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Financial news predicts stock market volatility better than close price

Financial news predicts stock market volatility better than close price
Financial news predicts stock market volatility better than close price
The behaviour of time series data from financial markets is influenced by a rich
mixture of quantitative information from the dynamics of the system, captured in its past behaviour, and qualitative information about the underlying fundamentals arriving via various forms of news feeds. Pattern recognition of financial data using an effective combination of these two types of information is of much interest nowadays, and is addressed in several academic disciplines as well as by practitioners. Recent literature has focused much effort on the use of news-derived information to predict the direction of movement of a stock, i.e. posed as a classification problem, or the precise value of a future asset price, i.e. posed as a regression problem. Here, we show that information extracted from news sources is better at predicting the direction of underlying asset volatility movement, or its second order statistics, rather than its direction of price movement. We show empirical results by constructing machine learning models of Latent Dirichlet Allocation to represent information from news feeds, and simple naıve Bayes classifiers to predict the direction of movements. Empirical results show that the average directional prediction accuracy for volatility, on arrival of new information, is 56%, while that of the asset close price is no better than random at 49%. We evaluate these results using a range of stocks and stock indices in the US market, using a reliable news source as input. We conclude 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.
2405-9188
Atkins, Adam
10d83781-9bd7-4e6c-aeaf-5a00c69b0ec6
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Atkins, Adam
10d83781-9bd7-4e6c-aeaf-5a00c69b0ec6
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Atkins, Adam, Gerding, Enrico and Niranjan, Mahesan (2018) Financial news predicts stock market volatility better than close price. The Journal of Finance and Data Science. (doi:10.1016/j.jfds.2018.02.002).

Record type: Article

Abstract

The behaviour of time series data from financial markets is influenced by a rich
mixture of quantitative information from the dynamics of the system, captured in its past behaviour, and qualitative information about the underlying fundamentals arriving via various forms of news feeds. Pattern recognition of financial data using an effective combination of these two types of information is of much interest nowadays, and is addressed in several academic disciplines as well as by practitioners. Recent literature has focused much effort on the use of news-derived information to predict the direction of movement of a stock, i.e. posed as a classification problem, or the precise value of a future asset price, i.e. posed as a regression problem. Here, we show that information extracted from news sources is better at predicting the direction of underlying asset volatility movement, or its second order statistics, rather than its direction of price movement. We show empirical results by constructing machine learning models of Latent Dirichlet Allocation to represent information from news feeds, and simple naıve Bayes classifiers to predict the direction of movements. Empirical results show that the average directional prediction accuracy for volatility, on arrival of new information, is 56%, while that of the asset close price is no better than random at 49%. We evaluate these results using a range of stocks and stock indices in the US market, using a reliable news source as input. We conclude 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.

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Accepted/In Press date: 1 February 2018
e-pub ahead of print date: 8 February 2018
Published date: 10 February 2018

Identifiers

Local EPrints ID: 417880
URI: https://eprints.soton.ac.uk/id/eprint/417880
ISSN: 2405-9188
PURE UUID: f65a9736-43c1-43cd-9dad-c0bb9424d7b1

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Date deposited: 16 Feb 2018 17:30
Last modified: 13 Aug 2018 16:31

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