Quantitative techniques and new methods for analyzing big data have increasingly been adopted by market participants in recent years. This includes computerized trading, use of big data, and machine learning or artificial intelligence.
Big data and machine learning have the potential to profoundly change the investment landscape. As the quantity and the access to data available have grown, many investors continue to evaluate how they can leverage data analysis to make more informed investment decisions. Investment managers who are willing to learn and to adopt new technologies will likely have an edge.
Machine learning and artificial intelligence may unleash new insights; however, investors need to better understand the current landscape and applications for data analysis before making significant investment in the technology.
A quantitative investor has access to real-time information, but organized data is not always readily available, and it needs to be analyzed to glean tradeable ideas.
The availability of new datasets, methods of analysis and more sophisticated computing has led to the growth in big data and the machine learning ‘revolution.’
The changes to the investment landscape will be profound. Big data will give an edge to quant managers who are willing to adapt and learn about new data and analysis methods. Machines have the ability to quickly analyze news feeds and tweets, process earnings statements, scrape websites, and trade on these instantaneously.
Big data and machine learning strategies are already eroding some of the advantage of fundamental analysts, equity long-short managers and macro investors, and systematic strategies will increasingly adopt machine learning tools and methods.
The transition won’t be without setbacks, though, as certain data may have no value and more complex techniques don’t always produce better forecasts.
Signals can now be found in data generated by:
As data sets get larger and more complex, investors need to use sophisticated data analysis techniques. The tools used for these tasks include machine learning (drawn from traditional statistics) or deep learning (inspired by the working of the human brain).
These techniques can be used to analyze data and design trading strategies. Select a technique to learn more:
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