Equity research is more valuable when times are bad
If you have ever watched a TV programme on financial channels such as CNBC and Bloomberg TV, you might have come across terms such as “analyst recommendation” of “Buy”, “Sell” or “Hold”. These refer to the work of equity research analysts, who are also known as “sell-side” analysts who work for brokerage houses and investment banks. These analysts, after studying a company’s financial information and talking to its management, make the call to buy, sell, or hold a specific company’s stock.
These recommendations are usually targeted at the “buy-side” i.e. fund managers who buy assets for the funds that they manage. Retail investors who buy on their own accounts, as opposed to institutional investors such as mutual funds, often cannot afford such research, although retail investor firms provide similar information at a more affordable price. The question for any investor, however, remains: How accurate are these recommendations? How about in times of economic turmoil, when it is especially important to possess quality information?
“Analyst earnings forecasts are less accurate during uncertain times, but when they make a stock recommendation, it has a stronger stock-price impact i.e. people are more likely to take the analyst’s recommendation,” explains Roger Loh, Assistant Professor of Finance at Singapore Management University.
Measuring the impact of stock recommendations
Loh, who co-wrote the paper, “Is Sell-side Research More Valuable in Bad Times?” with Ohio State University’s Rene Stulz, explains in his paper how he measures the impact and influence of an analyst’s recommendations:
“[We treat) recommendation changes as influential if the stock-price reaction is statistically significant, (and) we find robust evidence that both upgrades and downgrades are more likely to be influential during bad times compared to good times.”
Using data from Thomson Financial’s Institutional Brokers Estimate System (I/B/E/S), Loh measured the stock-price impact by utilising the cumulative abnormal return (CAR) of a stock counter on the day of the recommendation and the following trading day. Loh describes the CAR as the “cumulative return of the stock less that on an equally-weighted characteristics-matched size, book-to-market, and momentum portfolio”. Simply put, it measures stock price movement, be it upwards or downwards.
A few proxies were chosen to represent “bad times”:
- Crisis, which included the September to November 1987 period that enveloped the Black Monday stock crash, as well as the Long-Term Capital Management crisis in 1998;
- Credit Crisis, denoting the chain of events from July 2007 to March 2009 that triggered the Global Financial Crisis;
- Recession, which included three periods which fulfilled the U.S. National Bureau of Economic Research’s (NBER) definition of a recession; and
- Down Market, where “the prior three-month buy-and-hold market return is negative”.
The average two-day CAR for recommendation downgrades – from “buy” to “hold” and “sell”, and from “hold” to sell” – read like this (all readings in percentage points):
- Crisis: -2.560 v -1.602 (-0.958 difference) in non-Crisis periods;
- Credit Crisis: -2.810 v -1.599 (-1.210 difference) in non-Credit Crisis periods;
- Recession: -2.687 v -1.577 (-1.110 difference) in non-Recession periods;
- Down Market: -2.073 v -1.538 (-0.536 difference) in non-Down Market periods
The CAR differences also hold for upgrades:
- Crisis: 2.635 v 2.036 (0.599 difference) in non-Crisis periods;
- Credit Crisis: 2.778 v 2.032 (0.746 difference) in non-Credit Crisis periods;
- Recession: 2.936 v 1.994 (0.942 difference) in non-Recession periods;
- Down Market: 2.161 v 2.101 (0.061 difference) in non-Down Market periods
What these numbers show was that recommendation changes have more impact during bad times than they do during good times. However, it is also more difficult to correctly value a company during bad times.
“The other thing that also goes up is dispersion,” says Loh. “If you have ten analysts, they’ll have predictions all over the place i.e. they disagree more. The analysts would have a fainter idea about how earnings would be in bad times. If they told me to buy or sell the stock, it should have less impact. However, it has more impact. That’s the paradox.”
Acting on sell-side research
Loh explained the paradox with an analogy: “When you’re driving, and it’s really foggy, you’ll depend on the map more even though the quality of the map may not be so good. In good times, you’ll rely less on the map.”
He adds, “Investors learn about the company by observing the signal from the external party; in other words, an analyst’s recommendations. There are also the macro signals such as interest rates, which affect every firm. During bad times the macro signals are very noisy. Analyst will be producing firm-specific signals for the company, so if the macro signal is really bad, then I will need to rely on the analyst more, conditional on his output deteriorating less than the macro signal.”
How then should an investor assess an analyst’s recommendations?
“A lot of the numbers are predictable,” Loh says. “For example, Wal-Mart has been making several billion of dollars every quarter. You know how it’s going to move quarter to quarter. The management will also give guidance. Analysts talk to management as well so that they might have a relationship with the management of the company.”
“Suppose they have a number – for example, 50 cents per share – and this analyst is really accurate, and everyone thinks it’s 45 cents. This analyst would have the advantage because he would make the recommendation to buy this stock. If you have a history of getting it right, when everyone’s predicting 40 cents and you say 35 cents, then people would react.”
In other words, you might want to study an analyst’s previous recommendations before deciding to buy a company’s stock.
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