Pair trading technologies update

Pair trading was invented two decades ago, but automating its implementation has only recently become fashionable with independent traders. But once the spotlight is on, innovations come fast and furious. Here are a number of recent developments that I find interesting:


1. I mentioned previously the software called quant2ib. It is an API which allows us to get market data and send orders from a Matlab program to Interactive Brokers (IB). I have used it extensively for our trading, and it is as reliable as IB's native API. Their latest version now includes functions for constructing a "combo" security. This combo security can be pairs of stocks, ETF's, futures, etc. (with the notable exception of currencies), and the API allows you to get market data as well as to submit orders on a combo. This is a huge improvement because you can now automatically trade a pair of securities as one unit by submitting limit orders on the combo. (Previously, you would have had to submit market order on at least one side of the pair, and this would have required your program to continuously monitor the market prices and send orders when appropriate. Or else you had to give up using the API and manually enter a "generic combo" limit order in IB's TWS.)

2. Alphacet Discovery also has the ability to send limit orders on pairs, due to its partnership with Knight Trading. Besides, based on a demo that I have recently seen, they also now have great pairs portfolio and execution reporting functionality. (Full disclosure: I used to consult for them.)

3. IB itself has released a "Scale Trader" algorithm that can be applied to combos (see 1. above. Hat tip: Mohamed.) I can't explain this better than their press release: "... ScaleTrader algorithm allows clients to create conditions under which a long position in one stock is built while simultaneously creating an offsetting short position in the other. The ScaleTrader is named because investors can 'scale-in' to market weakness by setting orders to buy as the market moves lower. Similarly, sell orders can be 'scaled' into when a market is rising. The ScaleTrader algorithm can be programmed to buy the spread and subsequently take profit by selling the spread if the difference reaches predetermined levels set by the user." In other words, it allows us to automatically implement the "parameterless trading" or the "averaging-in" strategy that I blogged about previously without any programming on our part!

Speaking of pair trading, I will be teaching my first New York workshop in October.  (My editor inevitably picks touristy locations for these workshops. My London workshop takes place across the street from the Tower of London, my New York workshop is across from the new World Trade Center, and my Hong Kong workshop is in the "Golden Mile" shopping district of Tsim Sha Tsui.)

The Quants

Once in a while, a book about trading written for the general public contains some useful nuggets even for professionals.  Fortune's Formula was one. It introduced me to the world of Kelly's formula, Universal Portfolios, and the maximization of compounded growth rate. The Quants, by WSJ reporter Scott Patterson, is another. (Hat tip to my partner Steve for telling me about it.)

What is the most important take-away in The Quants? No, it is not that you should learn to become a master poker player or chess player before hoping to make it big, though you would think that given Patterson's exhaustive coverage of poker games played by the top quants. Among my own professional acquaintances, trader-poker-players are still a minority.

The most important take-away is what ex-employees said about Renaissance Technologies: "there is no secret formula for the fund's success, no magic code discovered decades ago by geniuses .... Rather, Madallion [Fund]'s team of ninety or so Ph.D.'s are constantly working to improve the fund's systems, ..."

In other words, though you may not have 90 Ph.D.'s  at your disposal, you can still work on continuously improving/refining your strategies, improving the engineering of your trading environment, and increasing the diversity of your strategies. And though you may still not archive 60-70% annualized returns every year, you will nevertheless enjoy stable returns year after year.

By the way, it is good to see my ex-colleagues Lalit Bahl, Vincent and Stephen Della Pietra mentioned in the book, all of whom left IBM to join Renaissance many years ago, and who are extraordinarily nice and friendly guys, quite in contrast to the norm on Wall Street.

A HFT primer

As a follow-up of my previous discussions on high frequency trading, I have invited guest blogger Jennifer Groton to share with us a quick survey of various common HFT strategies used by equities and FX traders.

==

High frequency trading strategies are under fire.  The recent trading spike in our national exchanges was duly noted as a short-circuit waiting to happen and drew immediate industry criticism of auto-trading robots. Before a witch-hunt ensues, perhaps a review of the common HFT strategies in stocks and Forex is in order. 

High-frequency firms employ a wide variety of  low-margin trading strategies that are implemented by professional market intermediaries who have invested heavily in technology. These firms claim that they make markets more efficient by enhancing liquidity and transparent price discovery to the benefit of investors.  The Forex market’s unique combination of high liquidity and low volatility make it an ideal environment for deploying HFT strategies, although many of the ideas and technology are from the equity markets.  The basic strategies fall into three categories: market-making, trending or predictive, and classic arbitrage.

Market-making strategies tend to focus on a single stock or currency pair.  Many firms in this area have been described as engaging in "rebate-capture trading", a reference to the credits that firms get for providing liquidity on most market centers.

The second group consists of mean-reversion and trending strategies. These utilize technical indicators for stocks or forex indicators for currencies, and seek to generate more return from individual trades.

The last group may involve a cross-section of trades from multiple markets.  The classic arbitrage strategy is a form of the “carry trade” that uses the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency.  If the market prices are sufficiently different from those implied in the model to cover transactions costs, then four transactions can be made to guarantee a risk-free profit.

High frequency trading is attributed with generating over 70% of the volume of trades on our equity markets.  Similar statistics are not available for forex markets, but speculating disguised as commercially necessary trades have been reported to be over two-thirds of the volume.  Liquidity and pricing transparency are the benefits offered by its advocates, but regulators and other market participants who disagree with this positive assessment are presently discounting these benefits.  Transaction taxes and time limits on orders have been proposed to mitigate the perceived risk created by HFT firms, but the wheels of Washington move slowly, even in crisis.  For the time being, there is no indication that their participation will be discontinued.   

Are flash orders to be blamed for Dow's 1,000 points drop?

Before the smoke is clear, fingers are already pointing at flash orders. See these two NYT pieces here and here. Our reader Madan has convinced me previously that flash orders can indeed be used to  front-run other traders, but until more evidence comes in, I am yet to be convinced that they are the main culprit. Couldn't old-fashioned automated momentum programs accomplished the same thing after an initial erroneous transaction price and/or quote was reported? Perhaps you know of discussions elsewhere on the blogosphere that bring more light to the issue?

An additional ETF pair

Many of you know that there are a number of dependable commodity-related ETF pairs that remain cointegrated ever since I mentioned them in 2006: IGE-EWC, IGE-EEM, IGE-EWA, EWA-EWC, etc. (Their latest zScores are available here to my book's readers and to Premium Content subscribers.) A recent visit to a client in South Africa prompted me to add a new one: EWA-EZA.

It is worth noting that for those country ETF pairs that cointegrate, their underlying currency cross-rates are often stationary as well. Now, there are several advantages in trading currency cross rates instead of ETF pairs. When trading a stationary cross rate, you can enter a limit order to enter and exit, but trading pairs of ETF's involve market orders on at least one side. Also, ETF's can sometimes be hard-to-borrow, and their margin requirements are much more onerous than that of currencies. However, the one major disadvantage in trading cross rates is that they are not always available on your brokerage. For example, based on the cointegration of EWA and EZA you would think that trading AUDZAR would be quite profitable. And you would be right, theoretically, except that AUDZAR is not available for trading on Interactive Brokers. If you know of a good Forex brokerage that have many emerging markets cross-rates for trading, especially those of Latin American countries, please let the rest of us know!

How do you limit drawdown using Kelly formula?

As many of you know, I am a fan of Kelly formula because it allows us to maximize long-term growth of equity while minimizing the probability of ruin. However, what Kelly formula wont' prevent is a deep drawdown, though we are assured that the drawdown won't be as much as 100%! This is unsatisfactory to many traders and especially fund managers, since a deep drawdown is psychologically painful and may cause you to panic and shut down a strategy prematurely.

