Revolutionizing Stock Market Prediction with Tensor Decomposition
Why Traditional Machine Learning Fails in Financial Markets
Markets reflect all available information, making prediction theoretically impossible
Asymmetric distribution of price movements creates training challenges
Financial data dominated by stochastic components and market sentiment
Market dynamics constantly evolve, invalidating historical patterns
Tucker Tensor Decomposition Meets Deep Learning
Stock prices are influenced by thousands of interacting factors - simple models cant capture these multi-way relationships
Markets exhibit patterns at multiple time scales - daily, weekly, monthly - that standard models miss
Large models overfit to noise, small models underfit - we need efficient expressiveness
Imagine you have a 3D cube of data representing how price, volume, and momentum interact over time. Tucker decomposition breaks this complex cube into smaller, manageable pieces while preserving the essential relationships.
Single direction, no tensor fusion
Single-scale CNN + LSTM
Multi-scale + Tucker + BiLSTM + Attention
QITF-Net draws inspiration from tensor networks used in quantum physics to efficiently represent high-dimensional quantum states. We apply these same mathematical principles to financial data - capturing complex interactions without needing a quantum computer.
5-Stage Hybrid Pipeline with Skip Connections
Dual-kernel convolution captures both short and long-term patterns simultaneously
Tucker decomposition captures complex multi-linear feature interactions
Bidirectional LSTM processes sequences forward AND backward for richer context
Dynamically weighs important time steps with 4 parallel attention heads
Dense layers with regularization for robust binary prediction
Dual kernels (3+5) capture patterns at different time scales - like looking at daily AND weekly trends together
Compresses 3rd-order tensors while preserving multi-linear relationships - quantum-inspired efficiency
Processes sequences both ways - understanding how past leads to present AND how future expectations affect current patterns
Direct information highways prevent vanishing gradients and enable deeper architectures
Why it works: Multi-scale CNNs capture patterns at different frequencies, Tucker decomposition compresses without losing relationships, BiLSTM understands temporal context from both directions, and attention focuses on the moments that matter most.
38 Engineered Indicators Across 5 Categories
All 38 indicators use .shift(1) to prevent look-ahead bias. This ensures only historical data is used for predictions, maintaining temporal causality.
predict(day=t, features=data[t])predict(day=t, features=data[t-1])df['RSI'] = rsi.shift(1)Advanced Techniques for Financial Data
Handles class imbalance by down-weighting easy examples and focusing on hard, minority-class samples. Prevents degenerate solutions where the model predicts only the majority class.
12 Experiments Across 4 Major Tech Stocks with Balanced Loss Training
| Model | Parameters | Accuracy | F1 Score | ROC-AUC |
|---|---|---|---|---|
CNN-LSTMOURS | 200K 45% smaller | 63.14% Best | 0.697 | 60.1% |
QITF-Net | 180K | 47.59% | 0.397 | 62.4% |
LSTM | 330K | 39.13% | 0.096 | 64.5% |
CNN-LSTM achieves best ROC-AUC of 76.22% on NVIDIA, showing strong discrimination ability
With balanced loss training, QITF-Net achieves 69.49% ROC-AUC on Tesla with proper precision-recall trade-off
Balanced BCE Loss with entropy regularization eliminates high-recall bias in tensor fusion models
Practical Applications in Financial Markets
Automated trading systems leveraging QITF-Net predictions for entry/exit signals
Portfolio risk assessment and hedging strategies based on movement predictions
Multi-asset allocation using predicted returns and uncertainty estimates
Dynamic spread adjustment and inventory management for market makers
How QITF-Net embodies the capstone values
First application of quantum-inspired tensor networks to finance
FastAPI backend, Next.js frontend, Docker deployment ready
Comprehensive codebase with documentation for community learning
Rigorous evaluation, temporal safety, multiple baselines
Building the Future of Financial AI
Samsung Innovation Campus AI/ML Capstone Project
A dedicated team passionate about applying cutting-edge machine learning techniques to real-world financial challenges. We combined theoretical innovation with practical engineering to build QITF-Net - achieving 60.48% average accuracy and 70.7% peak ROC-AUC across Tesla, NVIDIA, Amazon, and Meta stocks.
Identified limitations of traditional ML in finance, discovered Tucker decomposition potential for capturing multi-linear market relationships
Designed QITF-Net: Multi-scale CNN (kernels 3+5) + Tucker Tensor (rank 32) + BiLSTM (2 layers) + Multi-head Attention
Built production-grade ML pipeline with 20 technical indicators, temporal safety, and comprehensive cross-stock evaluation
Created full-stack demo with FastAPI backend, Next.js frontend, interactive 3D tensor visualizations, and real-time predictions
Roadmap for Next-Generation Financial AI
QITF-Net is built with the open-source community in mind. We believe in democratizing access to advanced financial ML techniques and contributing to collective knowledge.
Detailed README, code comments, and architecture explanations
Config-driven experiments with automatic checkpointing
Educational resource for tensor networks in finance
To make quantum-inspired machine learning accessible for financial applications, bridging the gap between theoretical physics and practical trading systems.