niharikabalachandra / Market-Risk-Management-with-Time-Series-Prediction-of-Stock-Market-Trends-
Market Risk Management with Time Series Prediction of Stock Market Trends using ARMA, ARIMA, GARCH regression models and RNN for time series analysis and prediction of short-term tends in stock prices.
☆21Updated 7 years ago
Alternatives and similar repositories for Market-Risk-Management-with-Time-Series-Prediction-of-Stock-Market-Trends-:
Users that are interested in Market-Risk-Management-with-Time-Series-Prediction-of-Stock-Market-Trends- are comparing it to the libraries listed below
- Reproduce the result of the paper "Deep Learning with Long Short-Term Memory Networks for Financial Market Prediction"☆19Updated 4 years ago
- Stock markets are an essential component of the economy. Their prediction naturally arouses afascination in the academic and financial w…☆21Updated 3 years ago
- By combining GARCH(1,1) and LSTM model implementing predictions.☆57Updated 6 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 is to practice applying Long Short-Term Memory network in deep learning to predict time series financial data. I selected Am…☆15Updated 7 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
- Reproduction of code described in the paper "Stock Market Prediction Based on Generative Adversarial Network" by Kang Zhang et al.☆25Updated 4 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
- Stock Price prediction using news data. The datasets used consists news and stock price data from 2008 to 2016. The polarity(Subjectivity…☆47Updated 7 years ago
- Time Series analysis with Python and ARIMA model to forecast Bitcoin price☆21Updated 6 years ago
- The repository contains the code for project for DS 5500 course at Northeastern.☆36Updated 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
- RNN - Stock Prediction Model using Attention Multilayer Recurrent Neural Networks with LSTM Cells☆39Updated 7 years ago
- Using past price data and sentiment analysis from news and other documents to predict the S&P500 index using a LSTM RNN. Idea replicated …☆32Updated 9 months ago
- ARIMA & GARCH models for stock price prediction☆17Updated 4 years ago
- Implement the model of Halperin and Feldshteyn for DJIA and SP500☆11Updated 5 years ago
- Deep Reinforcement Learning Framework for Factor Investing☆25Updated last year
- Compilation of technical analysis tools (EMA, Bollinger bands), fundamental analysis, machine learning models (LSTM, Random forest, ARIMA…☆12Updated 3 years ago
- LSTM stock prediction and backtesting☆14Updated 5 years ago
- Stock Price Prediction with PCA and LSTM☆14Updated 3 years ago
- This project aims to predict VOLATILITY S&P 500 (^VIX) time series using LSTM.☆99Updated 4 years ago
- Stock Market Prediction on High-Frequency Data Using soft computing based AI models☆20Updated 5 months 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
- 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
- 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
- Trained an LSTM model in python to predict prices after denoising the price signal using wavelet transformation method.☆8Updated 5 years ago
- Calculate technical indicators from historical stock data Create features and targets out of the historical stock data. Prepare features …☆30Updated 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
- This project explores stock trading modelling with the use recurrent neural network (RNN) with long-short term memory (LSTM) architecture…☆27Updated 5 years ago
- Codes for the paper 'Clustering Approaches for Global Minimum Variance Portfolio'☆23Updated 2 years ago