Peter Ciampi
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​​I'm a Private investor focused on closed-end fund events and statistical arbitrage of ETFs

Post 29. My report on the death of Momentum was greatly mistaken

8/24/2019

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Last September I suggested buying a momentum ETF, such as MTUM (10 billion dollar in assets), rather than SPY for those investors who prefer buy-and-hold. My post showed how MTUM had dramatically outperformed SPY in the previous 5 years. Embarrassingly in December I had to report that SPY outperformed MTUM in the last quarter - plus SPY won again in the first quarter of 2019.

But those two weak quarters (-4.38%) are less than MTUM’s better performance since then (5.09%). In fact quarter-by-quarter comparisons show MTUM outperforming SPY in 17  of the last 24 quarters. The table below has other tidbits. For instance, one might assume that MTUM is weak in down markets yet only once in the 5 down quarters of SPY (compare columns 2 and 6 ) was MTUM weaker than SPY. 

But we can’t ignore the fourth quarter of 2016 when MTUM  underperformed SPY by 5.40%. This quarter contributed importantly to the T-test on column 6  saying the differences in this sample are not significant -- but just barely with T=1.93 .
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Regardless of formal significance (or 4th quarter of 2016) I am overwhelmed by the net returns. The last row of columns 3 and 5 show that compounded quarterly, SPY has returned 88% over the last 24 quarters (6 years) while MTUM has returned 132% - an excess return of 44% !! 

Although my investment strategies are severely “labor-intensive” (to quote a friend) I do have some buy-and-hold investments. These numbers compel me to move the S&P portion of those investments  into MTUM.

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28. Per The musical Chicago, only 1 idea: Buy Europe (VGK) and China (FXI) ETFs when VIX is high.​

4/9/2019

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Some readers of this blog have noted that ideas such as SPY_at_noon or day_versus_night returns require the excessive effort of daily trading. For those readers and others this post offers an investment idea requiring far less effort. First I’ll show that day versus night returns are sensitive to VIX in the the US, in Europe and in China. These returns again require daily trading but I’ve included this high-maintenance strategy to complete the note in Post 26 and because it leads to a powerful low-maintenance strategy.  
Buy Europe (VGK) and China (FXI) ETFs when VIX is high.​

The first row of charts shows night minus day returns for all days and for low (0 to 14) VIX, second chart, medium(14 to 19) VIX and high(19 to 60) VIX. The first chart shows that nights are far more profitable than days in Europe and China. (But this effect doesn’t hold in the US.) The fourth chart in this row surprisingly shows that this overnight effect doesn’t hold when VIX is high. An academic mentioned that this overnight effect is know but not published because no-one can explain the phenomenon. Well it’s even more of a mystery why the phenomenon doesn’t hold at high VIX levels. 

The second row of charts parallels the first row but shows daily returns. Chart 1 shows 10 years of returns for all days. For instance, 2018 was down year for the US but the loss and more has been recovered in 2019. The second, third and fourth charts show returns for low, medium and high VIX. The high VIX chart is the most interesting. In the world’s three largest economic centers stock returns have been positive when VIX is high for the last 10 years. To obtain positive returns in 30 out of 30 cases is an amazing statistic. Especially noteworthy is the strength of Europe and China returns over the US.

 I can’t forget Richard Gere’s advice to Rene Zellwiger in the musical Chicago, “Keep repeating ‘We both reached for the gun’ because audiences only remember one thing.” 
If you’re only going to remember one thing from this blog. Please remember:
Buy Europe and China ETFs when VIX is high
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27 SPYnoon: napping or hibernating?

4/1/2019

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The absence of discussion about SPY noon isn't because it's stop working.  After SPY's painful fourth quarter I bragged that SPYnoon earned 1.4 % versus a -13.2 % loss in SPY itself. But no bragging about the current quarter since SPYnoon earned only 0.4%  versus a 12.5% gain for SPY. So the model is working but the weak results (1.8% in 6 months) are caused by low activation. Until the last 6 months there'd been a trade signal about once every 10 days. Since Oct 1 there's been a signal once every 25 days!

