# Real-Time Online Stock Forecasting Utilizing Integrated Quantitative and Qualitative Analysis

arXiv: https://arxiv.org/abs/2311.15218 · HTML: https://mlsanity.com/paper/2311.15218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 90 references that are in the map.
- First posted on arXiv 26 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts](https://mlsanity.com/paper/2001.05326/) (2020)
- [Improving Stock Market Prediction via Heterogeneous Information Fusion](https://mlsanity.com/paper/1801.00588/) (2018)
- [Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level](https://mlsanity.com/paper/1806.08890/) (2018)
- [Using Twitter Attribute Information to Predict Stock Prices](https://mlsanity.com/paper/2105.01402/) (2021)
- [Machine Learning for Stock Prediction Based on Fundamental Analysis](https://mlsanity.com/paper/2202.05702/) (2022)
- [S&P 500 Stock Price Prediction Using Technical, Fundamental and Text Data](https://mlsanity.com/paper/2108.10826/) (2021)

## Built on by (most cited first, 1 of 1)

- [An Agent Framework for Real-Time Financial Information Searching with Large Language Models](https://mlsanity.com/paper/2502.15684/) (2024)

## Abstract (arXiv)

The application of Machine learning to finance has become a familiar approach, even more so in stock market forecasting. The stock market is highly volatile, and huge amounts of data are generated every minute globally. The extraction of effective intelligence from this data is of critical importance. However, a collaboration of numerical stock data with qualitative text data can be a challenging task. In this work, we accomplish this by providing an unprecedented, publicly available dataset with technical and fundamental data and sentiment that we gathered from news archives, TV news captions, radio transcripts, tweets, daily financial newspapers, etc. The text data entries used for sentiment extraction total more than 1.4 Million. The dataset consists of daily entries from January 2018 to December 2022 for eight companies representing diverse industrial sectors and the Dow Jones Industrial Average (DJIA) as a whole. Holistic Fundamental and Technical data is provided training ready for Model learning and deployment. Most importantly, the data generated could be used for incremental online learning with real-time data points retrieved daily since no stagnant data was utilized. All the data was retired from APIs or self-designed robust information retrieval technologies with extremely low latency and zero monetary cost. These adaptable technologies facilitate data extraction for any stock. Moreover, the utilization of Spearman's rank correlation over real-time data, linking stock returns with sentiment analysis has produced noteworthy results for the DJIA and the eight other stocks, achieving accuracy levels surpassing 60%. The dataset is made available at https://github.com/batking24/Huge-Stock-Dataset.