PUBLICATIONS
There is nothing more practical than a good theory. My "day job" for the last 15+ years has primarily been as a quantitative portfolio manager at leading financial institutions. As someone with a passion for understanding the mechanics of financial markets, I have found that many models successfully applied in scientific fields are tremendously useful in addressing critical investment management problems. In order to facilitate this crosspollination, I have complemented my investment manager "day job" with an academic "night job". In that pursuit, I have been fortunate enough to meet and work extensively with some of the leading figures in Market Microstructure, Experimental Mathematics, Set Theory, Operations Research and Natural Language Processing. Most of our findings are kept proprietary. From time to time, however, we decide to publish some of them, hence the irregular frequency of the following publications.
For additional publications and working papers, visit: http://ssrn.com/author=434076. My Mathematical Reviews (MathSciNet) ID is 1026893.
RECENT PEERREVIEWED JOURNAL ARTICLES

AUTHORS 
TITLE 
REFERENCE 
INDEX 
NOTABLE
INNOVATION 

Bailey, David H.; Borwein,
Jon M.; Lopez de Prado, Marcos; Zhu, Jim 
Notices of the
American Mathematical Society, 61(5), pp. 458471. May 2014. 
We prove that high simulated performance is easily
achievable after backtesting a relatively small number of alternative
strategy configurations, a practice we denote “backtest overfitting”. Because
financial analysts rarely report the number of configurations tried for a
given backtest, investors cannot evaluate the degree of overfitting in most
investment proposals. This is one of the first Mathematical Finance papers
published in the Notices of the AMS, the official membership journal of
the American Mathematical Society. 


Mathematical Finance, 2014. Forthcoming. 
JCR (5Y IF = 1.662) 
Execution traders know that market impact greatly depends
on whether their orders lean with or against the market. And yet, the
literature on optimal execution strategies rarely incorporates order
imbalance in the modeling of transaction costs. We introduce the OEH model,
which considers this fact when determining the optimal trading horizon for an
order, an input required by many sophisticated execution strategies. 


A
Mixture of Gaussians Approach to Mathematical Portfolio Oversight: The EF3M
Algorithm

Quantitative Finance, 14(5), pp. 913930. 2014 
JCR (5Y IF = 0.957) 
We solve the "Nonic Polynomial problem" posed by Karl Pearson in the 1894 edition of the Philosophical Transactions of the Royal Society.
We apply quantitative methodologies originated in the Mathematical Theory of
Evolution to model the dynamics of investment styles within a fund. 


The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting and NonNormality 
Journal of
Portfolio Management,
2014. Forthcoming. 
JCR (5Y IF =
0.562) 
The Deflated Sharpe Ratio (DSR) corrects for two
leading sources of performance inflation: Selection bias under multiple
testing and nonNormally distributed returns. In this interview, Prof. Bailey speaks about our work. 


Algorithmic Finance,
3(1), pp. 2142. 2014. 
We introduce Stochastic Flow Diagrams (SFDs), a new
mathematical approach to represent complex dynamic systems into a single
weighted digraph. This topological representation provides a way to visualize
what otherwise would be a morass of equations in differences. 


Journal of Financial Markets,
17(1), pp. 4752. 2014. 
JCR (5Y IF =
1.505) 
Discusses implementation cautions with regards to VPIN
empirical studies. 


The Topology of Macro Financial Flows: An Application of
Stochastic Flow Diagrams 
Algorithmic Finance,
3(1), pp. 4385. 2014. 
We construct a network of financial instruments and show
how Stochastic Flow Diagrams (SFDs) allow researchers to monitor the
flow of capital across the financial system. Because our approach is dynamic,
it models how and for how long a financial shock propagates through the
system. 


An OpenSource implementation of the
CriticalLine Algorithm for Portfolio Optimization 
Algorithms, 6(1), pp. 169196. 2013. 
We fill a gap in the literature by providing a
welldocumented, stepbystep opensource implementation of the CriticalLine
Algorithm (CLA) in a scientific language. We discuss the logic behind CLA
following the algorithm’s decision flow. In addition, we have developed
several utilities that facilitate the answering of recurrent practical
problems. 