What might be causing this low activation? SPYnoon depends on world momentum of which Europe is a significant part. During the last 6 months there's been very few large morning rises in  Europe. If Brexit is causing this European weakness then SPY noon is only napping and will hopefully start activating more regularly. If systemic European problems, as mentioned below, are causing the weakness then SPY noon may be in for a long hibernation.

"BRUSSELS/FRANKFURT, April 1 (Reuters) - Euro zone inflation unexpectedly slowed in March, adding to the pressure on the European Central Bank (ECB) as it battles an economic slowdown which threatens to undo years of stimulus.
Headline inflation in the 19 countries sharing the euro slowed to 1.4 percent in March from 1.5 percent a month earlier, short of market expectations for a steady rate and also well below the ECB's target of almost 2 percent."

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26  The World’s Stock Markets Rise after Sunset

2/25/2019

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Last March I summarized 8 years of day versus night stock movements. This note 1) adds another year to that analysis, 2) shows year by year returns rather than just a summary and 3) tests for significance of differences. The day vs night effect was reported by Cliff, Cooper and Gulen's 2008 paper "Return Differences between Trading and Non-trading Hours: Like Night and Day." They reported only on the US. This note shows the effect around the world and finds, surprisingly, that the effect has been much weaker in the US during the last 10 years.

Below we see open and close time for 4 world centers - US, Europe, China and Japan - which represent 75% of the world’s GDP. To estimate day and night returns in these centers I use very liquid US-traded ETFs: SPY for the US; VGK for Europe; EWJ for Japan; FXI for China. Consider China which is open from 9pm to 4am. FXI's return from 4pm_9:45am approximates the China open period while FXI’s change 9:45am_4pm approximates the closed period( 4am_9pm). The same holds with Japan.
 VGK change from 4pm_11:30am approximates Europe open from 4am_11:30am, while VGK from 11:30am_4pm approximate Europe closed from 1130am_4am.
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This ETF-based method finds China,Japan and Europe consistently dropping while open and rising while closed. Specifically in only 1 year, 2018, out of 10 did Europe and Japan do better during the day than overnight. In only 2 years (2014 and 2017) out of 10 did China perform better during the day. Only the US had 4 years (2010, 2012, 2016 and 2019-to-date) better during the day than at night. The first Bar Chart is the most important showing night-day differences. For those wanting details the second chart shows day and night returns.
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Except for the US the results are highly significant.
US
Europe
Japan
China
T-stat
0.8
2.9
3.2
3.5
But can we capitalize on this phenomenon? No because of trading costs which are dominated by bid-ask spreads. FXI which represents China trades at 35 dollars with a bid-ask spread of a penny, ie .03%. If we bought at 945 and sold at 4pm there’d be 2500 buys and sells over 10 years costing 75%. Since the net profit from FXI was 100% most of the profit would go to trading costs with a residual return of only 2% per year, ie buying Treasury bills would have been a better play. This strategy fits Eugene Fama’s efficient market qualification which was - Some strategies beat the market but not when trading costs are considered.
 
Although I don’t currently see how to profit from this I’ll continue searching with filters such as VIX level and day of week. As always suggestions are more than welcome.


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25. Daytime and Nighttime returns during SPY’s 4th quarter plunge

12/30/2018

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Followers of this blog may recall the Feb 4, 2018 New York Times article reporting that most of the S&P’s gains occur overnight rather than during the day. I found (and sent two unanswered notes to the NY Times) that this phenomenon was concentrated in years 1994-2008 while from 2008 until Aug 2018, intraday and overnight returns were about equal:  68% intraday and 72% overnight. Now SPY's 14% plunge in the 4th quarter renewed my interest in intraday vs overnight.  Surprise. The 1994-2008 phenomenon has returned. During this quarter SPY’s intraday return has been negative 19 percent while its overnight return has been positive 5 percent.

