Category: Research

Does academic research destroy stock return predictability?

 

David McLean, Visiting Associate Professor of Finance at MIT Sloan School of Management

The first published paper that I know of to document that returns are predictable across stocks was published in 1972. In that paper, the authors showed that price level predicts returns in that low priced stocks tend to have higher returns than high priced stocks. Since then, this has been an active research area with numerous academic papers showing that various strategies based on observable firm traits (e.g., size, past performance) can predict returns across stocks.

In research conducted with Prof. Jeffrey Pontiff of Boston College, we asked how well these strategies perform after the strategy has been published in an academic journal. We replicated 82 different strategies that have been shown to predict stock returns in leading finance, accounting, and economics journals. We found that on average, the return of a strategy decays by 35% after a paper has been published. In other words, investors relying on a published strategy generating a 5% abnormal return should expect to make an average of 3.75% in abnormal returns during the years following publication.

Why does this decline happen? One explanation is that the abnormal returns of these strategies reflect returns to buying and selling mispriced stocks. When scholars discover a new trading strategy and publish a paper about it, investors begin to trade on that strategy, thereby pushing the prices of the stocks within the strategy towards “correct” or fundamental values.

Consistent with this idea, the post-publication decay is greatest in strategies that consist of larger stocks that are less costly to trade. These strategies decline by more than 35%, which seems reasonable because investors are more likely to follow a published strategy when the cost of trading in it is low. In contrast, when a strategy requires trading in smaller, more volatile, and less liquid stocks, investors are less inclined to try to exploit the strategy, and we find that such strategies decline less after the paper is published. Moreover, we find that after a strategy has been published, there is an increase in trading activity among the stocks in the strategy’s portfolio.

As for whether investors should follow strategies identified in academic papers, investors should on average expect to make 35% less on a strategy as compared to what is reported in a published paper. Investors should also keep in mind that these papers are written by researchers whose first priority is to better understand how financial markets work, and not to identify money making mechanisms. As a result, most studies do not estimate the costs of implementing the strategy, which can be substantial.

A silver lining in our results is the notion that academic research makes markets work better. What our findings suggest is that market mispricing is at least partially corrected once a study has drawn attention to it.

Prof. R. David McLean is visiting MIT Sloan from the University of Alberta. He recently coauthored the paper “Does Academic Research Destroy Stock Return Predictability” with Prof. Jeffrey Pontiff of Boston College.

“Dark Pools” can improve price discovery in open exchanges

When big investors want to execute trades but fear the size of the transaction could move the market, they often go to dark pools—alternative trading systems where orders are not publicly displayed. These opaque trading venues, now accounting for about 12 percent of equity trading volume in the United States, have sparked concern among regulators and in the financial press. With so many transactions occurring out of public view, critics warn that price discovery, the accurate determination of asset prices, will become more difficult.

Professor Haoxiang Zhu

But despite their sinister sounding name, dark pools can actually improve price discovery in open exchanges, I have found in my research, “Do dark pools harm price discovery?” Open a dark pool alongside a ‘lit’ pool, such as the New York Stock Exchange or Nasdaq, where orders are visible to all, and pricing can become more accurate on the open exchange.

To understand why this happens, it helps to consider investors to be divided loosely into two groups: informed and uninformed. Informed investors examine balance sheets, study analyst reports, read company press releases, and use advanced technology to monitor the market. When informed investors decide to execute a trade, they hope to profit from the information they gathered–and quickly.

Uninformed investors trade mainly for liquidity reasons. They may need to get cash to meet an obligation, or they may have cash they need to invest. They want to make their transaction regardless of a company’s fundamentals.

A dark pool presents different execution risks to informed and uninformed investors. This risk arises because a dark pool relies on matching, rather market makers, to execute trades. For example, if a dark pool has 300 shares to buy and 200 shares to sell, then only two thirds of each buy order is executed. Failure of execution is costly. (On an open exchange, those extra 100 shares to buy would be executed by market makers or liquidity providers). Informed investors have a high execution risk in the dark pool because they tend to trade in the same direction. Uninformed traders have a lower execution risk because their orders tend to be balanced on either side of the market.

This difference in execution risk tends to drive a greater proportion of informed investors to the open exchange and a larger share of uninformed investors to the dark pool. Having more informed investors trading on an exchange improves price discovery in the exchange and yield more accurate asset prices—precisely the opposite of what critics of dark pools fear.

While having non-displayed orders can help price discovery, dark pools are opaque in other, potentially harmful, ways. For example, dark pools typically do not disclose how they operate, and investors and the public often don’t know how the venues set execution prices or process orders. Increased oversight and closer scrutiny of dark pools could well be a very good thing for financial markets. But not displaying orders in itself needs not threaten price discovery in transparent venues. Instead, it could help.

Haoxiang  Zhu is Assistant Professor of Finance at the MIT Sloan School of Management

What is the true cost of government-backed credit?

The U.S. government is arguably the largest financial institution in the world. If you add the outstanding stock of government loans, loan guarantees, pension insurance, deposit insurance and the guarantees made by federal entities such as Fannie Mae and Freddie Mac, you get to about $18 trillion of government-backed credit. Through those activities, the government has a first-order effect on the allocation of capital and risk in the economy.

Professor Deborah Lucas

The question of what those commitments cost the public is important; accurate cost assessments are necessary for informed decisions by policymakers, effective program management, and meaningful public oversight.  My research and that of others has shown that if one takes a financial economics approach to answering that question — one that is consistent with the methods used by private financial institutions to evaluate such costs — it leads to significantly higher estimates than the approach currently used by the federal government.

At the core of the problem are the rules for government accounting, which by law require that costs for most federal credit programs be estimated using a government borrowing rate for discounting expected cash flows, regardless of the riskiness of those cash flows. That practice systematically understates the cost to the government because it neglects the full cost of risk to taxpayers, who are effectively equity holders in the government’s risky loans and guarantees.

An alternative approach to cost estimation — a fair value approach based on market prices — would fully take into account the cost of risk. Fully accounting for the cost of risk makes a significant difference:  An estimate of the official budgetary cost of credit programs in 2013 shows them as generating savings for the government of $45 billion, whereas a fair value estimate suggests the programs will cost the government about $12 billion.

The understatement of cost has important practical consequences. For example, it may favor expanding student loans over Pell grants because student loans appear to make money for the government. It also creates the opportunity for “budgetary arbitrage,” whereby the government can buy loans at market prices and book a profit that reduces the reported budget deficit, as it did in several instances during the recent financial crisis.

That perspective on how credit program costs should be measured is widely shared by financial economists, although until recently the issue has not received much attention by academics. That changed last month when the Financial Economists Roundtable (FER), of which I am a member, issued a statement on this matter, writing: “The apparent cost advantage of government credit assistance over private lenders is, in the opinion of the FER, primarily due to [government] accounting rules, rather than to any inherent economic advantage of the government.”

According to the FER’s statement, the solution to this undervaluation is to amend current accounting rules to require an approach to cost estimation that fully recognizes the cost of risk in the government’s credit programs. The group maintains (and I agree) that such a change “would make the true budgetary implications of credit assistance more transparent to program administrators, policy makers and the public.”

Prof. Deborah Lucas is the author of “Valuation of Government Policies and Projects.” She previously served as assistant director and chief economist at the Congressional Budget Office.