Stock Forecast Used AI

Documentize all

(Before)
Local file and Notion: https://www.notion.so/Financial-time-series-forecasting-with-AI-22fa35337f588040b81cdb54c78ca3a3

(After)
Github: https://github.com/deveworld/stock-forecast
Website: https://stock.worldsw.dev
Password is '250716'

Financial time series forecasting with AI

First method
→ Classification model: XGBoost (for Baseline), Transformer
Get stock data with pykrx, news with gnews
Sentiment analysis for the news with ‘tabularisai/multilingual-sentiment-analysis’
And add technical features with ‘ta’
Using class_weights in CrossEntropyLoss to unbalance classes

XGBoost: 43.5%, Transformer: 41.7%
⇒ Class Imbalance problem?

Trials (but all failed):
- Data Augmentation (Noise or SMOTE)
- Changing Threshold of Classification
- More technical features or less technical features
- Change model architecture from Transformer to LSTM
- Use FocalLoss Instead of CrossEntropyLoss
- Add EarlyStopping method to prevent Over-Fitting
- Increase dropout value
- Chaning LR
- Gradient Clipping

Okay. I feel like I'm missing something.
Turn a classification problem into a regression problem
→ RMSE: 0.0212

Impressive…. It’s even worse. Okay, regression is much harder than classification.
Roll back into classification

Add macroeconomic scale data using Fred
- KOSPI
- KRW/USD Exchange Rate
- 10-Year Treasury Constant Maturity Rate
⇒ Still low perf…

WHY??????