EMPIRICAL DATA/QUANTITATIVE ANALYST
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Friday, July 10, 2009
Empirical Data/Quantitative Analyst
Empirical Finance readers:
If any of our readers are interested in the position below, please email emackrell@advantageplacements.com (I told them our audience is a smart group--at least based on the intelligent and thoughtful commentary we get in our inbox everyday!).
Is Share Price Relevant?: 5/10
Investment Potential Rating: 5/10 (1 worst, 10 best)
============
Is Share Price Relevant?
Soosung Hwang
School of Economics, Sungkyunkwan University
Chensheng Lu
CPM Advisers
working paper
http://ssrn.com/abstract=1341790
Abstract:
This study examines the cross-sectional effect of the nominal share price. We endeavor to understand two interesting puzzles associated with share price. First, the nominal share prices of the US stocks have remained remarkably constant since the Great Depression despite inflation. Second, there is no consensus about the motivations for firms to split their stocks, since financial theory suggests share price is independent of its value. The findings indicate that share price per se matters in cross-sectional asset pricing: stock return is inversely related to its nominal price. It is shown that a strategy of buying these penny stocks can generate a significant alpha even after considering the transaction costs. The abnormal returns of these penny stocks are robust in the presence of other firm characteristics such as size, book-to-market equity, earning/price ratio, liquidity and past returns; and are also not explained by the existing factors. These results also cast some light on the stock-split phenomenon. Intuitively, if firm managers know that low price would generate higher future returns, they are more likely to split their stocks on behalf of shareholders.
Data Source:
The stocks and their prices come from CRSP, while the relevant financial data come from Compustat.
Data Specification:
The share price of a stock should have no relation to subsequent returns. After all, in isolation it is completely meaningless. That is, in theory it is meaningless. In practice of course, companies are often very concerned with their nominal share price and that is why we have seen stock splits that have kept share prices at a constant level since the Great Depression - even in light of the inflation since that time.
As the authors of this paper mention briefly, previous research (Benartzi, Michaely, Thaler and Weld (2007)) has shown that the average share price of stocks has stayed very constant around a level of $30/share since 1935. What the authors neglect to mention here is that this research also found that this resulted in tremendous excess commissions for brokerages. In the case of GE, for example, 99% of all commissions paid on trades in GE stock since 1935 would have been avoided by investors had the company never split its stock. In 2006, investors paid $100 million in commissions more than they would have if the stock was not split. Unreal.
Anyway, the point of this paper is that low price stocks outperform high price stocks. They sort all NYSE and AMEX stocks since 1926 in to five ranges, price < $5, $5 to $10 , $10 to $15 , $15 to $20 and price > $20. They then measure the monthly returns using a holding period of one year. (In other words, they rebalance these hypothetical portfolios annually, but present monthly returns in their table) They find that the lowest group of stocks outperforms the highest group by 0.831% per month before controlling risk factors. This result is statistically very significant.
The outperformance is also observed for NASDAQ stocks using the same methodology, though obviously the authors begin their observations much later, in 1963. Over the period of 1963-2006, low price NASDAQ stocks outperform high price NASDAQ stocks by 0.391% per month, although this results is of questionable statistical significance. The results for all firms on all three exchanges show outperformance by low price stocks of 0.53% monthly from 1963-2006.
An obvious explanation for these results is firm size. We already know that small market-cap companies outperform large ones. So if the low share price stocks are mostly small companies then these results are a lot less interesting. Another obvious explanation is liquidity, since illiquid stocks outperform and low price stocks are almost always less liquid than high price stocks. Both of these explanations could make the results unimpressive on a risk-adjusted basis.
The authors tackle both of these explanations. The try to make the case that share price still has an effect after controlling for size and liquidity. Unfortunately the data don’t seem to be cooperating. They split their sample, which is already split in to price ranges, in to five size categories. Not surprisingly, the only size category for which low price stocks outperform high price stocks is the smallest size firms.
They do the same thing for liquidity, breaking their sample in to five liquidity categories. Again, only among the most illiquid stocks do low price stocks outperform high price stocks. Putting these two findings together we can conclude that the preliminary results the authors report are probably completely explained by size and liquidity.