There is an easy way, though, that you can use Kelly formula to limit your drawdown to be much less than 100%. Suppose the optimal Kelly leverage of your strategy is determined to be K. And suppose you only allow a maximum drawdown (measured from the high watermark, as usual) to be D%. Then you can simply set aside D% of your initial total account equity for trading, and apply a leverage of K to this sub-account to determine your portfolio market value. The other 1-D% of the account will be sitting in cash. You can then be assured that you won't lose all of the equity of this sub-account, or equivalently, you won't suffer a drawdown of more than D% in your total account. If your trading strategy is profitable and the total account equity reaches a new high watermark, then you can reset your sub-account equity so that it is again D% of the total equity, moving some cash back to the "cash" account. Otherwise, you continue to keep the equity in the cash account separate from the equity of the trading sub-account.

Notice that because of this separation of accounts, this scheme is not equivalent to just using a leverage of L=K*D% on your total account equity. Indeed, some of you may be too nervous to use the full K as leverage, and prefer to use a leverage L smaller than K. (In fact, the common wisdom is that, due to estimation errors, it is never advisable to set L to be more than K/2, i.e. half-Kelly.) The problem with using a L that is too small is that, besides not achieving maximum growth, the portfolio market value will be unresponsive to gains or losses and will remain relatively constant. Using the scheme I suggested above will cure this problem as well, because you can apply a higher leverage L_sub to the sub-account (e.g. use L_sub = L/D%) as long as L_sub < K, so that the portfolio market value is much more sensitive to your P&L while still ensuring the drawdown will not exceed D%.

Has anyone tried this scheme in their actual trading? If so, I would be interested in hearing your experience and see if practice is as good as theory.

Conference on the sociology of quantitative finance

A new conference called Psi-Q will be held in London this June, featuring luminaries in the academic quantitative finance world, as well as risk and fund managers from various banks and hedge funds. Example topics:

• How did shared beliefs, practices, ways of calculating, and technical systems impact evaluation of asset-backed securities and CDOs before and during the credit crises?
• Was that Lucky or Good? Creating a framework for skill attribution in finance, business management and other risky endeavors.
• The “backing out” phenomena observed in options markets:  how traders use models to imply independent variables consistent with market observed pricing, and where enough traders can be wrong about the expected results and the backed-out positions can send the wrong message.

Sounds like an interesting bird's eye view of quantitative finance.

Pairs Trading Workshop in Hong Kong

For my readers in Asia, I will be conducting a pairs trading workshop in Hong Kong on March 10-11. This workshop is organized by the Technical Analyst magazine and is similar to the one I gave in London last year.
However, I have added a few useful insights based on audience feedback. As always, no prior knowledge of Matlab or advanced statistics is assumed. The numerous in-class exercises should be sufficient to bring your Matlab programming skills up to speed.

A method for optimizing parameters

Most trading systems have a number of parameters embedded, parameters such as the lookback period, the entry and exit thresholds, and so on. Readers of my blog (for e.g., here and here) and my book would know my opinion on parameter optimization: I am no big fan of it. This is because I believe financial time series is too non-stationary to allow one to say what was optimal in the backtest is necessarily optimal in the future. Most traders I know would rather trade a strategy that is insensitive to small changes in parameters, or alternatively, a "parameterless" strategy that is effectively an average of models with different parameters.

That being said, if you can only trade one model with one specific set of parameters, it is rational to ask how one can pick the best (optimal) set of parameters. Many trading models have a good number of parameters, and it is quite onerous to find the optimal values of all these parameters simultaneously. Recently, Ron Schoenberg published an article in the Futures Magazine that details a way to accomplish this with just a tiny amount of computer power.

The key technique that Ron uses is cubic polynomial fit of the P&L surface as a function of the parameters. Ron uses the VIX RSI strategy in Larry Connors' book "Short Term Trading Strategies That Work" as an example. This strategy has 5 parameters to be optimized, but Ron only needs to compute the P&L for 62 different sets of parameters, and the whole procedure only takes 58 seconds.

Although Ron has confirmed that most of the parameters that Connors picked are close to optimal, he did find a few surprises: namely, that RSI of period 3 or 4 is significantly more profitable than the 2 that Connors used, at least in the backtest period.

Now, for a true test of this optimization, it would be helpful if Ron performed this optimization withholding some out-of-sample data, and see if these parameters are still optimal in that withheld data set. Since he didn't do that, we need to wait for another year to find out ourselves!

Excel ADF test

Some readers have asked whether there is an Excel version of the ADF test for cointegration (mentioned in articles here or here.) You can download one such package here (Hat tip: Bruce H.).

And as always, you can download the Matlab version from spatial-econometrics.com.

Does Averaging-In Work?

Ron Schoenberg and Al Corwin recently did some interesting research on the trading technique of "averaging-in". For e.g.:  Let's say you have $4 to invest. If a future's price recently drops to $2, though you expect it to eventually revert to $3. Should you

A) buy 1 contract at $2, and wait for the price to possibly drop to $1 and then buy 2 more contracts (i.e. averaging-in); or
B) buy 2 contracts at $2 each;  or
C) wait to possibly buy 4 contracts at $1 each?

Let's assume that the probability of the price dropping to $1 once you have reached $2 is p. It is easy to see that the average profits of the 3 options are the following:
A) p*(1*$1+2*$2) + (1-p)*(1*$1)=1+4p;
B) 2; and
C) p4*$2=8p.

Profit A is lower than C when p > 1/4, and profit A is lower than profit C when p > 1/4. Hence, whatever p is,  either option B or C is more profitable than averaging in, and thus averaging-in can never be optimal.

From a backtest point of view, the Schoenberg-Corwin argument is impeccable, since we know what p is for the historical period. You might argue, however, that financial markets is not quite stationary, and in my example, if the historical value of p was less than 1/4, it is quite possible that the future value can be more than 1/4. This is why I never make too much effort to optimize parameters in general, and I can sympathize with traders who insist on averaging-in even in the face of this solid piece of research!

Selecting tradeable pairs: which measure to use?

A guest blog by Paul Farrington

One of the most important factors in statistical arbitrage pairs trading is the selection of the paired instruments.  We can use basic heuristics to guide us, such as grouping stocks by industry in the anticipation that stocks with similar fundamental characteristics will share factor risk and tend to exhibit co-movement.  But this still leaves us with potentially thousands of combinations.  There are some statistical techniques we can use to quantify the tradeability of a pair: one approach is to calculate the correlation coefficient of each pair's return series. Another is to consider cointegration measures on the ratio of the prices, to see if it remains stationary over time.

In this article I briefly summarise the alternative approaches and apply them to a universe of stock pairs in the oil and gas industry.  To measure how effective each measure is in real world trading, I back test the pairs using a simple means reversion system, then regress the generated win rate against the statistical results.  Some basic insights emerge as to the effectiveness of correlation and cointegration as tools for selecting candidate pairs.

Please visit http://www.paulfarrington.com/research/Selecting%20tradeable%20pairs.htm for details of my methodology and results.

Public service announcements for quants

   1.  Conference on 'Computational Topics in Finance', February 19/20, 2010, National University of Singapore. The topics will include using R/Rmetrics in finance, but the conference is by no means confined to R. See http://www.rmetrics.org/.

   2.  Consulting position (6-month renewable contract) available at a major Canadian bank in Toronto:  research in  various mathematical algorithms used for pricing of interest rate derivative instruments like swaps, caps, swaptions, FRAs. Please contact their recruiter at http://www.linkedin.com/pub/kevin-p-w-wang/6/899/29a.

   3.  A free copy of Chapter 8 of "High Probability ETF Trading" which I mentioned here is now available for download.

Are financial speculations really "harmful human activities"?

It is worrisome when not one but two eminent economists denounced financial speculation as "harmful human activities" in the short space of 2 weeks. (See Paul Krugman's column here and Robert Frank's here.) It is more worrisome when their proposed cure to this evil is to apply a financial transaction tax to all financial transactions.

Granted, you can always find this or that situation when financial speculation did cause harm. Maybe speculation did cause the housing bubble. Maybe speculation did cause an energy price bubble. In the same vein, you can also argue that driving is a harmful human activity because cars did cause a few horrific traffic accidents.

