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Developing High-Frequency Equities Trading Models

Savion Itzhaki
B.Sc. Electrical Engineering /B.A. Physics

Leandro Rafael Infantino
B.Sc. Industrial Engineering
Instituto Technologico De Buenos Aires
(ITBA), 2004

Technion - Israel Institute of

Technology, 2006






N0 8 2010

June, 2010


2010 Leandro R. Infantino and Savion Itzhaki. All rights reserved.


ereafter created.

Signature of Author:



Leandro R. Infantino
MIT Sloan School of Management
May 7, 2010

Signature of Author:
Savion Itzhaki
MIT Sloan School of Management
May 7, 2010
Certified by:

Roy E. Welsch
Professor of Statistics and Management
Science and Engineering Systems
Thesis Advisor
Accepted by:

Debbie Berechman
Executive Director, MBA Program
MIT Sloan School of Management

Developing High-Frequency Equities Trading Models

Leandro Rafael Infantino and Savion Itzhaki

Submitted to the MIT Sloan School of Management

on May 7, 2010 in Partial Fulfillment of the
Requirements for the Degree of Master of Business Administration
The purpose of this paper is to show evidence that there are opportunities to generate alpha in
the high frequency environment of the US equity market, using Principal Component Analysis
(PCA hereafter) as a basis for short term valuation and market movements prediction. The time
frame of trades and holding periods we are analyzing oscillate between one second to as high as
5 minutes approximately. We particularly believe that this time space offers opportunities to
generate alpha, given that most of the known quantitative trading strategies are implemented in
two different types of time frames: either on the statistical arbitrage typical type of time frames
(with valuation horizons and trading periods in the order of days or weeks to maybe even
months), or in the purely high frequency environment (with time frames on the order of the
milliseconds). On the latter strategies, there is really not much intention to realize equity
valuations, but rather to benefit from high frequency market making, which involves not only
seeking to earn profit from receiving the bid/ask spread, but also from the transaction rebates
offered by the numerous exchanges to those who provide liquidity. We believe that there are
more opportunities to capture existing inefficiencies in this arena, and we show how with very
simple mathematical and predictive tools, those inefficiencies can be identified and potentially
exploited to generate excess returns. The paper describes our underlying intuition about the
model we use, which is based on the results of short term PCA's on equity returns, and shows
how these results can predict short term future cumulative returns. We randomly selected 50 of
the most liquid equities in the S&P 500 index to test our results.

Thesis Supervisor: Roy E. Welsch

Title: Professor of Statistics and Management Science and Engineering Systems

We would like to thank all of those who helped throughout the thesis write-up. We would like to
start by thanking Prof. Roy E. Welsch for being our advisor in this thesis, for all of his guidance
and support, and for presenting us with this unique opportunity to work with him.
We also thank Prof. Jun Pan, Prof. Leonid Kogan, and Mark T. Mueller of MIT for helpful
comments and discussion throughout the process.
Finally, our special thanks goes to Neil Yelsey from QCap LLC. This thesis would not have
been possible without the Neil great devotion to this process. Neil has shared with us a great
amount of time and resources for this thesis and has been a real mentor.




AVAILABLE SOLUTIONS.. .ARE THERE ANY? ...............................































4 .1





4 .3



4 .4






1 Introduction
1.1 Description of the problem
The problem we want to solve is a long lived dilemma that traders, asset managers, insurance
companies, banks, private investors and even any human being would be more than happy to
solve, because a deterministic closed form type of solution would simply mean infinite wealth to
whomever discovers it. The problem we want to solve is twofold:
(i) A short term security valuation problem.
(ii) A regime identification problem, to understand if the market will act either in line, or
opposite of our valuation.
In particular, we want to build a predictive model to estimate future returns on US equities on a
high frequency environment, with approximate holding periods on the order of seconds to
minutes, to exploit a quantitative signal driven trading strategy. We are more than aware of the
challenge we face, and before trying to describe how we plan to address it, we want to ensure we
completely understand what type of problem and challenge we are dealing with.
We firmly believe that the recent paper written by Mark Mueller and Andrew Lo
("WARNING: Physics Envy May Be Hazardous To Your Wealth!" - March 2010) describes
very clearly the dilemma on how to understand the spectrum of challenges involved in solving
problems in finance and economics. They demonstrate that the systematic quantitative approach
(typical of hard core sciences such as physics) used to solve finance and economics problems
over time have led to significant advances. However, completely relying on it can lead to
dangerous pitfalls.
They defined taxonomy of uncertainty in to 6 levels of risk:
(i) "Level 1: Complete Certainty" ... "All past and future states of the system are determined
exactly if initial conditions are fixed and known-nothing is uncertain."
(ii) "Level 2: Risk without Uncertainty" ... "No statistical inference is needed, because we
know the relevant probability distributions exactly, and while we do not know the
outcome of any given wager, we know all the rules and the odds, and no other
information relevant to the outcome is hidden."
(iii)"Level 3: Fully Reducible Uncertainty" ... "situations in which randomness can be
rendered arbitrarily close to Level-2 uncertainty with sufficiently large amounts of data
using the tools of statistical analysis."

(iv) "Level 4: Partially Reducible Uncertainty" ... "situations in which there is a limit to what
we can deduce about the underlying phenomena the data. Examples include datagenerating processes that exhibit: (1) stochastic or time-varying parameters that vary too
and unknowable conditioning information."
(v) "Level 5: Irreducible Uncertainty" ... "This type of uncertainty is the domain of
philosophers and religious leaders, who focus on not only the unknown, but [also] the


Zen Uncertainty" ... "Attempts to understand uncertainty, are mere illusions;

there is only suffering."

The taxonomy is better illustrated by applying it to two problems: one from physics, the
harmonic oscillator, and one from Finance, an equity market-neutral mean-reversion trading
strategy. This simple illustration shows very clearly that when we introduce complexity to a
physics problem and take it to a higher level of uncertainty, solutions become non trivial and
extremely difficult. Analogously, when applying the same concepts on a very basic finance
problem, where complexities do not have to be added artificially but simply exist in its nature,
the problem shows the same level of challenges if tried to solve by a concise purely quantitative
"physics" type of solution, as the first oscillator problem when taken to high levels of
uncertainty. This paper shows how the uncertainties we usually deal with in finance problems are
on the higher levels, and therefore demonstrates how solving those problems with quantitative
techniques is extremely challenging. We are very conscious of this challenge, and writing this
paper simply helps us to understand that if we plan to solve a problem in finance through
quantitative methods and expect a high level of precision in the solution, we have extremely high
The good news is that in the environment we are dealing with, we don't care very much
about high precision. To generate alpha in a high frequency environment, using a large number
of securities and trading periods, we don't need to be extremely precise. We only need to be
slightly precise to generate decent alpha. To better illustrate this idea, we want to recall the
concept brought by Grinold and Kahn in their book "Active Portfolio Management". In
particular we want to recall the Fundamental Law of Active Management, which tells us that the

Information Ratio (IR) 1 on a trading strategy, is approximately proportional to the Information

Coefficient (IC) and to the Breadth by the following relationship:
IR = IC VBreadth

where the IC is defined as the coefficient expressing our skill, or the quality of our investment
decisions, and the "breadth" is the number of independent decisions made on a trading strategy
in one year, or also defined as "the number of independent forecasts of exceptional return we
make per year". The IC should be computed as the correlation between the forecasts and the
actual outcomes, by definition.
Let's build a numerical example to identify the advantage of operating in the high frequency
environment. Let's assume that a certain "star" portfolio manager manages a portfolio of the 500
stocks on the S&P 500. This seems like a reasonable number in order to be able to follow
responsibly all the fundamentals driving the prices of all the stocks, with the assistance of his
group of analysts, along the time. Let's also assume that based on his analyses he makes around
350 independent decisions every year on his portfolio. This means that he trades 350 times in his
portfolio over a year, representing a bit more than one trade per day, either for rebalancing,

buying, selling or shorting his stocks. For somebody aiming at a holding period of 2 to 5 years
per stock, this number of stocks and the number of independent decisions seem reasonable.
Translating this into Grinold's and Kahn's concept, this portfolio manager's strategy has a
Breadth of 350. Assuming he has an IC = 0.05 (which represents a very high skill and quality of
investment decisions), we expect him to obtain an Information Ratio of 0.93. This is a huge
Information Ratio, representative of a traditional low frequency portfolio manager who has such
an impressive IC as this one; remember this is the "star" portfolio manager. Now imagine a
simple high frequency trader, who works by himself and designs a quantitative trading strategy
on 3000 of the most liquid stocks in the US market. His strategy, just like ours, updates a trading
signal every second on the 3000 stocks. There are 23,401 seconds in a trading day, and
5,873,651 on a year. Assuming the trading strategy has average holding periods of 100 seconds
per stock, each stock trades around 234 times per day. Considering the 3000 universe of stocks
and 251 trading days per year, the Breadth is equal to 176,202,000. To achieve an Information
IR is defined as the ratio of residual return to residual risk. The residual return is the excess returns over a
benchmark (e.g. S&P 500 index), and the residual risk is the standard deviation of that excess return. It's a similar
concept to the Sharpe Ratio (where the benchmark is the risk free rate), but from a strict asset management
perspective where it is more common to analyze everything from a "benchmark" perspective.

