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Artificial Intelligence and the Buy-Side

May 02, 2018

The expression “information is power” applies to numerous facets of the professional financial industry but is particularly relevant for asset managers. Market conditions are continually impacted by information that is being generated, updated, and manipulated. Most investors require significant human and technological capital just to collect this data. Filtering through information and determining which pieces are operationally important can also be a monumental challenge. The application of artificial intelligence to “big data” has enabled quant traders to process a vast amount of information that would otherwise be materially impossible to analyze. 

In an article published by Market Brains titled “What the buy-side needs to know about data explosion”, the authors reference a conference hosted by a data provider company called RavenPack. Of the attendees, 85% represented the buy-side, while 36% of this same group were quantitative investors. An audience overwhelmingly composed of buy-side investors demonstrates the exponentially rising significance of artificial intelligence in allowing investment managers to manage large inflows of data. “Quantamental” investing, the combination of traditional fundamental analysis with contemporary machine learning methodologies, has become an increasingly prevalent investment approach. Fundamental analysis continues to be an important aspect of the investment decision making process, but it has limitations. The correlation between the lines on a company’s financial statements and their impact on an equities performance are not always apparent. There are also numerous human biases that, no matter how subdued, impact conclusions reached by investors. Not every investor, however, is convinced of the tremendous possibilities machine learning can provide.

A lack of understanding may be one of the more significant concerns for fund managers. While advanced technological practices have quickly become integrated into the financial sector, they have not had a prolonged connection with the asset management field. Traditional investors, who operated before the use of complex technological systems, may not grasp the concepts of machine learning. Another consideration is that, relative to traditional investment approaches, the use of artificial intelligence has a brief performance track-record. It may be difficult for investors to commit to an alternative strategy without the long-standing performance to support it. Despite the hesitations, investors are recognizing the need to integrate machine learning into their investment tactics. 

The ability to quickly and accurately scrutinize troves of data has become a source of competitive advantage for investment managers able to mould “big data” to their specifications. The availability of usable information often leads to better investment decisions and reduces risk of the unknown. Though there are hesitations among buy-side holdouts about dramatically shifting to an AI-enhanced investment strategy, the opportunities machine learning offer are substantial. 


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