No, we can't focus on a few catastrophes if we were to argue that financial speculation is harmful. We have to focus on whether it is harmful on average. And on this point, I haven't seen our eminent economists present any scientific evidence. On the other hand, as an ex-physicist and an Einstein-devotee, I can imagine some  thought experiments (or gedankenexperiment as Einstein would call them), where I can illustrate how the absence of financial speculation can clearly be detrimental to the interests of the much-beloved long-term investors. To make a point, a gedankenexperiment is usually constructed so that the conditions are extreme and unrealistic. So here I will assume that the financial transaction tax is so onerous that no hedge funds and other short-term traders exist anymore.

Gedankenexperiment A: Ms. Smith just received a bonus from her job and would like to buy one of her favorite stocks in her retirement account. Unfortunately, on the day she placed her order, a major mutual fund was rebalancing its portfolio and had also decided to shift assets into that stock. In the absence of hedge funds and other speculators selling or even shorting this stock, the price of that stock went up 40% from the day before. Not knowing that the cause of this spike was a temporary liquidity squeeze, and afraid that she would have to pay even more in the future, Ms. Smith paid the ask price and bought the stock that day. A week later, the stock price fell 45% from the peak after the mutual fund buying subsided. Ms. Smith was mortified.

Gedankenexperiment B: Mr. Smith decided that the stock market is much too volatile (due to the lack of speculators!) and opted to invest his savings into mutual funds instead. He took a look at his favorite mutual fund's performance, and unfortunately, its recent performance seemed to be quite a few notches below its historical average. The fund manager explained on her website that since her fund derived its superior performance from rapidly liquidating holdings in companies that announced poor earnings, the absence of liquidity in the stock market often forced her to sell into an abyss. Disgusted, Mr. Smith opted to keep his savings in his savings account.

Of course, our economists will say that the tax is not so onerous that it will deprive the market of all speculators (only the bad ones!?). But has anyone studied if we impose 1 unit of tax, how many units of liquidity in the marketplace will be drained, and in turn, how many additional units of transaction costs (which include implicit costs due to the increased volatility of securities) would be borne by an average investor, who may not have the luxury of submitting a limit order and waiting for the order to be filled?

Picking up nickels in front of steamrollers

When I was growing up in the trading world, high Sharpe ratio was the holy grail. People kept forgetting the possibility of "black swan" events, only recently popularized by Nassim Taleb, which can wipe out years of steady gains in one disastrous stroke. (For a fascinating interview of Taleb by the famous Malcolm Gladwell, see this old New Yorker article. It includes a contrast with Victor Niederhoffer's trading style, plus a rare close-up view of the painful daily operations of Taleb's hedge fund.)

Now, however, the pendulum seems to have swung a little too far in the other direction. Whenever I mention a high Sharpe-ratio strategy to some experienced investor, I am often confronted with dark musings of "picking up nickels in front of steamrollers", as if all high Sharpe-ratio strategies consist of shorting out-of-the-money call options.

But many high Sharpe-ratio strategies are not akin to shorting out-of-the-money calls. My favorite example is that of short-term mean-reverting strategies. These strategies not only provide consistent small gains under normal market conditions, but in contrast to shorting calls, they make out-size gains especially when disasters struck. Indeed, they give us the best of both worlds. (Proof? Just backtest any short-term mean-reverting strategies over 2008 data.) How can that be?

There are multiple reasons why short-term mean-reverting strategies have such delightful properties:

1. Typically, we enter into positions only after the disaster has struck, not before.
   
2. If you believe a certain market is mean-reverting, and your strategy buy low and sell high, then of course you will make much more money when the market is abnormally depressed.
3. Even in the rare occasion when the market does not mean-revert after a disaster, the market is unlikely to go down much further during the short time period when we are holding the position.

"Short-term" is indeed the key to the success of these strategies. In contrast to the LTCM debacle, where they would keep piling on to a losing position day after day hoping it would mean-revert some day, short-term traders liquidate their positions at the end of a fixed time period, whether they win or lose. This greatly limits the possibility of ruin and leaves our equity intact to fight another day in the statistical game.

So, call me old-fashioned, but I still love high Sharpe-ratio strategies.

In praise of ETF's

I have learned some years ago that ETF's are strange and wonderful creatures. Simple, long-only mean-reverting strategies that work very well on ETF's, won't work on their component stocks. (Check out a nice collection of these strategies in Larry Connors' book "High Probability ETF Trading". He has also packaged these strategies into a single indicator, the ETF Power Ratings, on tradingmarkets.com.) Simple pair trading strategies like the one I discussed in my book, also work much more poorly on stocks than on ETF's. Why is that?

Well, one obvious reason is that, as Larry mentioned in his book, ETF's are not likely to go bankrupt (with the notable exception of the triple-leveraged ETF's, as I explained previously), because a whole sector or country is not likely to go bankrupt. So you can pretty much count on mean-reversion if you are on the long side.

Another obvious reason is that though there are news which will affect the valuation of a whole sector or country, these aren't as frequent or as devastating as news affecting individual stocks. And believe me, news is the biggest enemy of mean-reversion.

But finally, I believe that the capital weightings of the component stocks also play a part in promoting mean-reversion. Typically, weighting of a component stock increases with its market capitalization, though not necessarily linearly. Perhaps large-cap stocks are more prone to mean-reversion than small-cap stocks? But more intriguingly, can we not construct a basket of stocks, with custom-designed weightings, with the objective of optimizing its short-term mean-reversion property? I (and others before me) have done something similar in constructing a basket of stocks that cointegrate best with an index. Can we not construct a basket that is simply stationary (with perhaps a constant drift)?

Now, perhaps you will agree with me that ETF's are strange and wonderful creatures.

The best environment for quantitative trading

Let me talk about a topic that is far more mundane than the usual high-brow theoretical discussions of strategies and algorithms, but that has no less long-term impact on the bottom line: what is the best office environment for research and execution of quantitative trading strategies?

I have worked in different office environments before, so I feel qualified to offer an informed opinion.

At Morgan Stanley, I huddled over a desk that is semi-partitioned from the rest of the office: nobody could see or bother me unless I or they stood up. At Credit Suisse, I shared an office with 2 other prop trading colleagues, one of whom was prone to freely sharing his opinion on various current affairs with his officemates. (On the other hand, he complained my biting an apple for lunch was too loud for him.) At Maple, a hedge fund in New Jersey, I shared an office with about 100 other colleagues on the trading floor, many of whom were prone to same opinion-sharing temptation.

Here at my own firm, I sit in solitude (except for my cat) in my basement office, my beloved classical FM streaming over the internet, my desktop electronically connected to my partner in our Chicago office, our trading servers at Amazon and elsewhere, and other clients and partners around the world, but unmolested throughout the day unless I voluntarily pick up the phone or answer an email or instant message.

Can you guess which environment is the one I find the most productive? Which one has the least stress? And which one contributes most to the bottom line of my employers/partners/clients?

(Hint 1: read Timothy Ferriss' book The 4-Hour Workweek. This guy checks his email only once a week.)

(Hint 2: my trading Sharpe ratio went from negative to >7.)

P.S. I look forward to meeting some of you at the Automated Trading Conference in London this Friday (my talk will start at 0900), and others at my pairs trading workshop on the preceding two days.

Are flash orders really so bad?

I confess I don't know much about flash orders, not being one of the Big Boys on the Street, until I read that the SEC is banning them. (For a clear diagrammatic explanation of flash orders, see here. For a refutation of some of the myths and misunderstanding surrounding flash orders, see here.)

It seems to me that flash orders can be understood as "request for liquidity" issued to various potential market makers/liquidity providers, not unlike the usual "request for quotes" (RFQ) common in other industries. They are issued when there is not enough liquidity on a specific exchange to satisfy an investor's need, and they ultimately benefit investors by lowering their transaction costs. The fact that high frequency traders are able to make lots of money by providing this liquidity is besides the point. Liquidity providers are supposed to make money by providing liquidity!

Some people, including Senator Charles Schumer and this New York Times op-ed, believe that flash orders are akin to front-running, a clearly illegal trading activity. But they are wrong. Front-running means that if you know someone is going buy a stock, you step in front of them
and buy it cheaply first, hoping to sell it to this slower buyer at a higher price. In the case of flash orders, the high frequency traders are instead selling this stock to the original investor, often at a lower price than available elsewhere and thus benefiting this investor, hoping that the prices will come down in the future after this liquidity need subsides. This is manifestly not illegal. This is what a market is built for!