Ratio similar to the "star" portfolio manager, this single-member team high frequency trader,
only needs to achieve an IC lower than 0.00008, which means he can be 13,000 times less
precise in his estimations than his low frequency peer! This is impressive! This reality has
encouraged us to try to attack the problem we have been discussing, given that the high
frequency environment allows us to be much less precise and still generate very attractive excess
returns. We totally agree and understand the magnitude of the challenges that Mueller and Lo
presented in their paper, so we tried to play in a world where the lack of possibilities to build
precise quantitative models was not a restriction to generate decent alpha; we wanted to play in a
world where "Physics Envy" was not so "Hazardous to our Wealth".
One important feature of the problem we want to solve is to try to predict opportunities that
arise from dislocations of a stock from its fair value, and trade on the expectation that it will
mean-revert 2. Knowing that most of the high frequency traders operate on the range of the
milliseconds and mainly try to predict movements in prices on liquid stocks just to be able to
place the right bids and asks and get rewarded with exchange rebates and bid ask spreads, we can
only assume that they are not very much focused on predicting a fair value of the stocks they are
trading, but rather try to earn profits by providing liquidity3 . This fact motivates us to think that
if any mispricing arises in the high frequency environment, the "ultra high frequency" 4 traders
are probably not going to arbitrage it away, given their holding periods are shorter, and given
that it is not in their interest to obtain profits by valuation. Let's now consider the effect
identified by the working paper done by Jakub Jurek and Halla Yang on Dynamic Portfolio
Selection on Arbitrage based on studying the mean reverting arbitrage opportunities on equity
pairs. They show how divergences after a certain threshold of mispricings do not precipitate
further allocations to arbitrage strategies, but rather reduce the allocation on these arbitrage
opportunities due to intertemporal hedging demands. This tells us in some way that when
2 We only expect this mean-reversion under conditions where momentum signal is not strong enough to cancel

the strength of this mispricing. We address in section 3.1 how we take into consideration a regime shift in the
environment that we are analyzing as an aggregate, which in return affects the significance or validity of the mean
reversion signal we observe and base our trades on.
3 A big issue that arises when observing the high frequency trading world is that the information that is
available in the exchanges regarding liquidity and depth of book are highly disturbing. Many quotes may have
hidden volumes in their bid/ask quotes. So for instance, if an investor looks to by stock A and sees an ask price of
$10 for the amount of 100 shares, it could most possibly be that there is a much larger quantity hiding behind this
quote of 10000 shares or even more. Not only can a trader hide his volume of the trade but also hide the quote all
together. This means that the information available is at times misleading and therefore high frequency traders tend
to test the market and "game" other traders in order to get a better picture of the real information, and make their
profits based on their ability to interpret this information. One may say it almost as if they are playing poker.
4 These are traders that have latencies below one millisecond from the moment they receive a signal from the
market till the time they execute the trade. This arena is mostly driven by technology.

mispricings are large enough to further break through this mispricing threshold, arbitrage
opportunities grow exponentially because the allocations to this strategies tend to diminish, even
when the dislocations grow. If we try to translate this concept just intuitively to the high
frequency world, we would analogously see that if some traders who are playing in the
millisecond environment face big price dislocations in the stocks they are trading, these
dislocations are very likely to be partially exploited in the ultra high frequency world because of
the higher trading frequency of these players. The part of the dislocation that was not "picked
up" by the ultra high frequency traders would then become available for lower frequency
players, who in turn may benefit from a further dislocation beyond the threshold. This will
additionally enhance a reduction in any possible existing allocations to the arbitrage (assuming
the effect described on the paper can be translated to the high frequency environment.) As Jurek
and Yang put it: "Consequently, risk-averse arbitrageurs (y > 1) who face infrequent
performance evaluation... "(in our analogy us: high frequency second to minute players) "...are

redicted to be more aggressive in trading against the mispricing than equally risk-averse
arbitrageurs who face more frequent performance evaluation." (In our analogy: ultra high
frequency traders.) Given there are not many players in the environment we chose to trade, and
that most players fall in the millisecond environment, we conclude that any possible arbitrageurs
in this higher frequency environment are likely to become less aggressive to opportunities given
they face "more frequent performance evaluations" in their algorithms. In addition, if these
opportunities appear in our second to minute environment, they may do in an exponentially
higher magnitude due to the allocation reduction effect. This simple analogy motivates us to
think there are more potential reasons to operate on the trading frequencies we chose.

1.2 Available Solutions... are there any?

Our plan was to explore available solutions to the problem we want to solve, and only then build
on them our proposed solution. Not surprisingly we didn't find much about it. In particular we
didn't find anything. So our further question was: are there no available solutions to our
problem? The answer is yes. There are. But as every single trading strategy on the financial
industry, they are kept under strict secret as part of proprietary information of those who exploit
For example, the results of the Medallion Fund of Renaissance Technologies LLC show
some clear evidence that there are people who have been successful in building decent solutions

to predictive problems in the high frequency environment5 : 35% average annual returns since
1982. These numbers are after fees, and in particular, Renaissance charges management fees of
5% and a profit participation of 36%, while all hedge funds operate on the 2%-20% fee structure.
There is no other fund in the history of financial markets that has achieved similar results. What
would we give for Mr. James Simons (Renaissance founder) to publish a paper with at least a
few of his trading strategies? However, this hasn't happened yet, and we predict with very high
confidence it will not happen any time soon.
Discouraged by the proprietary nature of high frequency trading strategies, we explored other
universes of lower frequencies, where we could find potentially applicable solutions or concepts,
that could eventually be translated to our universe of interest.
The first most basic and very illustrative example we want to use, is that used by the paper
published by Khandani and Lo in 2007 - "What happened to the quants?" - to analyze the quant
melt down of August 2007. In an attempt to explain what nobody could, which was to identify
what caused the sudden shock and the three-day simultaneous melt down of all the quantitative
driven quant shops in Wall Street, Khandani and Lo built a basic market neutral equity trading
strategy, based on the most basic concept of statistical arbitrage. The strategy consists of "an
positions consist of "losers" (underperforming stocks, relative to some market average) and the
short positions consist of "winners" (outperforming stocks, relative to the same market average).
Specifically, if wit is the portfolio weight of security (i) at date (t), then"


N (Rit-k





Note that this strategy positions a weight in each stock proportional to the dislocation of its
return to the market threshold, and that the aggregated stock weight adds to zero (here the name
of market neutral). This simple trading strategy implemented with k=1 day (which means that the
signal and rebalancing occurs every day, and also the "winners" vs. "losers" are evaluated daily)
delivers average Sharpe Ratios considering a null risk free rate (which in these days is nothing
too unrealistic) and avoiding transaction costs, starting at 53.87 in 1995 and ending in 2.79 in

5 The Medallion Fund's performance given here is not attributed solely to the high frequency strategies it uses
but also to other strategies and in other products as well. However, these numbers just come to illustrate why there is
a strong motive in this industry not to share information on trading strategies.

