Big Data, artificial intelligence (AI), machine learning, natural language processing (NLP) – for several years, we’ve heard how these technologies will transform investment management. Firms have invested untold capital in research in hopes of converting these trends into added revenue.
Yet for many of us, these technologies and what they can bring to the investment process remain cloaked in mystery. And that mystery has evoked existential fears: what do these developments portend for the future of human advisers? Who will pay a human to do what technology can do for free? And what about the risk of overfitting or the black-box effect? If an application generates alpha — or fails to — and we can’t explain why, we are hardly helping our firms, our clients or ourselves.
Nevertheless, despite such trepidations, the value of these technologies is clear. AI pioneers have leveraged these innovations and generated impressive results, particularly when these technologies function in tandem with human guidance and expertise.
With that in mind, we wanted to explore some of the more noteworthy iterations of AI-driven applications in investment management. And that brought us to Alexandria Technology and its use of NLP.
For a window into the New York City-headquartered company’s philosophy and for insight on progress in the financial technology space, we interviewed chief executive Dan Joldzic, CFA.
How would you define AI and natural language processing?
NLP is the classification of text, where the goal is to extract information from the text. Text classification can be done using rule-based approaches or AI. So, the AI component is not necessary for NLP.
Rule-based approaches are basically hard-coding rules or phrases to look up within text. This is also known as a dictionary approach. With a rule-based approach, a word or phrase needs to be manually introduced into the dictionary by a human/researcher.
When it comes to AI approaches, you are, in essence, allowing software to create its own dictionary. The machine is detecting words that occur together in sentences to form phrases, and then which phrases occur within the same sentence to form context. It provides for a much deeper understanding of text.
How can NLP applications inform the investment process?
We are living in a Big Data world and no single analyst or team of analysts can capture all the information on their positions. Natural language processing can first help by reading and analysing massive amounts of text information across a range of document types: capturing this information and standardising the text for companies, subject matter and even sentiment. The next step is identifying if the text has value. Once text is transformed to data, you can begin to see which sources can predict price movements and which are noise. This allows analysts to use the good sources to improve performance and potentially cut costs on the non-performing sources.
Let’s say you’re running an NLP application on an earnings call. What are you looking for?
The goal is to identify fundamentally driven information. It is not enough for a company spokesperson or CEO to say “we think we are doing really well”. We focus on statements that impact the bottom line. Are costs rising? Are they rising more or less than expected? It is not enough to look at statements in isolation. You need to focus on the context. For example: “our revenue was down 10 per cent for the quarter, which is much better than we were expecting”. Many, if not most, NLP systems may misconstrue this as a negative phrase in insolation but it is, in fact, a positive phrase, if one accurately comprehends the context.
What do you have your eye out for when the NLP is analysing a Wall Street Bets–type message board?
For one, our NLP had to learn a new language of emoji. You don’t come across rocket ships and moons and diamonds in earnings calls. So emojis need to be incorporated into our NLP’s contextual understanding. In addition, slang and sarcasm are much more prevalent in chat rooms. But here again, is where context matters.
Can you give an example of how Alexandria’s NLP was applied in an investment context and uncovered a hidden source of alpha?
The real power of NLP and Big Data is capturing information on a large panel of companies, countries or commodities. We can apply our NLP on something like 500 companies in the S&P or 1,000 companies in the Russell and identify positive trends within a subset of companies. We have found that the top 100 companies with positive statements in the S&P 500 outperform the index by more than seven per cent per annum.
Our clients are able to find alpha for a wide range of asset classes: whether they are short-term focused or long-term, fundamental, quantamental or quantitative, the alpha potential is real and measurable.
NLP applications in investing have moved from the obvious applications (on earning calls, financial statements, etc.), to assessing sentiment in chatrooms and on social media. What’s the next frontier?
It is still early for NLP applications. We started with news in 2012. Dow Jones publishes 20,000-plus articles per day, so it was very hard to capture all that information before NLP. Calls and filings were a necessary expansion because of the deep insight you get on companies from these documents. We still have a lot more to go with social media. At the moment, we are mostly capturing chatrooms that are geared toward investing. There is a much larger discussion happening about a company’s products and services that are not in these investing rooms. The larger the panel you start to capture, the more insight you can have on a company, before it even makes it to Wall Street Bets.
Tele-text is another information-rich source. Bloomberg or CNBC telecasts are not analysed for information value. Is the panel discussion on a given company or theme really helpful? We can actually measure if it is.
Beyond that, firms have so much internal text that we would expect to have a lot of value, from email communication to servicing calls or chats.
What about concerns that these applications could render human advisers obsolete?
Our systems are more automated intelligence than artificial intelligence. We are trying to learn from domain experts and apply their logic to a much larger panel of information. Our systems need analysts and advisers to continue to identify new themes and trends in markets.
From a client perspective, we help them do what they already do better from both an efficiency standpoint and from a risk and return perspective. In short, we are a tool to help investment professionals, not replace them.
Anything I haven’t asked that I should have?
I think one potential question would be: ‘are people actually using these tools?’. The short answer is yes, but we are still in the early days of adoption. At first, NLP and Big Data were a natural fit for systematic strategies but there is still some reluctance as far as how these tools can be trusted. The response is fairly simple, in that we have tools to allow for transparency where you can check the accuracy of the classification. The next question then becomes: ‘how does this work so well?’. That can be harder to explain at times, but we are using very accurate classification systems to extract insights from text, which tends to be from a fundamental perspective.
But NLP is not just a quantitative tool. Discretionary users can get even more insight on the companies or industries they cover and also screen the larger sector or universe that is not at the top of their conviction list. One response we hear from time to time is: “You can’t possibly know more about a company than I do.” We would never claim we do, but once you turn text to data, you can start plotting trends over time to help inform decisions. We will never replace the deep knowledge that analysts have, but we can be a tool to leverage that knowledge on a larger scale.
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All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.
Image credit: ©Getty Images/Yuichiro Chino