Heckyl Technologies, a leading news sentiment and market data analytics firm, has conducted a study to highlight how a trader can use sentiment scores to predict impending stock price movement. We have analyzed news sentiment data for 246 Canadian companies listed on Toronto Stock Exchange (TSX) from January 2016 to July 2016. On numerous occasions, our sentiment score has acted as a leading indicator for the share price movement of 213 companies or 87% of the total sample size.
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Sentiment Analysis is the use of Natural Language Processing (NLP), Text Analysis and Computational Linguistics to identify and extract subjective information from text. Every day, hundreds of thousands of news and opinions affecting publicly traded companies, commodities, and currencies, are made available in the public domain – through News Publications, Blogs, and even Social Media. Sifting through all this data in real-time, to determine which events and reports could potentially have a positive or a negative impact on the underlying Security, is not humanly possible.
Heckyl has developed its own proprietary Sentiment Analysis engine, that efficiently and accurately computes a Sentiment for companies, commodities, and currencies, based on news articles flowing into the system – in real-time.
Taxonomy is the science of the classification of things and when implemented effectively it helps us to retrieve relevant information in a timely manner.
The Best Taxonomy
The Global Industry Classification Standard (GICS) and Industry Classification Benchmark (ICB) are an industry taxonomy developed for use by the financial community, when classifying companies. These Sector classifications are probably the best-known taxonomy in the financial field. However, in trading since it is all about relevancy and contextual information, at Heckyl, we believed that a new level of taxonomy was needed that could analyse data – be it news, events or companies – at a much more granular level. Our taxonomy is therefore based on machine learning, which goes beyond keyword matching. Read the rest of this entry »
We have shown in previous posts, how, the new data sets that are available can be a key source of trading opportunities. In addition getting access to this information before it becomes ‘news‘ on the mainstream media is highly valuable.
However with there now being over 277,000 tweets, 571 websites created, 347 new blog posts and 72 hours of new video being added to YouTube every minute of every day, the volume of data to read and filter is growing exponentially. Read the rest of this entry »