2007. Although some of the stocks tested in this paper are those with highest illiquidity and
coincidentally where the greatest Sharpe Ratios come from, and therefore bias the returns to an
exaggerated upside, there are still very encouraging figures to believe on the power of statistical
arbitrage and mean-reversion trading strategies. Moreover, on the same paper, Khandani and Lo
show how simulated similar strategies on higher trading frequencies from 60 minutes to 5
minutes increase the cumulative returns in less than three months to 110% and 430%
What we conclude from this paper is that convergence of returns to a fair measure seems
plausible and adequate, and even more as the trading frequency increases.
One very important assumption of this basic strategy that we will certainly avoid when
constructing our solution7 is that it assumes that every security has a CAPM Beta close to one, or
a covariance similar to the covariance of the wide market with itself. This is not mentioned nor
intended, but it's a natural consequence of such a strategy given it expects mean reversion to a
market threshold estimated without considering any kind of significant covariance differences,
which we know exist.
Let's assume the following situation to illustrate our observation. Two of the stocks in the
market are an oil producer and an airline. Let's also assume that these two stocks are inversely
correlated, which is intuitively reasonable given the opposite exposure of both companies to the
price of crude oil. Let's assume the price of oil goes up, and the broad market of equities has
excellent positive returns too, in one day. If the broad market had a remarkable positive shock
(more than that of crude oil) due to some macroeconomic positive announcement, we expect the
oil company to rise in price more than the airline, given that the latter is negatively affected by
the oil price shock, while the previous benefitted. In other words, we know that the covariance of
these stocks and the market are different, and therefore they have different CAPM Betas.
Nevertheless, given the broad market average return threshold increased less than the oil
company's and more than the airline's, the model will send us a signal to "long" the airline and
"short" the oil company, ignoring completely that these Betas might not be close to one; and
missing the fact that given they are significantly different due to their inverse-correlation- one at
least has to be substantially different to one. We say the model ignores this because if the oil

important reference that also contributes to our motivation to explore mean reversion, as a previous
cornerstone of this paper, is the paper written by Lo and MacKinley in 1990 where they show how the meanreversion paradigm is typically associated with market over reaction and may be exploited to generate alpha.
7 This strategy has obviously been shown by Khandani and Lo only for illustrative purposes but we plan to take

from it the valuable concepts and address those assumptions that can potentially affect results significantly on a real
trading environment.

price continues to rise on the next day, the stocks will diverge further booking losses on the
P&L, regardless what happens to the broad stock market. The model on the contrary, expects the
market "strength" to be greater than anything else on the next period, bringing both stocks closer
to the market's intrinsic threshold return. This assumes predominance of the market "strength" to
drive the prices of both securities, and thus assumes a "Beta close to one", an assumption that we
have refuted in this given example. The strategy assumes that each stock's adequate threshold is
solely the broad equity market. If on the contrary, we could in some way consider the systematic
effect of oil prices in these stocks, and understand the stocks' different covariances with the
market, we would not expect them to mean revert to a common market threshold, but rather to a
threshold characteristic by their own components of risk. Mentioning this problem seems a little
bit obvious and at the same time naive to discover, as it represents the basic extremely ambitious
challenge every trader wants to address. We didn't discover anything the reader might not be
aware of, and we didn't mean to. We just wanted to identify this particular flaw, and will address
it in section 2.2, where we will comment on how we believe that Principal Component Analysis
can help us take care of these issues from a strictly mathematical and statistical point of view.
Another solution to predicting equity returns with quantitative methods we want to recall is
the approach given by Avellaneda and Lee (2010) in their paper. In particular, we want to recall
their definition of statistical arbitrage, because although we are not really "stat arb" players in
our strategy but rather end up falling in the "high frequency" 8 definition, we still believe that the
concepts of statistical arbitrage are frequently the source of potential alpha and valuation
dislocations we want to exploit. The definition in their paper is the following: "The term
statistical arbitrage encompasses a variety of strategies and investment programs. Their common
features are:
(i) trading signals are systematic, or rules-based, as opposed to driven by fundamentals
(ii) the trading book is market-neutral, in the sense that it has zero beta with the market
(iii)the mechanism for generating excess returns is statistical
The idea is to make many bets with positive expected returns, taking advantage of diversification
across stocks, to produce a low-volatility investment strategy which is uncorrelated with the
market. Holding periods range from a few seconds to days, weeks or even longer."
We plan a strategy that trades within the hour time frame and we close positions daily. This takes us to the
high frequency class, despite our plan to play a different game than most of the players in that arena.

As we can see we can pretty much be identified by their definition as stat arb players, but the
main difference we are going to have, is that we will not restrict ourselves to be market neutral,
and we will allow the strategy to take directional market risk.
The strategy, or "ancient" solution to the problem they wanted to address, was a simple
"pairs" type of trading strategy9 based on the expectation that one stock would track the other,
after controlling adequately for Beta in the following relationship, for stocks P and


In (Pt/Pto) = a(t - t o ) + # In (Qt/Qto) + Xt

In its differential version:


=cadt +fl



where Xt is a mean reverting process.

Avellaneda and Lee (2010) took this concept and built a model where the analysis of
residuals generated signals that were based on relative value pricing of a sector (or cluster, as we
like to call it). They decomposed stock returns to systematic and idiosyncratic components by
using PCA and statistically modeled the idiosyncratic component. Their general decomposition
looks like this:

=P adt + Z;flhFf + dXt


where FU represents the risk factors of the market/cluster under consideration.

The concept of this solution is pretty similar to an important issue that we want to address
(explore mean reverting nature by using PCA) and we will definitely build our ideas of mean
reversion based on a similar behavior. Our framing of the problem will be slightly different,
given we instead want to focus on the systematic components of risk obtained from the PCA and
use them as predictive factors of future returns, by fitting an OLS model. Therefore, establishing

9 Trade expecting the convergence of a pair of stock returns on the same industry or with similar characteristics

mean reverting thresholds will be achieved by a different model. But the very basic ideas are
consequences of this "ancestor" trade as described by Avellaneda and Lee in their paper.

2 The Model
With this initial model, we only aim to solve the first part of our problem which is "short term
security valuation" described in 1.1. We do not expect significant results coming from this
strategy, because it tackles only one part of the probleml".

2.1 The Benefits of Using Log Returns

In this thesis we used log returns to build the trading signal. The use of return as opposed to the
price of the stock is much more intuitive as the return is a normalized measure. For instance,
when assessing cross-sectional volatility of any universe of stocks, it is much simpler to use the
cross-sectional volatility of the returns of the stocks as they are already in a normalized form.
The prices on the other hand are drastically different in magnitude within the defined universe.
Thus in order to define thresholds for various strategies or assessment of regime changes, the
prices need to be standardized before any use can become of them which can introduce other
distortions in the model.
For example, a stock that trades at $0.50 compared to a stock that trades at $500 has very
different characteristics in regards to its liquidity, returns, trade volume, and so forth. By scaling
both stocks to the same price level, we are artificially creating two stocks that may look similar
but trade very differently. While the $0.50 stock can have a 10% return in one second of trade
(which will not be uncommon for stocks of that size), it would be very unlikely to see this kind
of move with a $500 stock.
The use of log Returns vs. real returns was done for several reasons. The main reason is that
log-returns are time additive. So, in order to calculate the return over n periods using real returns
we need to calculate the product of n numbers
(1 + r1 )(1.+ r 2 )


(1 + rn)

If r 1 is defined as follows:


P1 -PO




-o 1+r



10 We will see later in section 3 how introducing a "Regime Switching" analysis attacks the rest of the problem,
not attacked by the basic model analyzed in this section.

Then, the log return is defined as:

log (1 + r,) = log

- =

log(P 1) - log(Po)

Using log returns we can easily compound the returns over n periods as follows:
log(1+r 1 )+ log(1+r 2 )+ -+ log(1+rn) =


+ log P2 + -+ log




log(P1 ) - log(Po) + log(P2 ) - lo9P 1 ) + -+

log(Pn) - log(Pn-1 )

log(Pn) - log(PO)

Thus, compounding returns is as easy as summing up two numbers.