However the authors do make one very useful finding that they only gloss over. The January effect, which is the circumstance that stocks generally do much better in January than they do the rest of the year (because of tax loss selling in December), is very pronounced among low price stocks. So much so that you could almost implement this strategy in January only and spend the rest of the year doing something else. For the smallest size firms, low prices stocks outperform by 11.6% in January alone. Even for the largest size firms the low price stocks outperform by 6.6%. These results are much greater than the simple results of a January effect strategy. If I were the authors I would re-position this paper as a January effect paper and expand on this finding greatly.
Remarks:
At first I hesitated to even review this paper, but the topic is interesting and I think it makes a good point nonetheless. What we see here is an apparent anomaly that disguises other more robust anomalies that we already know about.
This is a really simple paper, so simple that it would make me somewhat nervous to implement it as a trading strategy. Also I found numerous typographical errors and confusing explanations. This leads me to believe this is very preliminary work and we might want to wait for the authors to get this paper published and rigorously peer-reviewed before relying on the results.
Nevertheless, the authors present some really interesting and believable results. Definitely something to consider, but probably not before you first consider the other anomalies like size and liquidity that are probably driving these results to a large extent.
Investment Potential Rating: 5/10
Sphere: Related Content
============
Is Share Price Relevant?
Soosung Hwang
School of Economics, Sungkyunkwan University
Chensheng Lu
CPM Advisers
working paper
http://ssrn.com/abstract=1341790
Abstract:
This study examines the cross-sectional effect of the nominal share price. We endeavor to understand two interesting puzzles associated with share price. First, the nominal share prices of the US stocks have remained remarkably constant since the Great Depression despite inflation. Second, there is no consensus about the motivations for firms to split their stocks, since financial theory suggests share price is independent of its value. The findings indicate that share price per se matters in cross-sectional asset pricing: stock return is inversely related to its nominal price. It is shown that a strategy of buying these penny stocks can generate a significant alpha even after considering the transaction costs. The abnormal returns of these penny stocks are robust in the presence of other firm characteristics such as size, book-to-market equity, earning/price ratio, liquidity and past returns; and are also not explained by the existing factors. These results also cast some light on the stock-split phenomenon. Intuitively, if firm managers know that low price would generate higher future returns, they are more likely to split their stocks on behalf of shareholders.
Data Source:
The stocks and their prices come from CRSP, while the relevant financial data come from Compustat.
Data Specification:
The share price of a stock should have no relation to subsequent returns. After all, in isolation it is completely meaningless. That is, in theory it is meaningless. In practice of course, companies are often very concerned with their nominal share price and that is why we have seen stock splits that have kept share prices at a constant level since the Great Depression - even in light of the inflation since that time.
As the authors of this paper mention briefly, previous research (Benartzi, Michaely, Thaler and Weld (2007)) has shown that the average share price of stocks has stayed very constant around a level of $30/share since 1935. What the authors neglect to mention here is that this research also found that this resulted in tremendous excess commissions for brokerages. In the case of GE, for example, 99% of all commissions paid on trades in GE stock since 1935 would have been avoided by investors had the company never split its stock. In 2006, investors paid $100 million in commissions more than they would have if the stock was not split. Unreal.
Anyway, the point of this paper is that low price stocks outperform high price stocks. They sort all NYSE and AMEX stocks since 1926 in to five ranges, price < $5, $5 to $10 , $10 to $15 , $15 to $20 and price > $20. They then measure the monthly returns using a holding period of one year. (In other words, they rebalance these hypothetical portfolios annually, but present monthly returns in their table) They find that the lowest group of stocks outperforms the highest group by 0.831% per month before controlling risk factors. This result is statistically very significant.
The outperformance is also observed for NASDAQ stocks using the same methodology, though obviously the authors begin their observations much later, in 1963. Over the period of 1963-2006, low price NASDAQ stocks outperform high price NASDAQ stocks by 0.391% per month, although this results is of questionable statistical significance. The results for all firms on all three exchanges show outperformance by low price stocks of 0.53% monthly from 1963-2006.