Another way to understand that flash orders are not at all front running is that anybody, including you and me, are free to put in limit orders at the same price as those of the high frequency traders, way ahead of time, in a specific exchange, and become liquidity providers ourselves. You don't have to wait for a "request for liquidity" before doing so. And presumably you will reap the same benefits as the high frequency traders. You are not taking any additional risks over the HF traders either, since if no requests for liquidity ultimately arrive, you are not any worse off for wear. You cannot begrudge the profits of the HF traders just because you didn't put the limit orders in place beforehand!

Maybe there are some other angles which I miss which can convince me that flash orders are evil. But until my kind readers convince me otherwise in the comments section, I will regard this piece of legislation as another SEC attempt at demagoguery.

Can a trader be a do-gooder?

It occurs to me that the only way in which a trader can become more than a completely selfish, self-enriching, narcissistic person is to trade well enough so that you can manage other people's money and thus saving these investors from crooks and charlatans (provided you are convinced you are not a crook and charlatan yourself).

Other traders have advanced other arguments in favor of trading. But I am not convinced by them.

They say that we provide liquidity to other long-term investors who may need to liquidate their investments. But then, this applies only to mean-reversal strategies. Momentum strategies take away liquidity from the market, and in some cases exacerbating price bubbles. Certainly not something your grandma would approve.

Others argue that momentum strategies help disseminate information about companies through quick price movements. But can't we just watch Bloomberg or CNBC? Do we really need some devious insiders to convey that information to the rest of us through price movements?

No, I think that independent trading should serve only one purpose (besides short-term self-sustenance): as training and preparation to become a fund manager. Once you graduated from independent trading, you then enter into the grand contest among all fund managers to see who can best serve and protect investors' assets, (and be rewarded according to your standing in this contest.)

I know, this is the idealistic way to look at things. Serving and protecting seem to be what policemen should be doing, not traders. But as in quantitative trading, I think it helps one becomes more successful in one's activities by having a simple guiding principle or model. And it doesn't hurt that in this case, the principle would also be conscience-nourishing!

Have you traded 10,000 hours yet?

Author Malcolm Gladwell, in his fascinating bestseller "Outliers: The Story of Success", cites neurological research showing that "10,000 hours of practice is required to achieve the level of mastery associated with being a world-class expert." This seems to apply across many different types of experts, whether they are "writers, ice skaters, concert pianists, chess players ... Even Mozart ... couldn't hit his stride until he had his ten thousand hours in".

Reflecting on my own experience, I have become consistently profitable only after 4 years of actual trading (research alone doesn't count -- real money need to be at risk.) So while the number of hours may not be exactly 10,000, the order of magnitude is about right.

So if your trading has not been profitable, ask yourself this: "Have I traded 10,000 hours yet?"

Using R to Test for Cointegration

Paul Teetor, who guest-blogged here about seasonal spreads, recently wrote an article about how to test for cointegration using R. Readers who don't want to pay for a copy of Matlab should find this free alternative with similar syntax quite interesting.

Interview on backtesting

I have given a 2-part interview (here and here) on the various nuances of backtesting on tradingmarkets.com. Most of the ideas have been covered in my book, but it does serve as a summary of what I consider to be the most important issues.

For those of you who are interested, I may be giving a workshop on general techniques in backtesting in London as well, in addition to my pairs trading workshop. Additional details will be available on epchan.com at a later date.

Are Triple Leveraged ETFs suitable for long-term holding?

Triple leveraged ETFs marketed by Direxion have been all the rage lately. The fund management company says that they do not recommend buying and holding these ETFs. But is there any mathematical justification for this caution?

Before I answer this, it is interesting to note that these ETFs (e.g. BGU is 3x Russell 1000, TNA is 3x Russell 2000) are managed as constant rebalanced portfolios, a concept I discussed before. In other words, the fund manager has to sell stocks (or futures) when there is a loss, and buy stocks (or futures) when there is a gain in the market value of the portfolio, in order to maintain a constant leverage ratio of 3. This is also identical to what Kelly formula would prescribe, a methodology discussed extensively in my book, if the optimal leverage f were indeed 3.

However, the optimal f for such market indices are quite a bit lower than 3. Both Russell 1000 and 2000 have f at about 1.8. This means that since the funds are leveraged at 3, there is a real possibility that sustained losses could ruin the funds (i.e. NAV going to zero unless new capital is injected, which, er..., reminds me of a Ponzi scheme). So I would argue that not only should an investor not hold these funds for the long term, the funds themselves should not be leveraged at this level. Otherwise, it is a disaster waiting to happen.

A free Matlab-to-Interactive Brokers API

For readers who do not want to pay for a commercial Matlab2IB API, Max Dama has put together a free alternative. Domenic has provided some additional sample Matlab codes for trading.

A user of the commercial product that I previously mentioned reports that "My problem with the matlab2ib product was that it did not have a function for all the Active X methods. ( for example the Market Scanners, Real time Bars and Fundamental Data methods are missing). I also had issues when I tried to steam in trades data(I'm not sure if the matlab2ib product allows you to even do this?)." Apparently Max's API has included these methods, though I have not personally tried them.

My interview, stop loss, and the Principle of Latest Information

You can find an interview of me in the July 2009 issue of Technical Analysis of Stocks & Commodities magazine. I mentioned in that interview and also in my book that I believe stop loss should only be applied to momentum strategies but not to mean-reverting strategies. I explained my reasoning better in my book than in the interview, and so I will paraphrase the explanation here.

In algorithmic trading, it is reasonable and intuitive that we should always make use of the latest information in determining whether we should enter into a position, whether that information is price, news, or some analysis. Let's call this the Principle of Latest Information. (If someone can think of a better or sexier name, let me know!)

So let's say we have a stock model based on price momentum, and we entered into a long position based on a recent positive return on price. A few minutes later, the price went down instead of up, causing a big loss on our position. If we now ran this momentum model again, very likely it would tell us to short the stock instead because of the recent negative return on price. If we did that, we would be exiting the previously long position and became flat. This is in effect a stop loss, and it follows strictly from adhering to our model and our Principle of Latest Information.

In contrast, suppose we now have a stock model based on mean-reversion, and we entered into a long position based on a recent drop in price. A few minutes later, the price went down further instead of up, again causing a big loss on our position. If we now ran this mean-reversion model again, it would definitely tell us to buy the stock again because of the ever cheaper price. The model would not ask you to exit this position and take a loss. Hence, adhering to the model and the Principle of Latest Information will not lead to a stop loss for a mean-reverting model.

(Now, if we hold this losing long position long enough, the model will incorporate new historical prices into determining its long or short signals as it retrain itself, as the Principle of Latest Information says it should! At that time, it may indeed recommend that we exit the previously held long position at a loss. But this adjustment takes place at a much longer time scale, and therefore cannot really be considered a stop-loss in its usual sense.)

More generally, I find that at every turn, and not only in the realm of stock trading, applying the Principle of Latest Information always help me to be disciplined and not be afraid to enter into new positions, take loss or endure a drawdown as the case may be.

A job opening for quants

Alphacet told me that they have a job opening for a quant who will be helping their clients backtest trading strategies, among other responsibilities. Given that Alphacet's clients include several major investment banks and hedge funds, this position should provide pretty good close-up view of how the major quantitative players operate.

A good book for quantitative traders

Larry Connors and Cesar Alvarez (the guys behind tradingmarkets.com) recently published Short Term Trading Strategies That Work, a nice collection of simple technical trading strategies that you can easily backtest and verify.

As I have argued in my own book, simple strategies are often the ones that work best. As with any published strategies, you may find that their backtest performance may not be as high as advertised if you test them on a different time period or a different security, or with different transaction cost assumptions; but the main value of these strategies is that they serve as an inspiration to trigger your own imagination and motivate you to refine them further.

(For e.g., though the book mainly covers long-only strategies, you can easily imagine the accompanying short strategies.)

To be quite honest, this is one of the few books on trading strategies that I actually manage to finish reading from cover to cover.