The second reason why we used log-returns was because normality holds when compounding
returns over a larger period. For example, if we assume that r, is normally distributed, than
(1 + ri) is also normal because normality is preserved under addition. For the same reason
(1 + r2 ) is normal. However, (1 + r1 )(1 + r2 ) is not normal anymore since normality is not
preserved under multiplication.
On the other hand, assuming log(1 + ri) is normal, than log(1 + r2 ) is also normal. For n
period returns we can say that the total return is a linear combination of normally distributed
variables, and thus it is also normally distributed.
Another reason for the use of log-returns is that prices are log normally distributed. So,
if log (1 + r1 ) is a random variable with a normal distribution, then


has a log-normal distribution. This is intuitive since prices are non-negative and can rise to
infinity, just as the log normal distribution is skewed to the right. Quoting the book
"Econometrics of Financial Markets' by Campbell, Lo and MacKinlay: " [...] problem that
afflicts normally distributed returns: violation of limited liability. If the conditional distribution
of Pt is normal, then there will always be a positive probability that Pt < 0.". This problem is
solved when taking log returns.

Finally, log returns are similar to real returns. In the high frequency space where we look at
one-second returns, the returns are fairly small and in approximation the log returns equal to the
real returns.
log (1 + r)

- r , r

2.2 The Use of Principal Component Analysis

Principal Component Analysis is a tool of multivariate statistics widely used in engineering and
sciences to explain the main components of the variance of the many datasets involved in the
problems scientists and engineers aim to solve. PCA helps identify the drivers of variance in
chromatography analysis, image processing, medical diagnosis, soil biology, etc. The good news
is that PCA has also been widely used in finance to explain the risk drivers of diverse assets,
given that one of the major variables used to explain the risk in financial assets is the variance of
their time series of returns. For example, the paper on "Statistical Arbitrage in the US Equities
Market" mentioned before (Avellaneda-Lee 2009) shows how one of the factors determining the
trading signals of their model is obtained through a PCA on stock returns. The paper shows how
PCA can play a major role in modeling risk in Finance and be used to build an alpha model. It is
over this concept - that the principal components represent an important role in determining
intrinsic risk and therefore value of securities - that we want to build on.
Instead of measuring the residual component of the stock returns, we use the principal
component for valuation using a simple linear predictive model we will describe in section 2.4.
Although our predictive method is different, and our time frames and holding periods shorter, we
still believe on the power that PCA has to explain intrinsic and fair value of financial assets, and
this concept around PCA where Avellaneda and Lee build on part of their paper, is the
cornerstone and motivation behind our trading model in the high frequency environment.
Another advantage of PCA in the time environment we are dealing with, is that traditional
fundamental factors used to determine valuation of securities for larger holding periods (months
and years) play a minor role in the valuations we are doing, and therefore, we really don't care
about the "identity" of the components of risk; we just want to capture them and ensure we
capture the relevant ones. For example, the Debt to Equity ratio, the Current Ratio, and Interest
Coverage of a company most of the time play a minor role in the price at which a certain security
will trade in the next 30 seconds. There are rather other factors affecting the high frequency
trading environment. For example, the computer driven algorithms implemented by high
frequency hedge funds, by broker dealers to execute their trades efficiently, or by asset managers

rebalancing their long term portfolios, appear to be more significant short term drivers of the
prices of securities. The factors driving these algorithms are very difficult to understand, identify
and follow using a traditional way of thinking and modeling as that of a typical equity analyst.
Using a PCA on a frequent basis seems like a more adequate tool in these cases to identify these
factors from a mathematical perspective and to update the changes in these factors as the
dynamics of the trading environment requires. The PCA will not inform us about the "identity"
of these factors as mentioned before, but we don't mind, simply because we don't care". In the
high frequency environment we only care about capturing the main factors of risk in a form that
will allow us to use them later as a predictive tool. A matrix of selected eigenvectors that can
afterwards be used to project the whole universe of stocks on a set of chosen principal
components seems intuitively more adequate to us, for building short term predictive models on
stock returns.
Another benefit of PCA is that the directions of the principal components are orthogonal by
construction. The fact that the eigenvectors of the variance-covariance matrix of returns of the
group of equities traded are orthogonal, allows us to express this time series with independent
components of risk. Although we cannot identify (at least directly) what type of risk these factors
represent, as we would on a typical low frequency fundamental based trading strategy, we are
able to determine completely that each of these factors is totally independent from one another
and represents a unique portion of uncorrelated risk of our universe. We can identify the weight
of each of these independent risk factors (by looking at their respective eigenvalues) and choose
not only how many of the independent components of risk we want to keep to build our model,
but also the amount of the total risk we are expressing with our choice. In addition, the
orthogonality property of our model will enable us to build a set of orthogonal uncorrelated
explanatory variables, more friendly to fit in a linear model, and to avoid multicollinearity.
Another important feature by PCA mentioned in the Avellaneda-Lee 2009 paper, is that it
has been used in the past in several trading strategies because it has identified risk factors
representing industry sectors without being as biased by market capitalization as other
methodologies. Translated to our world, we don't care much about identifying industry sectors,
but avoiding market capitalization bias is an important feature we would like to keep, and PCA
does that for us.

Even if we were able to identify them, we believe that the drivers for a change in the stock price in the
seconds to minutes time horizon may vary considerably at different points in time. Therefore, running a PCA in real
time will help us choose the highest risk components that are relevant for that period of time we are trading.

Finally, the last benefit of using PCA when building a mean reversion signal, is how it
addresses the flaw of the "CAPM Beta close to one" assumption of the basic equity marketneutral strategy mentioned in section 1.2. The PCA is based on the variance-covariance matrix of
returns' 2 and therefore considers all the covariances of the stocks involved in the analysis. When
solving for principal components, this method will allow us to build a predictive model that will
determine a particular threshold for each stock, based on a different linear combination of
principal components for each of them' 3 . Each of these "predicted returns" or "theoretical
returns" will have embedded in their construction all the covariance relationships of the
universe/cluster of stocks, and at the same time, will be a unique threshold for each security.
Using PCA in this way addresses the basic flaw of having to assume a general threshold for the
entire universe, with a CAPM Beta close to one for every security 14

2.3 The Use of Cumulative Returns

Given that we are dealing in a world where we entirely rely on mathematical models to realize
our predictions, we want to build these models to be the most possibly robust and reliable. This
is a simple and naive conclusion, but a very important one in determining the type of model we
want to build. If we are willing to build a linear model and use OLS to fit some parameters
(betas), we not only have to take care of multicollinearity and heterohedasticity in the
explanatory variables, but also must ensure that the residuals of the model are normally
distributed with mean zero and constant standard deviation. We can take advantage of the
Central Limit Theorem (CLT), and build a model to predict cumulative returns, with the
cumulative returns of the principal components. By using cumulative returns, we are using a
linear combination of stochastic variables to predict another linear combination of stochastic
variables, both normally distributed because of CLT15 . We can easily de-mean these series and
obtain a linear model with normally distributed residuals, with zero mean and constant standard
deviation, very friendly to fit in an Ordinary Least Squares (OLS) model. This is our main reason
for choosing cumulative returns in our model: to become better friends with the math, because
we need it as much as anything else to generate alpha.
12 Although

it can be done by avoiding the construction of the variance-covariance matrix by a Singular Value
Decomposition approach, as described in section 4.4
13 Obtained by a simple OLS model, or by a slightly more sophisticated NLS model, both discussed in sections
2.4 and 4.3 respectively
14 Flaw identified in section 1.2.
"5We assume that the CLT applies here.

2.4 Description of the Model

The strategy is broken up into the following steps:
Define the stock universe: We defined a universe of 50 stocks 1. These stocks were chosen
randomly from the S&P500. We collected the top of the book bid-ask quotes on the tick data for
each trading day during 2009'7.

