BirdiD / Stock-trends-prediction-with-macroeconomic-indicators
Stock markets are an essential component of the economy. Their prediction naturally arouses afascination in the academic and financial world. Indeed, financial time series, due to their widerange application fields, have seen numerous studies being published for their prediction. Some ofthese studies aim to establish whether there is a strong …
☆20Updated 3 years ago
Alternatives and similar repositories for Stock-trends-prediction-with-macroeconomic-indicators:
Users that are interested in Stock-trends-prediction-with-macroeconomic-indicators are comparing it to the libraries listed below
- By combining GARCH(1,1) and LSTM model implementing predictions.☆56Updated 6 years ago
- Market Risk Management with Time Series Prediction of Stock Market Trends using ARMA, ARIMA, GARCH regression models and RNN for time ser…☆21Updated 7 years ago
- In this project, we implement and compare the performance of several machine learning and deep learning algorithms in predicting the US s…☆53Updated 4 years ago
- Compilation of technical analysis tools (EMA, Bollinger bands), fundamental analysis, machine learning models (LSTM, Random forest, ARIMA…☆12Updated 3 years ago
- To create a data-web application deployed using the azure app service, which was made on Streamlit, the leading Pythonic data application…☆10Updated 2 years ago
- This project explores stock trading modelling with the use recurrent neural network (RNN) with long-short term memory (LSTM) architecture…☆27Updated 5 years ago
- Conversion of the time series values to 2-D stock bar chart images and prediction using CNN (using Keras-Tensorflow)☆40Updated 2 years ago
- Reproduce the result of the paper "Deep Learning with Long Short-Term Memory Networks for Financial Market Prediction"☆19Updated 4 years ago
- A Python implementation of a Hybrid LSTM-GARCH model for volatility forecasting☆27Updated 2 years ago
- Stock Price Prediction with PCA and LSTM☆14Updated 3 years ago
- ARIMA & GARCH models for stock price prediction☆17Updated 4 years ago
- https://arxiv.org/abs/2006.04992☆19Updated 3 years ago
- Stock Market Price Prediction: Used machine learning algorithms such as Linear Regression, Logistics Regression, Naive Bayes, K Nearest N…☆21Updated 4 years ago
- Multi-Factor Stock Profit Prediction Using EMD-ALSTM☆27Updated 5 years ago
- The random forest, FFNN, CNN and RNN models are developed to predict the movement of future trading price of Netflix (NFLX) stock using t…☆60Updated 3 years ago
- stock prediction with GAN and WGAN☆93Updated 2 years ago
- kennedyCzar / STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDAForecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning …☆133Updated 2 years ago
- An Empirical Study of Optimal Combination of Algorithms for Prediction-Based Portfolio Optimization Model using Machine Learning over Co…☆11Updated 2 years ago
- Stock selection and portfolio performance based on ESG Scores☆14Updated 3 years ago
- The repository contains the code for project for DS 5500 course at Northeastern.☆36Updated 5 years ago
- XGBoost is known to be fast and achieve good prediction results as compared to the regular gradient boosting libraries. This project atte…☆30Updated 5 years ago
- Financial risk analysis on a stocks portfolio through the VaR (Value at Risk), using Monte Carlo Simulation and Multiple Linear Regressio…☆20Updated 4 years ago
- Transformer and MultiTransformer layers for stock volatility forecasting purposes☆65Updated 3 years ago
- This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Networks to predict stock prices.☆83Updated 3 years ago
- Deep Reinforcement Learning Framework for Factor Investing☆24Updated last year
- Try to predict stock price with LSTM、GAN and DRL, exploring the features of news and technical indicators,which help improving perfomance…☆90Updated 5 years ago
- A hybrid model to predict the volatility of stock index with LSTM and GARCH-type input parameters☆22Updated 4 years ago
- Developing hybrid deep learning models by integrating Neural networks with (s,e,t)GARCH models to predict volatility in the Indian Commod…☆15Updated 3 years ago
- Simulate and estimate volatility by GARCH with/without leverage, riskmetriks. Compute Value-at-Risk and Test on VaR Violation☆20Updated 6 years ago
- Univariate_ARIMA_models, ARCH/GARCH Volatility Forecasting models, VAR model for macro fundamentals forecasts☆11Updated 4 years ago