AI and machine learning have become powerful financial analytics tools. By gathering and analyzing vast amounts of data, machine learning algorithms are able to detect patterns and trends, and identify potential risks or opportunities in an investment. These algorithms analyze historical data, identify correlations between different variables, and alert when pre-programmed thresholds are reached. The ultimate goal is to alert investors of potential risks or opportunities before they occur, and provide powerful insights that assist an investor’s decision making.
Machines cannot predict the actions of a company’s board of directors, or the timing of those actions. However, machine learning algorithms can analyze the data that creates the environment for such actions. For example, share buybacks will generally only occur once certain financial thresholds have been achieved. Within these favorable conditions, the board of directors can then take appropriate action at a time that is suitable for the company and its shareholders.
AnalytixInsight’s AI platform analyzes the presence of the financial conditions that would be favorable for a company’s board of directors to approve corporate actions such as buying back shares, cutting or initiating a dividend, or, acquiring or being acquired within the peer group. This is done by analyzing historic quarterly financial data of the company and computing this data in comparison to an algorithmically-selected peer group.
On February 2, 2023, Facebook-parent Meta Platforms’ (META:US) shares spiked 23% higher after the company announced a $40 billion share buyback, which also sparked a widespread rally throughout the technology sector. Did AnalytixInsight’s platform predict the likelihood of META’s share buyback? Yes.
In this scenario META was compared to a peer group of the FAANG stocks: Apple (AAPL:US), Amazon (AMZN:US), Netflix (NFLX:US), and Google (GOOG:US). Extensive computations on hundreds of financial data points produced the conclusion that META’s fundamentals did indeed justify a buyback of its shares.
A machine-created research report was then generated which contained the following excerpt:
Share Buyback - Is the company likely to buy back shares
In this section, we identify whether META-US is likely to buy back its shares. In order to screen for this event, we look for positive free cash flows and good liquidity in addition to a leverage, an earnings multiple and a current share price that are low enough to suggest that there is some pressure on management to buy back shares. If the company pays a dividend, we also confirm that its ending cash balance is more than the cash dividend in order to highlight the greater priority of paying a dividend versus buying back shares.
META-US’s fundamentals justify a buy back of its shares.
META-US is trading sufficiently below its 52-week high (currently 53.32% lower) and has low of price to earnings relative to peers reflecting low growth expectations. In addition, it has an adequate interest coverage, a relatively low debt to market capitalization and a positive free cash flow margin. META-US also does not pay a cash dividend. Overall, this combination of fundamentals suggests that a share buyback is justified. As a reference, the company’s cash balance is currently 10.40% of its market capitalization.
AI and machine learning are rapidly disrupting the way research is conducted within the financial industry. These tools are becoming invaluable to both research analysts as well as individual investors who are wanting to analyze mountains of data and arrive at a conclusion quickly.
AnalytixInsight’s platform is capable of over 100 billion daily computations and provides powerful analysis on approximately 50,000 publicly-traded companies. It generates insightful research reports on-demand using its proprietary natural language generation engine which creates easy to read narratives, comparative charts, and more.
Stay tuned for more blog posts about the industry and AnalytixInsight’s capabilities and product offerings.
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