Company Name



Google Inc
Apple Inc
Goldman Sachs Group InciThe Inc
International Business Machine s Corp
Sears Holdings Corp
Polo Ralph Lauren Corp


Colgate-Palmolive Co
3M Co


Chevron Corp
Boeing Co/The
Exxon Mobil Corp
Johnson & Johnson
Procter & Gamble Co/The



Schlumberger Ltd
Prudential Financial Inc
Biogen Idec Inc
Raytheon Co
Wal-Mart Stores Inc
Coca-Cola Co/The
Target Corp
Hewlett-Packard Co
Genzyme Corp
Pioneer Natural Resources Co
JPMorgan Chase & Co

16 The model should be adapted to a much larger universe of stocks. However, in order to work with a 1000
stock universe or more, clustering techniques are very critical to have. We will discuss this point further in section
17 The top of book is the best bid and ask in the exchange. Since there are many exchanges and top of books in
today's markets, we looked at the aggregated top of book and assumed that there is no issue with liquidity or latency
to the different exchanges in order to simplify the model.


Best Buy Co Inc

Hershey Co/The


Merck & Co Inc

Adobe Systems Inc


Kraft Foods Inc

Verizon Communications Inc
Microsoft Corp
Morgan Stanley


Sunoco Inc
eBay Inc


Cisco Systems Inc

AT&T Inc


Oracle Corp


Intel Corp
Valero Energy Corp


General Electric Co
Bank of America Corp


Pfizer Inc
Yahoo! Inc
Alcoa Inc
Goodyear Tire & Rubber Co/The


Advanced Micro Devices Inc

Motorola Inc



Table 1:

Staples Inc

The 50 companies that were analyzed in the thesis and their tickers

Intervalize dataset: We intervalized the tick data to one-second intervals. This was done by
taking the first quote in each second of the trading day and calculating the mid-price.
Pmid =


+ PBest-ask)/2

The use of mid-prices was convenient for testing the strategy. In the high-frequency world, the
stocks returns as they are calculated as the change in prices that the stocks were traded, reflect
little if at all fundamental movements in the stocks and more bid-ask movements. For instance, if
a stock trades as follows: 20, 20.1, 20, 20.1, these $0.01 movements are not real returns but just
jumps between the bid and the ask. If you are competing on speed and have the technology, you

can probably capitalize on these returns. A model like ours does not compete on speed but on
valuation of the future movements of the stock in larger time frames than these tick returns.
Thus, the use of mid-prices will eliminate to a great extent the bid-ask effect, and help us
concentrate on finding alpha elsewhere. In the example above, looking at the mid-price would
show us the following: 20.05, 20.05, 20.05, 20.05, so this stock's returns will be zero since we
are seeing no movements in the stock price.
Calculate log-returns: As described in section 2.1, the use of log returns is more suitable.
Therefore we calculated the log returns on the one-second mid-prices.
PCA: We used a Principal Component Analysis in order to decrease the number of
dimensions of the dataset without losing much information along the process. The inputs to the
PCA were the log-returns that were described above. The PCA is described below:

as a matrix with the time series of log returns of N stocks, for T seconds.
as a matrix with the average log returns for each of the N stock in the time series in each column:

Xt , Vn E {1,2,..., N}

M =

as the de-meaned matrix of log returns of the time series 8 , where:


time series is the original time series minus the mean across that time series. This is important for
the PCA to work properly. Also, when calculating the variance-covariance matrix, the de-meaning is needed by
definition: C = (X - M)T * (X - M )

as the variance-covariance matrix of Y, where:
YT *y

Get the eigenvectors and eigenvalues of the variance covariance matrix Z by solving:
Ex = Ax


| - AI|

where Ais the eigenvalue corresponding to eigenvector x.

Next, order the eigenvalues from largest to smallest. The largest eigenvalue is the single most
important component of risk in our universe. The more eigenvalues we choose the more risk we
are explaining in our model. Then, take the eigenvectors that correspond to the chosen
eigenvalues and place each vector in a column.

as the feature matrix with columns that are eigenvector of the variance covariance matrix E.
where N is the number of stocks, and k is the number of eigenvectors we chose to build the
feature vector. 19
D E RTxk

as the matrix of "dimensionally reduced returns", or principal components, obtained after

projecting Y on the principal-component plane defined by the highest k eigenvectors:
19 The number of eigenvectors that are used in the model depends on the amount of information need to build
our signal. The more eigenvectors we add the higher the precision of the model on the dataset we used to test it.
However, we are risking over-fitting by using too many eigenvectors. If all eigenvectors are used, the PCA will
return the original data set. In our model we back-tested to show that 4-5 eigenvectors (out of 50) hold over 80% of
the risk in general and thus we used 5 eigenvectors as a fixed number to simplify this part of the model. A more
comprehensive approach can be used by using OLS techniques such as building an autoregressive model where the
optimum number of autoregressive steps are determined with a Maximum Likelihood Estimation plus a penalty for
degrees of freedom type of function (later discussed in section 4.2), to better predict the best number of eigenvectors
should you choose to have it changing over time.

D =

[tpT * YT]T

After doing the principal-component analysis, we remain with a dimensionally reduced matrix of
log returns. These dimensions hold the main components of risk in our universe of stocks.
The next step is to build a prediction model. In Campbell, Lo, and MacKinlay, ("The
Econometrics of Financial Markets," 1997), they used a log-horizon regression of stock returns
on the short term interest rates as follows:

t+1 + -- + Tt+H







where rt is the log return at time t, and y1,t is the 1-month treasury bill yield at time t.
This regression predicts the accumulated future H-period log returns
rt+1 + - + rt+H

by looking at an indicator that relates to the accumulated historical H-period 1-month treasury
bill yields 20
In our prediction model we did something similar. We calculated the accumulated future Hperiod log returns. Then we ran a regression, estimated by OLS, on the future accumulated log

The results are as follows:

rt+1 +


r't+H =




Yk Dt-i,k

71 t+H,H


The indicator that Campbell, Lo, and MacKinlay used is actually the 1-month treasury-bill yield at time t in
excess of the mean of the historical 12 periods (including time t). However, it's a linear combination of the last 12
period yields, or in approximation a sort of scale done on the accumulated returns.
21 Later we will introduce some additional terms to this regression in order to incorporate regime switching
the prediction model, which we will compare to the current model.


as a matrix that holds the betas from the regressions for all the stocks. Each of the N stocks has k
corresponding betas in the columns of matrix B.
as the matrix of the new time series of the theoretical returns.

such that:


Dt-i , Vt E {1,2, -- ,T}


which is a matrix that holds the accumulated historical H-period dimensionally-reduced returns
given in D.
as our estimation of the accumulated (de-meaned) log-returns on the future H-periods based on
the regression that was done:
S = Dt*B
Each term in the vector S corresponds to a stock. However, in order to use this signal as a
comparison to the actual log-returns that we observe in the market, we need to add back the
original mean of the time series. Thus we will define,
S = S+Mt

as the trading signal that will be used in comparison with the prevailing log-returns in the
market. Our base assumption is that the principal components explain the main risk factors that
should drive the stock's returns in a systematic way, and the residuals as the noise that we would
like to get rid of. By using the dimensionally reduced returns, we were able to screen out much
of the noise that may have temporarily dislocated the stocks from their intended values. On this
basic mean-reversion model that does not consider regime switches, if we see that the last Hperiod accumulated log-returns have been higher than the signal, we assume that the stock is
overvalued and thus place a sell order. If the opposite happens, we put a buy order. This strategy
is basically a mean-reversion strategy, since we are taking the opposite position to what has
happened in the market by assuming mean-reversion to the estimated signal. As we mentioned in
section 1.2, this is based on the same "buy losers, sell winners" concept of the equity market
neutral strategy (Khandani-Lo 2007). We want to remind the reader at this point that this model
is only solving part of our problem. We will address it completely when we include the regime
switching algorithm.

