In active portfolio management, anomaly-based investment strategies are common. Identify and explain these strategies. Also explain what would be the main factors that contribute to the high returns of these strategies.
It is generally a given that there are no free rides or free lunches on Wall Street. With hundreds of investors constantly on the hunt for even a fraction of a percent of extra performance, there are no easy ways to beat the market. Nevertheless, certain tradable anomalies seem to persist in the stock market, and those understandably fascinate many investors.
While these anomalies are worth exploring, investors should keep this warning in mind—anomalies can appear, disappear, and re-appear with almost no warning. Consequently, mechanically following any sort of trading strategy can be risky, but paying attention to these seven moments could reward sharp investors.
1. Small Firms Tend to Outperform
Smaller firms (that is, smaller capitalization) tend to outperform larger companies. As anomalies go, the small-firm effect makes sense. A company's economic growth is ultimately the driving force behind its stock performance, and smaller companies have much longer runways for growth than larger companies.
A company like Microsoft (MSFT) might need to find an extra $6 billion in sales to grow 10%, while a smaller company might need only an extra $70 million in sales for the same growth rate. Accordingly, smaller firms typically are able to grow much faster than larger companies.
2. January Effect-
The January effect is a rather well-known anomaly. Here, the idea is that stocks that underperformed in the fourth quarter of the prior year tend to outperform the markets in January. The reason for the January effect is so logical that it is almost hard to call it an anomaly. Investors will often look to jettison underperforming stocks late in the year so that they can use their losses to offset capital gains taxes (or to take the small deduction that the IRS allows if there is a net capital loss for the year). Many people call this event "tax-loss harvesting."
As selling pressure is sometimes independent of the company's actual fundamentals or valuation, this "tax selling" can push these stocks to levels where they become attractive to buyers in January. Likewise, investors will often avoid buying underperforming stocks in the fourth quarter and wait until January to avoid getting caught up in the tax-loss selling. As a result, there is excess selling pressure before January and excess buying pressure after January 1, leading to this effect.
3. Low Book Value
Extensive academic research has shown that stocks with below-average price-to-book ratios tend to outperform the market. Numerous test portfolios have shown that buying a collection of stocks with low price/book ratios will deliver market-beating performance.
Although this anomaly makes sense to a point—unusually cheap stocks should attract buyers' attention and revert to the mean—this is, unfortunately, a relatively weak anomaly. Though it is true that low price-to-book stocks outperform as a group, individual performance is idiosyncratic, and it takes very large portfolios of low price-to-book stocks to see the benefits.
4. Neglected Stocks
A close cousin of the "small-firm anomaly," so-called neglected stocks are also thought to outperform the broad market averages. The neglected-firm effect occurs on stocks that are less liquid (lower trading volume) and tend to have minimal analyst support. The idea here is that as these companies are "discovered" by investors, the stocks will outperform.
Many investors monitor long-term purchasing indicators like P/E ratios and RSI. These tell them if a stock has been oversold, and if it might be time to consider loading up on shares.
Research suggests that this anomaly actually is not true—once the effects of the difference in market capitalization are removed, there is no real outperformance. Consequently, companies that are neglected and small tend to outperform (because they are small), but larger neglected stocks do not appear to perform any better than would otherwise be expected. With that said, there is one slight benefit to this anomaly—through the performance appears to be correlated with size, neglected stocks do appear to have lower volatility.
5. Reversals
Some evidence suggests that stocks at either end of the performance spectrum, over periods of time (generally a year), do tend to reverse course in the following period—yesterday's top performers become tomorrow's underperformers, and vice versa.
Not only does statistical evidence back this up, but the anomaly also makes sense according to investment fundamentals. If a stock is a top performer in the market, odds are that its performance has made it expensive; likewise, the reverse is true for underperformers. It would seem like common sense, then, to expect that the over-priced stocks would underperform (bringing their valuation back in line) while the under-priced stocks outperform.
Reversals also likely work in part because people expect them to work. If enough investors habitually sell last year's winners and buy last year's losers, that will help move the stocks in exactly the expected directions, making it something of a self-fulfilling anomaly.
6. The Days of the Week
Efficient market supporters hate the "Days of the Week" anomaly because it not only appears to be true, but it also makes no sense. Research has shown that stocks tend to move more on Fridays than Mondays and that there is a bias toward positive market performance on Fridays. It is not a huge discrepancy, but it is a persistent one.
On a fundamental level, there is no particular reason that this should be true. Some psychological factors could be at work. Perhaps an end-of-week optimism permeates the market as traders and investors look forward to the weekend. Alternatively, perhaps the weekend gives investors a chance to catch up on their reading, stew and fret about the market, and develop pessimism going into Monday.
7. Dogs of the Dow
The Dogs of the Dow are included as an example of the dangers of trading anomalies. The idea behind this theory was basically that investors could beat the market by selecting stocks in the Dow Jones Industrial Average that had certain value attributes.
Investors practiced different versions of the approach, but there were two common approaches. The first is to select the 10 highest-yielding Dow stocks. The second method is to go a step further and take the five stocks from that list with the lowest absolute stock price and hold them for a year.
It is unclear whether there was ever any basis in fact for this approach, as some have suggested that it was a product of data mining. Even if it had once worked, the effect would have been arbitraged away—for instance, by those picking a day or week ahead of the first of the year.
To some extent, this is simply a modified version of the reversal anomaly; the Dow stocks with the highest yields probably were relative underperformers and would be expected to outperform.
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