Using AI to Identify Undervalued Stocks for Investment

Approved

Relevance and Originality

The project addresses a timely and relevant topic: utilizing artificial intelligence for stock market predictions. The focus on identifying undervalued companies through financial metrics makes the study particularly applicable for investors. To enhance originality, the article could explore unique methodologies or innovative features that differentiate this model from existing stock prediction systems.

Methodology

The methodology employs a neural network-based approach using Keras and TensorFlow, which is suitable for the task. However, more detail on the architecture of the neural network, including layers and activation functions, would strengthen the methodology. Additionally, specifying how the financial metrics (PE, PB, DE, EPS growth) are calculated and selected for the model would improve clarity.

Validity & Reliability

The validity of the predictive model depends on the quality of the financial data used. Discussing the sources of data, as well as any potential biases, would enhance reliability. Including performance metrics such as confusion matrix details or feature importance could provide a deeper understanding of the model's effectiveness.

Clarity and Structure

The article communicates its objectives clearly but could benefit from improved organization. Structuring the content into distinct sections—such as introduction, methodology, results, and discussion—would help readers follow the progression of the research more easily. Clear headings and subheadings would further enhance readability.

Result Analysis

While the assessment of model performance using accuracy, ROC-AUC score, and cross-validation is promising, more specific results or examples would strengthen the findings. Discussing the implications of the model's predictions for investment strategies, as well as any limitations and areas for future research, would enrich the overall contribution of the project.