MATLAB as an Automated Execution System

I just published an article "MATLAB as an Automated Execution System". (It is available to readers of my book and subscribers to my Premium Content website.) It comes with example MATLAB codes executing a simple Bollinger-band high-frequency E-mini trading strategy.

As I mentioned before, I now find MATLAB to be a good platform not just for backtesting, but for automated execution as well. Of course, not all brokerages have API's that connect to MATLAB. My example codes are for submitting orders automatically to an Interactive Brokers account.

In general, I find that writing execution programs in MATLAB is a breeze compared to C++, Java or even C#. It takes about 1/5 the development time of a C++ program. Any performance limitations will probably not be due to MATLAB, but to the latency of your brokerage in updating positions and order status.

My pairs trading workshop in London

I will be holding a 2-day, hands-on, pairs trading workshop in London, October 14-15. It will be held in conjunction with the Automated Trading 2009 conference organized by the Technical Analyst magazine. Please see details here.

Seasonal trades in natural gas and gasoline futures

In my book, I mentioned 2 seasonal trades in natural gas and gasoline futures that have been consistently profitable for 14 years. (Mentioned here and here also.) And not only in backtest: I paper-traded them in 2006, and actually traded them in 2007-8, and all 3 years were profitable. How did they fare in this recession year? Quite poorly.

Depending on your exact entry and exit points, the gasoline trade lost about $2,500 per contract of RB. The natural gas trade lost about $7,700 per contract of NG.

You may have heard that natural gas price is at a 6-year low. In fact, we are not seeing any increase in industrial demand for natural gas. Apparently, somebody has forgotten to tell the nation's industrialists that an economic recovery is supposed to be under way.

Will I enter into these seasonal trades again next year? You bet I will.

Fios and EC2

As an algorithmic trader, I am constantly in search of a better physical infrastructure where I can connect via the internet to my execution broker at the highest speed and with the least possibility of outage, and at a reasonable cost.

To that end, I would like to mention Fios, a fiber-optics service from Verizon with download speed of 50 Mpbs, upload speed is 20 Mbps, both faster than your typical T-1 line (1.5 Mbps). Furthermore, it costs only $45/month. Hey, even Paul Krugman has installed it at his home!

(I haven't tried it myself, and would like to hear from those of you who have and see if it is time to say goodbye to T-1.)

And as I have reported earlier, I am also constantly looking for a good cloud computing platform so that I can run more strategies without cluttering my office with computers. Finding one will obviate the need for any big investment in internet connectivity at the office.

To that end, I have been trying out Amazon's EC2 for several months. I use it to run one of our strateiges, and I have to report that my experience is mixed.

Firstly, if you are not an IT person, it does take a lot of time (8 person-hours?) to get set up and running, especially with their securities precautions. The learning curve is steep.

Secondly, and more annoyingly, the instances sometimes fail to start properly, or fail to bundle properly. (Bundling means saving the software configuration for future use.) I am using Windows instances. Maybe those who use Linux instances have better experiences?

Thirdly, and most annoyingly, when a new instance is started, Windows often cannot automatically synchronize its clock with time.windows.com or any other internet clock. As a result, the time is often wrong. Now, this may not be a big deal for usual office work. But when your automated trading strategy depends crucially on the time of the day, it can be quite fatal to your profit. If anyone has experienced a similar problem with Window's clock and know a fix, please let me know!

Despite all these hassles, I am still running strategies on EC2, hoping that once EC2 get past the beta release, things will be better.

The upcoming Black Swan of Black Swans?

In a recent post (hat tip: Russell M.), Tyler Durden at Zero Hedge quoted a quant trader saying that "Anyone who is doing anything sensible right now is either losing money or is out of the market entirely", and that "liquidity deleveraging is approaching (if not already is at) critical levels", and finally the scariest part: "we have crossed into major statistically deviant territory, likely approaching a level that is 6 standard deviation away from the recent norms."

He pointed out that NYSE weekly volume is running about 9% below 52 wk average. But this may not necessarily be the result of deliberate hedge fund deleveraging or increasing risk-aversion by quant traders. From my personal experience, the usual opportunities for mean-reversion have just markedly decreased in the last few months, with much of the cash sitting on the sideline. I believe that quant traders are still ready jump in at any time to provide liquidity should the market demands it. I don't think that the recent market condition portends a 6-sigma event, but if one should occur, it may actually be a great profit opportunity for many short-term mean-reversion traders just as in those past 6-sigma events.

A reader comments on trading using Excel VBA and Factor Model

Thoughtful comments from a reader John S. from the UK on his experience with trading technology and models:

"I have been developing my own personal automatic trading systems using Excel VBA and based on rules I have developed over the years as an active private trader investor using both technical and fundamental data analysis.

One of the key merits in adopting an automatic trading system approach that has helped me is to avoid the temptation for manual interference and thereby improving profitability by maintaining consistency. I have found the challenge of developing a successful system very rewarding from a personal perspective as I recognise that there are many that have tried and failed. However one problem I have encountered is my ongoing desire to regularly modify and improve the system which I have found can become counter productive as there is a real danger that system development becomes an end in itself! I just can't seem to stop tinkering as soon as I come up with a new idea or feature!

One advantage of using Excel VBA that I have found is that it is inherently flexible as it facilitates the processing of data which can be important especially when using fundamental data as part of the system. In this respect I recognise that every trader is trying to build in an edge that will make the system more profitable. I have noticed that many traders seem to only focus on price by trying to seek an edge by looking at special indicators or combination of indicators etc. Combining price data analysis with a Factor Model approach is a challenge which is ideally suited Excel VBA as it can be easily used to process both fundamental and macroeconomic data into a form that can be integrated with price data analysis.

I recognise from your book that Matlab is more powerful than Excel VBA and may be just as flexible in integrating fundamental and macroeconomic data but I just wanted to draw your attention to benefits I have found using Excel VBA which may suit those who like myself are more comfortable in using Excel VBA and are reluctant to change. Other features that can be exploited that I have found helpful when back testing are automatically producing Price Charts that incorporate Entry and Exit points which provides visual reassurance that the system is working as intended as well as generating automatic Word reports recording key output for future reference.

I am sorry if I sound too much like an advert for Microsoft!"

Mean-reversion is getting stronger

As I mentioned in various previous blog posts, (e.g. see here), I believe mean-reversion strategies have been performing very well in the last year. Now here is an article (hat tip: Laurence) that provides concrete analysis to support this hypothesis. In fact, the author points out that most of the mean-reversion in recent years comes from the overnight close-to-open reversal.

A new service for retail investors

Here is a new low-cost service called Alerts4All that offers technical trading signals for retail investors. You can, for example, have an alert sent to you every time a "Double bottom" pattern occurs.

A much more advanced version of the service will be rolled out soon -- I saw a demo today where you can backtest your strategies online, combining different fundamental and/or technical variables as entry or exit signals. They also have some built-in models for you to adapt (e.g. a model based on The Little Book that Beats the Market by Joel Greenblatt.) More interestingly, you can look at other people's trading models and their historical and/or real-time performance.

Matlab or Alphacet it is not, but I think it will be quite useful for many retail traders. It might even be useful to professional traders who want a quick-and-dirty way to test out ideas.

Trader tax proposal will be the death knell for statistical arbitrage

U.S. Congressman Peter DeFazio, introduced H.R. 1068: “Let Wall Street Pay for Wall Street's Bailout Act of 2009”, which aims to impose a 0.25% transaction tax on the “sale and purchase of financial instruments such as stock, options, and futures.

Ladies and gentlemen, 0.25% is 50 basis points round-trip. Few if any statistical arbitrage strategies can survive this transaction tax.

And no, this is not "Wall Street paying for Wall Street's Bailout". This is small-time independent trader-entrepreneur like ourselves paying for Wall Street's Bailout.

Furthermore, this tax will drain the US market of liquidity, and ultimately will cost every investor, long or short term, a far greater transaction cost than 0.25%.

If you want to stop this insanity, please sign this online petition.

Finding seasonal spreads

I am pleased to introduce guest blogger Paul Teetor for today's article.

