these r the papers ill be also using Based on your research topic — "Stock Price Prediction Using Real and Simulated Data" — here are the Top 4 papers that are most aligned, ranked by relevance and usefulness:

🏆 Top 4 Papers for Your Literature Review

1. A Hybrid Quantum-Classical Model for Stock Price Prediction Using Quantum-Enhanced Long Short-Term Memory
Why it's top choice:
Introduces a quantum-enhanced LSTM, directly aligned with your interest in quantum algorithms.


Uses real stock price data and evaluates against classical models.


Discusses how quantum features improve prediction robustness — very useful for your “quantitative methods” presentation.


✅ Covers real data, machine learning, and quantum — core to your theme.

2. A Novel Stock Price Prediction and Trading Methodology Based on Active Learning Surrogated with CycleGAN and Deep Learning
A Novel Stock Price Prediction and Trading Methodology Based on Active Learning Surrogated with CycleGAN and Deep Learning and System Engineering Integration: A Case Study on TSMC Stock Data
Why it's excellent:
Uses CycleGAN to generate synthetic stock data, which is then used in deep learning models.


Applies active learning for better prediction efficiency.


It’s a real case study (TSMC stock), so results are practical and interpretable.


✅ Best paper for synthetic data + real stock + deep learning methods.

3. Research on Stock Price Prediction Method Based on the GAN-LSTM-Attention Model
 (PDF) Research on Stock Price Prediction Method Based on the GAN-LSTM-Attention Model
Why it’s strong:
Uses GAN to generate synthetic training data, then feeds it to LSTM with Attention.


Focuses on improving accuracy in complex time series.


Helps you talk about how generated data can improve sequential models (very relevant to your comparison).


✅ Very close to your simulation + ML combination theme.

4. Leveraging on Synthetic Data Generation Techniques to Train Machine Learning Models for Tenaga Nasional Berhad Stock Price Movement Prediction 
Leveraging on Synthetic Data Generation Techniques to Train Machine Learning Models for Tenaga Nasional Berhad Stock Price Movement Prediction 
Why it's useful:
Uses SMOTE and GAN-SMOTE for synthetic data generation.


Practical application on energy sector stocks (Tenaga).


Compares model performance with and without synthetic data.


✅ Simple, clear example of synthetic data boosting model accuracy.

⚠️ Skip or Use Sparingly:
Stock Price Prediction in the Financial Market Using ML Models – uses only real data, traditional ML (RF/SVM). Good for benchmarking, but not central.


Stock Price Prediction and Traditional Models – useful as a control or historical context, but doesn't involve ML or simulation.


COVID-19-based paper (PMC10513304) – too specific and off-topic unless you're focusing on pandemic-related volatility.



✅ Final Recommendation: Use These 4
Hybrid Quantum-Classical (QLSTM)


CycleGAN + Deep Learning (TSMC study)


GAN-LSTM-Attention Model


Tenaga Nasional with SMOTE/Synthetic Data


These give you a diverse but targeted spread of:
Quantum methods


Deep learning + synthetic data


Classic synthetic techniques (SMOTE)


Real case studies with market relevance