Drifting Away: Measuring Market Inefficiencies

Dennis Chung isn’t about to let a proliferation of computer hardware get in the way of a good study on global markets. The professor of accounting at SFU’s Beedie School of Business not only lets computer servers take up valuable space in his office – he has also allowed them to invade his home.

Chung, it turns out, is collecting financial data from markets around the world and around the clock – data that is memory intensive and requires serious computing power. The hardware as home decor may not sit well with interior designers, but it’s a boon to those investors following up on his research.

His recent work, which finds itself at the intersection of finance and accounting, builds on past studies focused on stock market inefficiencies.

Within the area, his work looks at how long it takes for the market to digest information. And the market, Chung points out, doesn’t always utilize the information at its disposal.

A 2011 study of his has asked a seemingly obvious but often overlooked question outside of academia: To what extent does the market utilize data, and to what extent can returns be predicted?

The study, co-authored with Beedie School of Business Assistant Professor Karel Hrazdil, is entitled “Market Efficiency and the Post-Earnings Announcement Drift (PEAD).” It was published in the journal Contemporary Accounting Research.

The tendency of stocks to experience PEAD is important to investors because it exposes the inefficiency of a market and market price. In a perfect world, once a firm’s current earnings become known, the information should be quickly digested by traders and incorporated into the stock price. But often that is not the case, which gives rise to the concept of PEAD.

Academic research to date on post-earnings announcement drift provides extensive evidence that firms with better (or worse) than expected earnings experience significantly positive (or negative) abnormal stock-price performance during weeks or even months following the earnings announcements. In other words, instead of spiking or plummeting, the stock price drifts for days on end in the direction of the sentiment of the earnings announcement.

At least one scholarly article has referred to the predictability of returns after earnings announcements as the “granddaddy of all underreaction events” resulting from investors’ underestimation to value relevant earnings information. The underreaction explanation, however, raises the question of why so-called arbitrageurs – those who would and should take advantage of the market price differences – do not take advantage of the mispricing opportunities, thereby eliminating the drift and reinforcing market efficiency.

Chung and Hrazdil’s findings support the theory that this drift results from investors’ underreaction to earnings news – which in turn raises the question of why informed investors do not fully price the news in quarterly earnings announcements and take advantage of the mispricing.

So what’s the takeaway for would-be efficient stock market traders and investors? When it comes to companies with less efficient market pricing in their stock valuation, there is opportunity for profit.

Chung’s hope is that by measuring market efficiency (or lack thereof), there can be a better understanding of PEAD – and that perhaps investors who are willing to do some extra work can capture more gains.

“In the end, it’s an indication that at least for some stocks maybe investors aren’t acting as fast as they should,” he says. “Our results confirm that idea.”

For institutional investors, Chung notes, “the measure is useful in identifying companies which are efficient or inefficient in terms of market valuation.”

More recent research by Chung and his colleagues has shown that, perhaps surprisingly, high capital markets liquidity in the form of high trading volume can also be a challenge for investors, particularly retail investors.

That research has looked at high-frequency trading conducted by those who can put through thousands of trades within a second. As it turns out, retail investors can’t compete with the high-frequency traders – and therefore capital markets may be vulnerable to favouring some traders over others.

To this end, Chung raises the spectre of investor protection. “You need a level playing field to not undermine the credibility of the market,” he says.

Some early results from this recent work on high-frequency trading have found their way into a paper co-authored with Karel Hrazdil in the Journal of Empirical Finance.

The paper, entitled “Speed of convergence to market efficiency: The role of ECN (electronic communication networks),” examined an “order traffic” variable – which can measure indirectly the extent of high-frequency trading in the market. It confirmed that heavier order traffic is associated with longer time to achieve market efficiency.

The issue is one of high priority for regulatory agencies globally.

In a 2010 document, the Securities Exchange Commission (SEC) asked two directly related questions: “Are there useful metrics for assessing the quality of price discovery in equity markets, such as how efficiently prices respond to new information?” and “What is the best approach for assessing whether the secondary markets are appropriately supporting the capital-raising function for companies of all sizes?”

By demonstrating that the speed of convergence can be a useful measure to assess how efficiently prices respond to new information, the results from Chung and Hrazdil’s study provide direct answers to the issues raised by the SEC.

Their results confirm that trading volume has the strongest impact on improving the speed of convergence to market efficiency for companies of all sizes. However, they also suggest that the effects of other factors such as investor sophistication are not uniform across large and small firms.

All the more reason why Chung hopes for market reforms that would not ignore the little guy in the global markets picture.