Elle Investments is a small “family&friends” office that primarily invests in securities trading on the US exchanges. We invest across all sectors of the economy, without any a-priori weight allocation.

Our portfolio return objectives are to maximize long-term growth – in short beating the overall market over the long-term. Over any shorter period, we are not tied down to a specific index – nor are we trying to be market-neutral or achieve a minimum absolute return.


While we believe that a strong understanding of mathematical techniques and statistical concepts are essential to the successful implementation of any portfolio management strategy, the guiding principles should be strongly grounded in a common-sense, practical-business approach. (Despite the Portfolio Manager being a Math Ph.D., he believes that many of the quantitative-based funds end up using too much math for its own sake.)

Instead, we believe that at the portfolio level the most promising long-term approach consists of a sound marriage of quantitative techniques and logical Fundamental analysis.


To maintain contact with reality, we find it helpful to describe our overarching portfolio management strategy using a vegetable garden metaphor, “Adaptive Harvesting”:

  • There is a time for planting and a time for harvesting – and each plant is ready for picking at its own time and pace.
  • Every day, your garden needs to be tended to. Some fruits are ripe to be harvested - which should be done promptly lest they rot on the tree; likewise, some fruits are growing up nicely but are not yet ripe and should be left on the tree for just a little bit longer.
  • Some plants have been infected by disease and need to be cured or cut off.
  • Tending to a garden has many attendant activities: planting, pruning, watering, fertilizing, educating yourself and nurturing your seedlings. And yes, there is a lot of science and knowledge that can and should be applied in doing so.


At the individual stock level, the opportunities we identify come in many forms: the investment universe is a veritable “zoo” and there are many different kinds of “animals” out there. Here is a partial list:

  • Babies in the bathwater: Stocks that have been beaten down as part of a macro movement in the economy or in their sector as part of an indiscriminate move (e.g. ODP, December 2018 – February 2019; VIAB, December 2018 – February 2019) .
  • Failure to distinguish: Stocks that have fallen in sympathy with a competitor – with investors failing to distinguish important differences (this happens often with biotech stocks when competitors release data).
  • Changing narratives: Companies that have performed (very) poorly in the past few years but that now are presented with a new channel of opportunities, often with a new and focused management and renewed energy (e.g. AMSC, April 2011 – January 2019).
  • Diamonds in the rough: Companies that are little understood and have been ignored but face great prospects (e.g. TUSK, October 2017 – June 2018).
  • Victims of fear: Companies that have been punished by an exaggerated level of fear but whose business prospects are asymmetrically good (e.g. CONN, September 2018 – ongoing; CHK, July 2014 – ongoing).
  • Semi-arbitrages: Inconsistent pricing in the market due to a merger announcement, volatility of a subsidiary/holding, etc. (e.g. SVVC, September 2018 – ongoing; AKBA/KERX, June 2018 – ongoing; CHK/WRD, October 2018 - ongoing).
  • Temporary setbacks: Companies that drop from a justified piece of bad news that may add some near-term uncertainty (such as an FDA filing delay), but whose long-term prospects remain unchanged (e.g. ACRX, October 2017 - ongoing).

Even though the storyline behind each of these types of investments is different, they all have something in common: they share an asymmetric risk/reward profile. They are great bets with very favorable (mathematical) expected values, albeit some may have high risk as a stand-alone bet (many companies in the biotech space have this characteristic).

The high risk dictates that any single position be small. However, in a wide portfolio, the risk is diversified away and is (eventually) uncorrelated with the market.

For these types of stocks, each position should be viewed as an individual bet which should be included in a well-diversified portfolio with each individual weight in the range of 1%-3%.


Markowitz-style Portfolio Optimization (the foundation of Modern Portfolio Theory) and its variants underlie the strategies of most asset management funds and for almost 70 years have permeated the thinking of Finance professionals.

Yet these models are fraught with both theoretical and practical issues.

As we do not wish to delve too deeply into theory here, we provide at the end a few select references of interest for those who are more scientifically inclined. But in short, our critiques of Modern Portfolio Theory can be summed up by the following three comments:

  • The parameters that go into the calculations are estimated from the past and may have little relevance to the future;
  • Complicated mathematical calculations and modeling give an illusion of precision and accuracy that is just not there;
  • The entire methodology is based on a fixed horizon which is the same for all securities (whereas our garden metaphor approach is much closer to reality).

The Adaptive Harvesting strategy, in contrast, is based on combining and refining well-accepted value and momentum ideas:

  • Qualitative analysis of company fundamentals can identify undervalued stocks – stocks that are poised for growth once investors recognize their true value;
  • Behavioral analysis either looks for catalysts that might cause a change in market sentiment or tries to identify ongoing trends in support of the underlying thesis;
  • Without going into details, at the portfolio level the collection of single-stock bets is managed with a methodology more akin to an “à la Thorpe” style than to the mainstream Mean-Variance portfolio models.


  • Chua, D.; Krizman, M.; Page, S. (2009). "The Myth of Diversification". Journal of Portfolio Management. 36 (1): 26–35.
  • Duke, A. (2018). “Thinking in Bets: Making Smarter Decisions When You Don't Have All the Facts”, ISBN-13: 978-0735216358.
  • Humphrey, J.; Benson, K.; Low, R.K.Y.; Lee, W.L. (2015). "Is diversification always optimal?". Pacific Basin Finance Journal. 35 (B): B. doi:10.1016/j.pacfin.2015.09.003.
  • MacLean, L.C.; Thorp, E.O.; Ziemba, W.T. (2011). “The Kelly Capital Growth Investment Criterion: Theory and Practice (World Scientific Handbook in Financial Economics Series 3)”, ISBN-13: 978-9814383134.
  • Markowitz, H.M. (1952). "Portfolio Selection". The Journal of Finance. 7 (1): 77–91. doi:10.2307/2975974. JSTOR 2975974.
  • Markowitz, H.M. (1959). Portfolio Selection: Efficient Diversification of Investments. New York: John Wiley & Sons. (reprinted by Yale University Press, 1970.
  • Poundstone, W. (2006). "Fortune’s Formula: The Untold Story of the Scientific Betting System that Beat the Casinos and Wall Street”, ISBN-13: 978-0809045990.
  • Taleb, N. (2005). “Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets (Incerto)”, ISBN-13: 978-1587991905.
  • Thorpe, E.O. (2018). “A Man for All Markets: From Las Vegas to Wall Street, How I Beat the Dealer and the Market”, ISBN-13: 978-0812979909.
  • Wong, C.X. (2011). “Precision: Statistical and Mathematical Methods in Horse Racing”, ISBN-13: 978-1432768522.