Authors: Morteza Maleki

Published on: April 16, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2404.10208

Summary

• What is new: This study introduces a novel approach by combining machine learning techniques with an enriched dataset of macroeconomic indicators and market data to predict downturns in the tech sector specifically.
• Why this is important: The challenge in predicting stock price movements, particularly identifying major downturns in the Information Technology Sector.
• What the research proposes: A combination of multiple regression and logistic regression analysis on historical stock prices, technical indicators, and macroeconomic data to forecast significant downturns.
• Results: The models were able to identify patterns that predict sector-specific downturns, improving investment strategies by anticipating market volatility.

Technical Details

Technological frameworks used: Machine learning

Models used: Multiple regression analysis, logistic regression

Data used: Macroeconomic indicators, market data

Potential Impact

Portfolio management, investment firms, and companies within the GICS Information Technology Sector could benefit or be disrupted by these predictive insights.

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