The Strategy Approval Decision: A Sharpe Ratio
Indifference Curve Approach 
Algorithmic Finance,
2(1), pp. 99109. 2013. 
The problem of capital allocation to a set of strategies could be partially avoided, or at least greatly simplified, with an appropriate strategy approval decision process. This paper proposes such procedure, by splitting the capital allocation problem into two sequential stages: Strategy approval and portfolio optimization. 


Journal of Risk,
15(2), pp. 344, Winter. 2012. 
JCR (5Y IF =
1.794) 
Introduced the Probabilistic Sharpe Ratio (PSR), a new uncertaintyadjusted investment skill metric that corrects the inflationary effect that NonNormality has on Sharpe Ratio estimates. It also determines the Minimum Track Record Length (MinTRL) needed to evidence skill. A Sharpe Ratio Efficient Frontier (SEF) arises, based on returnonrisk rather than returnoncapital. 


Balanced Baskets: A new approach to Trading and Hedging
Risks 
Journal of Investment Strategies,
1(4), pp. 2162. Fall, 2012. 

Introduced the notion of Balanced Baskets, which are portfolios of instruments that evenly spread risks or exposures across their constituents without requiring a change of basis, like PCA. It also developed the algorithms needed to compute such baskets in hedging as well as trading applications. Finally, it also contributed a new procedure for covariance clustering. 


Journal of Portfolio Management,
39(1), pp. 1929. Fall, 2012. 
JCR (5Y IF =
0.562) 
This paper has been cited by Market Regulators [1, 2, 3] for deepening their understanding of the phenomenon of High Frequency Trading (HFT), beyond the simple notion of "speed trading". In particular, it argues that at the heart of HFT is a new investment paradigm based on making decisions in Volume Time. 


Review of Financial Studies, 25(5), pp. 14571493. 2012. 
JCR (5Y IF = 5.367) 
Developed a new procedure to estimate the flow toxicity
impacting market makers, the Volume Synchronized Probability of Informed
Trading (VPIN). This metric has been shown to
anticipate liquidity crises (including the Flash Crash) and to be a good
predictor of toxicityinduced volatility. CFTC's HFT guidelines
cite this publication. 


Advances in Cointegration and Subset Correlation Hedging
Methods 
Journal of Investment Strategies,
1(2), pp. 67115. Spring, 2012. 

Introduced two new hedging methods, called DickeyFuller Optimization (DFO) and MiniMax Subset Correlation (MMSC). The former is a dynamic, cointegration based method while the latter is a static, balancedbasket method to evenly distribute exposure among portfolio constituents. It also generalized the BoxTiao Canonical Decomposition (BTCD) method. 


Journal of Trading,
6(2), pp. 813. Spring, 2011. 

It introduced the concept of "Market Makers' Asymmetric Payoff Dilemma", which characterizes a liquidity provider as the seller of a realoption to be adversely selected. Since that option cannot be dynamically replicated, a new contract is proposed to allow market makers to hedge such risks. 


Journal of Portfolio Management,
37(2), pp. 118128. Winter, 2011. 
JCR (5Y IF =
0.562) 
This has become one of the most read papers in Finance, according to SSRN. It analyses the "Flash Crash" from a microstructure perspective, and concludes that it was a liquidity crises which resulted from market makers receiving persistently toxic order flow for at least 2 hours before the crash actually unfolded. 


Journal of Alternative
Investments, 7(1), pp. 731. Summer, 2004. 