2.5 Simplifying Assumptions

One of our underlying assumptions was that we are able to buy or sell instantaneously at the mid
price. This means that once our signal tells us to buy (sell), we can execute at the same second
interval and at the mid-price. We found evidence that shows that the top of book quotes (best
bid/ask) do not change very much within the second. We also found that with these very liquid
stocks, there are usually several trades on the stock that are done in one second. So the fact that
we take the first quote at each second in the model and calculate the mid-price as our execution
price makes our assumption more realistic.
In addition to that being realistic, executing a mean-reversion strategy should position us as
market-makers. Thus, we are injecting liquidity into the system. This liquidity is a source of
income as we collect rebates plus bid/ask spread on every transaction that was executed based on
a limit order that we provided.
Another assumption we made was that transactions are made at mid-price level. This
assumption is conservative on a mean reversion liquidity providing strategy. Calculating returns
based on mid level of top of book helps mitigate the bid ask effect that is so dominant in the
space of high frequency trading. This is because the stock is not really changing value, in
between seconds. The price changes are mostly because of the market making effect of other
players in this domain.

It is important to note that if the model trades on a "momentum"2 regime, these effects
would be contrarian, and therefore we should analyze and weigh what percentage of the time the
model has been in the "momentum" and in the "mean-reversion" regimes, in order to better
understand the accuracy of the results in Sharpe Ratio, and how they might be affected by real
transaction costs.
In addition to these assumptions, we have to keep in mind that the strategy is purely
theoretical and has never been tested in the real market; it has only been tested on historical data.
We also assumed that we do not face any significant execution issues and that we are able to
execute the trades within the one-second interval where the signal was obtained. As naive as this
may sound, we believe that our strategy is very different than that of most of the high-frequency
players that are competing mainly on technology in higher frequencies, so we shouldn't be
extremely affected by our assumptions.
Finally, we assumed a time loss on computations of one second for every trading period. We
believe this to be extremely conservative.
While taking all these issues into account, it is very difficult to model these real world issues.
Building a likelihood of events could help come to a more accurate model, but it doesn't matter
how we look at it, the best way to know whether the model is working properly is by actually
trading with it.

2.6 Basic Model Results

This basic pure mean-reversion strategy has shown positive accumulated log returns in the first
quarter of year 200923, but has decreased in cumulative returns from the second quarter and all
through the end of 2009 (figure 1). The total accumulative log returns have reached -29.97% in
2009 with a very negative sharp ratio of -1.84 and volatility that resemblances the historical
average volatility of the S&P500 index (table 2).

'Momentum" is known as a trend following strategy, where opposed to mean-reversion, the trader expects the
returns of a security to further diverge from the established threshold, believing that it follows a trend effect driving
the price of the security, rather than the reversion to fair value, as though by mean-reversion strategies.
23 All together there were 251 trading days in year 2009, as seen in
the x-axis of the plot.

Cumulative Log Returns

30% -------- -20% -------




- -

-3 0 % - - - - - - - - - - - - - - - - - - - - - - - - - - -4 0 % -- - - - - - - - - - - - - - - - - - - - - - - - - - -








Figure 1: The plot of the accumulated log returns over a period of 251 trading days in 2009

Table 2:



Sharpe Ratio




Statistics of the strategy's performance across the entire year of 2009

In our process we recorded every single summary statistic per second for the entire year,
including t-stats, betas and Rsq coefficients. Due to the impossibility to bring billions of data
points altogether, we chose instead to present what we considered very representative examples.
To illustrate the variability of the betas we picked three stocks out of the 50 tested, that
express results we believe are quite representative, and that not vary that much along the
universe from what we present here, and graphed the beta of their first principal component2 4
over the course of a day, Oct-20-2009. We did the same for the t-stats of these betas to see
whether they are statistically significant in order to check the validity of the theoretical returns
that we built based on these betas. In addition, we brought the standard deviation of each of the
five betas over the course of that day. These stocks will be referred to as "stock A", "stock B",
and "stock C".

"The first principal component" means the principal component that holds the highest amount of risk in the
universe of stocks that we defined and in the trading frequency that we chose.

Results for stock A:

Beta of First Principal Component for Stock A

-1E-1 5


--- -- -





-0.4 -0 .6


- - -- - - -








Figure 2: Stock A's first principal component's beta over the course of the day

Table 3:







The standard deviation of the 5 betas of the five principal components of stock A

T-stats for Stock A



-- - -------

--- -

2 - -- -----

- --

- -



-2 ------








- 16:00

Figure 3: T-stats for the first principal component's beta for stock A over the course of the
t-statsl vol

t-stats2 vol



t-stats3 vol

t-stats4 vol

t-stats5 vol

Table 4: The standard deviation of the absolute value of the t-stats of the five betas for the
first five principal components of stock A
R-square for Stock A


- -










Figure 4: R-square coefficient for stock A over the course of the day


Results for stock B:

Beta of First Principal Component for Stock B
0.4 0.2 ------





- -









Figure 5: Stock B's first principal component's beta over the course of the day

Table 5:











The standard deviation of the five betas of the 5 principal components of stock B

T-stats for Stock B





- -


- -

0 - ---------



- -



- - - - -










Figure 6: T-stats for the first principal component's beta for stock B over the course of the
t-statsl voI

t-stats2 vol

t-stats3 vol

t-stats4 vol

t-stats5 vol

Table 6: The standard deviation of the absolute value of the t-stats of the five betas for the
first five principal components of stock B
R-square for Stock B







Figure 7: R-square coefficient for stock B over the course of the day


Results for stock C:

Beta of First Principal Component for Stock C




-- 1:1



---- - --08V

- --



Figure 8:

Table 7:

-- --


----- -


- - --



Stock C's first principal component's beta over the course of the day









The standard deviation of the five betas of the 5 principal components of stock C

T-stats for Stock C



- --2 -A

--- - --

-- --------

-4 --5






- - -



Figure 9: T-stats for the first principal component's beta for stock C over the course of the
t-stats1 vol

t-stats2 vol

t-stats3 vol

t-stats4 vol

t-stats5 vol

Table 8:

first five principal components of stock C
R-square for Stock C


















Figure 10: R-square coefficient for stock C over the course of the day



x 104





Tine inseconds


Figure 11: 3D plot of Beta for the principal components of all stocks in October 20 2009
20th 209
in October

0.5 -





Tire inseconds












Figure 12: Slice of Figure 11 to visualize the range of the Beta values

Figure 13: 3D plot of t-stats for the principal components of all stocks in October 20 2009

Figure 14: Slice of Figure 13 to visualize the range of the Beta values

3 Regime Switching Model

3.1 Description of the Model
From the results obtained in the first model we arrived to two possible conclusions:
(i) The expected returns we determined by our prediction model were not representative of the
market's behavior.
(ii) The expected returns were representative of the market's behavior, but only at certain times
and under certain conditions. The market could change behavior when certain conditions are
met. Knowing the fair value of a stock is not a sufficient reason for the market to trade
As we strongly believe in all the reasons that back our estimation of future returns, we will show
evidence that our second conclusion is correct. We will demonstrate what happens if we predict
when the market changes behavior and change our trading strategy accordingly. More
specifically we believe that there are two main regimes in which the market works: one regime is
"mean-reversion" , and the other regime is

"momentum" . This assumption would explain

why we have to change our trading strategy from mean-reversion"

to "momentum" when the


market conditions change in order to generate significant alpha.

Our expected "momentum" behavior in the high frequency environment is motivated by
many of the concepts developed by those who question the rational and efficient behavior in the
traditional low frequency arena. Yes, we refute the "Efficient Market Hypothesis" and strongly
believe in the "Adaptive Market Hypothesis" instead. Moreover, we believe that the latter
perfectly applies to the high frequency world 26 . There are no better words to describe these
thoughts as those used by Lo and Mueller in their 2010 paper:
"This perspective suggests an alternative to the antiseptic world of rational expectations and
efficient markets, one in which market forces and preferences interact to yield a much more
dynamic economy driven by competition, natural selection, and the diversity of individual and
institutional behavior. This approach to financial markets, which we refer to as the "Adaptive
Markets Hypothesis" (Farmer and Lo, 1999; Farmer, 2002; Lo, 2004, 2005; and Brennan and Lo,
This means that in a "mean-reversion" regime we expect the real returns to converge to the theoretical
returns, and in a "momentum" regime we expect real returns to further diverge from the theoretical returns.
26 Based on all the examples we gave on how "ultra high frequency" players make their decisions, it seems
closer to playing a poker game, than to solving a valuation problem. Therefore, irrationality in trading, as seen from
the "classic" point of view, could be very common in this arena.

