The world is awash in data like never before. From a person's morning Uber ride and favorite coffee spot, to the emails sent from their office--all these activities create massive amounts of data, but also behavioral and investment insights. Warren Buffett's investment style exemplifies the fundamental approach: "Which companies offer the best returns?" On the other hand, hedge fund manager James Simons of Renaissance Technologies is a notable example of the quantitative approach: "What is the best way to predict returns?" Both techniques have one thing in common--they seek excess return from the marketplace, or what is known as "Alpha". Today's infographic from GoldSpot Discoveries outlines quantamental investing as the blending of these two styles, human insight with computer power.

AI and quant strategies are nothing without solid fundamentals. As quantamental investing continues to gain traction, Elsen founder and CEO Zac Sheffer considers why human intervention will always be necessary. Investments are continuing to flow into funds that use Artificial Intelligence (AI) to make trading decisions, but in the past few months we've seen just how important it is to still have human involvement and good fundamental reasoning behind these strategies. In February, hedge funds that use AI in their trading processes experienced their worst month ever -- or at least since Eurekahedge created its AI Index to track the market in 2011. Overall it was a poor month for returns with the broader Hedge Fund Research Index falling 2.4 percent.

Using machines to read text as a way to enhance understanding of market movements is a topic of intense polarization and debate. Back in the 90s, work on natural language processing (NLP) involved teams of linguists and computer scientists attempting to code up rules of grammar. Recent work has focused on techniques like word embedding, the underlying idea that a word is characterized by the company it keeps; semantic similarities between words are based on their distribution in large samples of data. Newsweek is hosting an AI and Data Science in Capital Markets conference on December 6-7 in New York. The "bag of words" approach has been applied commercially in finance for more than 10 years.

Using machines to read text as a way to enhance understanding of market movements is a topic of intense polarisation and debate. Back in the 90s, work on natural language processing (NLP) involved teams of linguists and computer scientists attempting to code up rules of grammar. Recent work has focused on techniques like word embedding, the underlying idea that a word is characterised by the company it keeps; semantic similarities between words are based on their distribution in large samples of data. The "bag of words" approach has been applied commercially in finance for more than 10 years. But it can depend on the source of information being analysed: a rule-based approach can work pretty well for news articles that follow certain editorial processes, while social media proves much more challenging.