It developed a new risk framework for assessing hedge funds' loss potential, considering NonNormal and SeriallyCorrelated returns. It shows that the IID Normal assumption, ubiquitous in financial risk modeling, leads to a great underestimation of the loss potential of hedge funds. 
PEERREVIEWED ACADEMIC BOOKS

AUTHORS 
TITLE 
REFERENCE 
NOTABLE
INNOVATION 

Easley, David; 
High Frequency Trading: New Realities for
Traders, Markets and Regulators 
Risk
Books, 2013. 
An overview of high frequency trading (HFT) strategies,
with a particular focus on how low frequency traders can survive in a high
frequency world. Contributors include leading practitioners and academics in this field: Robert Almgren (Quantitative Brokers, New York University), Wes Bethel (Lawrence Berkeley National Laboratory), Ming Gu (Lawrence Berkeley National Laboratory), Terry Hendershott (U.C. Berkeley), Charles Jones (Columbia University), Michael Kearns (S.A.C. Capital, University of Pennsylvania), David Leinweber (Lawrence Berkeley National Laboratory), Oliver Linton (University of Cambridge), Albert Menkveld (University of Amsterdam), Yuryi Nevmyvaka (University of Pennsylvania), Richard Olsen (Olsen Ltd.), Oliver Ruebel (Lawrence Berkeley National Laboratory), George Sofianos (Goldman Sachs), Michael Sotiropoulos (Bank of America Merrill Lynch), Kesheng Wu (Lawrence Berkeley National Laboratory), and JeanPierre Zigrand (London School of Economics). 

Complutense University, 2011. 
This is the author's second doctoral dissertation. The
generalization of electronic markets and ubiquitous automation of financial
transactions has rendered many established models and theories obsolete. This
work presents a new scientific framework for the study of some of the most
relevant questions concerning High Frequency Trading. 


Díaz de Santos,
2003. 
This is the author's first doctoral dissertation, which
dealt with portfolio optimization, risk management and capital allocation to
hedge funds. Once hedge funds' hidden risks are taken into account, optimal
allocations are much smaller than proposed by the standard Markowitz
approach. 
WORKING PAPERS AND BOOKS
AUTHORS 
YEAR 
TITLE 
NOTABLE
INNOVATION 
2014 
We introduce Kinetic Component Analysis (KCA), a
statespace application that extracts the signal from a series of noisy
measurements by applying a Kalman Filter on a Taylor expansion of a
stochastic process. We show that KCA presents several advantages compared to
other popular noisereduction methods such as Fast Fourier Transform (FFT) or
Locally Weighted Scatterplot Smoothing (LOWESS). 

Bailey, David H.; Borwein, Jon M.; Lopez de Prado,
Marcos; Zhu, Jim 
2013 
Most firms and portfolio managers rely on backtests (or
historical simulations of performance) to select investment strategies and
allocate them capital. Standard statistical techniques designed to prevent
regression overfitting, such as holdout, tend to be unreliable and
inaccurate in the context of investment backtests. We propose a framework
that estimates the probability of backtest overfitting (PBO) specifically in
the context of investment simulations, through a numerical method that we
call combinatorially symmetric crossvalidation (CSCV). We show that CSCV
produces accurate estimates of the probability that a particular backtest is
overfit. 

2013 
We develop a framework for informing the decision of stopping a portfolio manager or investment strategy once it has reached a drawdown or time under water limit for a certain confidence level. Under standard portfolio theory assumptions, we show that it takes three times longer to recover from the expected maximum drawdown than the time it takes to produce it, with the same confidence level. We also derive a closedformula expression for drawdowns, in the more general case of firstorder seriallycorrelated outcomes. 

2013 
Growth Optimal Portfolio (GOP) theory determines the path of bet sizes that maximize longterm wealth. This multihorizon goal makes it more appealing among practitioners than myopic approaches, like Markowitz's meanvariance or risk parity. The GOP literature typically considers riskneutral investors with an infinite investment horizon. In this paper, we compute the optimal bet sizes in the more realistic setting of riskaverse investors with finite investment horizons. 

2012 
We propose a new trade classification method "in bulk", and show that its accuracy is comparable to that of "tradebytrade" classification methods, such as the "tickrule", with much smaller data requirements and greater explanatory power over the trading range. 