AI in investment management spots event-driven opportunities that others missed

March 5, 2020

When I first thought about investing I asked my clients in the financial industry what I should be doing to even become an investor? The number one answer was “to find my strategy/niche” or a sector that I can research and develop an expertise in. I decided not to try too hard by starting to learn algorithmic trading or advanced HFT strategies, but search for a more traditional and feasible tactic. That’s exactly why event-driven stood out as the most reasonable idea of them all.

Event-driven Investing

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. It’s particularly attractive, because firstly - it’s pretty easy to rationally explain a stock’s directional move prior to or after an event. Secondly, it’s evergreen. Every business, large or small, is cyclical and has its own ups and downs and this tendency is not subjected to change in the future.

Mispricings always 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.

In general, most funds employ specialist teams who spend time analyzing potential risks, news, financial statements and balance sheets to recommend an action only after a full, in-depth review. Such analysis always involves assessing the fundamentals, possible outcomes, the economic environment and the direction of the stock price after the event takes place.

Merger Arbitrage

Let’s look at probably the most popular event-driven strategy, which is Merger Arbitrage. In this strategy, the investment process is mainly focused on equity-related instruments of companies which are engaged in a corporate transaction. Such strategies are focused on trading equities around the M&A announcement date. Money managers will avoid the advanced stages of such special situations because of increasing complexity surrounding the deals, which as a consequence can reduce the possible returns. Such scenario involves a larger than usual risk. An M&A deal can always collapse and as a result the price of the stock may drop. However, if an investment decision is made early in the M&A stages and the deal eventually gets done, the strategy will likely make money. In this case, let’s look at LinkedIn and its share price jumping from $131.08 to $192.21 on the day Microsoft announced its acquisition.

AI in Investment Management Linkedin Stock Price

Distressed Debt

Another example is Distressed Debt Investing. It can turn out to be extremely lucrative for those who know what they’re doing. This strategy involves taking positions in the debt of distressed companies, often just after an announcement of insolvency, administration proceedings or filing of Chapter 11 bankruptcy. After such an announcement the company’s stock will trade at a diminished price. When companies are experiencing significant trouble, traditional money managers look to sell their positions, but there are also investors who we often hear about, walking away with large sums of money. An event-driven fund manager will be looking to identify such situations and inspect companies where he believes the market is undervaluing the potential returns. Distressed Debt Investing is all about seeking profit from a possible turnaround. In most cases, an investor can still walk away with money, even if a company collapses. The risks are certainly a bit higher than in Merger Arbitrage, but those who take good risk measures can put up significant returns.

Distress and Recovery Cycle

Activist Investors Involvement

“Never doubt that a small group of thoughtful, committed, citizens can change the world. Indeed, it is the only thing that ever has.”
― Margaret Mead

Let’s take Carl Icahn, a well-known activist investor, and a corporate raider for an example. Icahn’s efforts have unlocked billions of dollars of shareholder and bondholder value and have improved the competitiveness of American companies. As an investor, he is known for buying large amounts of a company’s stock and then pressuring the company to make significant changes to increase the stock’s value.

A recent burst of activist pressure has allowed event-driven money managers to outperform other investors. Opportunistic investors have largely benefitted from opportunities created by activist investors. In the recent case of Twitter, after its share price rose by 8% in a single day after a surprising $1B activist move by Elliott Management or in 2012, when Carl Icahn announced buying out over $2B worth of Apple shares and pressured them to give more money back to the shareholders and the stocks rose by 5%.

Carl Icahn

Other strategies

Other examples of corporate special situations include regulatory changes, earnings announcements, succession issues, divestitures, turnaround, huge layoffs/trouble/antitrust bodies, corporate relocations, pension issues and more. Identifying such events as they happen in real-time often turns out to be crucial for portfolio strategies of investors.

AI in Investment Management Events in Stock ChartAlternative Data In Event-Driven Strategies

Alternative data flows from more and more sources. For fund managers and individual investors, it is very important to leverage uncommon sources in their strategies. With the technology being made available for more, gradually many investors turn to a quantitative approach. The rest of them, without a solid data foundation, lose ground in the competitive market. The sheer amount of unquantifiable data that can be implemented in investment strategies presents many new opportunities for money managers. With the presence of Artificial Intelligence in investment management, management consulting, financial research or investor relations there is a growing trend to turn into alternative data sources to seek insights and recommendations. One of them is processing massive amounts of textual data from global news.


