve how financial institutions manage risk and allocate capital by making their predictive signals far more stable.

Tom: I think the core message is that we have been missing this critical temporal trade-off, right?

Jane: We are moving into an era where adaptive, dynamic learning is necessary for truly understanding complex systems like the stock market.

Lu: It's a beautiful marriage between statistical theory and cutting-edge deep learning architecture.

Meng: I’m excited to see how this will scale up in real-world deployment across different global financial markets, too.

Lalam: The Label Horizon Paradox tells us that the future is not about fixed targets, but about finding the most impactful horizon for optimal learning.

Tom: Thank you all for sharing your insights today and I hope listeners are excited to read The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting themselves before we head into our next topic.

Conclusion: Tom: So, we're finally reaching the end of our conversation about "The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting," but I feel like we need one last moment to tie everything together.

Jane: It’s important to remember that this isn't just a technical tweak; it’s a fundamental shift away from assuming training labels must perfectly align with the ultimate goal.

Lu: I think the biggest intellectual leap here is recognizing that the signal and noise accumulation have a distinct, measurable temporal trade-off, rather than being static properties.

Meng: From an engineering viewpoint, this means we' aren't just adding more data; we're intelligently optimizing *how* we train on existing data by dynamically selecting the best label horizon for maximum practical impact.

Lalam: The cultural shift here is realizing that financial markets are complex systems that require dynamic, not fixed, supervision to improve the robustness of our AI systems.

Tom: I agree with Lalam; it's all about acknowledging that market dynamics require a smarter approach to learning than we've been using for years.

Jane: It really demonstrates how much our previous assumptions were flawed when trying to predict something as chaotic as global markets.

Lu: The paper shows us where the true intelligence lies in the signal-to-noise ratio, not just in the architecture of a network.

Meng: I hope this makes sense for practical deployment so that we can start building more resilient trading models with less wasted training time.

Lalam: This allows our AI systems to learn with a sophisticated understanding of time itself, improving how they help people navigate financial uncertainty.

Tom: It’s fascinating how the "Label Horizon Paradox" is truly changing the conversation about supervision in financial forecasting.

Chen-Hui Song, Shuoling Liu, Liyuan Chen

E Fund Management Company, Limited, Guangzhou, Guangdong, China.

cs.LG

Submitted: 2026-08-19

Updated: 2026-08-20

Importance score: 72/100

The gist: The paper, "The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting," proposes a bi-level optimization framework to improve financial forecasting by rethinking supervision

Key concepts

Label Horizon Paradox
The paradox is that forcing training labels to match final prediction targets does not always work in real-world financial data. This suggests that traditional assumptions about how data should be labeled are fundamentally flawed, requiring a new approach to supervision.
Signal vs. Noise Accumulation
This refers to the competition between useful information (signal) and irrelevant data (noise) over time. In fast-moving markets, noise accumulates quickly, making it disadvantageous for predictive models to wait for a full target window before making a prediction.
Bi-level Optimization Framework
This is a proposed technical improvement where the model treats all possible intermediate time horizons as candidates. It allows the system to dynamically learn which specific horizon is most informative, moving away from a single fixed label.

Terminology

Summary

The paper, The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting, proposes a bi-level optimization framework to improve financial forecasting by rethinking supervision targets.

Empirical Performance and Real-World Constraints:

The framework's effectiveness is demonstrated through downstream portfolio backtesting results on the CSI 500 dataset (Table 8). Crucially, the testing methodology incorporates real-world constraints: Transaction costs (commissions and stamp duties) and execution slippage are strictly incorporated into the backtesting framework to accurately reflect real-world trading frictions.

The findings indicate that applying this bi-level optimization method consistently improves downstream trading performance across almost all backbone architectures. Specifically, the methodology achieves multiple benefits:

  1. It not only enhances the Annualized Return (Ann. Ret) but also effectively reduces the Maximum Drawdown (MDD) and Annualized Volatility (Ann. Vol) in most cases.

  2. Consequently, the risk-adjusted returns, measured by the Sharpe Ratio, exhibit substantial and consistent improvements over the standard training paradigm.

Computational Efficiency Analysis:

The authors provide an efficiency analysis to benchmark the computational overhead of their proposed method (Table 9). This analysis compares the per-epoch training time of a standard predictive model against its bi-level counterpart using a single NVIDIA H20 GPU on the CSI 1000 universe, which was chosen because it is the largest universe considered in our experiments, thus presenting the heaviest computational load.

The results demonstrate that:

  • with a single inner-loop step, the bi-level method adds a moderate amount of computational overhead compared with standard training, and remains practically implementable across all tested architectures.

