## Momentum Anomaly and Baseball Cards Friday, 24 June, 2016

**A very interesting paper related to fundamentals of momentum anomaly:**

**Title: **Stock Market Anomalies and Baseball Cards

**Link:** http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2798951

**Abstract:**

We show that the market for baseball cards exhibits anomalies that are analogous to those that have been documented in financial markets, namely, momentum, price drift in the direction of past fundamental performance, and IPO under performance. Momentum profits are higher among active players than retired players, and among newer sets than older sets. Regarding IPO under performance, we find that newly issued rookie cards under perform newly issued cards of veteran players, and that newly issued sets under perform older sets. Our evidence is consistent with the predictions of Hong and Stein (1999) and Miller (1977).

**Notable quotations from the academic research paper:**

"Financial economists have documented the existence of simple strategies that earn unusually high or low returns despite the fact that the strategies do not load heavily on common risk factors. For example, Jegadeesh and Titman (1993) document that during holding periods of 3-12 months, stocks that have performed well in the past 3-12 months continue to outperform stocks that have performed poorly in the past 3-12 months. In addition, Ritter (1991) documents that firms that have recently gone public underperform their peers over their first three years of public trading.

The point of this study is to use a non-financial laboratory in which some, but not all, of theories trying to explain momentum should apply. If we find momentum, this would be evidence that momentum can exist naturally in markets without the bells and whistles of dynamic growth rates, dividends or mutual funds.

Our laboratory is the market for baseball cards. Baseball cards have a long history, dating all the way back to the late 1860’s. By 1991, sales of baseball cards reached $1.2 billion annually. Although baseball cards produce no cash flows, their market values can be substantial. For example, the T206 Honus Wagner, which was produced from 1909-1911, has been sold for as much as $2.8 million. Because there have been long periods of time over which their values have appreciated, baseball cards have often been perceived as investment vehicles.

Most theories of momentum do not apply to this market. There are no growth options, dividends, or mutual funds.

Among the behavioral theories, Hong and Stein (1999) is the one that should most apply to the market for baseball cards. In their model, momentum arises because information gradually diffuses across the investor population. If Hong and Stein theory is valid, then we should find momentum not only in financial markets, but in any market with gradual information diffusion such as the market for baseball cards. Obviously, there are many significant differences between the market for baseball cards and the stock market. One difference is the level of investor sophistication. In the stock market, there are many hedge funds that can arbitrage away inefficiencies and keep prices in line with fundamentals. In the baseball card market, there are dealers who are relatively sophisticated, but much of the activity in this market is driven by children. Moreover, whereas it is common to short stocks, there is little (if any) short selling of baseball cards. Hence, the opportunity for arbitrage is severely limited in the baseball card market. Because of these differences, if gradual information diffusion is truly a source of momentum profits, we should expect momentum to be significantly stronger in the market for baseball cards because the participants are generally less sophisticated and there are fewer opportunities for arbitrage. Consistent with this prediction, we find that short run (3 month) momentum strategies earn 5.6% per month, whereas momentum strategies in the stock market earn less than 1% per month.

Hong and Stein provide additional testable predictions in this market. Active players play up to 162 regular season games per year in addition to the postseason, whereas retired players do not play any games. If gradual information diffusion causes momentum, then momentum should be stronger among the cards of active players than retired players, because there is little to no new information released about the ability (or performance) of retired players. Consistent with this prediction, we find that when the 3 month momentum strategy is restricted to retired players, the strategy earns only 1.63% per month, but when the 3 month momentum strategy is restricted to active players, the strategy earns 9.42% per month.

We test also IPO effect (as in Miller (1977))by analyzing the performance of rookie cards and new sets. A card is a considered a “rookie card” if it is the player’s first appearance on a regular issue card from a major card company. Players often have rookie cards before they play in the major leagues, and some players with rookie cards never make it to the major leagues. Like young firms, there is less information about rookies so it is more difficult to determine their quality/ability. Moreover, when sets are first released, there is a lot of uncertainty over the number of sets produced and how other collectors will value the sets. Hence, according to Miller (1977), we should expect rookie cards and new sets to underperform. Consistent with this prediction, we find that rookie cards and new sets have cumulative abnormal returns of –6.6% and –5.7% (respectively) over the 12 months following their release, both of which are statistically significant (t = 2.8 and 2.8 respectively)."

