Can investors create profitable trading strategies based on academic findings?

“There have been a ton of studies on earnings announcements, and the evidence is pretty compelling,” says Matthew Lyle, associate professor of accounting and management reporting at Kellogg. However, “these studies are not necessarily designed to mimic how traders actually act or what information they actually incorporate into their decisions.”

So Lyle and co-author Teri Lombardi Yohn, of Emory University, investigated whether investors could use results from earnings announcement research to make profitable trades. They have worked hard to keep the investor’s point of view in mind. As they write in their article, they wanted to create “a method that looks more like workable trading strategies” than previous research has.

They did this by creating portfolios filled with historical trading data to mimic a plausible investor portfolio, then applying a “train test approach” (common in machine learning) to simulate the different ways which a genuine investor might attempt to trade on profits. announcement news. This allowed the researchers to discern how an investor might make the most of stock movement patterns that tend to lead or follow earnings announcements.

In particular, they looked at three broad conclusions from past academic research to see how helpful each might be to a typical investor.

Lyle and Yohn’s analysis revealed that some, but not all, have proven to be useful trading strategies. But more broadly, their findings supported the idea that real investors could benefit from understanding these patterns of stock movement.

“The paper addresses, I believe for the first time, the question, ‘Is there really something economically there when it comes to search results on results announcements?'” Lyle said. . “And the answer is yes.”

Simulate real investors

Lyle says the hardest part of the study was designing it with an investor’s perspective front and center and considering what they’re likely to know and what they don’t. is not likely to know at the time it makes trading decisions.

For example, when building their portfolios and designing their train test approach, the researchers chose to assume that the imaginary investor was aware of the three main patterns surrounding earnings announcements that have been documented in previous research. These findings were:

1) After a company announces surprising earnings, the shares of that company tend to drift for a few days or even weeks, up for positive surprises, down for bad ones.

2) A company’s stock price is usually boosted before earnings are announced, perhaps because more investors are thinking about it.

3) Earnings tend to be more positively surprising when an earnings announcement is earlier than its expected date – and more negatively surprising when the announcement is delayed, suggesting companies seem more eager to share good news only bad news.

Lyle and Yohn’s imaginary investor might wonder if an investment strategy could reconcile all three models at once? Should one or both take precedence over the others? Lyle says previous research hasn’t addressed these questions.

Often, studies assume that investors have no knowledge of earnings announcement patterns found in previous research. It seemed unlikely to Lyle and Yohn. At the same time, Lyle argues, many of the common approaches used in such studies also assume that an investor has too much awareness.

For example, many of these studies use what is called an “event temporal” approach, which groups together events that have occurred over a period of time and treats those events as if they were happening at the same time.

Event time is smart for helping researchers understand stock market movements, Lyle says, “but from an investor’s perspective, as you approach today, you only have the information based on today and on the past, to actually trade on .You can’t see tomorrow.

Lyle and Yohn sought to address the limitations of this “anticipation bias” by relying only on what an investor would have actually known on any given day.

The researchers also had their simulated portfolios containing at least 30 stocks. This was intended to mimic the real-life diversification that many investors are mindful of, as opposed to the less diversified and less realistic portfolios that academics often use.

In their portfolios, the researchers’ train-test approach simulated various ways an investor might attempt to trade on the earnings announcement. In some portfolios, for example, they allowed a long delay after the earnings announcement before trading a stock, as some previous research has suggested that a stock’s drift after the earnings announcement can last several weeks. In others, they moved faster. From there, the researchers were able to focus on the most profitable strategies.

A dominant model

The researchers’ findings reveal interesting insights for implementing trading strategies around earnings announcements.

First, they found that a stock’s drift pattern after earnings announcement can generally be traded profitably for the first one to four days. This is a much smaller trading window on earnings announcement updates than suggested in previous research.

Moreover, it seems that investors do better when they only pay attention to drift patterns after the earnings announcement, rather than also trying to incorporate stock movement patterns just before these announcements or in the event of a rescheduling. Lyle acknowledges that this surprised him; he had assumed that the most profitable strategies would be those that succeeded in incorporating more than one of these documented patterns.

Ultimately, he says, he thinks non-post-announcement drift models don’t prove progressively useful because it’s essentially a trade-off: when an investor chooses to invest, that investor must give up on something, and since the patterns overlap across different companies for most of the time, it behooves investors to pick the one that tends to produce the most stock moves, and that is the post-earnings announcement drift pattern.

The underlying reasons why this pattern was the dominant pattern were not discussed in this article – and Lyle hopes the study will help provide a new way to examine the reasons in future research.

A message to future researchers

Lyle thinks his findings have important messages to convey.

The first is for investors: even after designing a methodology that incorporated many of the constraints faced by real investors, Lyle and Yohn still found potentially profitable strategies in the patterns surrounding earnings announcements. For Lyle, this suggests that “some of the results in the academic literature appear to be real and remarkably robust.”

Lyle says the other central message of his paper is for academics: maybe it’s time to update some of the common methodologies used in this area of ​​research that prevent the results from being generalizable to real investors.

“I think if we’re going to say anything about how much an investor leaves on the table, it’s important for us to provide some insight into how they would actually trade based on the patterns identified in academic research.”

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