## What is the Capacity of Smart Beta Strategies ? Wednesday, 16 November, 2016

**An academic paper related to multiple smart beta strategies:**

**Authors: **Ratcliffe, Miranda, Ang

**Title: **Capacity of Smart Beta Strategies: A Transaction Cost Perspective

**Link:** https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2861324

**Abstract:**

Using a transaction cost model, and an assumption for the smart beta premium observed in data, we estimate the capacity of momentum, quality, value, size, minimum volatility, and a multi-factor combination of the first four strategies. Flows into these factor strategies incur transaction costs. For a given trading horizon, we can find the fund size where the associated transaction costs negate the smart beta premium, assuming current rebalancing trends and holding constant other market structure characteristics. With a trading horizon of one day, we find that momentum is the strategy with the smallest assets under management (AUM) capacity of $65 billion, and size is the largest with an AUM capacity of $5 trillion. Extending the trading horizon to five days increases capacity in momentum and size to $320 billion and over $10 trillion, respectively.

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

"We study the capacity, in terms of AUM, of smart beta strategies—momentum, quality, size, value, and minimum volatility. We also study a strategy combining the first four of these factors. All of these smart beta strategies can be implemented with transparent, third-party indices and directly traded as ETFs. The strategies are also long only.

We base our analysis on a transaction cost model developed by BlackRock, Inc. that is used on a daily basis by different investment teams. In line with a sizeable microstructure literature building on Glosten and Harris (1988) and Hasbrouck (1991), the transaction cost model includes both fixed-cost and non-linear market impact components. The parameters of the model are updated on a daily basis, based on trading executed by BlackRock across all of its portfolios. For example, BlackRock traded over $340 billion in US equities during January to March 2016, an indication of the amount of data that is used to calibrate the model. Thus, the transaction cost model gives an estimation that a large asset manager would face in executing trades in ETF securities.

We define capacity as the breakeven hypothetical AUM at which the associated turnover transaction costs exactly offsets the historically observed style premium.6 Since this calculation is sensitive to the assumption of the magnitude of the premium, we present results varying the premiums. A key variable that determines smart beta capacity is the turnover of the factor, and we assume recent rebalancing trends are a good representation of the expected turnover going forward.

The exercise we conduct in this paper is hypothetical and involves several unrealistic assumptions. We assume that all trading takes place in a given interval—over one day, and over a longer horizon of five days. We assume that market structure characteristics of the factor vehicles, like turnover (measured as two-way, annualized), and of the market itself, like no entry and exit of stocks in these strategies, are held fixed as the flows come in. We gauge capacity only by transaction costs incurred by inflows, and so ignore the funding costs of those flows (which could come from other stocks or asset classes). We are not saying the transaction cost estimates are definitive measures of capacity of smart beta strategies—but they are informative in that they measure an important real-world trading friction that reduces returns earned by investors.

As expected, the strategy with the smallest capacity is momentum—the style factor with the highest turnover. Momentum has an estimated breakeven AUM of $65 billion. If trading occurs over one day, we find that the breakeven AUM for size is the largest, at approximately $5 trillion, followed by minimum volatility, which is above $1 trillion. However, if trading is allowed to occur over five days, which is common for larger trades, instead of over one day, the capacity of momentum increases from $65 billion to $324 billion. Finally, the combination of value, size, momentum, and quality factors has an estimated breakeven AUM of $316 billion and $1.5 trillion over trading horizons of one and five days, respectively. In reality, it is likely that many aspects of the markets—including the composition of the stocks in the factor strategies themselves—will change before flows of this magnitude are realized. What is important is the large size of these numbers, rather than the absolute numbers themselves, which indicate that transaction costs have to be very large in order to have a significant effect in reducing returns to investors in smart beta strategies. Put another way, capacity considerations in smart beta are likely to come from economic sources other than trading costs."

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## A Reversal-Based Trading Strategy around Earnings Announcements Wednesday, 2 November, 2016

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

#307 - Reversal During Earnings-Announcements

**Authors: **Jansen, Nikiforov

**Title: **Fear and Greed: a Returns-Based Trading Strategy around Earnings Announcements

**Link:** http://www.wsj.com/public/resources/documents/FearandGreedJPM0922.pdf

**Abstract:**

This study documents that earnings announcements serve as a reality check on short-term, fear and greed driven price development: stocks with extreme abnormal returns in the week before an earnings announcement experience strong price reversal around the announcement. A trading strategy that exploits this reversal is profitable in 40 of the last 42 years and earns abnormal returns in excess of 1.3% over a two day-window.

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

"In this study, we develop a trading strategy around earnings announcements that seeks to profit from predictable reversals of fear and greed driven price development in individual stocks. We argue that earnings announcements are logical events around which to center such a trading strategy, because they convey fundamental information about asset prices and thus have the potential to “break” irrational price development. Moreover, because of heightened information asymmetry in the period just before an earnings announcement, price development is probably particularly susceptible to excessive fear or greed. That is, if uninformed investors observe sharp price changes just before an earnings announcement, they may attribute these to the informed trading of insiders; start to excessively trade in the same direction themselves; and thus cause an overreaction. We therefore predict—in the spirit of Warren Buffett’s advice—that stocks that experience sharp price changes just before an earnings announcement will experience price reversal at the time of the announcement itself. We test this prediction with a trading strategy that on the earnings announcement date takes (1) a long position in stocks that experienced extreme negative abnormal returns in the week prior, and (2) a short position in stocks that experienced extreme positive abnormal returns in the week prior.