News certainly moves markets so this 24 percent difference is startling if one compares news volume in the 17.5 hours between 4pm and 9:30 - China and other Asian countries trading between 9 and 4am and European countries trading between 5am and 9:30  - versus news volume in the 6.5 hours between 9:30 and 4pm when news comes only from the US plus 2 hours of Europe trading. Your comments will be appreciated.

Seeing this intraday loss (SPY fell 19%) might support an intuition that trading SPY at noon hasn’t been the best strategy during this period but actually it’s been positive. GDP-based momentum returned 2 percent.  So while buy-and-hold lost 15%, SPY-at-noon was positive.

Over the longer term since since this blog started on July 11, 20  SPY-at-noon (+6%) and buy-and-hold (+7%) have been about equal.

Enjoy 2019

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24. Recently, Buy-and-Hold Momentum Fails but Intra-day Momentum’s Success Continues.

12/15/2018

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On Sept 16th I suggested a momentum ETF, ticker MTUM, as a buy-and-hold alternative to daily trading at noon. History showed that in almost every period during the past 5 years MTUM had beaten SPY. Well, my suggestion couldn’t have had worst timing. Since Sept 16th SPY has dropped by 10.6% while MTUM dropped by 13.3% - a 2.7% excess loss! So buy-and-hold momentum has underperformed during this large fourth quarter drop -- but it shouldn't be discounted. In fact since Jun 2013  MTUM still trounces SPY by 39% compounded  ( 101% to 62% ). It's worth reading a recent discussion of momentum (while noting that it's written by Cliff Asness an apologist for momentum) :
https://www.aqr.com/Insights/Perspectives/Fama-on-Momentum

Intra-day GDP-based momentum, on the other hand, continues to perform well. During afternoons since Sept 16th SPY dropped 4.4% while SPY-at-noon gained 1.1% with a win ratio of 57%. One might say a 57% success rate "doesn’t impress" but SPY’s every-afternoon win ratio was 45%. During the prior 9-year prior period when SPY-at-noon's success was 69%, the all-afternoons success rate was 56%. So even during this violent SPY drop period trade-SPY-at-noon has maintained its 12-13% win ratio advantage over all afternoons.

On the question: Is buy-and-hold-SPY better than trade-SPY at noon? this quarter answers a strong NO (minus 10.6% versus plus 1.1% -- an excess of 11.7%). Over the longer period, however, July 2017 (when I started this blog) through Dec 2018, it's YES (6.7% to 4.1%) albeit with a lot less a lot less effort but a lot more risk.
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23.   A Stock Competition.              The US vs the World vs Momentum:      The Winner is?

9/15/2018

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Several readers of this blog don’t have time for the daily trading required by my SPYnoon strategy and so prefer buy-and-hold SPY. And Yes that strategy has exceeded mine -although with higher risk. However, if one prefers buy-and-hold consider the strategy outlined here.
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The central theme of this blog series has been momentum: GDP weighted momentum to trade SPY at noon (Most Posts) or self-momentum with ETF bonds (Posts 20-22). Momentum is more and more accepted. Fama and French called it the premier anomaly and French even publishes momentum returns (These are for long-short portfolios and so different from long-only ETFs.)  To capitalize on this strategy Blackrock issued an ETF in 2013, ticker MTUM, which buys US stocks exhibiting momentum. It now has 10 billion dollars in assets. Unsurprisingly some of its largest holding are Amazon, Microsoft, VISA, Boeing, MasterCard and Netflix all of which are touching new highs.

How has it performed since its inception in 2013? The answer needs to consider that SPY returns have dramatically outperformed the world since then. In fact it's possibly the largest 5 year outperformance of the US stocks since World War II. Rows 1-4 below show SPY's return of 99.8% trouncing developed Europe (35.4%), Japan (38.1%), and emerging markets including China (23.8%). But row 5 then shows MTUM trouncing even SPY by 144.% to 99.8%. It’s 5-year return has been stunning. Moreover, the last 5 rows show victory wasn’t concentrated in one year;  MTUM beat SPY in each of the 5 years .