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Finding Seasonal Spreads

By Paul Teetor

A seasonal spread is a spread which follows a regular pattern from year to year, such as generally falling in the Spring or generally rising in October. To find seasonal spreads, I've been using ANOVA, which stands for analysis of variance. ANOVA is a well-established statistical technique which, given several groups of data, will determine if the groups have different averages. Importantly, it determines if the differences are statistically significantly.

I start with several years of spread data, compute the spread's daily changes, then group the daily changes by their calendar month, giving me 12 groups. The ANOVA analysis tells me if the groups (months) have significantly different averages. If so, I know the spread is seasonal since it is consistently up in certain months and consistently down in others.

The beauty is that I can automate the process, scanning my entire database for seasonal spreads. A recent scan identified the spread between crude oil (CL) and gasoline (RB), for example. The initial ANOVA analysis indicated the CL/RB spread is very likely to be seasonal. This bar chart of each month's average daily change demonstrates the seasonality. (Click on the graph to enlarge it.)

The lines show the confidence interval for each month's average. Notice how May and June are definitely "up" months because their confidence interval is entirely positive (above the axis). Likewise, November and December are definitely "down" months. For all other months, we cannot be certain because the confidence interval crosses zero, so the true average change could be either negative or positive. The conclusion: Be long the spread during May and June; be short during November and December.

For more details, please see my on-line paper regarding ANOVA and seasonal spreads.

- Paul Teetor

The limitation of Sharpe ratio

Just as one should not trust VaR completely, one should also beware of high Sharpe ratio strategies. As this Economist article pointed out, a strategy may have a high Sharpe ratio because it has so far been accumulating small gains quite consistently, but it could still be subject to a large loss when black-swan events strike.

Personally, I am more comfortable with strategies that do the opposite: those that seldom generate any returns, but always earn a large profit when financial catastrophes occur.

The peril of VaR

This Quebec pension fund lost some $25 billion due to non-bank asset-backed commercial paper (ABCP). Their Value-at-Risk (VaR) model did not take into account liquidity risk. As usual, the quants got the blame. But can someone tell me a better way to value risk than to run historical simulations? Can we really build risk models on disasters we have not seen before and cannot imagine will happen?

(Hat tip: Ray)

Kelly formula revisited

Some discussions on Kelly's formula with a reader Steven L:

Q:

"I am more than half way through your book and am stuck at a concept that I can't seem to find an answer in any other forum.

I have read Ralph Vince's "Portfolio Management Formulas," which uses Kelly's formula to calculate an optimal "fraction" of the bankroll to bet on each trial. So a trader can calculate a fraction of his total trading account value to risk on each trade. What I am referring to is the so-called "fixed-fractional" trading. There exists an optimal fraction that will maximize the geometric growth rate of the trading equity, in theory anyway.

However, in the money management chapter of your book, you use Kelly's formula to derive an optimal "leverage." This seems to be in conflict with what I learned from Ralph Vince, since leverage is usually great than unity and fraction is usually less than unity. I can't seem to make a connection between these two concepts. I have also seen the same optimal leverage formula in Lars Kestner's Quantitative Trading Strategies and asked the same question on some forums, but no one was able to give me a clear satisfactory answer. It would be greatly helpful if you can help me sort out the confusion."

A:

I don't have Ralph Vince's book with me, but if I recall correctly, his formulation is based on discrete bets (win or lose, no intermediate outcome), much like horse-betting or in a casino game. My approach, or rather, Professor Ed Thorp's approach, is based on continuous finance, assuming that every second, your P&L could fluctuatate in a Gaussian ("log-normal") fashion.

For discrete bets where you could have lost all of your equity in one bet, surely one should only bet a fraction of your total equity. For continuous finance, there is very little chance one could have lost all of the equity in one time period, due to the assumed log-normal distribution of prices. Hence one should bet more than your equity, i.e. use leverage.

Q:

In example 6.2 in your book, the portfolio consists of only long SPY, which has little chance of going to zero. So I can see how it is reasonable that you use the continuous finance approach and apply the optimal leverage to scale up the return.

But let's assume that the portfolio consists of a single strategy that buys options. Suppose this strategy will lose most of the time due to time decay but will make profit once in a while due to black-swan events. I don't think it's a good idea to bet the entire portfolio equity on each trade for this strategy. Can you still apply the continuous finance approach in this case, since in reality trading is like making discreet bets? Should we expect the mean and variance of this strategy automatically result in an Optimal Leverage that is less than one? So that we actually need to risk a fraction of the account equity per trade?

A:

The formula I depicted in the book is valid only if the P&L distributions are Gaussian. If one expects a fat-tailed distribution due to black-swan events, a different mathematical model needs to be used, though it can still be within the continuous finance framework. However, for simplicity's sake, if the distribution looks multinomial (e.g. high probability of "Win a lot" v "Lose a lot"), then you may model it with fractional betting just like a casino game.

Algorithmic Trading Technology Update

Lately a number of new (at least to me) technologies useful to the algorithmic trader came to my attention:

1) Matlab2IB API

I said in my book that it is difficult to use Matlab as an execution platform. As Max has pointed out, this is no longer true. This inexpensive API connects Matlab to your Interactive Brokers' account. It allows you to retrieve historical data, get real-time quotes, and send orders. In other words, all the basic functions you need to create your own execution engine.

2) R

Many people (hat tip: Steve H.) know that R is an open-source (i.e. free) alternative to Matlab. I find that there is also an API that connects R to Interactive Brokers, though I have not tried it myself.

3) Trade Ideas

Trade Ideas (hat tip: Russell M.) is a complete automated trading platform that provides connections to different brokerages (scottrade, IB, TD Ameritrade, etc.)

4) Amazon EC2 cloud computing platform

Running out of PC's to run your myriad strategies? Try Amazon's EC2 cloud computing platform. For a modest hourly fee, you get access to an instance of either Linux or Windows environment, and you can add as many instances as you want. The connection speed is supposed to be at least 10x T-1 line, well-suited to high frequency traders . Here is some other performance benchmarks.

Hedge funds move to "easy-to-understand liquid strategies"

See this interesting article (registration required) on FT on the state of the hedge fund industry. Paul Tudor Jones, Citadel, and Fortress Investment Group are all said to be moving to "easy-to-understand liquid strategies", otherwise known as "statistical arbitrage".

(By the way, I have been urging traders to do just that in my book.)

How is the job market for quants these days?

Felix Salmon claimed in this post (hat tip: J. Rigg) that the quant job market is alive and well. However, I haven't heard much from the usually diligent headhunters in the last few months, which doesn't bode well. Maybe some of our readers can comment on the current state of the quant job market?

In that same post, Felix wondered whether to incorporate the extraordinary period of 2008 as part of backtesting data. Actually, I don't see much of a problem here -- of course one should include 2008. The only reason a trading model would have performed poorly in 2008, as opposed to 2006, 2007 or 2009, would be that its parameters are fitted too tightly to historical data. If you try out some parameterless trading models like I advocated, 2008 is not that unusual except for its higher volatility.

The enduring profitability of mean-reversion strategies

Some readers have doubts about my assertion that mean-reversal models continue to be very profitable during this whole year of financial and economic disasters. So I backtested the mean-reversion strategy in Example 3.8 of my book with the most recent one-year SP1500 data. Without transaction cost, the Sharpe ratio is 4.8. Even after subtracting 10 b.p. round-trip transaction cost, it is still at 3.5.

Since the strategy was constructed over a year ago while I was writing the book, this most recent backtest is done on unseen data, with absolutely no look-ahead bias!

Josh Brolin on day trading

Actor Josh Brolin ("Milk", "W", "No Country for Old Man") said on Charlie Rose that his trading portfolio had a 57% return this year. His strategy is based on entering on reversal from a trend. Sounds pretty reasonable to me: maybe some of our readers can backtest this.

Below is the full interview, beginning with Sean Penn, then goes on to Gus Van Sant, then finally Josh Brolin mentioned his day-trading at the very end of the 1 hour show.



Interested? He is starting a multi-million dollar hedge fund to manage your money.

My book on Quantitative Trading is published

My book on Quantitative Trading has been published and is now available from Amazon.com. Many thanks to all of you for your ideas, comments, and support!

