Machine Learning methods to analyze large and complex datasets: There have been significant developments in the field of pattern recognition and function approximation (uncovering relationship between variables). These analytical methods are known as ‘Machine Learning’ and are part of the broader disciplines of Statistics and Computer Science. Machine Learning techniques enable analysis of large and unstructured datasets and construction of trading strategies. In addition to methods of Classical Machine Learning (that can be thought of as advanced Statistics), there is an increased   focus on investment applications of Deep Learning (an analysis method that relies on multi-layer neural networks), as well as Reinforcement learning (a specific approach that is encouraging algorithms to explore and find the most profitable strategies). While neural networks have been around for decades10, it was only in recent years that they found a broad application across industries. The year 2016 saw the widespread adoption of smart home/mobile products like Amazon Echo11, Google Home and Apple Siri, which relied heavily on Deep Learning algorithms. This success of advanced Machine Learning algorithms in solving complex problems is increasingly enticing investment managers to use the same algorithms.

While there is a lot of hype around Big Data and Machine Learning, researchers estimate that just 0.5% of the data produced is currently being analyzed [Regalado (2013)]. These developments provide a compelling reason for market participants to invest in learning about new datasets and Machine Learning toolkits.  

 

Eoin Treacy's view

Here is a link to the full report.

Over the years I’ve seen a great deal of commentary about the petrodollar and the oil economy and no doubt that has been lynchpin of economic growth for much of the last century. After all every country uses oil but not every country produces it and the fact it is denominated in Dollars gives the USA, as the onetime largest consumer, an important advantage. However, if we look forward rather than backward, there is a compelling argument for considering that the data driven economy is what is likely to drive economic growth in future. 

If we are prepared to stand back and look at the evidence not on an individual basis but rather as a trend we can possibly see the future. The companies curating massive data collection such as Apple, Alphabet/Google, Amazon, Facebook, Tencent, Baidu, Alibaba, JD.com, coupled with mobile phone networks like Verizon, AT&T, Vodafone, CK Hutchinson, China Mobile etc. and companies like Tesla and Netflix have the raw materials to cater product offerings and marketing to specific interests and demographics which offers an avenue to acquire a great proportion of the consumer’s monthly spending. There relative performance is a testament to the success of the architecture they own and the increasingly high barriers to entry to acquiring novel data sets. 

The evolution of machine learning in finance is being pioneered by hedge funds and investment banks because it represents the next frontier where they can develop an edge not readily available to the public. This article from Bloomberg may also be of interest. 

It is not possible to completely depend on machine learning and artificial intelligence for all decisions but what they offer is a massive support to evidence-based decision making that was never possible before. That is a gamechanger for investment potential in both investing and business because it has the potential to decrease risk by reducing the number of unknowns. On its own machine learning is a driving force behind a secular bull market and that is before we consider how well it can be integrated with other rapidly developing technologies in heathcare, energy and consumer products.