During these years market drops have been short-lived and small. Between Jul 15, 2015 and Sept 1, 2015 SPY dropped -9.0% and MTUM -7.9%. This year between Jan 26 and Feb8 SPY dropped -10.4% and MTUM -10.5%. So during drops momentum equaled or outperformed SPY although it's important to note this limited data isn't significant.
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With the market having only a few small drops during these 5 years we can’t answer a crucial question: Do momentum stocks drop more than other stocks in falling markets? with any certainty. In rising markets, however, momentum performance has been compelling so for an investment with one side historically beating SPY consider a momentum based ETF such as MTUM.
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22. Arbitraging Bond ETFs with Momentum

9/2/2018

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Can bond ETF be arbitraged? One suspects a “Yes” answer because the underlying bonds of a bond ETF trade infrequently meaning ETF prices are only an estimate of their portfolio. We test this question on four bond ETFs with 10 years of data  using a casual binning method supported by regression statistics.
 The  strategy is:    Buy at 4pm; Sell at 9:45 next morning. 


One predictive factor we study is the ETF’s Premium or Discount (P/D) , defined as ETF price minus NAV (value of the individual bonds) divided by NAV at 4pm. When P/D is positive traders expect the value of the bonds to rise. When it’s negative they expect the bonds to fall. 

A second factor is momentum - but not as defined in this blog series which uses GDP-weighted momentum of world stock prices to estimate SPY. Instead it's self momentum, that is, if a bond ETF rises in the afternoon, does it continue overnight? 

We’ll show the answer is “Yes” Both factors have predictive power although municipals and investment-grade ETFs have inverse momentum. That is when they rise in the afternoon they fall overnight and vice versa. 

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Factor 1. Premium/Discount (P/D). We create 7 bins of P/D ranges containing the avg overnight change for days in these bins.

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P/D levels affect overnight returns although in different directions. International bonds do best with a 4pm Premium. Municipals do best with a 4pm discount.

Seeing the bins without linear slopes suggests a 3 degree polynomial. HYG, LQD and MUB have significant 2nd and 3rd degree coefficients. 

Trading statistically  row 2, column 2 says: Buying EMB every day when its Premium was between 1.5% and 5.0% returned: 187 *.05%= 9.3% (There were 187 days with such a premium; Trading costs of .01% reduce daily return to .05%)

Factor 2: Intraday Momentum, the change in the ETF between 11:45 and 4pm, is a second factor influencing the overnight returns.
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EMB (International) follows direct momentum. Examples are .15% when the intraday rise was between .5% and 1.5%;  With intraday drops between -.5% and -1.5% it drops overnight -.06%.

HYG (High-Yield) has a convex effect. It rises overnight after a large afternoon increase; It’s flat with other changes but also rises with large drops, ie rise .17% when afternoon change drops between -.5 to -1.5%. 

LQD (Investment-grade) and MUB (Municipals) have inverse momentum. After afternoon rises they drop overnight. After drops they rise overnight. Negative slopes of -.12 and -.25 with strong T’s confirm this.

A surprise was that LQD, bonds rated high by S&P, Moodys' ... have much stronger coefficients than HYG, high-yield, which is the area of most SEC concern..

Trading statistically row 8, column 3 (blue highlight .17%) says: Buying HYG  when its afternoon change was between -1.5 and -5% profit would be:   58 * .16 = 9.0% . (There were 58 days with such a change; Trading costs of .01 reduce return to .16)

So Momentum and P/Ds produce tradable returns -  but combining factors further improves results. Let’s expand our bins to 7-by-7.

In the matrix for HYG the rows are the P/D as listed above.The columns are the afternoon change. Column 1 represents (-1.5% to -0.5%}  
Col 2: {-.5% to -.3%},   .., Col 7: {.5% to 1.5%}].

Specifically, row 2, col 1 are those days where P/D is between .5% and 1% and afternoon change is between 1% and 1.5%. Content of {4, .59} represents 4 days with an average overnight return of 0.59%.

HYG avg overnight returns by P/D (rows) and by 1145to4pm change (columns)
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Those in blue suggest cases for going Long but surrounding cells should be similar.  For instance row7, col1 {29,.23} has similar bins to its right and above it so Buying has good probability. Row7, col4 {20,.10} has losing bins to its left and right so may be just noise.

Col 1 of this matrix says go Long only in rows 1, 2, 6 and 7 for an average return of .23% whereas Table 2, row 7, col 3 with no P/D restriction, has an average return of .20% thus showing the power of combining factors.

Many statisticians are skeptical of binning because it doesn’t fully use the data. The 7-by-7 matrixes have only 49 data points rather than the 2200.

So it’s less than optimal but it’s visually helpful. And it’s consistent with regression which does use the full data set. The blue entries on the left and right are consistent with cells {3,3}, {3,4} and {3,8} in Table 2. Blue in entries in the bottom row are consistent with cell {3,8} in Table 1

Additionally binning has similarities to Artificial Intelligence. Machine learning techniques such as nearest neighbors and k-clustering somewhat create bins - albeit based on heavy computations rather than personal choice as I did. 

Tim Leung, Director of Computational Finance at the University of Washington and an ETF researcher has reviewed some results. Being a Princeton mathematician he’s leary of binning but he did offer a comment: “This is an interesting experiment. As for all ETFs, Premium /Discount  is a natural candidate factor, but combining it with other factors, such as Momentum, can potentially identify good tradable signals. One natural question, which speaks to the broader impact of this research, is whether these factors have similar effects on ETFs of other asset classes ."

Conclusion. 1) Premium/Discount and 2) Momentum at 4pm can predict  overnight returns of bond ETFs. Below is a summary from Tables 1 and 2:

Momentum, the strongest factor, is inverted for investment-grade and municipal ETFs. That is when the ETF rises strongly in the afternoon it falls overnight. When if falls strongly it rises overnight. 

In terms of momentum strength, both the binning method and standard regression coefficients show that all 4 ETF types are arbitragable. Investment-grade being more so than high-yield was quite surprising. 
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Combining factors, shown above for HYG, produces even more powerful returns.

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Post 21: Momentum Investing Again

7/23/2018

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The attached article (long but worth a read)  from the July 23rd Financial Times, "Hedge Fund Killer," discusses a shift in investment strategy from stock selection to factors with momentum being a key factor. The article caught my attention because momentum (GDP-weighted ) underlies my system. Now their momentum is long term, not daily, but nevertheless it made me revisit pure momentum. 

Previously, Post 14. “World-GDP weighted momentum works; US-weighted momentum doesn't” I couldn't find self-momentum in SPY, ie, morning increases in SPY weren't followed by afternoon moves.Because I couldn’t find momentum in stocks I explored bond ETFs instead. The three ETFs were:

LQD holding investment grade bonds
HYG holding high-yield. bonds rated BB or lower by Moody's, S&P and FitchEMB holding government bonds in emerging countries

The results were quite surprising. It appears in a quick analysis that high-yield bond and international bond ETFs do have self momentum. (LQD’s lack of overnight momentum differs dramatically from the other two and needs an explanation.). A later post will examine these results in more detail.


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(Coincidentally,Clifford Arness, featured in the article, is one of the authors of a paper I referenced in Post 4, “Fact, Fiction and Momentum Investing”

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2435323

AQR is at the vanguard of a revolution that aims to identify the core drivers of markets. By mimicking investment strategies at a lower cost, these factor-focused funds could become a cheaper alternative.By Robin Wigglesworth

AQR’s Clifford Asness is moving billions of assets into factor investing, which analyses the persistent drivers of markets. Below: Nobel laureate Eugene Fama
FT montage; Bloomberg

In 2001 Clifford Asness, a cerebral but fiery-tempered hedge fund manager with a penchant for comic book memorabilia, penned a paper arguing that his industry’s skills were “overstated”. It went down like a lead balloon.
Mr Asness was inundated with irate calls from some of the industry’s biggest names, and even got the occasional glower at school events attended by other hedge fund fathers. “I got yelled at by a lot of famous people,” he recalls.
He survived the opprobrium. Mr Asness’s company AQR is today a major player in the hedge fund industry. Its $226bn of assets under management outstrip even Ray Dalio’s Bridgewater Associates. But rather than a hedge fund, AQR could now arguably be better described as a hedge fund killer.
AQR is at the vanguard of a revolution quietly sweeping through the asset management industry: “Factor investing”, which in theory breaks down market returns into their basic components, researching what drives them and trying to systematically exploit their characteristics.
Factor investing is part of the broader world of computer-powered “quantitative” finance. But rather than scour markets and oceans of data for fleeting signals, factors are the big, persistent market drivers that in theory exploit timeless human foibles, such as our tendency to favour glamorous stocks over solid ones. Financial academics argue that a lot of what asset managers do is take advantage of these well-known patterns, anomalies and inefficiencies. But if one can do so systematically and cheaply, why pay for an expensive fund manager?
“Before, market drivers were like gods in the sky — mysterious and often unfathomable. But with factors we can now understand what actually drives performance,” says Marko Kolanovic, head of quantitative research at JPMorgan.
Think of factors as the basic ingredients of a solid meal. By deconstructing and finding the healthiest components, fans say they can be reassembled into a better-balanced, tastier diet. In other words, a more diversified, robust and cheaper investment portfolio than one built with traditional, blunt asset classes like stocks and bonds.
Recent results have been mixed, with many factor-focused funds — including AQR’s — suffering a mediocre or dismal 2018. But many pension funds, endowments and even retail investors are still embracing this new approach. Black-Rock estimates that there are $1.9tn of assets in dedicated factor strategies, and predicts this will swell to $3.4tn by 2022.
Some have even gone so far as to call AQR the “Vanguard of hedge funds”, a reference to the passive investing group founded by Jack Bogle that has helped popularise cheap, index-tracking funds for the masses and unsettled the mutual fund industry in the process.
That may be a step too far to describe an investment group that still boasts plenty of expensive hedge fund strategies, some of which have also struggled in 2018. But it is “what we want to be. It’s aspirational,” Mr Asness admits. It is a description tacitly endorsed by Mr Bogle himself, who has said that among hedge funds AQR “is the one I hate the least”.
Beating the market
In 1992 Eugene Fama and Kenneth French, two professors at the University of Chicago Booth School of Business, published a paper that showed how investors could beat the stock market’s returns — the “beta” in finance jargon — by taking advantage of two simple factors: the tendency of small or cheap companies to outperform over time.
Factors are often called risk premia because they represent the extra compensation investors receive for taking on some specific risk. Many factors have been known for decades. Some pioneering “quant” investors influenced by academic research started to exploit factors in the 1970s. But the Fama-French paper was a bombshell, largely because Prof Fama is the father of the “efficient markets hypothesis”, which argues that investors cannot consistently beat the market.
“The king of EMH said that there were factors that had positive outperformance,” says Rob Arnott, head of Research Affiliates, a factor-focused investment group. “It blew people away.”
In 1989, Prof Fama took on a precocious PhD student as an assistant. Under his tutelage, Mr Asness wrote his thesis on a new factor, momentum, on how stocks that have gone up tend to continue to rise, and falling stocks tend to keep sliding.
Given how it went against Prof Fama’s thesis it was “nerve-racking” telling him the dissertation, recalls Mr Asness. But Prof Fama says he was more upset that his protégé later chose the grubby world of investing over academia.
It worked out fine for Mr Asness. AQR is a privately held company, but according to filings by Affiliated Managers Group, which owns a minority stake, its revenues jumped 39 per cent to $1.3bn last year, and its net income rose over 50 per cent to $807m. For comparison, Man Group — the world’s biggest publicly listed hedge fund group — notched up revenues of $1bn and profits of $384m in 2017.
This has made its co-founders billionaires. Mr Asness is worth $3.6bn, and David Kabiller and John Liew both have an estimated $1.25bn, according to Forbes. A fourth co-founder, Robert Krail, retired for health reasons some years ago. But the rest of AQR’s quantitative analysts are not doing badly either. AQR spent $366.9m on compensation, benefits and other related staffing expenses last year, or more than $400,000 on average for its 914 employees (which includes 73 PhDs).
