stock_prediction

Predict stock using various models

Quantitative Analysis

1. Statistical Methods: Employing statistical techniques such as time series analysis, regression analysis, and factor models.
2. Machine Learning: Using algorithms like support vector machines, decision trees, and random forests to identify patterns and make predictions.

Econometric Models

1. ARIMA (AutoRegressive Integrated Moving Average): A popular time series forecasting method used to predict future stock prices.
2. GARCH (Generalized Autoregressive Conditional Heteroskedasticity): Modeling and predicting the volatility of stock returns.

Artificial Intelligence and Deep Learning

1. Neural Networks: Using deep learning techniques such as:
   1. LSTM (Long Short-Term Memory) networks,
   2. convolutional neural networks (CNNs), and
   3. recurrent neural networks (RNNs) to model and predict stock prices.
2. Reinforcement Learning: Implementing reinforcement learning algorithms to optimize trading strategies through trial and error.