Event-driven or opportunistic investing is a strategy associated with exploiting stock mispricings occurring before, during or after corporate events (also called catalyst or special situations) like restructurings, M&As, spinoffs or bankruptcies. Mispricings tend to arise when public companies are involved in special situations because the stock can become artificially inflated or depressed due to speculations from market players. Money managers tend to look at things from their own perspective and value their stock differently than others. While a company subject to a catalyst may not seem like a good investment opportunity, some sophisticated investors are willing to accept the increased risk. At Sigmoidal we are looking for similar opportunities with our MarketMove news analytics engine. Instead of looking at raw numbers, spreadsheets, and balance sheets, the system is scanning global news. Currently, it analyses 200,000 articles/day to find companies whose market valuation may be wrong in light of recent events that could lift a share price up. A reliable, but still speculative strategy is to follow the actions of activist investors. Their intentions are filed with the SEC under Schedule 13D and they can be a source of potential event-driven investment opportunities. However, companies that are targeted by activist investors are more-or-less in far from perfect conditions which pose a relatively high risk when considering an investment.
Recent Activist Investments
In light of recent situations stirring around Twitter and a recent activist investment of $1B from Elliott Management, let’s take a look back at similar events in the past. Between 2011 and 2014, we saw an increase in the number of activist funds in the US from 19 to 162, with their assets increasing from $68B to $205B. Back in the early 2000s, institutional investors were heavily reluctant to back activists, as they were viewed as a rather disruptive force. With recent successful activist investments, the fears are mitigated with the realization that successful campaigns can turn a struggling company around. In terms of long-term performance, in the 2004-2016 period impact investors have increased their capital by 1,400%. This stands out when comparing to the asset growth of alternative investors (hedge funds/private equity) of 304% over the same time.x One of the recent examples of successful activist investment campaigns was pushing eBay to sell StubHub and its Classifieds ad business, which is still in progress. We saw eBay’s CEO stepping down in September and a significant divestment in the sale of StubHub for almost $4B. Starboard Value - the main activist investor in eBay who owns more than 1% in the business noted an over 4% after increasing the pressure on eBay’s management. On January 22nd, 2019 news websites announced that Elliott Management took a $1.4B stake in eBay the stock also jumped by 7%.
How AI can discover activist investments before/after a market catalyst takes place?
With MarketMove we can identify market-moving scoops as they take place in the capital markets. With precise algorithms, the platform identifies opportunities in shareholder activism across news articles, TV headlines and more. The system is using Named Entity Recognition models and advanced neural Topic Modelling to identify when the press releases about activist investors. Over the last month, the MarketMove algorithms identified several events related to new activist campaigns and shareholder activism in real-time, including, among others:
- New activist campaigns:
- Sachem Head Capital Management building a new stake in Olin Corporation
- Elliott Management unveiling a $3.4B stake at SoftBank
- Tenzing Global Management buying up 5% of Noodle & Co.
- Activist fund Amber raising stake in Lagardere above 10% from 5.3%
- Third Point calling for Prudential breakup as it takes near-$2bn stake
- Macellum Advisors and Ancora Advisors have taken a combined stake of more than 10% of Big Lots
- Shareholder activism:
- Kirin urged to sell-off cosmetic venture by activist investor
- Blackwells Capital urges Colony Credit to replace the investment team
- Activist investor Coast Capital renews calls for FirstGroup break-up
- RMB Capital has called on Japanese clothing company Sanyo Shokai Ltd. to sell itself
- Pershing Square Capital Make Moves in Chipotle Mexican Grill, Tivity Health, and 3 Other Stocks
How Information Extraction Works
Natural Language Processing is an increasingly popular field of research related to Artificial Intelligence. There are many reasons why textual data has business value. In the capital markets sector, we deal with enormous amounts of written content sourced from news, SEC filings or social media every day. One of the core processes we leverage to discover essential financial news is Named Entity Recognition (NER). NER can be described as a way to find and classify people, organizations, locations, dates, etc… It is often used as a foundation for thorough document information extraction that allows taking the algorithms even further. At MarketMove, we are using proprietary information extraction algorithms, but also state-of-the-art NER models like spaCy. SpaCy is a free and open-source library, which has made NLP much simpler and available for a wider audience. What’s best about the library, is that it also supports the custom addition of arbitrary classes to the entity-recognition model. What it means for us is that we can train the spaCy models on our own labeled datasets and parse textual documents to discover significant market events like the ones given above.
Extracting The Real Meaning From Text
By the time we have correctly extracted the Named Entities from the text, we can move a step forward to making our AI try to understand the deep meaning of a document. In information extraction, we often speak about the concept of a triple. A triple represents a couple of entities and a relation between them. In our case, for example, is (, , ) where and are the related entities. The relationship between those two is . By looking at the snippet below: We can clearly see how the two entities: and are related and form a triple, this technique also allowed us to identify a potential spin-off within Prudential. The algorithms that we use help get structured information (like numbers) from unstructured text based on grammar and syntax. The tricky part is to correctly match the two entities with the right relation, but there are several models and techniques like the linear TF-IDF or, more sophisticated, Convolutional Neural Networks that help with this.
How do we make sure that the named entities are correct?
The MarketMove engine is supported by a proprietary PostgreSQL database of 6M+ global companies and individuals. Each NER extracted from an article is cross-checked in the database to verify its correctness via an API. This allows us to not only analyze news in scale, but also provide accurate news feeds for each company - small or large - present in our database.