An obvious explanation for these results is firm size. We already know that small market-cap companies outperform large ones. So if the low share price stocks are mostly small companies then these results are a lot less interesting. Another obvious explanation is liquidity, since illiquid stocks outperform and low price stocks are almost always less liquid than high price stocks. Both of these explanations could make the results unimpressive on a risk-adjusted basis.
The authors tackle both of these explanations. The try to make the case that share price still has an effect after controlling for size and liquidity. Unfortunately the data don’t seem to be cooperating. They split their sample, which is already split in to price ranges, in to five size categories. Not surprisingly, the only size category for which low price stocks outperform high price stocks is the smallest size firms.
They do the same thing for liquidity, breaking their sample in to five liquidity categories. Again, only among the most illiquid stocks do low price stocks outperform high price stocks. Putting these two findings together we can conclude that the preliminary results the authors report are probably completely explained by size and liquidity.
However the authors do make one very useful finding that they only gloss over. The January effect, which is the circumstance that stocks generally do much better in January than they do the rest of the year (because of tax loss selling in December), is very pronounced among low price stocks. So much so that you could almost implement this strategy in January only and spend the rest of the year doing something else. For the smallest size firms, low prices stocks outperform by 11.6% in January alone. Even for the largest size firms the low price stocks outperform by 6.6%. These results are much greater than the simple results of a January effect strategy. If I were the authors I would re-position this paper as a January effect paper and expand on this finding greatly.
Remarks:
At first I hesitated to even review this paper, but the topic is interesting and I think it makes a good point nonetheless. What we see here is an apparent anomaly that disguises other more robust anomalies that we already know about.
This is a really simple paper, so simple that it would make me somewhat nervous to implement it as a trading strategy. Also I found numerous typographical errors and confusing explanations. This leads me to believe this is very preliminary work and we might want to wait for the authors to get this paper published and rigorously peer-reviewed before relying on the results.
Nevertheless, the authors present some really interesting and believable results. Definitely something to consider, but probably not before you first consider the other anomalies like size and liquidity that are probably driving these results to a large extent.
Investment Potential Rating: 5/10
Friday, July 3, 2009
The Good News in Short Interest: 9/10
Investment Potential Rating: 9/10 (1 worst, 10 best)
============
The Good News in Short Interest
Ekkehart Boehmer
University of Oregon - Charles H. Lundquist School of Business
Zsuzsa R. Huszar
NUS Business School - Finance Department; California Polytechnic State University
Bradford D. Jordan
University of Kentucky - Gatton College of Business and Economics
Journal of Financial Economics, forthcoming
http://ssrn.com/abstract=1405511
Abstract:
We study the information content in monthly short interest using NYSE-, AMEX-, and NASDAQ-listed stocks from 1988 to 2005. We show that stocks with relatively high short interest subsequently experience negative abnormal returns, but the effect can be transient and of debatable economic significance. In contrast, we find that relatively heavily traded stocks with low short interest experience both statistically and economically significant positive abnormal returns. These positive returns are often larger (in absolute value) than the negative returns observed for heavily shorted stocks. Because stocks with greater short interest are priced more accurately, our results suggest that short selling promotes market efficiency. However, we show that positive information associated with low short interest, which is publicly available, is only slowly incorporated into prices, which raises a broader market efficiency issue. Our results also cast doubt on existing theories of the impact of short sale constraints.
Data Source:
The data on short interest come from the exchanges themselves. Although the authors don’t specify, I assume they merely downloaded csv files from the exchanges’ websites, each of which provide this data for free. (There are also many services that aggregate these data.) Other relevant data used in the study come from CRSP. The period tested is 1988-2005.
Data Specification:
Not surprisingly, a lot of research has come out recently on the topic of short selling. Traditionally, theory has held that constraints on short selling prevent the impounding of negative information in to stock prices and, since there is no similar constraint to going long, more positive information is impounded in to prices than negative. Thus, constraints on short selling impede market efficiency (and regulators would be foolish to try to limit its use, but we can save that discussion for another day).
