Do not buy stocks that are in the news

What happens when a stock suddenly becomes much more volatile? In a paper written jointly with Turan Bali and Yi Tang, we show that such stocks earn unusually high contemporaneous returns. However, the price run-up is reversed in the next three to six months. Therefore, sudden volatility increases predict future underperformance. Stocks in the top 10% of the volatility change in the prior month underperform otherwise similar stocks by 1.22 % in the next month!

We investigate the reason for such a surprising pattern. This result seems to contradict our intuition that investors demand to be compensated for the exposure to high price volatility. However, we do find that stocks with persistently high volatility levels earn higher returns than otherwise similar stocks. Rather, this phenomenon applies only to stocks that experience sudden volatility increases. Most of the volatility increases that we observe are transitory, such as that the stock’s volatility levels converge back to the long-run average in the next few months.

In order to figure out why stocks that experience volatility increases earn high contemporaneous returns we need to answer why price volatility could suddenly increase. We find that sudden increases in volatility tend to coincide with announcements of firm-specific news. The types of news that lead to large subsequent volatility increases are quite diverse and include announcements of corporate earnings, new promotional campaigns, the loss of customers, new information about credit, M&A activity, earnings restatements, news about legal problems, changes in the top management ranks, and new information about firm operations and performance, and so on. All these announcements have the potential to carry significant implications for the firm value. However, since most of these announcements are non-routine, investors may have trouble quantifying their impact on the stock price and will end up disagreeing about the post-announcement valuation. Moreover, in the aftermath of the announcement, investor confidence about the accuracy of their firm valuation will be weakened, and they will respond more strongly to the subsequent news stories and pundits’ opinions. Therefore, a decrease in the confidence level will lead to an increase the subsequent level of stock volatility until investor confidence in their valuations is restored.

Importantly, the high post-announcement level of price volatility serves as a barrier to short selling. And high costs of short selling will make it difficult for the relatively pessimistic investors to sell shares and put the downward pressure on prices.

    Here is an example that illustrates the mechanism through which price volatility increases short selling costs.

    Suppose that a brokerage house requires that the short seller maintain a margin of 30% (which is a common requirement across brokerages, and the lowest margin allowed by the regulators is currently 25%). Suppose further that the short seller believes that the current stock price of $10 per share is too high and will decline to $9 per share. Therefore, when the short seller sells short 100 shares of the stock, she needs to deposit $300 in the margin account. Given that she expects to earn $100 ($100 shares * ($10 – $9)), the expected return on the investment is equal to $100/$300 = 33.33%.

    Now suppose that instead of steadily decreasing to $9 per share, the price initially increases by 10%, to $11/share. The equity value of the position thus drops to $200 (calculated as the $300 (margin) + 100 shares * $10 (the initial short-sale proceeds) – 100 shares * $11 (the current liability)). The new margin is equal to $200/(100 shares * $11) = 18.18%. In order to comply with the margin requirement, the short seller or her broker needs to reduce leverage by purchasing and returning 39.40 shares at the current price of $11/share. This would bring the margin back to 30%. Barring any further price increases until the share price finally converges to $9, the profit that the short seller will walk away with is $21.20 (calculated as 100 shares * $10/share (the initial short-sale proceeds) – 39.40 shares * $11/share (the cost of repurchasing shares to comply with the margin requirement) – (100 shares – 39.40 shares) * $9/share (the final liability)). And the actual return on the strategy will decline from the expected 33.33% to only 7.07%, which is about one-fifth of the originally anticipated return.

    If the initial price increase is instead equal to 12%, the short seller will end up losing 0.7% on the position. And if the initial price increase is equal to 30%, both the initial margin and the short-sale proceeds will be used to prematurely repurchase shares and the short seller will end up losing 100% of the initial margin investment.

To sum up, volatility increases tend to coincide with firm-level news announcements. Given the non-routine nature of most news, investors tend to disagree how to quantify the impact of the news on the firm valuation, even though they may agree on the positive or negative quality of the news. Some investors will come up with higher post-announcement valuations than other investors. However, the high level of stock price volatility in the aftermath of the news announcement serves as a barrier to short selling. As a result, the relatively more pessimistic investors are forced to sit on the sidelines, while the optimistic investors end up driving prices to the level of their own optimistic valuations. In the future, as the dust settles and investors come to an agreement on how to quantify the impact of the news, and the resulting price tends to be lower than the initially high valuation of the optimistic set of investors. This explains the subsequent underperformance of stocks that experience high volatility increases.

The bottom line: do not buy stocks that are in the news. You are likely to be too late to experience the initial price run-up but will surely experience the subsequent price decrease back to the fundamentals.

The paper can be downloaded from the link:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1362121

Can investors successfully time the market?

In these uncertain and volatile times, investors often wonder if the stock market can be timed, such that low returns can be avoided and high returns can be realized. There is much evidence that markets indeed can be timed.  Various signals help predict market downturns so that investments can be taken out of the stock market and safely parked in cash or treasury bills.  When the signals indicate high future market returns, investments can be moved back into the stock market.  

There is a variety of signals that were shown to predict stock market movements.  For example, signals based on financial ratios, such as the price-to-earnings ratio (P/E), the book-to-market ratio (B/M), and the dividend yield (D/P); signals based on interest rates (both short- and long-term rates); signals based on the maturity spread (defined as the yield spread between long- and short-term treasury bonds/bills); signals based on the credit spread (the yield difference between BAA- and AAA-rated corporate bonds); signals based on the implied volatility index (VIX); and so on.  The intuition, for example, for the P/E-based signal is the following: when stock prices are high relative to earnings (the P/E ratio is high) stocks are considered to be overvalued and thus expected to earn low returns.  And vice versa: when price levels are low relative to earnings (the P/E ratio is low), stocks are cheap and are expected to appreciate and earn high returns.    

There has been plenty of empirical evidence presented that the above indicators work in predicting stock market movements and therefore can be used to successfully time the market. However, a recent paper by Andreas Neuhierl and Bernd Schlusche ( “Data Snooping and Market-Timing Rule Performance”) disputes these findings.   The authors convincingly show that much of the documented success of market timing strategies can be attributed to data snooping.  

In this context, the term “data snooping” simply means that previously-reported successful strategies were discovered by searching through many different parameters for the market timing indicators until an indicator that works has been found.   The “successful” indicator was thus found by pure luck (or by intensive and deliberate search) and is not guaranteed to work in the future.

After the authors correct for data-snooping biases, none of the market-timing strategies beat a buy-and-hold strategy.  A small exception is a strategy based on signals from several indicators at once, which only works for the period of 1981-1994.

And, of course, why would market-timing strategies work if any investor can take advantage of them?  By paying attention to market timing signals, investors bring prices in line with the fundamentals, and the indicators cease to be useful in predicting market movements.  It is entirely possible that there might be a strategy out there that has never been reported and it works precisely because only a select few know about it!