2009), is a far cry from theoretical physics, and calls for a more sophisticated view of the role
that uncertainty plays in quantitative models of economics and finance."
In addition we also want to recall the harmonic oscillator problem used as an example of a
"Level 4 Risk" problem in Lo-Mueller 2010. As mentioned before, the type of risk we deal with
in finance problems is usually characterized by this level of risk. The problem used as "Level 4
Risk" example was a "Regime Switching" harmonic oscillator of the following characteristics:

= I Wt



1(t)) Xz2t)

xi(t) = Ai cos(wit + <i)

, i = 1,2

"...where the binary indicator I(t) determines which of the two oscillators x (t) or x (t) is
generating the observed process x(t), and let I(t) be a simple two-state Markov process with
the following simple transition probability matrix P:"
I(t) = 1

IW - 1) =1
I(t- 1) 0

(1 - p

I(t) = 0
1 -p)

Given all these ideas, our challenge is now to look for a regime switching signal. Being
consistent with our thoughts, we believe that the principal components are the best representation
of the main risk in the universe under study, and it is also from them that we will obtain the
signal to indicate if the market is in the "mean-reversion" or in the "momentum" regime.
Our first basic underlying and very intuitive idea is to expect the momentum regime to be
related to the sprouting of dislocations in the market. One example of this type of behavior is
given by all the dislocations in the financial crises (widening of "on the run" vs. "off the run"
bond spreads in the LTCM crisis, all sorts of irrational credit spreads in the Credit Crunch in
2009 , etc.) where momentum strategies happen to be very successful.
In particular, we will look at dislocations in the main components of risk, which are indeed
the principal components. To identify these dislocations we created a variable that we called the
"Cross Sectional Volatility of the Principal Components", which is nothing more than the
standard deviation of the stock returns projected on the selected eigenvectors obtained from the
PCA at a certain point in time. This Cross Sectional Volatility, oD(t),could be explained as

JD(t) =


k -- 1

Vt E t1,2, ---, T}

where D is the time series of the "dimensionally reduced returns" mentioned in section 2.4, and
k is the number of eigenvectors chosen to obtain the dimensionally reduced returns, dt; is the
"dimensionally reduced return"

(out of the k defining our reduced-space) at time t. dt is

the cross sectional mean at time t defined as:



On our following analysis we identified the following behavior. As the short term changes in oD
appeared to be more pronounced - identified by very narrow peaks in the aD time series cumulative returns from the basic mean-reversion strategy seemed to decrease. One better way of
looking into this behavior is rather looking at the "changes" in uD over time, rather than the
value itself. Let's define V)as:

where ip is the continuous time form of the change in aD in time
We define the time series of cumulative returns of the basic strategy as:

p = p(t),

We want to somehow include the intuition we learned on the graphs we show below, and capture
"short term" aD (t) changes (or "peaks"), and measure the aggregate value of these changes over
a period of time. We believe that this framework should create clearer "momentum" signals that
We will keep the continuous time forms to better illustrate the concepts, although all the problems are being
solved in discrete time, using the discrete time series of returns obtained from our dataset.

would otherwise not be captured by using a second per second comparison of the change


We modeled V) as a chain of discrete and iid 28 steps along a certain period of time H (the
same length as the parameter H used in section 2.4 to describe our strategy). In order to capture
the level of one-second changes in the cross sectional volatility across the time frame H, we will
define the measure EH, as:


EH(t) =

(t - i)] 2,

Below we can observe the behavior between 0 D(t), EH(t) and p(t). In particular, we identified
that there is pretty consistent negative correlation between EH (t - 1) and p(t) throughout the
year. This finding is extremely useful to predict the return of the basic mean reversion strategy in
the next second, and therefore, would allow us to identify the strength of the mean reversion
signal and identify the appropriate regime. Below is an illustration of this analysis done on three
representative trading days.

Stands for independent and identically distributed

The letter "E' was chosen because it is defined very much like a "Euclidean distance" between vectors
o-D(t -1)
and o-(t - 2) , Vt E {t - H + 1,---, t}


__- 1_1_

__11_- - --

.,w4 -.1 ,--A4,iNWF-!-Fi,









- -.- _ ________

February 10 2009:

I-------L -------



. 11 -




0.18% -

0.09% -




L -,

--- - - -



-2.50% 9:30







Figure 15: The top plot shows the cross sectional volatility of the dimensionally reduced time
series, aD (t) over the course of the day. The bottom plot shows the cumulative returns p(t),
over the course of the day.

*~ 0.002











Figure 16: A plot of the one-second Euclidean distance EH (t) over the course of the day
correl[EH(t- 1),p(t)] = -0.4574

July 20 2009:


>, 0.10%






0 %


--- - - - - -


-- ---- -












Figure 17: The top plot shows the cross sectional volatility of the dimensionally reduced time
series o-D (t) over the course of the day. The bottom plot shows the cumulative returns p(t),
over the course of the day.
















Figure 18: A plot of the one-second Euclidean distance EH (t) over the course of the day
correl[EH(t- 1),p(t)] = -0.2150

December 3 2009:
0.15 0X









0. 20/c


0. 10 /C





0. 00/C
-0. 10 /C
-0. 200%c


---- -









Figure 19: The top plot shows the cross sectional volatility of the dimensionally reduced time
over the course of the day.






0c o













Figure 20: A plot of the one-second Euclidean distance EH(t) over the course of the day




This finding is extremely consistent with our basic intuition: The sprouting of principal
components dislocations at time t induces negative returns on a mean reversion strategy at t+l, or
in better words, the sprouting of principal components triggers momentum.
We now only have to model these discrete changes in the dislocations of principal
components, capture some order of magnitude of these changes in a period of time of our
interest, and build a "regime switching" signal. This signal would tell us to either agree with
basic mean-reversion signal and trade according to our base strategy, or refute this signal
because of evidence of "momentum", and trade opposite of our base strategy.
The regime switching strategy would then follow:
EH(t)- EH(t - 1)

at time t. If this value happens to be greater than zero, we understand that the "aggregate value of
principal components dislocation" seems to be increasing and we should therefore, trade on
"momentum". If this happens to be equal or lower than zero, then we stick to the mean-reversion
signal of the basic model. As we can see, "momentum" seems to be linked to the "acceleration"
of oD (the cross sectional volatility of principal components). This is totally intuitive to us. On
the next section we will show an analysis of the results of the combined regime switching

3.2 Regime Switching Model Results

After applying the "Regime Switching" signal to our basic mean reversion strategy we improved
the results significantly:
Cumulative Log Returns

70% ---------60%


30% -20%


10% ~~-0%







Figure 21: The plot of the accumulated log returns over a period of 251 trading days in 2009

Table 9:


Sharpe Ratio

Max Drawdown

Statistics of the strategy's performance across the entire year of 2009

4 Potential Improvements
Because of the scope of our problem we did not have time to investigate certain issues that could
affect our alpha model. However, below are several suggestions for improving the model.

4.1 Clustering
One such issue is the scaling to thousands of stocks vs. the universe of 50 stocks that we
investigated. Assuming that our chosen stock universe is representative of a much larger universe
of stocks could seem a bit questionable. For instance, when looking at a broader range of stocks
we will run into fewer liquid stocks and one can argue that this would affect the sparsity of the
variance-covariance matrix which in turn would give us a very different principal component
analysis. In order to tackle this problem we can cluster the stocks into smaller buckets, each
characterized by their principal components.
For example, if we have 1000 stocks that we want to divide into 10 buckets of 100 stocks
each, we can simply run a PCA on the entire universe. We then regress each stock's return with
the first principal component. The 100 stocks that show the highest beta will be placed in one
bucket. We then subtract the first component from the stock returns and do a PCA on the
residual. We repeat the process for the next highest principal component. This is done until all
stocks are placed in a certain bucket. Although there may be an overlap between buckets, it can
easily be resolved by comparing the size of the beta of the stock that shows up in more than one
bucket and placing it in the bucket in which it has the highest correlation to the bucket's
principal component (highest beta). One problem with this method is that the last clusters will be
defined by using lower eigenvalue principal components, and therefore bucketed by using very
"noisy" eigenvectors.
Another idea for clustering, especially for those who want to preserve universe wide PCA
characteristics across every cluster and at the same time reduce the "noisy" eigenvector effect
mentioned before, is to simply do a naive random clustering. This method will preserve the
universe wide PCA characteristics in every cluster if we keep the same eigenvectors for every
cluster. We can do this by taking the mean of all the eigenvectors of the same hierarchy across
every cluster. However, this would bring a different eigenvector noise, given that each of them
would no longer represent exactly its corresponding principal component in the cluster. This
seems trivial and unsophisticated, but may make help keep a similar PCA for every cluster.
Notice that the 1000 stocks that we had earlier, will be now divided into 10 buckets of 100 stocks

each. Now doing a PCA on the smaller universes would be 100 times faster for each bucket
compared to the universe wide PCA. This is because the variance-covariance matrix is smaller
by a factor 10 both in the row and in the column dimensions. Doing so for all 10 buckets will
give us a unified PCA but 10 times faster.