This paper is about the performance of heavily shorted stocks versus lightly or un-shorted stocks. Market pundits usually assume heavy short interest is a bearish indicator because it signals that many investors anticipate the price to decline. But there are other interpretations; One is that shorting is usually used for hedging or arbitrage and thus says nothing about the valuation of the stock. Another is that shorting implies future buying because all short sales will need to be covered at some point in the future, therefore it may in fact be a bullish indicator. On balance, though, the academic literature has shown support for the conventional wisdom and found underperformance of heavily shorted stocks.
This paper is one of the first to actually compare the difference between high-short interest stocks and low-short interest stocks. The results are somewhat surprising.
Using the short interest data of the prior month, the authors rank all stocks based on the short interest ratio, which is the ratio of total shares shorted divided by shares outstanding, then observe the performance of those stocks in the subsequent month. There are a lot of stocks with zero short interest, about 15% of them, so when the authors to group stocks based on short interest ratio they have a lot more stocks in the 1st percentile portfolio than any of the others.
Results:
The results may surprise you. Heavily shorted stocks do underperform, but the outperformance of lightly or un-shorted stocks is far more significant. Hmm.
Stocks in the 99th percentile portfolio (the highest short interest stocks) on average experience negative absolute returns of 0.1% per month. The stocks in the 1% portfolio (the lowest short interest stocks) on average experience positive absolute returns of 2.1% per month. This is an impressive result.
After controlling for various risk factors through multifactor regressions, the authors find that the 99% portfolio produces -1.2% alpha monthly, and the 1% portfolio produces +1.4% alpha monthly. This means that a theoretical long/short portfolio adjusted for risk could produce 2.6% per month, or 36% annually!
In the full sample that the authors test, there are 45 stocks in this 99th percentile group, and 232 in the 1st percentile, so it the investor should be able to diversify himself fairly well.
Investment Strategy:
Each month, rank all stocks based on short interest ratio and assign each a percentile rank. Buy stocks in the lowest percentile and short stocks in the highest percentile. Rebalance monthly.
OR
If you’re not the shorting type, just rank the stocks as above but simply go long the stocks in the lowest percentile and rebalance monthly. Even doing this provides pretty good alpha.
Implementation Issues and Remarks:
It is nice to see a Wall Street rule of thumb formalized by academic work. I think this is what we can see in this paper. However it is important to note that this paper does not account for some very obvious costs, most noticeably the margin costs associated with shorting, but also the mere transactions costs of rebalancing a large portfolio so frequently. Nevertheless, I do feel the paper provides convincing evidence that following short interest data is not a fruitless exercise.
Another risk to investors is that short selling will be outlawed entirely. I would guess there is a nonzero probability of this happening sometime in the foreseeable future, and if it does clearly this strategy is worthless.
There is also reason to believe that the results of this paper persist for periods well beyond one month. Figure 3 of the paper charts the cumulative returns in months t=0 through 5 for stocks sorted in month t-1. In other words, it looks like we could hold a portfolio for a period of up to six months and still generate some good returns, although the authors do not fully test such a strategy in this paper.
Investment Potential Rating: 9/10
Sphere: Related Content
============
The Good News in Short Interest
Ekkehart Boehmer
University of Oregon - Charles H. Lundquist School of Business
Zsuzsa R. Huszar
NUS Business School - Finance Department; California Polytechnic State University
Bradford D. Jordan
University of Kentucky - Gatton College of Business and Economics
Journal of Financial Economics, forthcoming
http://ssrn.com/abstract=1405511
Abstract:
We study the information content in monthly short interest using NYSE-, AMEX-, and NASDAQ-listed stocks from 1988 to 2005. We show that stocks with relatively high short interest subsequently experience negative abnormal returns, but the effect can be transient and of debatable economic significance. In contrast, we find that relatively heavily traded stocks with low short interest experience both statistically and economically significant positive abnormal returns. These positive returns are often larger (in absolute value) than the negative returns observed for heavily shorted stocks. Because stocks with greater short interest are priced more accurately, our results suggest that short selling promotes market efficiency. However, we show that positive information associated with low short interest, which is publicly available, is only slowly incorporated into prices, which raises a broader market efficiency issue. Our results also cast doubt on existing theories of the impact of short sale constraints.
