Data is the basis for all sound investment models. In addition to traditional sources, such as company filings, investment managers are turning to non-traditional data sources for a more complete investment model. Non-traditional data includes information that is publicly available on the internet, but often difficult to get in a structured, easy to digest format. Non-traditional data sources include things like retail prices across vendors, store locations in a region, customer sentiment ratings, influencer opinions in blogs and forums, company news, and world news.
In a recent article, Osman Ali, Portfolio Manager, Quantitative Investment Strategies, GSAM said this about non-traditional data sources. “With the growth and availability of non-traditional data sources such as internet web traffic, patent filings and satellite imagery, we have been using more nuanced and sometimes unconventional data to help us gain an informational advantage and make more informed investment decisions.”
Of course, bottom up analysis could not have been possible without the internet and the data would not be as usable without data extraction software. Takashi Suwabe, Portfolio Manager, Quantitative Investment Strategies, Goldman Sachs Asset Management, said in a recent interview, “Access to new types of data, along with the ability to capture and process that data quickly, has given us new ways to capture investment themes such as momentum, value, profitability and sentiment.”
To formulate sound investment recommendations, analysts need up-to-date information from many sources and web data extraction is making this possible. In the article, Mr. Suwabe went on to say, “We identify strong businesses with attractive valuations, positive sentiment and a strong connection with positive themes that are trending in the markets.” Tracking themes and sentiment would have been impossible without the internet and extremely prohibitive without the ability to continuously extract data into a structured format.
We are poised for another wave of innovation, “turning the data into knowledge” using machine learning and artificial intelligence. With easy data extraction comes potentially more data than a human can consume, that’s where machine learning comes into play.
With close to 2,000 artificial intelligence start-ups and $21 billion in venture capital, we are closer than ever to machine learning and AI being an imperative to any financial services firms.
To stay competitive, firms need to consume more data, which takes more time to process. Machine learning will help process the data and learn from it much faster. With both traditional and non-traditional data considered, equity researchers and their clients will make better investment decisions.