"In any battle, the right combination is used to win the battle with the unusual." This famous saying from "The Art of War" is still applicable in today's M&A market. When listed companies go into battle in mergers and acquisitions, the right combination is the M&A strategy, while the unusual victory is often hidden in a string of difficult numbers - the valuation model.
Especially when the target of the merger is not another glamorous listed company with complete data, but a "dark horse" enterprise lurking in the non-public market, with sparse data and asymmetric information, equity pricing becomes a key variable that determines the success or failure of the transaction. In the past, this was a subjective game based on experience, feeling, and "analogy", but now, AI, the "behind-the-scenes man" who was once regarded as a technical player, is coming to the fore.
Today, Goheal will talk to you: When AI begins to understand financial statements, disassemble equity, and predict the future, how does it gradually intervene in the valuation world of mergers and acquisitions, and use data to make the originally vague pricing logic of non-listed companies reasonable, well-founded, and even interesting?
American Goheal M&A Group
You will find that this is not only a valuation revolution, but also an upgrade of the cognitive system of acquirers.
The story begins with the encounter between an AI analyst and a "rough estimate" company.
In the traditional M&A world, "non-listed equity pricing" is often jokingly called a "metaphysics"
It is hard to imagine that mergers and acquisitions involving hundreds of millions or even billions of funds often rely on "looking at historical transaction cases + financial forecasting models + expert judgment" in the key link of pricing. Especially when the target is a non-listed company, P/E ratio? Can't get it. Public statements? Not released. Comparable companies? Not satisfactory.
Therefore, such a scene often appears:
Company A acquires a medical technology company, and the other party offers 2 billion, with the reason that "we will grow 10 times in the next three years";
Company B wants to acquire a new energy unicorn, which has not made a profit in the past three years in the financial statements, but the founder said "Look, Tesla didn't make a profit that year."
The problem is that listed companies, as "public buyers", cannot just listen to stories. They need a set of scientific methodologies to endorse the transaction price, explain the rationality of the valuation to the regulatory authorities, and convey confidence to investors. In the multiple restructuring projects that Goheal participated in, we found that the AI model is solving this valuation pain point.
How will AI end? It is not metaphysics, but using models to explain logic
Rather than saying that AI is "valuing", it is better to say that it is "polishing pricing logic."
The AI valuation framework adopted by Goheal is not a mysterious "black box prediction", but a complete set of "valuation logic modeling" system based on multi-source data, machine learning as the engine, and qualitative factors as boundary control. It is roughly divided into three steps:
The first step is multi-dimensional data collection and cleaning. For non-listed companies, AI models will capture the operating characteristics of enterprises from unstructured information such as industrial and commercial annual reports, bidding, social media public opinion, industry information, and customer reputation, forming similar "financial snapshots" and "growth trajectories".
The second step is industry mapping based on analogy. The model compares the target company with hundreds of historical comparable companies, including industry cycles, profit models, R&D intensity, revenue structure, etc., to generate a "valuation range". For example: a non-listed company with AI chip design capabilities and a gross profit margin of more than 50% in the past three years may be matched by the model to 10 cases that have been listed or acquired with similar indicators, and their average PS multiples become the reference anchor point.
The third step is scenario simulation and sensitivity analysis. AI will build a cash flow forecast model under different market scenarios, and cooperate with Monte Carlo simulation or deep learning prediction to generate an "expected return curve" and use it to back-calculate the valuation.
In this way, the valuation of non-listed companies is no longer a "future story" told by mouth, but a data-driven "probability map". And Goheal's AI system is the cartographer of this map.
In practical cases, how accurate is the AI valuation model?
After all, valuation is not for beauty, but for transaction. The success or failure of the model does not depend on how good the prediction is, but whether it can win the trust of investors, regulators and the board of directors.
Take a recent merger and acquisition in the consumer technology industry operated by Goheal as an example: listed company X intends to acquire a non-listed company Y that mainly engages in smart hardware by cash + additional issuance of shares. However, since Y company has not been audited for three years, the traditional valuation path cannot be applied. The Goheal team launched the AI model for auxiliary pricing, and completed the following work in just one week:
1. Construct a comparable company group: match the valuation data of 50 similar companies at home and abroad;
2. Clean the entire network data of Y company: extract growth indicators from dimensions such as industrial and commercial data, official website crawlers, video number comments, and employee evaluations;
3. Construct an expected return model: Based on the compound revenue growth rate and EBITDA forecast, AI recommends a valuation range of 1.18 billion to 1.35 billion yuan.
Finally, this valuation model was included in the appendix of the restructuring report, helping the acquirer to successfully respond to the question of "reasonable pricing of non-listed equity" in the second round of inquiries from the exchange, and achieved unconditional approval.
Not only that, we also found an unexpected surprise: the AI model also showed its prowess in speeding up due diligence. Traditional due diligence takes 30 to 45 days, while the AI-assisted model can give a structured report within 7 days, greatly improving transaction efficiency.
So the question is: Will AI replace human appraisers?
Of course not. As Goheal's research team concluded: "AI is responsible for calculating probabilities, and humans are responsible for judging logic." The final landing point of the valuation still requires the M&A team to make qualitative judgments based on negotiation strategies, transaction structures, regulatory tolerance and other factors.
Goheal Group
The advantage of the AI model is not to give you a "only answer", but to provide you with a starting point that is more reliable than experience and a clearer range than ambiguity, so that valuation is no longer a "game result" hidden in a folder, but a "data path" that can be publicly verified.
At the same time, it can also help investors predict the direction of the company's value after the acquisition, use models to see the future in advance, and use simulations to reduce risks.
Just like the joke we often say: "AI can't decide whether you buy or not, but it can help you pay less "tuition fees."
Back to reality: Is your valuation logic keeping up with the times?
From experience to AI model building, the valuation world is quietly changing. And with the regulatory authorities' requirements for transparency in M&A valuations, transactions without "scientific pricing" in the future may not even pass the inquiry letter.
Then the question is: Is your company negotiating a non-listed equity merger and acquisition? Are you still used to using the "rough multiple method" to value? If the transaction enters the review stage, can you use the model to explain "why it is worth so much"?
Welcome to leave a message in the comment area for discussion. Goheal is willing to explore the new valuation paradigm in the AI era with you, connect capital and logic with models, and use data to escort M&A decisions. We believe that the future M&A world will not only be dominated by the negotiation table, but also led by AI to innovate the way of thinking.
[About Goheal] Goheal is a leading investment holding company focusing on global mergers and acquisitions. It has deep roots in the three core business areas of acquisition of controlling rights of listed companies, mergers and acquisitions of listed companies, and capital operations of listed companies. With its profound professional strength and rich experience, it provides companies with full life cycle services from mergers and acquisitions to restructuring and capital operations, aiming to maximize corporate value and achieve long-term benefit growth.
