This project focuses on predicting stock returns using deep learning methods. Specifically, 1D Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Artificial Neural Networks (ANN) are employed to analyze historical stock data and forecast future returns.
The dataset used for this project consists of historical stock prices and related financial indicators. It includes information such as opening and closing prices, trading volumes, and other market-specific features. The dataset is obtained from reliable financial data sources or APIs.
The main objective of this project is to build predictive models that can effectively estimate stock returns for future time periods. By analyzing patterns and trends in historical stock data, the deep learning models aim to capture intricate relationships between various features and the target variable (stock returns). The project aims to create models that can provide valuable insights and assist in making informed investment decisions.
Data Preprocessing: The historical stock data is preprocessed to handle missing values, outliers, and perform necessary feature scaling. Time series-specific preprocessing techniques like differencing or log transformations may be applied to make the data suitable for modeling.
Feature Engineering: Additional features can be engineered from the available dataset, including lagged variables, technical indicators, and market sentiment indicators. These engineered features provide additional information and improve the model's predictive power.
Convolutional Neural Networks (CNN): CNNs can be used to extract spatial and temporal patterns from the input data. They are especially useful when dealing with sequential data such as time series. The model architecture includes convolutional layers, average pooling layers, and fully connected layers.
Long Short-Term Memory (LSTM) Networks: LSTM networks are well-suited for capturing long-term dependencies and handling sequential data. They can effectively model the temporal nature of stock prices and capture patterns over different time horizons.
Artificial Neural Networks (ANN): ANNs can also be employed to learn complex relationships between features and stock returns. They consist of multiple hidden layers and utilize activation functions to introduce non-linearities into the model.
Model Training and Evaluation: The dataset is split into training and testing sets. The deep learning models are trained on the historical data and evaluated on the unseen test set. Evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), and R2 Score are used to assess the performance of the models.
Hyperparameter Tuning: The models' hyperparameters, such as learning rate, number of layers, and activation functions, are tuned using techniques like grid search or random search. This helps to optimize the model's performance and generalization ability.
data/: Contains the dataset used for the analysis.
notebooks/: Jupyter notebooks containing the code for data preprocessing, feature engineering, model training, and evaluation.
models/: Saved models or model artifacts, including trained CNN, LSTM, and ANN models.
README.md: Overview and instructions for the project.