Getting a seat at the decision-making table is no easy task. Typically only experienced business veterans are involved in key decision making, but machine learning and deep learning are changing that, and in ways you might not expect. Leaders are relying more on data-driven analytics and AI algorithms instead of experience and know-how, especially in today’s dynamic and ever-changing business conditions. With deep learning, businesses can quickly analyze tremendous amounts of real-time data with minimal biases. The insights garnered help augment the decision-making process for long-term asset managers.
Whereas traditional financial modeling involves human discretion and cannot scale to incorporate the rapid growth of secured and unsecured data, machine learning is equipped and able to meet market needs with data-centric, unbiased decision-making. With its deep-learning neural networks, machine learning has the ability to account for new, incoming data, and provide the most statistically accurate forecast. The impact is real; it’s positive; and it’s tangible. The results prove how prudent it is to apply machine-learning technology to more than just the “nice-to-haves” in life. It’s time to start avoiding financial errors and oversights, and put machine learning to work for your investments.
Hasn’t machine learning been around in investing?
Yes, to a degree. When it comes to investment technology, machine learning itself is not new. However, deep learning is. From the early 2000s until 2010, machine-learning techniques were resulting in decent predictivity, but those predictions were only for short to mid-term trading timeframes. The data sets for longer-term horizons at the time were not rich enough to make long-term, insightful decisions with the same techniques.
While there were opportunities to put machine learning to work for quantitative hedge funds, long-term active management and valuation techniques remained relatively unaffected. Some quantitative desks did crop up among large investment shops, working to identify major trends and regime changes to capitalize on, but human analysis still ruled when it came to predicting future stock performance.
Successfully making investment decisions for the long-term (e.g. 6-24 months) isn’t a simple feat, especially given the many factors in play over the course of even one year that will affect a stock’s price compared to the day-to-day impacts. Knowing which of these factors is most important at any given time, and which will be most important one year from now is a challenging task that rarely – if ever – follows easily identifiable past trends. But new advances in deep learning have given confidence and reliability to put the technology to work for long-term asset managers.
The neural networks behind-the-scenes
It’s the use of deep neural networks that have allowed for machine-learning algorithms to scale to datasets of significant magnitude, and generalize future performance with minimal overfitting. Even though the theoretical predecessors for deep neural networks have existed for decades, the previous expense of distributed computing and scalable data storage solutions made it almost impossible to apply deep learning in the real world until around 2010. And even then, applications were largely experimental such as the Google Brain project founded in 2011.
A seat secured still carries hesitations
The advancements have been significant, yet AI technology still brings along reservations for some. These mainly lie around “black box” fears, which boil down to the lack of clarity and understanding investors have in explaining the outputs and decisions behind systems driven by deep-learning technology.
The reality is that explaining the insights generated in a completely human way may be impossible, likened to someone trying to explain a “gut-feeling.” However, we can understand the most important factors that contribute to artificial intelligence decision-making and their relative importance. Additionally, when specific applications of artificial intelligence are designed properly, there is no opportunity for the system to act on outside data, or take actions outside of its designated function.
Regardless of whether or not we can fully understand the decision-paths and recommended outputs of machine-learning algorithms, one thing is clear: Both active and passive long-term asset management strategies can benefit from new advances in deep learning.
For active managers, deep learning can help generate strong and difficult-to-find investment ideas, assist with market timing, and raise red flags to risky investments. And if we look at the future of passive management, there are new possibilities for funds to run entirely on machine learning and deep-learning algorithms that will be just as inexpensive to operate as an index fund. It can be hard to rationalize today, but the benefits outweigh the hesitations. When it comes to investment decisions, machine learning is proving its worth and is here to stay.