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This project analyzes the predictability of Apple stock price movements using deep learning techniques across historical data and key corporate milestones. The objective is to evaluate whether machine learning models can achieve reliable directional prediction in financial markets.
The study employs multiple architectures, including LSTM, BiLSTM, and CNN-LSTM, along with walk-forward cross-validation and engineered financial features. It incorporates milestone-based features to assess the impact of corporate events on stock behavior.
Results indicate that while models capture certain patterns, their predictive accuracy remains close to random, supporting the Efficient Market Hypothesis. However, insights highlight the importance of volatility, event timing, and feature design in financial modeling.
This project provides a practical evaluation of deep learning limitations in stock market prediction and offers directions for future research
Applied Data Science assumes a significant role in the backdrop of massive data generated from millions of devices. Currently, the daily data output is over 2.5 quintillion bytes, likely to touch 1.7 Mb of data per second per person on the planet in the future. The 12-month PG Level Advanced Certification Programme in Applied Data Science and Machine Learning enables learners to build deep tech capabilities and apply their learnings to make data-driven business decisions for their organizations. TalentSprint is offering this programme in partnership with the Wadhwani School of Data Science an AI, IIT Madras and IITM Pravartak, India’s leading research hub at IIT Madras. Participants will experience a unique learning process that includes masterclass lectures, hands-on labs, hackathons, workshops, industry interactions, and a campus visit for fast-track learning.