TRADING ON NEWS SENTIMENT DATA IS HOW GOLDSKY ACHIEVES ITS INFORMATION ADVANTAGE

Systematic trading is an ever-evolving competition. One key arena in that contest: the inputs used in quantitative trading models.

Over the years, the list of data sets that quants use has expanded from those based on historical prices, to statistical cross-asset correlations, to exposures to unique risk factors and beyond. To get an edge, many quant researchers and portfolio managers are continually looking for unconventional, independent factors that make sense. News represents a rich source of data sets for developing trading strategies.

Many features of news-based information make it especially appealing. Real-time news, though less structured than conventional fundamental valuation measures, usually encodes the first clues of major changes affecting a company. You could, for example, use news fragments to build statistical forecasting models that dynamically adjust price targets.

Social Media 

Another promising source of information is social media, systems in which the feedback on content can be Systematic trading is an ever-evolving competition. One key arena indirectly measured. Sharing items, for example, tends to separate noteworthy content from ambient noise. Replies and discussions provide on-the-spot feedback about opinions and emotional responses.

Aggregated data on news supply and demand can be used to detect abnormal spikes. Often, such spikes happen alongside major company events. When you track such analytics for a large portfolio of stocks, you can identify the market focus and hot-spot stocks in real time.

The advantages of news-related data are obvious—significant advances began only recently with the now availability efficient machine-learning techniques and high-performance computing hardware.

Backtesting 

Researching signals and backtesting strategies require large amounts of sample data. You need both broad news coverage and deep historical securities data to evaluate performance.

For generated news and third-party content, using two levels of machine-readable news-derived data. Story-level analytics calculate quantifiable metrics for individual news stories—sentiment and impact, for example. Company-level analytics aggregate information for individual companies to track continuing developments.

A key finding: Performance was consistently better for the small-cap portfolios. One explanation is that because of the lack of analyst coverage and market attention, it takes longer to price in fundamental changes. Such market inefficiencies could make news-based analytics stronger predictors.

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