4.2 Selection of Eigenvectors

One of the things that could be done better to increase predictive power in the model is work on
the criterion to select the number of eigenvectors (or principal components) to use after the PCA.
As you may recall, these selected eigenvectors construct the feature matrix that transforms our
original time series to a reduced-space of returns. These reduced-space returns ultimately build
the explanatory time series of variables to use in the linear model to predict cumulative future
We suggest the use of a simple autoregressive model on the time series of the number of
eigenvectors that maximize the Sharpe Ratio at each trading period. The approach should consist
on the following steps:
(i) Compute an optimization of the number or eigenvectors used vs. the Sharpe Ratio ex-post, at
the end of each trading period
(ii) Record this optimum number of eigenvectors on a vector (let's call this number of
eigenvectors, or principal components: et)
(iii)We should allow for a reasonable time to pass in order to provide robustness to the time
series which expresses the optimum number of eigenvectors at each time (we suggest one
third of the trading day.) We can predict these optimum number of eigenvectors to be used in
our main predictive model, by using the past returns of the model on that day as a validation
dataset. The suggested way to compute this is by building an AR model of the following

et =

#1 et-I +

f 2 et- 2 + f 3 et-

-- +

fp et-p +


, -qt,p ~

(0, U2)

where et-p is the number of eigenvectors at time period (t - p) that would have generated
the maximum Sharpe Ratio, and et is the predicted optimum number of eigenvectors to use at

time (t) to build our trading signal. 30 The errors are assumed normally distributed with
constant variance.
In addition, we suggest the use of a Maximum Likelihood Estimation function31 and the Akaike
or Bayesian Information criterions (AIC or BIC) to pick the optimum number of autoregressive
steps in the AR model. The criterions introduce a penalty for each increment in the number of
autoregressive steps to a log likelihood function used to determine p*, which is defined as the
optimum number of autoregressive steps p. The problem to solve for p* should be approached as
(i) Start by specifying the maximum possible order P. Make sure that P grows with the sample
size but not too fast:




lim -=


(ii) Define,
p* = arg max - L (0; p) - penatly (p)
0:sp:s p T

where the penalty p is as follows:

penatly (p) with AIC criterion=-

p Nz

In T

penatly (p) with BIC criterion=

T p Nz

and T is the number of values in the time series to autoregress.

L (0; p) is the following log likelihood function with vector of parameters 0 = o2 and
p number of autoregressive steps:


The optimum value at each period can be easily computed at each step, and recorded in a vector to be used

later, by a simple iteration that computes the Sharpe Ratio of the trading model for each number of possible
31 Most of the concepts in sections 4.2 and 4.3 have been obtained from Leonid Kogan's lectures on "Analytics
of Finance" in the MIT Sloan School of Management.






f2 et-2

fl3 et-s


flp et-p)z

We should be aware that in larger samples BIC selects a lower number than AIC.

4.3 NLS
Another suggested improvement for the predictive model of cumulative returns, would be to
make it a little bit more sophisticated and allow for a function to compute weighted lagging
observations of the principal component time series of returns, or as previously called, the
dimensionally reduced time series of returns.
We recall the basic model we suggested:

rt+1 +

+ rt+H =



Dti,k +llt+H,H




We now introduce a "Beta Function" of the type:


0) =

f (X,A,0

rt+1 +

+ rt+H =


- X)

b,H 1 (i, 0) Di,1


-- X" (

+-- +

bH,k (i,


Dti,k + ?7t+H,H


The introduction of this function that will perform optimum weighting of principal components
will introduce much more flexibility and accuracy to the model, but this will no longer be an
OLS problem; this will be an NLS type of problem.3 3

This is nothing more than the original time series of returns projected over the feature vector of chosen
eigenvectors after the PCA.
3 Consistent with the risk levels defined in Mueller and Lo's paper mentioned before, this would be fitting a
problem still assuming the 3rd Level of risk (Fully Reducible Uncertainty), and in our view to the most complex of
its boundaries where non-trivial challenges such as non-linear parameter estimation arise.

4.4 Other
The challenge of the two suggestions is the intensity of the computer power needed to solve the
models on a dynamic high frequency strategy. As final suggestions we want to recommend the
following numerical methods to solve some of the problems presented in this paper:
(i) To compute the PCA we suggest not getting into covariance matrix computations. Instead we
suggest a Singular Value Decomposition (SVD) approach of the type:

where the columns of VT are the eigenvectors of MT * M , the columns of U are the
eigenvectors of M * MT , and the diagonal terms in Y are the square roots of the eigenvalues
of MT * M

The matrices containing eigenvalues and eigenvectors can be computed much more
efficiently and quickly using numerical methods such as the Jacobi algorithm, that would
save a tremendous amount of time in computer driven algorithms.
(ii) Our other suggestion is to solve the NLS problem when including the weighting functions on
the model of principal components to predict future cumulative returns, also by using
numerical methods such as the Marquardt Levenberg algorithm, which is probably the
convergence algorithm that provides a better tradeoff between the complexity to implement
and code and the quality and computation time.
(iii)Lastly, we suggest trying the use of GPU (graphing processing units) as sources to enhance
computing speed as the problem gets more computationally intense and databases get larger.
As certain GPUs have hundreds of cores in them, it is possible to divide the mathematical
complex problem into hundreds of smaller problems that will be done in parallel. This can
dramatically reduce the latency of certain computations such as calculating the variancecovariance matrix of a very large universe of stocks.

5 Conclusion
In conclusion, we achieved our initial objective of presenting evidence that there are significant
opportunities to generate alpha in the high frequency environment in the US equity markets. In
particular, we found sources of potential alpha in the arena characterized by holding periods
between what we called the typical "ultra high frequency" 34 environment and traditional
statistical arbitrage environment.
We achieved our goal by building a model that:
(i) Determined short term equity valuation by predicting cumulative returns based on the
cumulative sum of principal components.
(ii) Identified regimes by analyzing the aggregate change in the dislocations of these principal
components, measured by computing their cross sectional volatility.
Although there are many assumptions that in reality may erode our estimated alpha due to real
transaction costs 35 , the fact that we used very liquid equities in our model, should mitigate this
potential erosion. We do not suggest that it may not happen, we simply believe that a Sharpe
Ratio of 7, considering the universe of liquid stocks that we used, is strong enough evidence to
suggest that there are significant opportunities to generate excess returns in our environment. In
addition, we also provided evidence that the "Fundamental Law of Active Management" works
well. We can see in section 2.6 that the summary statistics of the cumulative-return predictions
made on a given day are quite weak from a statistical significance perspective. Nevertheless, this
lack of precision did not prevent us from obtaining significant theoretical alpha in our model.
Overall, we believe that the high frequency environment offers a wide range of opportunities
to generate alpha; even more, if we consider how we can benefit from the "Fundamental Law of
Active Management". We believe that we provided strong evidence to support this hypothesis by
presenting a plausible trading strategy as an example.

See definition in section 1.1, footnote #3.


We assume that trades were executed at mid market prices, and that we can execute a trade based on the last

mid-price within the same second we recognized the trade. See simplifying assumptions in section 2.5

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