Some riskless profit, and why it exists

Numerous commentators have pointed out the enormous yield spread between agencies debt (Fannie/Freddie) and US Treasuries.

Here are some links kindly provided by a reader: 10 yr Fannie/Treasury, 5 yr Fannie/Treasury, 10 yr Freddie/Treasury, and 5 yr Freddie/Treasury.

Currently their spreads are above 150 bp. Since the US government has nationalized Fannie and Freddie, this 150 bp is a riskless profit. As the blog Accrued Interest has pointed out, one reason this riskless profit exists is hedge fund deleveraging: nobody has the risk appetite to arbitrage this spread at a meaningful scale.

Brad Setser, a blogger at the Council of Foreign Relations, suggests that the Chinese government, who does have a lot of cash to benefit from this high yield, should go ahead and buy up these agencies debt. However, if you read the Chinese blogs and online comments, there is enormous internal pressure for the government to spend some of this money on infrastructure projects, social security, health care, etc., so I doubt that the Chinese government will have stabilizing the US mortgage market at the top of its agenda. As a result, arbitrageurs out there should have no fear that this opportunity will disappear any time soon.

How does the financial crisis affect quantitative trading?

Now that we are reasonably sure the financial world is not coming to an end yet, it is reasonable to ask how quantitative strategies have been faring under this extreme market stress. Despite reports of massive hedge fund deleveraging and negative YTD returns, I believe quantitative strategies, especially statistical arbitrage, have survived the period relatively unscathed. But here are a few of my thoughts:

1) The paltry 10% annual returns that a mediocre statarb fund can deliver is suddenly looking pretty good when the risk-free rate is under 1% and a prolonged bear market is on the horizon.

2) Mean-reversal models continue to beat momentum models in this crisis environment, as in previous crisis environments. This is not surprising because market returns have completely dominated specific returns, and of course market returns have been highly mean-reverting lately.

3) Models involving shorts are under some tumoil because of regime-change induced by new and ever-changing short-sale regulations. (For a while, I even have difficulties locating SPY for hedging purposes!)

4) Models are generally trained on data with far lower volatility than is recently realized. (Even incorporting VIX in a model does not guarantee that it can match realized volatility any better.)
As a result, P&L's fluctuations are also much higher than usual, which induces deleveraging as a risk-management measure, which drains liquidity from the market, which in turn leads to still higher volatility. The usual viscious cycle.

5) Political risks in an election year have further reduced leverage and increased volatility. What if there is an assassination? What if the wrong party got elected? What if the paper-trailess electronic voting machines cause another dispute for a month? The nightmares will continue at least until the morning of Nov 5.

6) Normally, lack of liquidity in the market is good for statarb models since they profit from renting out temporary liquidity. However, this profitability assumes that there are buyers of last resort for the market: the long-term investors, the mutual funds, Warren Buffet, etc. When they are absent, statarb investors can be left holding the bag. Fortunately, Warren Buffet & Co. has indeed stepped in and we statarb traders can breathe a sigh of relief.

7) I have been paying particular attention to 3 websites since the crisis began in order to judge whether I should return to my normal leverage: the Ted spread (I am waiting for it to return to below 2), the Calculated Risk blog, and Paul Krugman's blog. This crisis is caused by panic in the credit market, so we should look for credit market returning to normal before declaring victory. The VIX? Not so much because I believe it is backward-looking in this environment.

8) Watching Fannie, Freddie, Lehman, AIG, WaMu, Wachovia, Iceland, and the initial bailout bill failed feels like reading Chapter 8 of Harry Potter and the Deathly Hallows: "The Ministry has fallen. Scrimgeour is dead. They are coming." The Dark Lord is taking over our economy.

Webinar on algorithmic trading system

Recently I participated in a webinar on using an algorithmic trading system called Alphacet Discovery. A link to the webinar can be found here. Some previous research using this system was described here.

Index change strategy

Some years ago, I traded a simple index change strategy: buying stocks to be added to the SP500 index at the market open right after the index change announcement and exiting the position at the close, and similarly shorting stocks to be deleted. The results were mediocre at best.

However, new research by University of Edinburgh Business School suggests that a similar strategy works well for FTSE350 stocks (Hat tip to J. Rigg for the link). The trick is to predict which stocks are to be added or deleted 30 days before the announcement ("review date"), buy/sell the stocks, and close out the positions just before the review date.

Since the criteria for inclusion in the FTSE index is well-defined (and primarily based on market capitalization), it should not be hard for the interested traders to make their own predictions and profit from this rebalancing.

Behavioral finance we can all use

In their new book "Nudge: Improving Decisions About Health, Wealth and Happiness", U of Chicago economist Richard Thaler (of behavioral finance fame) and Harvard law professor Cass Sunstein gave a few pieces of personal finance advice, one of which coincided with my point in a previous post: buy insurance with the largest deductible available. The others are: don't invest much in your employer's stock, don't pay points on mortages, and don't pay for extended warranties. The book is reviewed in the NYT Book Review.

Predicting SP500 futures using investor sentiment

Ronald Domingues, an economics graduate student, has done an interesting study of how well a group of qualified investors with superior skills can predict the movement of the SP500 index. This group of qualified investors are selected by their track records of making correct predictions, and as a result of submitting their predictions going forward, they are eligible to be notified of the average predictions of other qualified investors, thus enabling them to make a better informed investment decision.

In other words, the elite will benefit from the collective wisdom of other elites -- sort of like the real world, isn't it?

How well does it work in practice? Well, they correctly predicted whether SP500 index will go up, down, or flat, a whopping 65.2% of the time. The details can be found on his website, where you can also sign up to see if you can join the elite.

More on parameterless trading model

I have written before that my ideal trading model is one that has no parameters, and what ways there are to accomplish this. Actually, I forgot to mention that a trading strategy proposed by Dr. Andrew Lo discussed previously is in fact parameterless, and the technique is so general that it can be applied to any mean-reverting strategy.

The technique is simply this: maintain a long (or short) portfolio with capital proportional to the distance between a supposedly mean-reverting measure and its long-term mean value.

For e.g. if you are pair-trading PEP vs KO, and you believe that the spread between PEP and KO is mean-reverting, then this spread is the mean-reverting measure you should employ.

As the spread moves away from its mean, keep buying (or shorting) the spread in equal dollar amount. And as the spread reverts, keep selling (or buying) the spread in the same dollar amount. What this dollar amount should be depends on: a) the total buying power you possess, b) the expected maximum deviation of the spread from its mean, and c) how often you intend to buy/short. Note that point c is not a parameter: it is arbitrary and limited only by transaction costs, technology, and other operational issues. As for the expected maximum deviation, it can be obtained by observing the history of the spread since inception.

This scheme thus obviates the need for entry or exit thresholds, and with them, the possibility of data-snooping bias. (You may still want to impose an entry threshold based on transaction cost consideration - but that would not count as a free parameter.)

What are we hedging here?

I wrote a blog article last year on why hedging isn't always better. The more I try to practice what I preached, the more I am convinced that most of the time, we are hedging the wrong risks.

Hedging should not be about reducing volatility in our portfolio. If reducing overall volatility is our goal, we should simply reduce leverage, as I have argued in my previous article. If volatility in a particular industry group is too much for us, (banks? brokerages? energy stocks?), just reduce the capital allocation in that group.

Sure, if hedging does increase your overall Sharpe ratio, go ahead and hedge to your heart's content. Kelly's formula tells us that the higher the Sharpe ratio, the higher the compounded growth rate of your wealth. The problem is, many of us hedge even when doing so do not clearly increase Sharpe ratio. A further problem is that we can achieve this maximum growth rate only if we use the high leverage recommended by Kelly's formula, but this leverage often exceeds what our brokerage would allow us. It is not clear that it is beneficial to waste our buying power on the hedge if we can only operate at sub-optimal leverage.

To me, hedging should be about eliminating the risk of ruin (equity reduced to zero) due to unexpected, catastrophic events. (Many sophisticated hedge fund managers cannot even meet this simple survival criterion, giving lie to the whole notion of "hedge" funds.)

