The financial industry is inherently a data-driven industry. Data streams in via indicators and indices, and traditional financial data flows out as income statements and balance sheets. However, there are various sources of information in addition to the traditional fundamental data. This can be supplemented with macro, sentiment, and sector level data indicators. Data can stream monthly, weekly, daily and also in ticks.
Given the insights that all this data can provide, it only makes sense for an investment firm to have a data driven strategy to go along with a more traditional equity and fixed-income strategy line-up. At City Different Investments, we’ve developed a proprietary data strategy based on something called machine learning.
Over the next few blogs, we want to share key components of that strategy with you and how it benefits from data analysis advancements. But first, let’s look at what machine learning actually is and what it is not.
What is Machine Learning
Machine learning is a subfield of artificial intelligence (or AI) — the broad term given to any computer process meant to mimic human intelligence or decision-making. Machine learning is the practice of giving computers the ability to learn without necessarily programming them to do so.
According to Thomas W. Malone, the founding director of the MIT Center for Collective Intelligence, machine learning can be “descriptive” to tell you what happened; it can be “predictive,” meaning it can tell you what will (or may) happen; and it can be “prescriptive,” telling you what action you should take next.
From this, you can see how machine learning can be valuable in the investment management world.
Machine learning cannot happen without data. For instance, when you first tag yourself in a picture using Google Photos, the program learns what you look like through that data tag. It then learns more by looking at all of your photos and guessing what other photos include you. The system asks you to confirm, then tags you in multiple photos — by describing your face, predicting other instances of your face, and prescribing tags for various photos.
While the thought of AI and machine learning sounds invasive , there are far more benefits and advancements that simply would be impossible without a continuously learning AI system. For example, take Google Translate — a program that would not exist without a machine learning system scouring millions of articles and images in multiple languages across the internet.
Netflix recommendations. Predictive text. The Google search engine. Chatbots. Self-driving cars. All of these applications rely on machine learning. In fact, so many recent developments in AI have centered around machine learning, the two concepts are deeply intertwined and soon may be interchangeable.
Again, all machine learning systems need data. Lots of it. The more, the better. Luckily, the financial sector and investment firms like City Different also have a lot of data. This is why these data analysis advancements (like machine learning) are so important for the work we do.
Next time, we’ll look at the application of machine learning in investment management. More specifically, why should an advisor pay attention to how stock picking is done using machine learning and why it is important not to miss out on this investing style.
If you have any questions about how we work with financial advisors and clients, the investment strategies we manage, or our investment process, contact us here.
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