• Corpus ID: 221761458

Using Machine Learning and Alternative Data to Predict Movements in Market Risk

  title={Using Machine Learning and Alternative Data to Predict Movements in Market Risk},
  author={Thomas Dierckx and Jesse Davis and Wim Schoutens},
  journal={arXiv: Computational Finance},
Using machine learning and alternative data for the prediction of financial markets has been a popular topic in recent years. Many financial variables such as stock price, historical volatility and trade volume have already been through extensive investigation. Remarkably, we found no existing research on the prediction of an asset's market implied volatility within this context. This forward-looking measure gauges the sentiment on the future volatility of an asset, and is deemed one of the… 

Tables from this paper


Bridging the divide in financial market forecasting: machine learners vs. financial economists
Overall, it is confirmed that advanced forecasting methods can be used to predict price changes in some financial markets and whether these results question the prevailing view in the financial economics literature that financial markets are efficient is discussed.
Financial news predicts stock market volatility better than close price
It is concluded that volatility movements are more predictable than asset price movements when using financial news as machine learning input, and hence could potentially be exploited in pricing derivatives contracts via quantifying volatility.
Stock market one-day ahead movement prediction using disparate data sources
The results suggest that diversifying the knowledge base of financial expert systems can benefit from data captured from nontraditional experts like Google and Wikipedia, and combining disparate online data sources with traditional time-series and technical indicators for a stock can provide a more effective and intelligent daily trading expert system.
An intraday market risk management approach based on textual analysis
Evaluation results provide strong evidence that unstructured (textual) data represents a valuable source of information also for financial risk management - a domain in which, in the past, little attention has been paid to unstructuring data.
Market Trend Prediction using Sentiment Analysis: Lessons Learned and Paths Forward
Investigation of the potential of using sentiment attitudes and also sentiment emotions extracted from financial news or tweets to help predict stock price movements finds that at least for certain stocks, integrating sentiment emotions as additional features into the machine learning based market trend prediction model could improve its accuracy.
Backpropagation and recurrent neural networks in financial analysis of multiple stock market returns
  • Jovina Roman, A. Jameel
  • Computer Science
    Proceedings of HICSS-29: 29th Hawaii International Conference on System Sciences
  • 1996
It follows that the effect of learning temporal information was not substantial on the prediction accuracy for the stock market returns, and a selection criterion is proposed in this paper to make this choice effectively.
The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices
It was found that Twitter sentiment and posting volume were relevant for the forecasting of returns of S&P 500 index, portfolios of lower market capitalization and some industries, and KF sentiment was informative for the forecast of returns.
Predicting stock market price using support vector regression
Predict results from WinSVR models are compared with actual price values of DSE to evaluate the model prediction performance and a new approach which uses different types of windowing functions as data preprocess for predicting time series data is introduced.
Stock Market Prediction via Multi-Source Multiple Instance Learning
To improve the prediction for stock market composite index movements, the consistencies among different data sources are exploited, and a multi-source multiple instance model is developed that can effectively combine events, sentiments, as well as the quantitative data into a comprehensive framework.
Predicting short-term stock prices using ensemble methods and online data sources
It is shown that the use of features extracted from online sources does not substitute the traditional financial metrics, but rather supplements them to improve upon the prediction performance of machine learning based methods.