Short selling involves selling a stock or an asset that an investor does not own. Activist short sellers comprise a category of hedge fund activists who benefit from stock price declines and typically act as whistleblowers and highlight alleged corporate frauds or financial jugglery by disseminating reports in the public domain. They profit from short-selling the stocks of the target company.
The ongoing battle between Hindenburg Research and the Adani Group is a case in point. The scathing report by the US firm alleged Adani indulged in stock manipulation and accounting fraud, hurting investor response to Adani Enterprises’ Rs20,000 crore follow-on public offering (FPO) and effected a sharp correction on Adani group stocks, wiping out trillions in stock value.
In 2014, Gotham City Research alleged that the Spanish firm Let’s Gowex was perpetrating a massive accounting fraud, following which the latter filed for voluntary insolvency five days after Gotham published a damning report. A year later, Iceberg Research began releasing a series of research reports targeting the (then) largest Asian commodity trader–Noble Group, which was eventually delisted. Major activist short sellers include Citron Research, Copperfield Research, Glaucus Research, and Muddy Waters Research.
Even as we debate the merits and demerits of short-selling activist investors, the fact is that publicly listed companies are increasingly putting out volumes of information, which makes it difficult for investors to process all the available data reliably and efficiently.
It’s here that algorithmic trading (also known as automated trading, algo-trading, or black-box trading) helps in implementing trade orders with the help of automated pre-programmed trading instructions. Considering variables like volume, price, and time, the programs send small slices of the order to the market over a period, according to MordorIntelligence.
The market growth for algorithmic trading is projected to be significantly influenced by the financial services industry’s broad adoption of AI, machine learning (ML), and big data. Algorithmic trading was $13 billion as of 2021, according to research from IMARC Group. The figure would have undoubtedly risen since, especially after the pandemic-driven digital drive. According to TRADE’s January 2022 Algorithmic Trading Survey, hedge funds increasingly use algorithms to trade most of their portfolios.
HFT, a subset of algo-trading, is a very complex process, which explains why it’s typically used mostly by large institutions like proprietary firms, investment banks, and hedge funds. In 2023, nine out of ten hedge fund traders will use AI to achieve portfolio returns, according to Market Makers.
But the very same AI tools can be used to short-sell a stock that is perceived to be priced much above market norms compared to its fundamentals. Traders who can spot such a stock can write an algorithm to take advantage of a possible price drop. You can also use algorithms to trade futures and options, which are common ways of short-selling.
Many automated trading platforms support short-selling, thus giving traders an opportunity to buy back the very stocks they sold at a lower price before the delivery time. This can, of course, result in both a huge profit and a huge loss, too, depending on how strong or weak your bet was.
Algorithms can streamline such trading decisions. For instance, AI makes it easier to forecast the future value of assets, given that it can assess innumerable variables. With AI, you can also specify the short positions to be closed when the stock drops to a certain value. Activist short sellers can combine their domain expertise with AI to identify the stocks they want to short. As an example, you can write an AI program to help you buy futures contracts of a company if its trading volume falls to a specific low level.
And even if you’re not a programmer, an online community of global algorithmic traders like MQL5.com offers features such as real-time market data, live chat, a forum, and hundreds of ready-made short-selling algorithms.
But how will people judge such active short sellers if they know that algorithms are involved in the investment process as advisers? In a paper titled ‘Will the use of machines to identify negative activist investments backfire?’, Paweł Niszczotaa and Dániel Kaszasb, researchers from the Poznań University of Economics and Business, Institute of International Business and Economics, in Poland, set out to find an answer to this question.
“The findings of this study suggest that investment funds that use sophisticated algorithms to identify short-selling targets should not be punished more than those that rely on humans for the same task. We conclude that such use of technology is judged to be socially acceptable,” they concluded.
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