Kati8/ICLR_2023_2025 huggingface dataset:
authors: Manaswini Swamy
authors: Arunima Shukla
authors: James Purtilo
reviews: 
Comments: The paper introduces a neural network-based method for predicting stock trends by analyzing company interdependencies and news impact.
Strengths: None. The paper significantly misses the mark for ICLR standards, displaying a conspicuous absence of the analytical depth and methodological rigor that characterizes scholarly research. Its presentation and investigative approach lack the innovation, thoroughness, and scholarly discourse expected in an academic publication.
Weaknesses: W1: Lack of Specificity and Detail. Several sections of the paper could benefit from more detailed information, such as specific algorithms used, neural network architecture, and a clearer explanation of the variables included in the model. The broad statements and lack of detailed data or theoretical backing make it difficult to fully assess the validity of the claims.

W2: The paper lacks a comparison with existing models or approaches. This omission makes it challenging to gauge the actual advancement this research proposes.

W3: The paper fails to clearly convey complex procedures, particularly around the data collection and neural network modeling. This lack of clarity could hinder readers' understanding.

W4: The research's basis on a hypothetical situation, rather than real-world data and scenarios, may detract from its applicability and relevance. It creates a simulation-like environment which may not account for all real-world variables and uncertainties, decreasing the robustness of the findings.

W5: The paper lacks a thorough literature review, which is crucial for situating any research within the context of existing knowledge. Additionally, there are very few references (only two!), and those included are not from peer-reviewed sources, which could call into question the research's grounding in established academic discourse.
Recommendation: 1: strong reject
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 1 poor
Clarity: 1 poor
reviews: 
Comments: This submission studied a hypothetical scenario where the companies' stock prices rely not just on their progress, but also on the performance of their interdependencies (e.g., their clients, suppliers and competitors). The authors tracked stock values over time and assesses news sentiment’s influence, presented a comprehensive view of news-event-driven market dynamics.
Strengths: * This submission studied an important problem, i.e., how companies' interdependencies influence their stock values.
Weaknesses: * This submission is not a scientific writing, with nearly no references at all, and model designs / data source details missing. I do not think this paper should even pass the pre-screening.
Recommendation: 1: strong reject
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 2 fair
Clarity: 1 poor
reviews: 
Comments: This work firstly collected stock news and their numerical data via stocknewsapi.com, and then applied regression analysis. Finally, it presents a picture of integrating more features into a neural network model for trend prediction. This manuscript is not ready for review because it lacks a clear research niche, a sufficient literature review, a proposed novel approach as a solution, and comprehensive evaluations to support arguments.
Strengths: n/a
Weaknesses: It lacks a clear research niche, a sufficient literature review, a proposed novel approach as a solution, and comprehensive evaluations to support arguments.
Recommendation: 1: strong reject
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 1 poor
Clarity: 1 poor
reviews: 
Comments: This paper proposes a neural network approach for forecasting equity market movements through examination of corporate relationships and media coverage effects. However, the work falls considerably short of the expectations established for ICLR submissions. The contribution demonstrates insufficient intellectual sophistication, limited methodological soundness, and inadequate scholarly presentation. The research methodology appears to lack the originality, comprehensiveness, and academic rigor necessary for publication at a premier venue.
Weaknesses: W1: Insufficient Precision and Elaboration. Multiple portions of the manuscript would greatly benefit from greater specificity regarding implementation choices, including the particular algorithms employed, the design specifications of the neural network infrastructure, and a transparent definition of the features integrated into the predictive framework. The reliance on generalized assertions coupled with minimal empirical substantiation or theoretical justification creates ambiguity regarding the credibility of the central findings.

W2: Missing Comparative Analysis. The paper does not establish a baseline comparison against alternative methodologies or competing techniques. This significant gap prevents readers from understanding what novel contributions or performance advantages the proposed approach offers relative to existing solutions.

W3: Insufficient Exposition of Technical Content. The elucidation of intricate technical elements—particularly regarding information acquisition procedures and neural network construction—lacks adequate clarity. This communicative deficiency may compromise comprehension for the intended readership.

W4: Reliance on Artificial rather than Empirical Data. The investigation utilizes synthetic or hypothetical scenarios in lieu of genuine historical market data and authentic real-world conditions. This methodological choice raises concerns about the practical utility and relevance of the findings, as the controlled experimental setting may not capture the full spectrum of actual market complexities and variability.

W5: Inadequate Engagement with Existing Literature. The manuscript fails to provide a comprehensive review of pertinent prior work necessary for contextualizing the research within the scholarly landscape. The citation list is remarkably sparse—containing merely two references—and these sources lack credentials from peer-reviewed publication channels, which undermines the scholarly legitimacy and evidence-based foundation of the investigation.
Recommendation: 1: strong reject
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 1 poor
Clarity: 1 poor