Smart Algorithms to predict buying and selling of stocks on the basis of Mutual Funds Analysis, Stock Trends Analysis and Prediction, Portfolio Risk Factor, Stock and Finance Market News Sentiment Analysis and Selling profit ratio. Project developed as a part of NSE-FutureTech-Hackathon 2018, Mumbai. Team : Semicolon

The coding has been done on Python 3.65 using Jupyter Notebook. This program fetches LIVE data from TWITTER using Tweepy. Then we clean our data or tweets ( like removing special characters ). After that we perform sentiment analysis on the twitter data and plot it for better visualization. The we fetch the STOCK PRICE from yahoo.finance and add it to the data-set to perform prediction. We apply many machine learning algorithms like (random forest, MLPClassifier, logistic regression) and train our data-set. Then we perform prediction on untrained data and plot it with the real data and see the accuracy.

Sentiment Analysis on Financial Stocks. Sources of information are Twitter, News, Yahoo Finance Statistics.

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FinTechForge is an open-source modular platform designed to provide smart, AI-powered financial tools and insights. From sentiment analysis of financial news to secure authentication and dashboards, this project empowers developers, analysts, and students to build and extend finance-focused applications.

The application is a cloud service that provides the functionality of performing sentiment analysis on stock market and financial data. The application can be hosted on Google App Engine and makes use of many of the GAE services like Search Service, MemCache, DataStore etc. Given the name of a company, data from various sources like Twitter, Facebook Graph, Google News, Google Finance etc is aggregated. For each source, different models have been pretrained using some prior data. Using different models provided us with a chance to utilize different Machine Learning methodologies based on the type of data from each source. The various techniques that we have built and tested on are :Naive Bayes, Multinomial and Bernoulli text representations, KNN.

This repository consists of customized word embedding focused on banking and finance terms which will be helpful in analyzing and classifying financial sentiments or stock price sentiment analysis.

Scrape Reddit Finance Forums for Social Sentiment Analysis

Benchmark pipeline for evaluating language models on financial tasks, including sentiment analysis and credit scoring. Supports over ten tasks with modular design for easy integration of new tasks. Provides automated performance metrics for standardized evaluation, benefiting researchers and practitioners in finance.

Project on engagement and stock price analysis of CEOs on Twitter. Extracted the data from Twitter API and Yahoo Finance and implemented sentiment analyzer, topic modeling (LDA), stock price regression and engagement analysis to determine the factors that make a CEO influential

FinBERT is a pre-trained NLP model to analyze the sentiment of the financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. For the details, please see FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. The project was deployed on the flask. Providing an indicator to buy, hold or sell stock based on the sentiment predicted for the following news headline/article.

This project was completed with the intention of helping Tesla stock investors better understand how to make decisions where the stock market is very volatile by training different models through historical and social media data analytics. Behavioral economics shows that public emotions can profoundly affect individual behavior and decision making. In order for investors to utilize it, business analysts must understand the behaviors and attitudes of the public within the finance context. Nowadays, social media perfectly tracked by data reflects the public emotions and sentiment about stock movement. Also, tremendous stock marketing news can be used to capture a trend of stock movement. The fundamental trading and decision making for main techniques rely on expert training and prediction. This article concentrated on tweets and stock news, and I applied sentiment analysis and machine learning models, especially, XGBoost to tweets and news extracted from Elon Musk tweets, Nasdaq and New York Times News about Tesla. Only by understanding the values and priorities of the public sentiment of Tesla stock will investors be able to make significant decisions. In addition, I conducted two models- ARIMA and RNN(LSTM) in forecasting the Tesla stock price. I compare their results with the prediction performances of the classical ARIMA and RNN.

This projects allows users to predict stock prices through the use of scikit-learn to train a support vector regression on a Google Finance dataset (apple in this case). The code produces a graph showing the 3 model used: RBF, Linear, and Polynomial (RBF turned out to be the best one). The Machine Learning model can be adjusted to Keras, as well, to adapt it to Neural Networks. A further upgrade might be prediction of stock prices by using sentiment analysis and price history.

The key arguments for the low utilization of statistical techniques in financial sentiment analysis have been the difficulty of implementation for practical applications and the lack of high quality training data for building such models. Especially in the case of finance and economic texts, annotated collections are a scarce resource and many are reserved for proprietary use only. To resolve the missing training data problem, we present a collection of ∼ 5000 sentences to establish human-annotated standards for benchmarking alternative modeling techniques. The objective of the phrase level annotation task was to classify each example sentence into a positive, negative or neutral category by considering only the information explicitly available in the given sentence. Since the study is focused only on financial and economic domains, the annotators were asked to consider the sentences from the view point of an investor only; i.e. whether the news may have positive, negative or neutral influence on the stock price. As a result, sentences which have a sentiment that is not relevant from an economic or financial perspective are considered neutral.

Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers

Sentiment Analysis on 10-k filings based on Loughran and McDonald paper (2011)

Analyzed sentimental information of Yahoo Finance Conversation messages to forecast stock movements using 4 sentiment analysis algorithms in Python

In this the stocks data of QCOM has been downloaded from https://finance.yahoo.com/ and india-news-headlines data from https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DPQMQH.Further did data cleaning and pre processing.The did sentimental analysis to find sentiment of india news headlines as well as its polarity,subjectivity,compound using textblob and vador .Then merged stocks and inh data,on which I applied regression model in order to predict Adj Close of stocks by textual data analysis.

尝试将金融舆情作为量化因子进行分析。

Causal factor graph + agentic sentiment engine for quant finance. 52-node graph with Claude/GPT analysis, 3D visualization, regime detection, and signal propagation.

This project is designed to scrape all articles on Yahoo! Finance based on the inputted stocks and provides a Sentiment Analysis using the Transformers library.

This is my original product, I have builded AI based Finance Analyst which is based on AI Agentic Workflow concept, it uses yahoo finance for fetching stock data and duckduckgo as search engine to query the web to perform sentiment analysis as per latest news about the stock, you will need to configure your own perplexity key to use my product.

Programming with torch is becoming fun day by day. And with that finetuning a LLM is becoming interesting too! I have fine tuned FinBERT LLM an opensource finance transformer model to give stock market prediction based on the news headlines or financial articles with the help of sentiment analysis on the text input.

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Retrieved financial institutions data using Twitter’s API, created a database, and performed pre-processing of this data • Created an AI module which classified each tweet into three different sentiment groups: Positive, Negative and Neutral Developed a POC which predicted the stock price of these financial institutions based on the different sentiment groups

Sentimental Analysis for Finance using Bilstm and bert tuning

GPT-3 and GPT-Neo zero-shot solution for financial sentiment analysis

Gives out the financial news of the top companies in a user specified country along with the average sentiment rating

AutoTraderBot is a Python-based automated trading bot designed to execute trades in the financial markets using machine learning sentiment analysis. It utilizes Alpaca as the brokerage platform for executing trades and Yahoo Finance data for backtesting.

Source code for 'Enhancing TinyBERT for Financial Sentiment Analysis Using GPT-Augmented FinBERT Distillation.' Demonstrates knowledge distillation for optimizing sentiment analysis in finance.