# Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices

arXiv: https://arxiv.org/abs/2312.03758 · HTML: https://mlsanity.com/paper/2312.03758/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Temporal Relational Ranking for Stock Prediction](https://mlsanity.com/paper/1809.09441/) (2018)
- [Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction](https://mlsanity.com/paper/1712.02136/) (2017)
- [Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model](https://mlsanity.com/paper/1808.01560/) (2018)
- [ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting](https://mlsanity.com/paper/2012.06289/) (2020)

## Built on by (most cited first, 3 of 3)

- [StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction](https://mlsanity.com/paper/2409.08281/) (2024)
- [AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction](https://mlsanity.com/paper/2407.18324/) (2024)
- [Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion](https://mlsanity.com/paper/2603.19286/) (2026)

## Abstract (arXiv)

Predicting stock market is vital for investors and policymakers, acting as a barometer of the economic health. We leverage social media data, a potent source of public sentiment, in tandem with macroeconomic indicators as government-compiled statistics, to refine stock market predictions. However, prior research using tweet data for stock market prediction faces three challenges. First, the quality of tweets varies widely. While many are filled with noise and irrelevant details, only a few genuinely mirror the actual market scenario. Second, solely focusing on the historical data of a particular stock without considering its sector can lead to oversight. Stocks within the same industry often exhibit correlated price behaviors. Lastly, simply forecasting the direction of price movement without assessing its magnitude is of limited value, as the extent of the rise or fall truly determines profitability. In this paper, diverging from the conventional methods, we pioneer an ECON. The framework has following advantages: First, ECON has an adept tweets filter that efficiently extracts and decodes the vast array of tweet data. Second, ECON discerns multi-level relationships among stocks, sectors, and macroeconomic factors through a self-aware mechanism in semantic space. Third, ECON offers enhanced accuracy in predicting substantial stock price fluctuations by capitalizing on stock price movement. We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility.