So how much did quantitative strategies actually lose last quarter?

The numbers have started to come in: Morgan Stanley lost $480MM last quarter due to quantitative trading -- about 10% of operating profits.

Hedge fund replication

I wrote about how hedge fund returns can be replicated with simple factor models. I just learn that IndexIQ, a company in Rye Brook, NY, has just launched such products available to retail investors as managed accounts.

More discussion on returns, risk and leverage

Previously I discussed an important debate on whether it is better to increase a portfolio's return by taking on more risks (e.g. holding high-beta stocks), or by increasing leverage but holding low-risk assets. A reader Mr. F. Sudirga has kindly send me some other research papers supporting the conclusion that increasing leverage is the preferred way.

In a paper titled "Risk Parity Portfolios", Dr. Edward Qian at PanAgora Asset Management argued that a typical 60-40 asset allocation between stocks and bonds is not optimal because it is overweighted with risky assets (stocks in this case). Instead, to achieve a higher Sharpe ratio while maintaining the same risk level as the 60-40 portfolio, Dr. Qian recommended a 23-77 allocation while leveraging the entire portfolio by 1.8. The stock-bond dichotomy is for illustration only -- the results can be improved further by including other asset classes such as commodities.

The only reservation I have with all this enthusiasm with increasing leverage is one that many risk-managers are aware of: most of the research uses concepts such as standard deviations to measure risk. But as the LTCM debacle as well as the recent subprime mortgage meltdown has reminded us, risky events have fat-tailed distributions. Therefore, one should be very wary of using standard deviation as the sole determinant of leverage.

Whatever happened to the XLE-USO spread?

Recently Mr. Teetor, a subscriber of mine, has posted an enthusiastic comment on trading the XLE-USO spread that I suggested. While Mr. Teetor has a lot of success trading this spread, I must say that I have lost faith in the cointegrating characteristic of this spread because of two reasons:

1) The spread appeared to have experienced a regime-shift since the historic backtest period before August 2006: the out-of-sample performance of the spread since then did not support cointegration; and

2) The fundamental argument in support of cointegration between XLE and USO fell apart upon closer investigation.

The two reasons are, I believe, intertwined. Unlike GLD (part of a much more cointegrating spread that I discussed and tracked in my premium content area), USO does not actually hold commodity assets in its portfolio. It holds nearby futures contracts in oil. When the USO fund started trading in April 2006, its price per share was very close to the spot oil price. Now, however, USO is trading at about $53, while spot oil price is at about $70.6. How can a fund that is supposed to reflect oil price diverge so much from it after a year and 5 months? The reason is that the oil futures market has been in contango since 2005 or so, i.e. far month futures costs more than the nearby contracts, which results in negative roll-yield for long position in oil futures. In the historic period from which the XLE-USO cointegration relation was established, oil futures market exhibited backwardation: far month futures cost less than nearby futures. This regime shift partially explains the breakdown of the cointegration relation in the present out-of-sample period.

The lesson I have learned from all these is to avoid analyzing cointegration relation when either side of a spread involves futures contracts at different points of the forward curve, at least on a time-scale when the shape of that curve might change. (I argued before that XLE, the other side of the spread, can be modeled as an average over the entire forward curve.) Meanwhile, the fund manager of USO would really have done investors a much better favor by getting their hands dirty, leasing some oil storage tanks and buying some real oil assets rather than keeping their hands clean and dealing in futures contracts alone. After all, retail investors like myself can just as easily buy oil futures ourselves, but we can't very well go out and rent an oil tank.

CIO Magazine Innovation and IT Strategy blog

Ms. Elana Varon who writes the CIO Magazine's Innovation and IT Strategy blog quoted me today in saying that some quantitative investment models are over-engineered. This old article of mine is an elaboration of my view on this.

The Perils of Momentum Strategies

Not so long ago I was an agnostic with respect to choosing between mean-reverting and momentum models: I felt that depending on the particular model or environment, each can be profitable. Lately, however, I am increasingly skeptical about the long-term profitability of momentum models. The main reason is the increasing competition among traders, algorithmic or otherwise.

As I mentioned in my previous post, when more and more traders decide to adopt mean-reverting strategies, all they do is to eliminate the trading opportunity. The market becomes efficient, and nobody makes any money, but nobody loses either. In contrast, when more and more traders decide to adopt momentum strategies, the momentum will be established sooner and sooner. For e.g. in the case of event-driven strategies which are mostly momentum-based, the new equilibrium price will have been established almost instantaneously after the event is publicly disclosed. Under this circumstance, any momentum trades that are entered just a little bit late will not only suffer zero profit, but will likely suffer losses as mean-reversion almost inevitably takes over. But how soon do we need to enter in order to avoid this fate? (It can't be too soon either because often a trend need to be established first in order to trigger an entry signal.) It is unfortunately a moving target as competition increases: 1 day earlier might work now, but may not be sufficient a few months from now. (The exit trade also suffers the same problem, as we don't know how long the momentum will last.) It is a dangerous game to play.

Indeed, time is often a friend of the mean-reversion trader: the longer s/he waits, perhaps the more profitable the trading opportunity. And if s/he enters too early and suffers a loss, s/he can always double the position. As I explained in a previous article, stop-loss should generally not be applied to mean-reverting trades on a short time-scale. So even if the trader does not double-up the position, an eventual re-couping of the loss is more than likely. On the other hand, time is an enemy of the momentum trader: if s/he loses the first-mover advantage and suffers heavy loss, I argued in that article that a stop-loss is advised, and thus the loss is forever locked-in.

Given this asymmetry, it is no wonder that algorithmic traders have been warning me long ago that it is hard to find a profitable momentum trade. And I was silly enough not to pay heed to them until now.

Further debate on factor models

A reader from a hedge fund (who wishes to remain anonymous) sends me some thoughtful comments about factor models. He has graciously allowed me to reprint them here:

"With regards to your blog entry, 'The Robin Hood regime': this weekend I was actually also thinking about the philosophy behind factor models which you allude to in the post. I am wondering if you have any other thoughts as to what service factor models provide? Relegating them to 'just arrogant bets on the correctness of the managers' convictions' isn’t completely intellectually satisfying to me.

I look at factors as such: the returns I get for exposure to various factors can come either because the market is inefficient and systematically misprices those factors (alpha), and/or because I am providing some service via the exposure (and collecting some kind of risk premium associated with that service). My question #1 to you is, are you convinced that all of the returns to factor models are indeed simply from risk premiums and not alpha? If alpha exists, it’s less clear that a service needs to be provided to the market, at least to me.

However, let’s assume (as I believe your boss did) that in the long run, the market is efficient. Then, you will be compensated for factor exposure only by bearing some risk or providing some service. In my mind, some particular conviction of a manager doesn’t necessarily qualify for a risk factor in and of itself - I think we agree on that point. But are there possible fundamental, valuation-based explanations behind these factors? Perhaps low VALUE companies are generally those companies with bad recent performance but which are expected to turnaround / mean-revert (as you somewhat suggest in your post) and the risk you bear when buying a low P/E company is “turnaround risk”. Or perhaps high MOMENTUM companies are companies riding an industry trend and you are bearing “trend continuation risk”. So, my question #2 to you is, are you convinced that there are no such explanations?

If factor models do indeed work, it seems to me that there must either be real risks behind the factors, or alpha, or both."

And here is my response:

"I believe the service that some value factors provide is the efficient allocation of capital to those companies that deserve them, just like any value investors do. In this case, the factors hope to identify these companies faster than humans can, and therefore bring capital to them sooner. I have no argument with these factors as they also provide liquidity, albeit on a longer time-scale. However, with regard to various momentum factors, they are in fact just betting on certain behavioral characteristics of investors, or on the slow dissemination of news, etc. You can argue that they provide a service by improving the efficiency with which information about companies disseminate. But the problem is that once everybody are using these momentum factors, the market becomes efficient and any further bets generate losses.

So I am quite willing to accept that many of these (momentum) factors represent alpha, but these factors are generating more losses as more investors employ them. I am also willing to accept that many of the (value) factors represent risk premia. As more investors employ these, the profit goes to zero, but fortunately not negative as the risk also disappears."

The "failed" factors have reverted

As I said in my CNBC interview, investors just got to be patient with the factor models. Sure enough, we are seeing reports that the large drawdown suffered by these models has already reverted as of Friday.

The Robin Hood regime

It has become apparent to me in the last month that there has been a massive transfer of wealth from the gigantic hedge funds running factor models to many day-traders with accounts less than $10M. I call this the Robin Hood regime (regime being a common technical term referring to a particular trading environment, as in "this is a mean-reverting regime"). Many, many day-traders that I heard from have had one of their best months in a long while. Is this just luck, or is there a deeper explanation?

I believe that there is a philosophical difference between factor models and many of the mean-reverting strategies that day-traders like to employ, a difference that works to the day-traders' favor. I recall a wise musing from one of my former bosses: he believes that a trading strategy will be profitable in the long run only if it performs a service for other market participants. The service that mean-reverting strategies performs is the provision of liquidity, in particular, short-term liquidity. What service does factor models provide? They seem to be just arrogant bets on the correctness of the managers' convictions. For e.g. I believe that stocks with good earnings will rise in value. Or, I believe that stocks with increasing price momentum will continue in that momentum. True, most of the time the convictions of the best managers are correct, and many of these convictions are actually mean-reverting as well (for e.g. the "value" factors). But on average, a factor model may take away as much liquidity from the market as it provides. And sooner or later, some of these convictions are wrong. Maybe not wrong for very long, but long enough to cause investors' panic. This may be part of what we are seeing recently.

Now am I advocating that every gigantic fund simply just switch from factor models to pure mean-reverting strategies? No: that would be impractical when the portfolios involved are in the tens of billions. If everybody run mean-reverting strategies, there will hardly be any mean-reversion left to profit from. (Look what happened to pair-trading in the last few years.) When you are an investor in a multi-billion fund, and you expect the fund to deliver higher returns than the risk-free rate, you just have to accept that high short-term returns volatilities will be part of the bargain, just like any long-term investments.

CNBC Interview video

Here is the link to my interview on why quantitative models are losing money of late:

CNBC interview

Folks, I will be appearing on CNBC tomorrow (Tue, August 14) for a live interview about quantitative trading with Maria Bartiromo. The segment will be aired around 3:20 pm ET.

A reader's comment on quant funds' losses

A reader of mine (who wish to remain anonymous) pointed out that most of the losses seem to come from low-frequency trading models, while high frequency models continue to perform superbly. This also confirms my own experience. My enthusiasm for high frequency trading was expressed previously here and here.

An update on why quantitative funds are losing money recently

A story just came through Dow Jones newswire ("How Black Boxes Became Pandora's Boxes" by Spencer Jakab) suggesting that recent losses are due to factor models gone bad. Given my expressed distaste for such models, that should have been my first guess instead of blaming the "exotic" models!

Why are quantitative funds losing money these days?

The New York Times today has an article about several well-known quantitative hedge funds incurring significant losses in recent months. I was quoted in saying that traders running similar quantitative models could contribute to market volatility. This is certainly true if the strategies are trend-following. What puzzles me, however, is that most statistical arbitrage strategies are mean-reverting: they buy during investors' panic, and sell during investors' euphoria, and should be richly rewarded in this volatile market by providing sorely needed liquidity. And indeed, from my own experience as well as hearing from other traders, mean-reverting strategies are performing very well recently. So where did those losses come from? My guess is that, as I have observed before, many traditional stat arb strategies are getting boring and generating diminishing returns, and therefore many of the quantitative researchers are driven (by their own professional pride or their bosses) to come up with more exotic and higher-return strategies that ultimately may not stand the test of time. For us quants, remembering Occam's razor and that our job is to generate returns as opposed to producing brilliant mathematical models is often a hard lesson to learn.

Currency trading resources

For those of you who are into currency trading, this blog looks interesting. You can also subscribe to a free daily email newsletter called "The Daily Pfennig" from www.everbank.com.

More on news-driven trading

For readers who are interested in news-driven trading, here is another article. This article pointed out a contrarian view offered by Richard Oldfield, a fund manager, who says “price movements in response to news are exaggerated, providing an opportunity to those who do not base too much on what has happened in the last hour or 24 hours.” [my italics] I am not sure whether Mr. Oldfield's statement is based on any statistical research or not -- as far as I can ascertain, his book "Simple But Not Easy” cannot be purchased anywhere in North America. However, I should point out that this statement is in contradiction to an abundance of research done on Post Earnings Annoucement Drift (PEAD): the phenomenon that stocks with positive earnings news continue to trend up for a long time. Furthermore, if indeed price movements in response to news are exaggerated (contrary to the findings of PEAD), it would seem to suggest a reversal trade rather than suggesting that the news can be ignored!

Fair value of currencies

In the same issue of the Economist magazine I cited previously, there is an article about the valuation of currencies based on 13 quantitative models that Morgan Stanley developed. They found that the most overvalued currency (against the US dollar) is the New Zealand dollar, while the most undervalued currency is the Japanese Yen.

What about the Chinese Yuan that arouses much hoopla in Congress? The models found it to be almost exactly fairly valued.

News-driven algorithmic trading

There is an article about algorithmic trading in the latest issue of the Economist magazine, where it says that one-third of all stock trades in the US are due to algorithmic trading. This should not surprise us. What is more interesting is its mention of the electronically tagged news products that are coming out of Dow Jones and Reuters, which purportedly enable computers to buy or sell stocks immediately upon the release of a news item. The data suppliers regard these news products as some kind of secret high-tech weapons: "Dow Jones claims the business is so secretive that it cannot divulge details of customers." Is this hype justified?

Actually, to get a taste of news-driven trading, you don't need to pay a hefty fee to buy one of these products. You can just monitor the regularly scheduled economic news release (consumer confidence, new homes sales, crude inventories, etc.), trade the relevant futures, and proceed to make millions.

The fact that most of us who monitor these economic news releases haven't yet made our millions is an indication whether these news products will help you do the same. The information contained in the news is often difficult to interpret. Even the initial price reaction to the news may be wrong, leading to swift reversal after an apparent initial trend. And finally, what's wrong with scanning for sudden price movemenets, and then check for possible news to confirm that the price movement is due to the release of new information?

Pair trading stocks and the life-cycle of strategies

I have discussed in various articles trading the spreads between pairs of ETF’s, or between a basket of stocks against an ETF using cointegration technique. There is, however, a glaring omission, as I haven’t yet mentioned the classic statistical arbitrage strategy: pair-trading stocks.

There are pros and cons on applying cointegration to pair-trading stocks. On the pro side: because of the large number of stocks, we can enjoy a highly diversified portfolio that improves the validity of our results. Even if a number of spreads fail to cointegrate going forward, we can count on a larger number of spreads that still do. (For e.g. my USO-XLE spread fell apart, while GLD-GDX spread is still tightly cointegrated.) There are 2 main cons: 1) stocks are subject to various specific risks which may render our purely statistical model useless, especially in M&A situations. Therefore it is customary to remove such stocks from our portfolio when they are involved in special situations – however, by the time the news is public we may have incurred substantial loss already; also 2) because of the technique’s long history, it became known to many hedge funds and indeed students of finance, and therefore pair trading stocks has not been very profitable, especially in the period 2003-2005. Here I plotted the excess returns of the strategy as applied to US bank stocks from 20010102-20041231. (Excess returns means credit interest on margin balance is not included.)

Interestingly, when a strategy becomes too popular and less profitable, many traders start to abandon it, or at least reduce their trading capital invested in the strategy. After a while, its popularity decreases, and the profitability recovers! This life-cycle of strategies reveals itself as mean-reversion of strategies, on top of mean-reversion of stock prices. In our case, this strategy recovery starts in 2005, and is still in full-force. Here I plotted the excess returns of the strategy as applied to US bank stocks from 20050103 to 20070531:

The average annual excess return in 2005-now is about 7.7% (on one-side of capital), and the Sharpe ratio is 0.8. Since I have applied the technique on only one industry group, diversification is limited and therefore the Sharpe ratio is low. For the interested readers, they can attempt to apply this technique to more industry groups and perhaps generate a higher Sharpe ratio. Even with just one industry group, this trading strategy may be a good complement to a portfolio heavy on trend-following strategies and therefore require a reversal model to smooth out the returns.

I have started a model portfolio in my subscription area to demonstrate this strategy which will be updated daily around 3pm ET. Other details of the strategy will be detailed in an accompanying article there as well.

The effect of terrorism on forex trading

Here is an interesting and thoughtful article, with reference to an academic study, on how terrorism affects forex trading.