Analysis of high–frequency financial data using non–linear models

returns and, in Essay 2, we utilised weekly data to build a trading strategy or portfolio. The main reason that we used daily/weekly data is that we needed to decrease the trade number to account for the trading fees.

News information and stock prices are high–frequency data. Recent research has used ANNs or HMMs to analyse such high–frequency financial data. For instance, Lahmiri (2014) presents a forecasting model that integrates the discrete wavelet transforming and backpropagation neural networks for predicting financial time series. The model uses low– and high–frequency components, obtained through the decomposition of the financial time series data by discrete wavelet transformation, as input variables for forecast future stock prices. Arévalo et al. (2016) use deep neural networks to forecast the next one–minute average price. The deep neural networks model is trained on the current time (hour and minute) as well as the n–lagged one–minute

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pseudo–returns, price standard deviations and trend indicators. Cartea and Jaimungal (2013) employ an HMM to examine how the intraday dynamics of the stock market have changed and how to use this information to develop trading strategies at high frequencies.

These existing studies use high–frequency financial data, only considering stock prices to predict stock price movement. In our future work, we will consider high–frequency news information and stock prices to build a prediction model based on neural networks or HMMs.

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Appendix: RavenPack News Analytics (RPNA)

In this research, we use data on public news from RPNA, Edition 3.0. RavenPack (http://www.ravenpack.com/) is one of the most well–known providers of news analytics data. Another well–known provider is Thomson Reuters News Analytics (TRNA). News analytics is a relatively new tool based on AI and designed to improve the understanding of news events.

RPNA collect corporate news items (from the year 2000) from all public sources, including the Dow Jones, Barron’s and the Wall Street Journal. There are two categories of editions: Global Equities and Global Macro. Global Equities editions contain only entities that are classified as companies. These companies include over 40,000 listed stocks from the world’s equity markets, which spread across the Americas, Europe and the Asia–Pacific. Global Macro editions contain all entities that are not classified as companies. RavenPack analyses news on over 200 economies, delivering data on more than 138,000 places, 2,500 financially relevant organisations, 155 currencies and 82 commodities. In this thesis, we use the Global Equities editions dataset.

There are 39 fields used to describe each news item in the Global Equities editions (and 27 fields per news item in the Global Macro editions); these include the time stamp, the entity ID, the company name, the relevance of the news, the event category, the event sentiment, the novelty of the news, the composite sentiment score of the news, the story event count and the story ID. Some of these fields, such as event category and event sentiment, are generated by RPNA through content analysis.

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The ‘time stamp’ is used to record the UTC at which the news occurred. RavenPack uses the ‘relevance’ variable to differentiate between news items where the corporation is the main object of the original news source and news items where the name of the corporation is mentioned only tangentially. The relevance variable attributes values from 0 to 100 and a score of 100 indicates that an article is highly relevant.

For a news story with a relevance score of 100, the ENS represents its novelty value. The first story to report a categorised event will receive a novelty score of 100. The novelty scores of subsequent stories about the same event will follow a decay function (i.e., 100, 75, 56, 42, 32, 24, 18, 13, 10, 8, 6, 4, 3, 2, 2, 1, 1, 1, 1, 0 …).

The ESS measures whether a particular news item contains favourable or unfavourable information about the underlying corporation. This variable represents the news sentiment for a given entity, ranging from 0 to 100, where 0 indicates extremely negative news, 50 indicates neutral news and 100 indicates extremely positive news.

In terms of the sentiment, RavenPack uses a proprietary computational linguistic analysis algorithm to quantify positive and negative perceptions of facts and opinions reported in the textual content of the news (Shi et al., 2016a). The core of this algorithm can be divided into two steps. First, a group of financial experts manually tag a set of stories and build up a historical database of words, phrases, combinations and other word–level definitions that have affected the target company, market or asset class. Then, the text in the specific news story is compared with the historical database and the

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sentiments score is generated by automated computer classification using a Bayes Classifier.

Many researchers use RPNA to analyse the financial market. For instance, Smales (2014) examines the market reaction of leading Australian stocks to stock–specific news flow over an extended period. The study concurs with previous literature that news items are critically relevant to identifying significant effects. Akbas et al. (2016) find that high short interest is predictive of negative public news based on RPNA. Shi et al. (2016a) analyse how the hourly return volatility of S&P100 stocks from 2000 to 2010 are linked to the various linguistics–based sentiment scores of the news releases.

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