reviews: 
Comments: This manuscript addresses stock market trend prediction but presents substantial deficiencies that warrant rejection. The work lacks organizational clarity, making it difficult for readers to distinguish between foundational concepts, methodological contributions, and experimental design. The incorporation of LLM-based services through ChatGPT emerges only toward the conclusion, yet this reliance on pre-trained models represents a fundamental aspect that should be disclosed prominently from the outset. Critical experimental details remain unexplained, including dataset selection, preprocessing procedures, and train-test partitioning schemes. The connection between findings presented in Section 3 regarding optimal time period combinations and the justification for employing LLMs is poorly articulated. The reported accuracy improvements are marginal (0.72 to 0.73), raising questions about whether LLM integration provides meaningful value. Visualization elements lack proper documentation, and tangential discussions, such as the treatment of the Volatility Index, do not substantively support the paper's central claims. The manuscript does not adequately demonstrate empirical advantages of the proposed approach relative to conventional methodologies, and the overall contribution remains nebulous and open to doubt.
Weaknesses: * The organizational framework is inadequate, preventing clear delineation between related work, methodological innovation, and experimental configuration, imposing unnecessary cognitive burden on readers.
* The review of existing literature is superficial, lacking a dedicated section for prior research and failing to incorporate pertinent domain literature. References to [A systematic review of fundamental and technical analysis of stock market predictions] and [Neural network techniques for financial performance prediction: integrating fundamental and technical analysis] would strengthen this section.
* The proposed methodology lacks transparent articulation regarding its specific nature and distinguishing characteristics.
* ChatGPT dependency is concealed until late in the manuscript, despite being instrumental to experimental outcomes. The authors utilize existing LLM capabilities rather than developing their own, a distinction requiring upfront acknowledgment.
* Dataset identity, transformation procedures, and partitioning schemes remain unspecified and ambiguous.
* The relationship between findings in Section 3 (optimal time period sequences) and the rationale for LLM application is unexplained.
* Section 4's accuracy computation methodology is undocumented. Given performance figures between 0.7-0.72 and modest gains from LLM utilization (0.72-0.73), the utility of this approach appears limited. The section additionally purports to investigate causal mechanisms but lacks precision, with results appearing incongruent with stated objectives.
* Figure 1 omits y-axis designation and clarifying caption; Figures 2 and 3 contribute minimal interpretive value. Language choices like "We were pleased with the accuracy" deviate from scholarly conventions.
* Section 5's discussion of the VIX appears disconnected from core methodology and objectives, and experimental evidence does not substantiate claimed advantages of LLM application.
* The manuscript provides no comparative evaluation against established competing techniques.
* The work's substantive contributions remain obscured and contestable, with misalignment between experimental design and stated motivation.
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