Building Machine Learning Algorithms

Obtaining useful information about market opportunities from huge amounts of data require vast resources, both in terms of time and funds. The good news is there is the AI-based technology that is able to perform some parts automatically, and therefore improve the efficiency of the whole process. A system using AI algorithms – Document Classification, Named Entity Recognition, to name a few – can spot certain changes on the market without human intervention.

1. Collect the data from the online and local sources

sources 1
Let’s focus on a particular, real-life example. An investor needed information about changes in the management structure of companies in NYSE. Before leveraging the AI solution, he employed a dedicated team of research analysts traversing thousands of web articles, tweets, and social media posts looking for recent changes in the structure of the companies. The first step we need to do is to aggregate data from valuable sources. So far, our client’s analysts read online business newspapers and monitored selected Twitter accounts looking for structural change information.

2. Filter out the irrelevant materials

Our system has entirely automated that work by scraping newspapers and tweets, producing a steady feed of information ready to use by the analysts, putting the information in one place.

3. Extract knowledge from text

Screen Shot 2017 10 25 at 15.05.27
We’ve already managed to save a lot of time by showing only the most valuable information. However, there’s still a lot of work to do. Working on raw documents is hard. We need to extract the information that the client needs.And AI helps us in that as well!
Data scientists call this technique named entity extraction. It identifies important information to the investor in the text, based on given criteria. In this case, we extract the company name, position, and the reason, and put it in a table row that we can use later. At this point, it can be exported into JSON, CSV, or made accessible via API.

Real-time News Analytics For Investors

At Sigmoidal we help investors gather and process crucial unstructured data with AI. The Sigmoidal MarketMove™ engine started as a news analytics platform, which with the use of Natural Language Processing helped identify fresh investment opportunities. At an early stage, the tool processed textual data from over 250 news sources and searched for specific documents like new regulations. Currently, the engine is very versatile and fits various use cases. MarketMove discovers specific investment opportunities for a fund manager, performs due diligence, or identifies fraud for a risk consulting corporation. MarketMove™ scans multiple media resources in 7 languages and matches demand in different even more demanding uses.

Just in the last month, the MarketMove™ platform identified several spin-offs. As a result, it discovered GSK prospective joint venture with Pfizer or a logistics company Transplace merging with Celtic International. The tool also helps investors identify opportunities among distressed companies. For instance, MarketMove™ addressed the potential takeover of a French furniture manufacturer. The company was recently placed in receivership. It also underlined a jewelry retailer, Links of London, going into administration. In addition, it identified wafer biscuits maker, Rivington Biscuits, selling their assets, and filing for insolvency. In conclusion, MarketMove™ found over 230 distressed companies over the last 30 days.

Use In Event-Driven Strategies

Michael, Sigmoidal’s product manager, explains that the MarketMove™ engine finds use in stock market forecasting and time series analysis. “The datasets accessible by our API contain multiple data points for each collected news article and there are 80,000 new articles a day from 1,000+ news sources. Every single record matches a specific company and/or tickers with a sentiment score and an accuracy metric. As a consequence, unquantifiable data get quantifiable and accessible for various stock market prediction models.” he says. Leveraging MarketMove™ enables data engineers to run more accurate and better performing stock market prediction models.

“Investors also use MarketMove™ in opportunistic strategies.”, he continues. “Money managers often rely on special situations in the market. Events like carve-outs or restructurings cause movements in the stock prices. Our algorithms detect such events early. For instance, when a company files an M&A agreement or media sentiment surges prior to a huge layoff our system can detect it and alert our users. When leveraging such strategies, timing is very, very important.”

AI in Investment Management Tesla Cuts Jobs Market Reaction

Currently, the MarketMove™ engine tracks the reputation of more than 6,000,000 companies 10,000,000+ individuals globally. The system then assesses their involvement in the news around the globe and allows them to set up custom alerts exactly when special situations occur. The MarketMove™ engine is also available as a web-based application. With an easy to use interface the platform enables users to discover new companies or individuals based on recent market events.

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