reviews: 
Comments: This work investigates the application of Contrastive Predictive Coding (CPC) for improving financial time series forecasting through the automatic generation of feature embeddings that capture latent market dynamics without requiring manual feature engineering. Recognizing that conventional forecasting approaches struggle with the inherent volatility and stochasticity of financial markets, the authors conduct an empirical comparison between CPC-augmented models and baseline approaches (including LSTMs, linear regression, and naive persistence strategies) using data from currency markets (USDJPY, USDSGD, EURGBP). The empirical findings indicate that CPC-enriched models, particularly when paired with linear regression, achieve superior performance relative to conventional benchmarks, suggesting that CPC successfully extracts meaningful, transferable representations applicable across different currency pairs. Furthermore, the analysis reveals that trading strategies informed by CPC embeddings exhibit improved Sharpe ratios compared to passive buy-and-hold strategies, pointing toward CPC's utility for constructing dynamic trading methodologies within broader financial markets.
Strengths: 1. Although CPC is an established technique, deploying it within the financial domain—a particularly challenging environment characterized by stochasticity and unpredictability—represents a meaningful research direction. This application effort contributes value by examining whether CPC's feature learning capacity extends to non-deterministic environments, notwithstanding the need for additional methodological refinements.
2. The evaluation encompasses multiple currency pairs (USDJPY, USDSGD, EURGBP) rather than focusing on isolated cases, demonstrating an attempt to validate CPC's transferability across different market conditions. This multi-currency assessment suggests the approach captures transferable market insights, though more comprehensive experimental validation would strengthen such conclusions.
Weaknesses: 1. The contribution exhibits limited originality, as it primarily adapts CPC—an existing 2018 technique—with minimal substantive modification. The primary methodological enhancement involves introducing domain-specific negative sampling informed by Black-Scholes pricing theory; however, this alone does not constitute a sufficiently substantial innovation for high-impact publication. Greater methodological advancement would be necessary to constitute a meaningful scientific contribution.
2. The experimental evaluation is constrained in scope, notably lacking comparisons against recent advances in time series forecasting (e.g., iTransformer, PatchTST, DLinear) and contemporary self-supervised learning approaches designed specifically for temporal data (e.g., TimeDRL, TS2Vec, TS-TCC). This omission of competitive baselines undermines confidence in the results and obscures whether the proposed method truly represents an advancement over contemporary approaches.
3. The paper exhibits structural and presentation deficiencies that compromise readability and conformity with academic conventions. Specifically, the reference formatting diverges from ICLR standards, and the paper's organization is non-standard, with the Related Work section insufficiently demarcated and the Methodology section conflating experimental details that should occupy a distinct section. Adopting conventional structural conventions and improving formatting would substantially elevate the paper's clarity and alignment with journal expectations.
Recommendation: 1
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 1