Data Source:
The data on short interest come from the exchanges themselves. Although the authors don’t specify, I assume they merely downloaded csv files from the exchanges’ websites, each of which provide this data for free. (There are also many services that aggregate these data.) Other relevant data used in the study come from CRSP. The period tested is 1988-2005.
Data Specification:
Not surprisingly, a lot of research has come out recently on the topic of short selling. Traditionally, theory has held that constraints on short selling prevent the impounding of negative information in to stock prices and, since there is no similar constraint to going long, more positive information is impounded in to prices than negative. Thus, constraints on short selling impede market efficiency (and regulators would be foolish to try to limit its use, but we can save that discussion for another day).
This paper is about the performance of heavily shorted stocks versus lightly or un-shorted stocks. Market pundits usually assume heavy short interest is a bearish indicator because it signals that many investors anticipate the price to decline. But there are other interpretations; One is that shorting is usually used for hedging or arbitrage and thus says nothing about the valuation of the stock. Another is that shorting implies future buying because all short sales will need to be covered at some point in the future, therefore it may in fact be a bullish indicator. On balance, though, the academic literature has shown support for the conventional wisdom and found underperformance of heavily shorted stocks.
This paper is one of the first to actually compare the difference between high-short interest stocks and low-short interest stocks. The results are somewhat surprising.
Using the short interest data of the prior month, the authors rank all stocks based on the short interest ratio, which is the ratio of total shares shorted divided by shares outstanding, then observe the performance of those stocks in the subsequent month. There are a lot of stocks with zero short interest, about 15% of them, so when the authors to group stocks based on short interest ratio they have a lot more stocks in the 1st percentile portfolio than any of the others.
Results:
The results may surprise you. Heavily shorted stocks do underperform, but the outperformance of lightly or un-shorted stocks is far more significant. Hmm.
Stocks in the 99th percentile portfolio (the highest short interest stocks) on average experience negative absolute returns of 0.1% per month. The stocks in the 1% portfolio (the lowest short interest stocks) on average experience positive absolute returns of 2.1% per month. This is an impressive result.
After controlling for various risk factors through multifactor regressions, the authors find that the 99% portfolio produces -1.2% alpha monthly, and the 1% portfolio produces +1.4% alpha monthly. This means that a theoretical long/short portfolio adjusted for risk could produce 2.6% per month, or 36% annually!
In the full sample that the authors test, there are 45 stocks in this 99th percentile group, and 232 in the 1st percentile, so it the investor should be able to diversify himself fairly well.
Investment Strategy:
Each month, rank all stocks based on short interest ratio and assign each a percentile rank. Buy stocks in the lowest percentile and short stocks in the highest percentile. Rebalance monthly.
OR
If you’re not the shorting type, just rank the stocks as above but simply go long the stocks in the lowest percentile and rebalance monthly. Even doing this provides pretty good alpha.
Implementation Issues and Remarks:
It is nice to see a Wall Street rule of thumb formalized by academic work. I think this is what we can see in this paper. However it is important to note that this paper does not account for some very obvious costs, most noticeably the margin costs associated with shorting, but also the mere transactions costs of rebalancing a large portfolio so frequently. Nevertheless, I do feel the paper provides convincing evidence that following short interest data is not a fruitless exercise.
Another risk to investors is that short selling will be outlawed entirely. I would guess there is a nonzero probability of this happening sometime in the foreseeable future, and if it does clearly this strategy is worthless.
There is also reason to believe that the results of this paper persist for periods well beyond one month. Figure 3 of the paper charts the cumulative returns in months t=0 through 5 for stocks sorted in month t-1. In other words, it looks like we could hold a portfolio for a period of up to six months and still generate some good returns, although the authors do not fully test such a strategy in this paper.
Investment Potential Rating: 9/10
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