reviews: 
Comments: The paper aims to overcome manual feature engineering in financial time series by focusing on automatic feature generation. To do so, it considers the application of unsupervised contrastive learning to the domain of financial time series. Specifically, the paper proposes an implementation of Contrastive Predictive Coding (CPC) to extract meaningful features from the data to be used in downstream forecasting tasks. Embeddings are optimized by maximizing the similarity with subsequent windows of observations (positive samples) while separating from random noise (negative samples). The extracted embeddings are paired with a linear regression model, improving performance over simple baselines in financial tasks.

Despite the interesting application and some promising results, I think the paper suffers from critical weaknesses that prevent me from recommending acceptance.
Strengths: - Developing time series methods that are robust to inherent strong stochasticity in the data is significant.

- The application of CPC to the financial domain seems novel.

- Some of the presented results are promising.
Weaknesses: Major:
- I did not find the paper’s motivations convincing. The first sections only mention that CPC was never evaluated in the financial domain. I think this is more of a necessary condition rather than sufficient. The paper would be much stronger if convincing arguments are provided on why CPC is a good choice for tackling stochasticity and why is preferable w.r.t. other unsupervised representation learning methods.

- The contextualization of this paper within the current literature is very poor. Only 3 references are provided in the literature review section: CPC, an application to anomaly detection, and an LSTM-based architecture. Overall, only 11 references are provided. I think the paper would benefit from a better presentation, up to recent works, of time series unsupervised representation learning [1] and forecasting [2], preferably discussing relevance to the financial domain.

- The presentation and organization of the paper’s content is very poor. A considerable portion of the paper (~3 pages in total) is dedicated to presenting naive baselines and details about the standard ML pipeline (preprocessing, normalization, windowing, train/test split), while all the important figures, describing the architecture and presenting the main results, are piled in the Appendix with minimal text. I think the authors should consider moving to the main text all tables/plots that they believe are relevant to support their claims.

-  The learned embeddings are only evaluated in conjunction with linear regression, which severely limits the impact of the work, especially given the targeted venue.

- The paper claims LSTMs struggling to learn from the considered dataset and being outperformed by linear regression and naive baselines (Sec. 3.2-3.3-3.4). However, Figure 7 (in Appendix) is the only result supporting this. Furthermore, the experimental pipeline does not consider a validation set for early stopping and model selection, which is arguably fundamental for deep architectures. Given the sensitivity of the paper's claim, I think it should be supported with more empirical evidence, e.g., learning curve, hyperparameter optimization.

Minor
- There are some inconsistencies in the notation. For example, $x_t$ is found on both sides of Eq.2;  notation is inconsistent between Eqs.1-2 and between Eqs. 3-4-5-6. 

- The definition of the CPC-LR acronym is missing.

- Figure presentation could be improved, e.g., all plots in Fig. 8 are missing axis labels; captions are very concise; text in Fig.3-5-7 is small. 

- The paper might benefit from a more gentle introduction to some domain-specific terminology, e.g., allocations, buy-and-hold, return plots.


[1] Meng, Qianwen, et al. "Unsupervised representation learning for time series: A review." arXiv preprint arXiv:2308.01578 (2023).

[2] Benidis, Konstantinos, et al. "Deep learning for time series forecasting: Tutorial and literature survey." ACM Computing Surveys 55.6 (2022).
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 1
Clarity: 1