Machine learning and the power of big data can help achieve stronger investment decisions

NA | March 03, 2020

Machine learning and the power of big data can help achieve stronger investment decisions
Sarah Ryerson, President of TMX Datalinx, is certain we don’t need to worry about that. And it’s safe to say we can trust her opinion with data being her specialty, as well as having spent five years at Google before joining TMX.She applies her experience on Bay Street by helping traders, investors and analysts mine the daily avalanche of data that pours out of TMX every day.If information is power what will we be doing with data in the future? Ryerson has the answer, explaining that we will be mining data for patterns and signals that will help us draw new insights and allow us to make better investment decisions.Ryerson is bringing real-time, historical and alternative data together for TMX clients. It’s all about picking up the signals and patterns that the combined data set that will deliver.She also affirms that she is aiming to make this information more accessible. This will be done through platforms where investors can do their own analysis via easy-to-use distribution channels where they can get the data they want through customized queries. Ryerson notes, “Machine learning came into its own because we now have the computing power and available data for that ‘iterate and learn’ opportunity.”
Ryerson knows that for savvy investors to get ahead of algorithms, machine learning or artificial intelligence (AI), they need more than buy-and-sell data. This could be weather data, pricing data, sentiment data from social media or alternative data. “When you combine techniques to the vast amounts of data we have that’s where we can derive new insights from combinations of data we haven’t been able to analyze before.One of the most important elements of AI that data scientists realize is that algorithms can’t be black boxes. The analysts and investors using them need transparency to understand why an algorithm is advising to buy, sell or hold.

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Our reports of market studies on “Self-Service BI” began in 2012 as “Collaborative BI,” followed by “Collective Insights” in 2016. Since that time, we expanded the criteria to include important topics such as data storytelling and governance. As much of the data aligns across multiple years, we offe


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