There are generally thought to be a “big five” in factors — size, value, momentum, volatility and quality. But over the years, academics have discovered scores more across different asset classes. While some of the research is well established, it is mainly in the past decade that interest has exploded.
Of the $1.9tn in factor strategies, BlackRock divides the industry into “proprietary factors” that typically reside in mutual fund structures ($1tn), “enhanced factors” in hedge fund vehicles ($209bn) and $729bn of “smart beta” exchange traded funds, cheaper vehicles that tilt towards one or several investment factors.
Andrew Ang, head of factor investing at BlackRock, argues that the falling cost has been the primary catalyst. “Cars were invented in the late 1800s, but it wasn’t until Ford’s Model T that they took off. And it was because of cost,” he says. To describe the impact, he uses another metaphor:
“Asset classes are like watching TV in black and white, while factors is like viewing it in colour.”
By in theory replicating what a lot of professional money managers do at a fraction of the cost, factor investing puts more pressure on fees. This is why Mr Asness thinks AQR can play the same disruptive role for hedge funds that Vanguard did for mutual funds.
“It is part of our business to be the Vanguard of hedge funds. It’s not all of our business, by any means. But to take some of the basics and say you should get this for lower fees,” he says. “What [hedge funds] are doing as a group is good, but simple. And they’re kicking up a whole lot of dust around it.”
Reversion fear
Probably the first institutional investor to embrace factor investing was PKA, a $39bn Danish pension fund. In 2011 it started shifting its entire portfolio towards a more focused stream of risk premia, shrinking the number of asset managers it employs from 25 to just five. One of the managers it has kept working with is AQR, and Nils Ladefoged, the PKA executive who led the restructuring, is full of praise for the firm.
“They look at an asset and decompose it into various factors and think of the best way to harness them,” he says. “They are academic, but also conscious of the practical issues.”
PKA might have been one of the first investors to shift towards factors, but it was not the last. A State Street investor survey found that two-thirds now use factors to at least analyse their portfolios, and a third said it was their most important method. Indeed, factors are now an integral part of the industry.
Nonetheless, factor investing has plenty of detractors — even from its proponents. “It is aggressively oversold right now,” says Mr Arnott, another pioneer of the industry. “It shouldn’t be seen as a panacea. Factors can become materially expensive, and performance will mean-revert. Some factors have been performing better precisely because they’ve been getting inflows.”
Work done by Vincent Deluard, a strategist at INTL FCStone, illustrates this pitfall. In November 2016 he built a portfolio of S&P 500 stocks that failed to qualify for any of the five big US factor ETFs. But this “basket of deplorables” has since outperformed a basket of smart beta ETFs. This is probably because many smart beta ETFs appear to be negatively correlated to interest rates, possibly because the data that academics crunch in their hunt for factors mostly only stretches back three decades — a period characterised by a secular decline in rates. Interest rates are now nudging higher, and this could be an Achilles heel for some factors.
“Investment managers will sell anything that can sell. Some of it is fine, but some of it is flimsy,” says Prof Fama. And even the ones that are robust will not always work. “A lot of people don’t know what they’re buying, but they’re buying a risk,” he adds. “If they’re willing to do so they will get a higher return over time. But there are long periods where by chance they just don’t turn up.”
That seems to be the case now, with many of the biggest factors simultaneously suffering a stinker in 2018 — hurting many big quant funds, including those managed by AQR. For example, its $4.8bn Style Premia Alternative Fund and $9.1bn Managed Futures Strategy Fund are down 7.4 per cent and 4.8 per cent this year. But the investment group is undeterred.
Playing with emotions
Mr Liew first met his future AQR co-founder at the University of Chicago, when another student pointed out Prof Fama’s assistant and whispered: “That guy [Asness] is ridiculously smart. He’s probably smarter than the professor. But the thing is, he knows it. He can be really insufferable at times.”
Mr Asness insists that his mentor is clearly the smarter one, but cheerfully admits that he does not always succeed in hiding his insufferableness. He also has a legendary temper, renowned smashing a series of computer monitors during a rough patch in the financial crisis. He said later that the reports were exaggerated: “It happened only three times, and on each occasion the computer screen deserved it.”
But Mr Asness uses his emotional reaction as an example of why factors endure in the long run. Many of the anomalies that factor investing exploit are deeply rooted in human psychology, which is why they are not weeded out over time. And as long as even quants can fall prey to these foibles, then factors will have a bright future, he argues.
“It’s a high pressure world. And when your factor is not working, it’s not an easy time,” he says. “But if we can’t keep emotion out of our own brains, it’s pretty good news for the factors, for the idea that investors aren’t perfectly rational is the reason these factors work. No matter what the times bring, we will stick to what we believe in and keep doing it.”
‘It is part of our business to be the Vanguard of hedge funds — to take the basics and say you should get this for lower fees’ ‘Factor investing is aggressively oversold. It shouldn’t be seen as a panacea. Performance will revert to the mean’
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20. China, Europe and the US like (No, "love") high volatility periods

