Social media and web scraping can impact the positions in your portfolio. To explore this further, we caught up with Mike Anderson, an Arootah consultant who has more than 26 years of experience as a head trader, COO, and CCO at some of the largest and most prestigious hedge funds in the world. Anderson brings experience as a head trader and risk manager across equities, commodities, derivatives, and futures for both new firms, as well as funds looking to expand and grow.
Investors expect asset managers across the globe to outperform their benchmarks. But that’s no easy task. Traditional fundamental analysis using common data sources and techniques no longer give asset managers a distinctive edge.
As a result, asset managers are increasingly turning to artificial intelligence (AI) and machine learning (ML) to develop algorithms — complex mathematical equations — that can quickly turn real-time data, including social media, into actionable insights like buying and selling stocks.
And whether you believe in investing using algorithms based on these data sources, they can affect the names in your portfolio.
Algorithmic Trading: Current and Future State
In the U.S. stock market and many other developed financial markets, algorithmic trading now accounts for 60-75% of overall trading volume, according to Select USA. Such strategies allow investors to lower trade expenses and increase their profitability.
The global algorithmic trading market is expected to reach $18.8 billion in 2024, compared to $11.1 billion in 2019; a compound annual growth rate of 11.1% over this five-year period. The major growth drivers of the algorithmic trading market include the increasing demand for fast and effective order execution and reducing transaction costs. As any portfolio manager or investor knows, these are two key measures that can have a material impact on the fund’s performance.
That said, algorithms are just part of the strategy. The equations are only as good as the data that powers them. In the past, asset managers would rely on sources such as press releases and news stories to inform their trading decisions. To gain an edge in today’s markets, investors are relying on “alternative data.”
Adding Social Media into the Mix
Take the explosion of social media. In an industry that strives to keep pace with rapidly changing market conditions, investors ignore social media at their peril. Companies, investors, and consumers can instantly share and consume information through sites such as Twitter, Facebook, LinkedIn, TikTok, Reddit, and Instagram. Big data companies are using social media to create data sets and algorithms to help funds trade the swings and volatility of the markets. For example, algorithms employ natural-language processing tools to find keywords and measures when a story is “rising up” in the media food chain, such as from blogs to newswires, to indicate that it may be important enough to act on.
We all remember GameStop and AMC, both fueled by nothing other than attention on social media. Perhaps what was so fascinating about these instances is that they shed light on the fact that the right person, or “influencer,” could, in essence, have the power to potentially ruin a portfolio by placing selling pressure on your names.
Advanced quantitative analysts and data scientists also use these data sets to spot patterns and create new investment ideas that set them apart from the competition. Alternative data research and AI firms are even designing data sets to meet unique investment objectives of each customer or fund and dynamically determine the optimal fund allocation across uncorrelated portfolios. These assessments are made at lightning speed, allowing the most promising investment strategies to be brought to market at a fraction of the time it would normally take.
Alternative data is fast becoming mainstream. News and data companies like Bloomberg and Thomson Reuters now offer alternative data products and services. And 75% of hedge funds already use social media and social-driven news feeds to inform investing decisions, according to Greenwich.
Funds and family offices are also using techniques such as these to create distinct advantages to outperform their comparative indices, which has been increasingly difficult over the last 20 years.
The Bottom Line
ML, AI, and big data can impact certain holdings in a portfolio without regard to economic or financial factors.
Even if you have high conviction in a stock, it’s a good idea to keep an eye on this type of data to understand how popular sentiment, including social media, can impact the names in your portfolio now, rather than fighting against the tide later.