We find that, over the two day window of the earnings announcement date and the day following, both positions are highly profitable. On average, the long position earns abnormal returns of 1.49%, and the short position earns 1.20%. We furthermore show that these return reversals are about 60% larger than around non-earnings announcement dates, and thus are significantly more pronounced than short-term return reversals documented in the prior literature. We also show that our strategy (1) is profitable in 40 of the 42 years in our sample; (2) is similarly profitable in “bear” and “bull” markets; (3) and is significantly profitable for both large firms and high volume stocks. Since the year 2000—using a conservative transactions costs estimate of 70 basis points for a round trip trade—we find that our strategy generates abnormal returns of 0.76% after transaction costs, or 95% on an annualized basis. We conclude, therefore, that prices are subject to sentiment-driven price development in the period of elevated information asymmetry just before earnings announcements, and that the announcements themselves serve as a reality check on that price development."

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## Quantopian & Quantpedia Trading Strategy Series: Reversals in the PEAD Friday, 28 October, 2016

Our sucessful __ Quantopian & Quantpedia Trading Strategy Series__ continues with a third article, written by Matthew Lee, focused on Reversal During Post-Earnings Announcement Drift (Strategy #238):

https://www.quantopian.com/posts/quantpedia-trading-strategy-series-reversals-in-the-pead

(Click on a "View Notebook" button to read a complete analysis)

What is the logic behind this strategy? Jonathan A. Milian in his paper "Overreacting to a History of Underreaction" explores the possibility that well known cross sectional anomalies can reverse over time. And he picks the premier anomaly - Post Earnings Announcement Drift. He finds that stocks with the most negative previous earnings surprise actually exhibit the most positive returns very shortly after the subsequent earnings announcement.

The academic paper speculates that it seems that due to well-documented history of investors underreacting to earnings news, investors are now overreacting to earnings announcement news. Investors position themselves in alignment with the expectation of the PEAD effect so when the next earnings announcement comes, the overcrowding of investors pushes the market beyond efficient, resulting in the correction of investor sentiment and a negative correlation for firm's earnings news in the following days. However classical PEAD (post-earnings announcement drift) literature examines mainly quarterly portfolio returns while this academic paper focuses on 2-days retun therefore it is probable that PEAD still holds and both anomalies exists concurrently.

Matthew Lee from Quantopian performed an independed analysis of initial findings of Jonathan A. Milian's original academic paper (over a sample period from 2011 - 2016 compared to the Milian's 2003 - 2010). What Matthew found? Overall, he found his results to be consistent with Milian's results for all strategy's holding periods from 2-10 days. The best result was a hold period of 9 or 10 days following the Earnings Announcement, rather than 2 days. Firms in the highest decile of past earnings surprise underperform stocks in the lowest decile by -1.78% over a hold period of 10 days (compared to -1.59% over a hold period of 3 days found in the paper).

So, what is the trading algorithm?

1. Each day, pick stocks in the S&P500 which have Earnings Announcements the next day

2. Go short on stocks in the highest decile of previous earnings surprise, long on stocks in the lowest decile of previous earnings surprise

3. Hold for a period of 10 days, then close the position

As always, the final OOS equity curve looks really good:

Nice work Matthew!

You may also check first or second article in this series if you liked the current one. And stay tuned for the next ...

## Tail Protection of Trend-Following Strategies Friday, 21 October, 2016

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

#118 - Time Series Momentum Effect

**Authors: **Dao, Nguyen, Deremble, Lemperiere, Bouchaud, Potters

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

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

**Abstract:**

The performance of trend following strategies can be ascribed to the difference between long-term and short-term realized variance. We revisit this general result and show that it holds for various definitions of trend strategies. This explains the positive convexity of the aggregate performance of Commodity Trading Advisors (CTAs) which -- when adequately measured -- turns out to be much stronger than anticipated. We also highlight interesting connections with so-called Risk Parity portfolios. Finally, we propose a new portfolio of strangle options that provides a pure exposure to the long-term variance of the underlying, offering yet another viewpoint on the link between trend and volatility.

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

"In this paper we have shown that single-asset trend strategies have built-in convexity provided its returns are aggregated over the right time-scale, i.e., that of the trend filter. In fact, the performance of trend-following can be viewed as swap between long-term realized variance (typicaly the timescale of the trending filter) and a short-term realized variance (the rebalancing of our portfolio). This feature is a generic property and holds for various filters and saturation levels. While trendfollowing strategies provide hedge against large moves unfolding over the long time scale, it is wrong to expect a 6 to 9 months trending system rebalanced every week to hedge against a market crash that lasts a few days.