7/8/2018

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Post 15 discussed an academic article, “Volatility-Managed Portfolios,” linked below, that’s been mentioned in the financial press and has interest from hedge funds. Instead of buy-and-hold it proposes reducing your stock portfolio when volatility rises. Here's its thesis. Sharpe ratio, which equals return divided by volatility, is the most widely used measure of risk-adjusted return. Since volatility is predictable, when it’s high it will more often than naught stay high; Returns, however, are unpredictable, as detailed by the 2013 Nobel prize winner. The arithmetic is: do more investing when the denominator, ie,volatility, is low (and less when it’s high) because this yields higher Sharpe ratios, ie better risk-adjusted return.

By happenstance while reading this article I was battling volatility in my buy SPY-at-noon model and noticed that when volatility is high my model weakens. Specifically, when volatility exceeds 28 SPY-at-noon had negative returns, and so I wrote that post supporting the article.

But recently I revisited SPY from the buy-and-hold strategy suggested by my physicist friend and surprisingly the article’s thesis doesn’t hold with ETFs representing the world’s largest centers. They have increased dramatically more in high VIX periods than in low VIX periods. The table lists returns of ETFs, representing  China, Europe and the US, grouped by quintiles of the VIX over nine years. Notice that China, return of 91% in the top two quintiles versus -33% in the other three, and Europe, return of 114 % in the top two versus -14% in the lower three, do even better that the US (93% in the top two versus 45% in the lower three. So all centers have performed better in high VIX periods. I haven't computed annual volatility but it seems clear that these world proxies would not have yielded better risk-adjusted return by lowering investment in high-volatility periods.

To check influence from the end of the financial crisis I dropped July 2009 until June 2010 and results are similar.
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​As always if you have comments please send them. Meanwhile I’m sending this to the authors for possible comment

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2659431

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    May23 Return .31%


    Out-of-Sample return 62%








    Updated at 12 and 4:15pm on trade days

    Author

    Peter Ciampi is the Managing Director of CEF Events LTD, a British Virgin Islands business company and the Managing Partner of Time-Zone Arbitrage,a Delaware LP. Both companies invest in special situations of closed-end funds and statistical arbitrage of international ETFs.

@ 2017 Peter Ciampi
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