Using AI to Identify Undervalued Stocks for Investment

Approved

Relevance and Originality

The project addresses a significant challenge in investment strategies: identifying undervalued stocks using artificial intelligence. This focus is highly relevant, given the increasing interest in data-driven decision-making in finance. The application of AI to financial metrics like PE, PB, DE, and EPS growth reflects originality, although incorporating novel data sources or techniques could enhance the innovative aspect of the research.

Methodology

The methodology utilizes Keras and TensorFlow to develop a neural network for predicting undervalued stocks, which is appropriate for the task. However, further details on the data collection process and the specific architecture of the neural network would strengthen the methodology. Clarifying how financial metrics are selected and processed for model input would also improve transparency.

Validity & Reliability

The validity of the predictive model relies heavily on the quality of the financial data used. Discussing the sources of data, sample size, and any preprocessing steps taken to ensure data integrity would enhance reliability. Additionally, outlining the rationale for chosen evaluation metrics (accuracy, ROC-AUC score, cross-validation) and how they specifically relate to stock prediction would provide a stronger foundation for the findings.

Clarity and Structure

The article presents its ideas clearly, but organizing content into distinct sections—such as methodology, results, and discussion—would enhance readability. More structured headings would help guide readers through the research process. Including visual representations, such as diagrams of the neural network architecture or performance metrics, could further aid comprehension.

Result Analysis

While the project aims to create a predictive model, a deeper analysis of preliminary results or insights gained from model performance would strengthen the article. Discussing specific findings regarding stock predictions, potential investment strategies, or limitations of the model would enhance the overall impact. Additionally, exploring practical implications for investors based on the model's predictions would provide significant value to the research.