For instance, let's assume that the worst one-day drop in the market index can be 20%. Furthermore, let's assume that you are able to endure a 30% reduction in equity during one trading period. Then you should not be afraid to have a net long exposure of 150% of your equity. In other words, not only should you not hedge, but you should go ahead and leverage your long-only portfolio 1.5 times.

I believe this notion of hedging, or buying insurance, extends to all spheres of our lives. We should avoid ruin, not mere losses. Otherwise, you will be paying too much on the insurance policy over the long term. In other words, max out the deductible on your insurance policy!

Have oil stocks exhausted their run?

Floyd Norris, the chief financial correspondent of The New York Times, suggested in his blog today that we are looking at "The Beginning of the End for High Oil Prices". What is the basis of his optimism? He argued that oil stocks have been lagging oil prices lately, and therefore equity investors must believe that high oil prices are causing demand destruction which will eventually reduce oil prices and oil companies earnings.

I beg to differ.

Look at the long-standing spread relation between an oil stock ETF and an oil commodity ETF, e.g. XLE vs USO, which I have been commenting on and tracking since October 2006. At the moment, this spread is within 1.4 standard deviations of its historical value. See my table here (subscription required). In other words, oil prices and oil stock prices are at approximately their long-time historical average. I would hardly call that suggestive of the beginning of the end.

Futures markets have no effect on spot prices

NYTimes columnist and Princeton economist Paul Krugman has previously (see my link here) argued that the trading of oil futures should have no effect on spot oil prices, contrary to what many politicians and pundits think. Here is his latest elaboration of that argument.

Statistical electoral vote predictor: Update

For readers who have been tracking the Gott and Colley presidential electoral vote prediction, they will notice a sudden switch over to a predicted Obama victory in the last few days. That is because polls from OH, VA and MO are now available -- surprising because the 3 states are not hitherto known for their Democratic leanings.

It seems to me that, after all, the stability of prediction at this early date is quite questionable due to the paucity of state polls, a point already made by Dr. Colley.

Statistical model predicts a McCain victory?

There has been a lot of buzz lately about a simple statistical model proposed by astrophysicists Prof. Gott and Dr. Colley that uses the median polls of each state to predict the November electoral vote. (For our un-American readers, the electoral vote is what determines the outcome of a general election, not the popular vote, in case the nightmarish 2000 election has not already drilled this fact into the world's collective consciousness.)

Dr. Colley has set up a website to track daily such polls to gauge the mood of the states. The authors have tested this method on the 2004 election, as well as numerous sporting events outcomes, and found it to be highly predictive.

Right now, they are betting on a McCain victory.

But there is one caveat that many bloggers have pointed out, and it is the same caveat that I have previously applied to the predictive accuracy of political futures market such as intrade.com. The caveat is this: polls (and futures market) change with time. And at different times, they predict different election outcomes. So for example, at this point (June 2008), the polls predict a McCain victory, while the futures market at intrade.com predicts an Obama victory. Who is right?

The answer is: neither. As Dr. Colley has explained to me, no backtest as far back as the June of an election year has been conducted. (Their research was based on polls from September onwards.) So we do not know if the June polling prediction has any accuracy. Similarly, as I pointed out before, the futures market can swing violently even on Election Day, even in the last hours of an election.

One advantage of the Gott and Colley method though, is that the predictions resulting from median poll statistics are remarkably stable over time. In 2004, there was very little movement in the electoral tally from September through election day. Extrapolating this result, we can be somewhat more confident of their prediction vs. Intrade.com's, even at this early date.

And in any case, I have observed that the political futures markets are highly mean-reverting, implying that the current large 20 points spread between the Obama and McCain futures is destined to decrease in the coming months.

As an arbitrage trader, I have therefore proceeded to short the Obama future.

Pre-order my book from Amazon.com

A reader told me that he can now pre-order my book "Quantitative Trading: How to Build Your Own Algorithmic Trading Business" from Amazon.com. The scheduled publication date is November 24, 2008.

Parameterless trading models

A portfolio manager that I used to work for like to pronounce that his trading models have "no free parameters". As is customary in our secretive industry, he would not elaborate further on his technique.

Lately, I begin to understand what a trading model with no free parameter means. It doesn't mean that it does not contain any lookback period for calculating trends, or thresholds for entry or exit. I think that would be impossible. It just means that all such parameters are dynamically optimized in a moving lookback window. This way, if you ask: "Does the model have a fixed profit cap?", the trader can honestly reply: "No, profit cap is not an input parameter. It is determined by the model itself."

The advantage of a parameterless trading model is that it minimizes the danger of overfitting the model to multiple input parameters. (The so-called "data-snooping bias".) So the backtest performance should be much closer to the actual forward performance.

Now, it is quite computationally challenging to optimize all these parameters just-in-time for your next order, but it is often even more difficult to do that in a backtest, given that a multidimensional optimization need to be performed for each historical bar. As a result, I personally have seldom traded parameterless models, until I get to research my regime-switching model. That model is almost parameterless (I left out a few parameters from optimization because of a lack of time, not because of any technical difficulties).

The reason backtest optimization can now be done within a few minutes is due to my use of Alphacet Discovery's server-based optimization engine. There may be other optimization software out there that performs similar functions efficiently -- I welcome comments from the reader.

Regime switching paper

A preprint version of my Regime Switching and Machine Learning article can be found on my premium content area.

Machine Learning + Regime Switching = Profitability?

My article on a trading strategy based on regime switching and machine learning techniques is now available on Automated Trader magazine (subscription required). The software I used to research this model is Alphacet Discovery, an industrial-strength backtesting, optimization, and execution platform.

Are high oil prices due to hedge fund speculation?

The economist Paul Krugman advances an interesting argument today in the New York Times against the idea that high oil prices are due to hedge fund speculation.

He believes that speculative buying can lead to persistent high prices (which has been the case for the last few years) only if there is physical hoarding. Yet oil inventory level has been normal for this period.

Indeed, I have been trying to find a mean-reverting strategy to trade oil and oil-related assets for some time now. So far, none have outperformed (even on a risk-adjusted basis) just buy-and-hold energy stocks for the long term!

5%: an important number for real estate investors

Equity investors like to check out a company's price/earnings ratio before they invest in its stock. Likewise, real estate investors should do the same before buying a house. The equivalent of price/earnings ratio for real estate is the price/rent ratio, or inversely, the rent/price yield.

What is a reasonable rent/price yield for US residential real estate? According to Morris Davis of the University of Wisconsin-Madison, and Andreas Lehnert and Robert Martin of the Fed, the long-term average is 5% (i.e. the annual rent of a house should be about 5% of its market value). As the Economist magazine has reported, at the height of the US housing boom, this figure dropped to as low as 3.5%.

Currently, this ratio is at about 4.3%, which implies that average US housing price has to drop another 14% in order to return to its historical fair value.

Can quantitative traders profit from this prediction? Well, we can always short the S&P/Case-Shiller Home Price Indices futures at the Chicago Mercantile Exchange.

A combination momentum and mean reversal model based on earnings annoucements

Mark Hulbert of the New York Times just discussed 2 momentum strategies investigated by professors David Aboody, Brett Trueman and Reuven Lehavy.

Strategy A: pick stocks in the top percentile of 12-month returns. Buy them (individually) 5 days before their earnings announcements and sell them just before the announcement.

Strategy B: pick stocks in the top percentile of 12-month returns. Buy them (individually) 5 days immediately after their earnings announcements and hold them for 5 days.

Strategy A is very profitable: the annualized excess return is 47% before costs. (To be taken with a grain of salt due to the large transaction costs associated with trading momentum strategies, especially if small-cap stocks are involved.) Strategy B is very unprofitable: the annualized excess return is -43% before costs.

So what are the ways we can make best use of this research?

Naturally, instead of buying the top percentile after the earnings announcements, we should have shorted the stocks, thus making Strategy B a reversal strategy instead.

Furthermore, what about the bottom percentile of stocks? Should we have shorted them prior to the announcements, and bought them after the announcements? If so, we would have a very nice dollar-strategy for you statistical arbitrageurs out there!