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## Information Ratio Analysis of Time-Series Momentum Strategy Thursday, 16 June, 2016

**A related paper has been added to:**

#118 - Time Series Momentum Effect

**Authors: **Ferreira, Silva, Yen

**Title: **Information ratio analysis of momentum strategies

**Link:** http://arxiv.org/abs/1402.3030

**Abstract:**

In the past 20 years, momentum or trend following strategies have become an established part of the investor toolbox. We introduce a new way of analyzing momentum strategies by looking at the information ratio (IR, average return divided by standard deviation). We calculate the theoretical IR of a momentum strategy, and show that if momentum is mainly due to the positive autocorrelation in returns, IR as a function of the portfolio formation period (look-back) is very different from momentum due to the drift (average return). The IR shows that for look-back periods of a few months, the investor is more likely to tap into autocorrelation. However, for look-back periods closer to 1 year, the investor is more likely to tap into the drift. We compare the historical data to the theoretical IR by constructing stationary periods. The empirical study finds that there are periods/regimes where the autocorrelation is more important than the drift in explaining the IR (particularly pre-1975) and others where the drift is more important (mostly after 1975). We conclude our study by applying our momentum strategy to 100 plus years of the Dow-Jones Industrial Average. We report damped oscillations on the IR for look-back periods of several years and model such oscilations as a reversal to the mean growth rate.

**Notable quotations from the academic research paper:**

"Similar to Moskowitz, Ooi and Pedersen, we focus on the momentum of individual assets. We study the technical rule (moving average of past returns) for one asset, therefore avoiding the portfolio effect that is important for cross-section momentum. This work adds to the paper of [ Moskowitz, T. J., Ooi, Y. H., Pedersen, L. H.. Time series momentum.] by looking at the information ratio of the time series momentum strategy. Our work also contributes to the literature of linking momentum to cycles/regimes. However, contrary to the previous studies, we do not associate economical episodes to the regimes. Our approach is to divide and transform the data in a way such that the final asset returns are as close as possible to stationary. We believe that our work is new in this respect.

We study momentum by looking at the risk adjusted performance measured by the information ratio (IR) as a function of the look-back lag used to construct the portfolio. Our main new contribution from a mathematical point of view, is to present in close form the risk associated with the momentum strategy. Previous works calculate the same expression for the average return as given here, however they do not calculate the standard deviation of the strategy. Furthermore, we analyze the stability of the results across time as non-stationary effects become important in explaining the results. We find that both autocorrelation and mean drift of the random process are important in the final performance of the strategy. In particular, for look-back periods up to 4 months, the most important effect is the autocorrelation; and for look-back periods larger than 4 months to 1 year, the drift. However, in contrast with previous studies, we find that the mean drift is the most important factor after 1975.

In case I, all the autocorrelations are zero, performance comes from the drift. In case II all performance comes from autocorrelation. Lag is in weeks.

"

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## Trend Model via Difference Between Long- and Short-Term Variance Wednesday, 8 June, 2016

**Related to CTA/trendfollowing strategies:
Authors: **Bouchaud, Dao, Deremble, Lemperiere, Nguyen, Potters

**Title: **Tail Protection for Long Investors: Convexity at Work

**Link:** http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2777657

**Abstract:**

We relate the performance of trend following strategy to the difference between a long-term and a short-term variance. We show that this result is rather general, and holds for various definitions of the trend. We use this result to explain the positive convexity property of CTA performance and show that it is a much stronger effect than initially thought. This result also enable us to highlight interesting connections with Risk Parity portfolio. Finally, we propose a new portfolio of options that gives us a pure exposure to the variance of the underlying, shedding some light on the link between trend and volatility, and also helping us understanding the exact role of hedging.

**Notable quotations from the academic research paper:**

"In this paper, we have shown that a single-asset trend has a built-in convexity if we aggregate its returns over the right time-scale. This becomes apparent if we rewrite the performance of the trend as a swap between the variance defined over long-term returns (typically the time scale of the trending filter) and the one defined over short-term returns (the rebalancing of our portfolio). This feature appears to hold for various filters and saturation levels.

**The importance of these 2 time-scales has been underlined, and it is clear that the convexity (and the hedging properties) are only present over long-term time scales (as defined by the trending filter itself): it is wrong to expect a 6-month trending system rebalanced every week to hedge against a market crash that lasted only a few days.**

We also turned our attention to CTA indices, and particularly the SG CTA Index. We have proposed a simple replication index, using a very natural un-saturated trend on a pool of very liquid assets. Assuming realistic fees, and fitting only the time-scale of the lter, we get a very good correlation (above 80%), and capture the drift completely. This shows again that CTAs are simply following a long-term trending signal, and there is little added value in their idiosyncrasies.

However, this also shows us that a CTA does not provide the same hedge a single-asset trend provides: some of the convexity is lost because of diversication. We however have found that CTAs do offer an interesting hedge to Risk-Parity products, which we approximated with a very good precision by long positions on the main asset classes.A ll in all, these results prove that a trending system does offer protection to long-term large moves of the market.

We then turned our attention to the link between trend and volatility. We found that a simple trending toy-model shares an exposure to the long-term variance with a naked straddle. The difference is the fact that the entry price for the straddle is fixed by the at-the-money volatility, while the trend pays the realized short-term variance. We then propose a very clean way to get exposure to this short term variance by using the trending toy-model as a hedging strategy for a portfolio of strangles. This is a simple, model-free portfolio that offers the same pay-off than traditional variance swaps."

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