The media often uses colorful phrases to explain stock moves that are downright inexplicable. “Animal spirits,” “irrational exuberance,” and “FOMO” were all used to describe Tesla’s staggering 740 percent gain and the dazzling meme stock rallies in 2020.

Economists have a poetic device of their own to explain these price moves: growth expectations. Standard asset pricing models, such as the discounted cash flow model and the consumption capital asset pricing model, use cash flow growth expectations—expectations about future profit—as a key input to explain stock prices. This is a subjective measure that varies from one investor to another, and a 1 percent increase in the expected profit for a company should produce about a 1 percent increase in its stock price, according to such models.

But there may actually be reverse causality between growth expectations and prices, proposes Chicago Booth PhD student Aditya Chaudhry. He finds that changes in growth expectations affect prices substantially less than currently assumed by standard asset pricing models—and the reason for this, he writes, is that market demand doesn’t move much in reaction to changes in expected returns.

To investigate his hypothesis, Chaudhry analyzed mutual fund trading activity from January 1984 through December 2021. When a fund indexed to the S&P 500 receives a $10 million investment, it typically uses that money to buy all the stocks in its portfolio in proportion to the current holdings to maintain its relationship with the index. (If $10 million is cashed out of the mutual fund, there would be proportional sales.) These purchases do not reflect any new information or changes in the manager’s growth expectations for those stocks, so aggregating these types of trades across many funds creates a way to view stock price changes that are unrelated to fundamentals.

With these data, Chaudhry was able to see how prices changed in response to this “uninformed” trading. Then, using the earnings-per-share forecasts of equity analysts at institutional firms such as Goldman Sachs and Morgan Stanley, he tracked how analysts’ growth expectations changed in response.

A 1 percent increase in stock prices by uninformed trading caused analyst EPS growth expectations to increase by 0.41 percent for the following year. This outcome, Chaudhry writes, is large enough to have real-world relevance—and is too big to be explained away by randomness.

Having seen how analysts react to uninformed price moves, Chaudhry revisited the impact of growth expectations on asset prices in standard asset pricing models. He developed a framework to predict asset demand using investors’ beliefs about a company’s profitability (their growth expectations, in other words). This required finding a way to measure investors’ beliefs, which he did by recognizing that investors learn information from analysts’ reports.

With machine learning, he homed in on analysts’ beliefs, weighting their influence on investors and teasing apart the portion of forecasts that were specific to an analyst’s particular beliefs and expectations versus the portion due to a company’s news and stock-price moves (which, as he had established, affected growth expectations). This helped him view how analyst-specific signals influenced investors’ beliefs about expected growth and changed demand for a stock.

In the final step of his study, Chaudhry measured the impact of these growth expectations on prices. With a regression model, he statistically analyzed the price movements that followed analyst reports.

Initially, the model assumed that the analyst influence on investors’ beliefs did not vary, nor did the sensitivity of investors’ asset demand to growth expectations. With these assumptions, the regression implied that a 1 percent increase in annual investor growth expectations increased stock prices by only 0.07 percent. Later, Chaudhry used Securities and Exchange Commission reports of mutual fund stock holdings to create individual demand curves for each fund and ran the same regression fund by fund.

Aggregating those results, Chaudhry determined that a 1 percent increase in annual investor growth expectations increased stock prices by 0.16 percent—still a far cry from 1 percent. Growth expectations have less of an impact on asset prices than standard pricing models currently suggest because of the low sensitivity of demand to expected returns, he concludes.

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