We dissected the performance of the SG CTA Index in terms of a simple replication index, using and un-saturated trend on equi-weighted pool of liquid assets. Assuming realistic fees, and fitting only the time-scale of the filter (found to be of the order of 6 months) we reached a very strong correlation (above 80%) with the SG Index, and furthermore fully captured the average drift (i.e. our replication has the same Sharpe ratio as the whole of the CTA industry). However, our analysis makes clear that CTAs do not provide the same hedge single-asset trends provide: some of the convexity is lost because of diversification. We however have found that CTAs do offer an interesting hedge to Risk-Parity portfolios. This property is quite interesting, and we feel it makes the trend a valid addition in the book of any manager holding Risk Parity products (or simply a diversified long position in both equities and bonds).

Finally, we turned our attention to the much discussed link between trend-following and long-volatility strategies. We found that a simple trend model has exactly the same exposure to the long-term variance as a portfolio of naked strangles. The difference is the fact that the entry price of the latter is fixed by the implied volatility, while the cost of trend is the realized short-term variance. The pay-off of our strangle portfolio is model-independent and coincides with that of a traditional variance swap - except that the latter requires Back-Scholes assumptions. In other words, the option strategy is a better hedge and therefore its price should be higher than realized volatility. The premium paid on option markets is however oo high in the sense that long-vol portfolios have consistently lost money over the past 2 decades, while trend following strategies have actually posted positive performance. So, even if options provide a better hedge, trend following is a much cheaper way to hedge long-only exposure.

All-in-all, our results prove that trending systems offer cheap protection to long-term large moves of the market. This coupled with the high statistical significance of this market anomaly, really sets trend-following apart in the world of investments strategies. A potential issue might be the global capacity of this strategy, but recent performance seems to be quite in line with long-term returns, so there is at presence little evidence of over-crowding."

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## Zero-Crossing Variant of Pairs Trading Strategy Friday, 14 October, 2016

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

#12 - Pairs Trading with Stocks

**Authors: **Donninger

**Title: **Is Daily Pairs Trading of ETF-Stocks Profitable?

**Link:** https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2816288

**Abstract:**

Pairs trading is a venerable trading strategy. There is agreement that it worked fine in the far past. But it is less clear if it still profitable today. In this working paper the universe of eligible pairs is defined by the holdings of a given ETF. It is shown that the stocks must be from ETFs which select high-quality, low-volatility stocks. The usual closeness measure presented in the literature performs poor. The paper presents a simple and clearly superior alternative based on zero-crossings. The strategy performs with the correct universe and the improved pairs selection rule before trading costs quite fine. It depends on the assumed trading costs if this is also in real-trading life the case.

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

"The seminal paper on pairs trading is Gatev et. al. [1]. They authors did not invent the strategy. It was in common use since the 1980s. The pairs are formed from a universe of stocks. There is a one year formation period. Each stock is normalized to 1 at the beginning of this period. One selects for each stock the closest neighbor. The distance measure is the summed up squared daily difference of the normalized prices.

The initial results with the distance method were rather disappointing. Pairs trading is based on mean-reversion. The distance measures if the stocks stick together. But sticking together and mean-reversion are two different concepts. Vidyamurthy proposes zero-crossings as an alternative. One counts the number of times the spread moved above or below the mean-spread. But this measure is also not satisfactory. It is known from the theory of Brownian-motions that zero-crossings are much more likely in the first few steps of the motion. If one starts at zero a small up- followed by a larger down-move is a zero crossing. The path moves in the following away from zero and a crossing gets very unlikely. The situation is somewhat different for a mean-reverting process but the general behavior is still the same. A zero (or mean) crossing does also not create a profit. The interesting case is a crossing which started initially outside the two-sigma band. This is the main distance function. A larger number of crossings is of course better than a lower one. For two pairs with the same number of crossing the distance is used as a secondary measure. But a pair with 5 crossings is always closer than a pair with only 4. The strategy defines also a minimum number of crossings (usually 4). A pair with less crossings is never traded.

The strategy does not use overlapping formation periods. The set of tradeable pairs is determined each month (every 21 trading days). The formation window is like in most studies a year (252 trading days). But an open position is not automatically closed at the end of the trading period. An open position is – if mean reversion does not happen before – closed after 30 trading days. There are usually pairs from the previous formation period open. It makes no sense to close a position which was entered at day 20 of the trading period just because a new formation calculation is performed. The strategy does not reset the spread to zero at the end of the formation phase. It uses the mean and the standard deviation from the formation period also in the trading phase. A position is only opened, if the spread is between 2 and 4 standard deviations. It is unlikely that the spread is by chance larger than 4 deviations. A very large spread is a sign that the pair is in divorce. As an additional stop-loss an already open position is closed if the spread gets larger than 8 standard deviations. This stop-loss is only triggered a few times but it avoids some

really disastrous losses.

As already noted simulated trading is done from 2011-01-01 till 2016-07-26. The strategy has an overall profit of 144.4%, a monthly Sharpe ratio of 1.16 and a max. relative drawdown of 8.2%."

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