A Study of the Influence of Factors on the Models of the Market Price of Common Stock and Its Elasticity with a Neutral Approach to Dividend Policy as an Artificial Intelligence Tool

In the world of public companies, the price of a single share is more than just a number on a screen; it is a reflection of a company's health, its future promise, and the trust investors place in it. For the owners of these companies, the ultimate goal is to maximize this value, ensuring their wealth grows over time. However, reaching this goal is complicated by a persistent debate: how much of a company's profit should be paid out to shareholders as dividends, and how much should be kept to fund future growth? Some theories suggest that paying dividends doesn't matter at all, while others argue that the decision is crucial. A third, more balanced perspective suggests that a company can find a "neutral" path, one that respects the interests of all stakeholders and ensures long-term stability. This middle ground is where a new study by Sergey Krylov of Ural Federal University begins, aiming to understand exactly how different financial levers pull the price of a stock up or down when a company follows this neutral approach.

The researcher set out to build a precise map of these financial forces. He focused on a specific set of factors that determine the market price of a share: how much profit the company makes per share, how efficiently it uses its money to generate returns, the overall discount rate the market applies to future earnings, the return rate investors demand, and the rate at which dividends are expected to grow. The challenge was that these factors do not act in isolation; they are tangled together in complex mathematical models. To untangle them, Krylov employed a method known as deterministic factor analysis, which he treats here as a form of artificial intelligence. In this context, the "intelligence" is not a robot making decisions, but a systematic, computer-ready process of prediction. It works by taking a known starting point, changing one specific factor at a time while holding everything else steady, and calculating exactly how much that single change shifts the final price. This allows a company to see, with clarity, the isolated impact of a decision before it is made.

To test this approach, the study applied these models to a hypothetical public company named "Gamma." The researchers established a baseline scenario using data from the end of a reporting year and then created a forecast for the following year, adjusting the key factors to see what would happen. They observed that when the market's capitalization level, which acts as a discount rate for future cash flows, rose from 15.3% to 16.1%, the expected share price dropped significantly by 104.839 rubles. Conversely, when the company's earnings per share increased from 157.48 rubles to 164.53 rubles, the share price climbed by 78.817 rubles. The study also tracked how these changes affected the "elasticity" of the stock price, a measure of how sensitive the price is to changes in specific factors. For instance, the sensitivity of the price to the company's return on investment shifted slightly as other conditions changed, demonstrating that the relationship between these variables is dynamic and responsive.

The results of this simulation provided a clear, step-by-step breakdown of how each factor contributed to the final outcome. When all the changes were combined, the net effect was a modest increase in the share price of 12.145 rubles. This small gain was the sum of several larger, opposing forces: the drop caused by the higher market discount rate was partially offset by the gains from higher earnings and improved investment returns. The study also revealed that if the dividend growth rate remained constant at 5%, it had no immediate effect on the price change in this specific scenario, highlighting how some factors can remain neutral under certain conditions. By isolating these effects, the method allows managers to predict exactly how a specific decision, such as altering the dividend policy or improving investment efficiency, will ripple through to the stock price.

This work confirms that using a systematic, chain-substitution method to analyze these complex models is a viable and effective tool for financial management. It offers a way to move beyond guesswork, providing a structured way to forecast how a company's actions will be perceived by the market. The study suggests that by understanding the precise influence of each factor, a public company can better manage its stock price and its dividend policy in a way that supports sustainable development. While the method relies on the assumption that factors change independently, which is a simplification of the real world, the results offer a powerful framework for decision-making. The research concludes that this approach, framed as an artificial intelligence tool for prediction, can help companies navigate the complexities of the stock market, ensuring that their strategies are not just hopeful guesses, but calculated steps toward maximizing shareholder value.