liuyanjun-001's starred repositories
StockPricePrediction
Predicting stock market is very tough due to its volatile nature. It depends on geopolitics, the global economy, physical and psychological factors, and many more. Machine Learning and deep learning techniques will be used for making predictions in the stock market
MasterThesisHNGDeepVola
Discrete Volatility Models Using Deep Learning. Improving the approach of Horvath, Blanka and Muguruza, Aitor and Tomas, Mehdi, Deep Learning Volatility (January 24, 2019). Available at SSRN: https://ssrn.com/abstract=3322085 or http://dx.doi.org/10.2139/ssrn.3322085
deep-volatility-models
Volatility models for stock prices using deep learning and mixture models.
NN-StochVol-Calibrations
We implement the paper: Deep Learning Volatility
DepressionEstimation
Bachelor Thesis - Deep Learning-based Multi-modal Depression Estimation
MasterThesis-1
In my Computer Science Master's Thesis, I experimented with advanced deep learning models and introduced a new model, cNSVM, to refine the estimation of connectivity in financial time series data, facilitating enhanced market understanding for improved data-driven decision making in finance.
MTech-Project
This Thesis work is based on Prediction of Financial Stock price prediction using Deep learning techniques
UnivariateTimeSeriesForecast
PhD Thesis: "Data Science in the Modeling and Forecasting of Financial Timeseries: from Classic methodologies to Deep Learning"
unsupervised-learning-dimension-reduction
Python notebook for dimension reduction implementations using PCA
Blog-Principal-Component-Analysis
Step by step explanation of PCA. Please read along with implementation notebook.
PCA-from-Almost-Scratch
This is a notebook used to explain what PCA is, why it's useful, how to perform it, and how to apply it to a real problem.
Multilinear-Subspace-Learning-Algorithms
Python notebooks implementing multilinear PCA (MPCA, UMPCA) and LDA (UMLDA) algorithms using Tensorly.
sentiment-analysis-using--PCA
the sentiment analysis is done on US Airline dataset by using PCA
SentimentAnalysis_Clustering
Sentiment analysis, clustering, Gaussian mixture model, PCA
amsterdam-airbnb
Performed exploratory data analysis, data cleansing and modeling of Airbnb dataset containing 130k records to capture customer preferences and existing market in Amsterdam using Python. Created visualizations to get insights into the data and applied 4 statistical data models using Scikitlearn package and performed model evaluation in which linear regression model achieved the highest accuracy rate of 65%. Provided 3 recommendations to investors based on sentiment analysis and data visualization.
Sentiment_Analysis
This project aims to accurately gauge investor’s sentiment of stocks using social media platforms such as Twitter, and Stocktwits. While fundamental factors, such as revenue, cost, profit, and cashflow, play a vital role in the valuation of a publicly traded stock, at the end of the day the price is determined by supply and demand. The sentiment of buyers is a great indicator of demand and could provide insights towards the movement of price in the future and aid in finding opportunities for profitable trades.
Stock-Sentiment-from-News
Used NLP to generate investing insight by applying sentiment analysis on financial news headlines from Finviz to understand the emotion behind the headlines and predict whether the market feels good or bad about a stock.
MovieReview_Analysis-DL
By using sentiment analysis with IMDB movie reviews, we can extract customer’s sentiment about the movies from their reviews in a low-cost and efficient way, so that the platform or film investors can choose the popular movies or similar movies that contain favorable elements and get benefits from it.
financial-market-sentiment-analysis
A ML project that aims to develop a powerful sentiment analysis model for financial markets, analyzing news, social media data, and other textual sources. It also aims to provide real-time sentiment scores and actionable insights to aid investors and traders in making informed decisions and optimizing their strategies.
NIFTY50-Daily-Trend-Prediction-Using-NLP-Python
This project predicts stock market performance using sentiment analysis of tweets collected using snscrape library. The sentiment is visualized using histograms and bar plots. It can be useful for investors and traders as an additional indicator for stock market predictions.
SentimentAnalysisForFinancialNewsNotebook
Building the model of a financial news sentiment classifier. Financial news headlines will be classified as positive, negative or neutral (from an investor point of view)
MasterThesis
Python Code of Master Thesis on "Sentiment-Driven Behaviour of Retail Investors", Submitted August 2023
MSc-Thesis-Cryptocurrency-Return-Forecasting-Using-BERT-Based-Sentiment
Cryptocurrency Return Prediction Using Investor Sentiment Extracted by BERT-Based Classifiers From News Articles, Reddit Posts and Tweets ---- Master's thesis project for the program of M.Sc. Economics and Management Science at Humboldt University of Berlin
nlp-crypto-project
Repo for the "Using Deep Pre-Trained Language Models to Understand Investor Sentiment and Volatility for Cryptocurrencies," UC Berkeley independent study project.
5600website
Forecasting Volatility in Financial Time Series with the Contextual Factors of Weather Events, Labor Conditions, Investor Sentiment, and Bond Yields
Financial_sentiment_analysis
It contains the sentiment analysis for financial news headlines from the perspective of a retail investor.
twt_ipo_puzzle
Investor's Sentiment extracted from Twitter and IPO Puzzles