  • This overhead is manageable because in quantitative finance, where the signal-to-noise ratio is typically low and models tend to use relatively modest parameter sizes to control overfitting, which keeps the overall training cost in a comparable range.

  • Furthermore, the bi-level approach offers a significant advantage by effectively avoiding the need for extensive repeated training runs for horizon selection, which would otherwise involve training tens or hundreds of separate models, leading to much higher total computational cost.

The analysis cautions that while the absolute per-epoch times in Table 9 are reported, they are not meant to be directly compared as indicators of model quality or efficiency, due to the varying architectures and parameter counts among the tested models. The primary takeaway is thus the relative overhead of bi-level training versus standard training for each given model.

Improvements for AI systems

As a diligent AI researcher, I have analyzed the provided paper, The Label Horizon Paradox, and identified a fundamental paradigm shift in how we approach financial forecasting models. The core finding—that training labels should not rigidly mirror inference targets—necessitates an architectural overhaul of current machine learning pipelines.

Below are the specific improvements to AI systems based on this research, followed by a detailed description of what these improved systems can accomplish.

The following changes must be integrated into any deep learning system designed for high-noise time-series forecasting (e.g., stock prediction):

1. Implementation of Dynamic Label Selection (DLS):

  • Modification: Replace the static, single target label (r) with a set of candidate proxy labels R delta = r delta in 1,,). The system must be trained not just to predict r, but to learn which intermediate horizon delta* provides the optimal signal.

  • Target: This overcomes the closer-is-better intuition, allowing the model to find a superior supervisory signal that is neither too short (lacking information) nor too long (overwhelmed by noise).

2. Integration of Bi-Level Optimization (BLO):

  • Modification: Implement a structured training regimen consisting of two phases:

  • Phase I: Warm-up Epoch (N warm): Train the model on the arithmetic mean of all standardized proxy labels (t) to establish a robust, foundational feature representation. This mitigates shortcut learning and initial gradient collapse.

  • Phase II: Bi-Level Adaptation: Enable an iterative optimization process where the model learns a weight vector lambda (via softmax over candidate horizons). The system then performs an inner loop update on the support set (Bin) to learn optimal weights theta*(lambda) and an outer loop update on the query set (Bout) to refine lambda. This ensures the model adapts its supervision strategy based on real-time validation error.

3. Dynamic Weighting Scheme (lambda):

  • Modification: The system must not simply average all potential labels (Naive Averaging) nor treat them equally (Multi-Task Learning). It must autonomously assign weights lambda delta to each horizon delta, prioritizing those that maximize the predictive correlation with the final target r.

  • Target: This allows the model to dynamically emphasize high-value windows (e.g., in Scenario 2, where a short window is optimal) while down-weighting noisy or saturated windows (e.

4. Signal-Noise Decomposition Integration:

  • Modification: The system must be designed with an internal mechanism that models the temporal trade-off between two competing processes: Information Gain (alpha(delta)) and Noise Accumulation (K(delta + delta 0)).

  • Target: This guides the learning process toward the optimal equilibrium delta* where marginal information gain equals marginal noise penalty, ensuring generalization is not merely a function of complexity but of temporal efficiency.

An AI system incorporating these improvements will possess capabilities far exceeding traditional financial models:

1. Autonomous Optimal Label Identification:

The system can autonomously determine the most effective training signal (delta*) for any given market condition, moving beyond pre-defined scenarios (e.g., interday vs. intraday) without human intervention or brute-force searching hundreds of configurations.

2. Enhanced Predictive Accuracy and Stability:

By avoiding the pitfalls of rigid alignment, the system achieves significantly higher Information Coefficients (IC) and Rank Correlation (rho) than standard benchmarks across all market indices (CSI 300, 500, 1000) and demonstrates superior stability metrics (ICIR).

3. Robust Generalization in High-Efficiency Markets:

The system is proven to maintain its edge even in highly efficient markets like the S&P 500. It can successfully extract subtle, short-lived alpha factors that are immediately priced in (Scenario 1) while capturing sustained momentum (Scenario 2), without being distracted by noise in longer windows.

4. Superior Economic Performance under Stress:

The system is uniquely suited for dynamic risk management. Under periods of extreme market stress or downtrends, the adaptive nature of the label selection allows it to maintain its predictive power and significantly reduce Maximum Drawdown (MDD) compared to static models, leading to a superior Sharpe Ratio in real-world portfolio simulations.

5. Computational Efficiency:

By using the BLO framework—specifically designed with a single-step inner loop update after a warm-up phase—the system achieves these sophisticated gains without the prohibitive computational cost of training dozens of separate models for every possible horizon.

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