QuantDesk® Machine Learning Forecast

for the Week of November 27th, 2017

Protecting Your Long Only, Bottom Up, Deep Value Fundamental Portfolio

Erez Katz writes about Reducing Turnover Through Quantitative Research

by Erez Katz, CEO and Co-founder of Lucena Research.

We have been experiencing two very different types of incoming leads lately. One is centered around blockchain technology, and in particular cryptocurrency trading. The other stems from growing interest in quantitative analysis technology by traditional deep value fundamental analysts. Since we’ve yet to form a comprehensive research strategy for cryptocurrency trading, I’d like to defer this discussion to a later time. For today, I’d like to discuss how machine learning can be used effectively to enhance human intelligence in the context of fundamental buy and hold research.

What Is A Deep Value Fundamental Portfolio?

Value investing is a style of investment which focuses on specific stocks that are priced below their intrinsic value. Individual stocks are valued mainly based on their companies’ fundamental and financial performance. Most analysts have gathered domain expertise through many years of research and intimate knowledge of very few companies which in turn serve as the basis of highly concentrated portfolios. The concept of quantitative analysis has been mostly foreign to deep value fundamental analysts until recently.

What Has Changed?

First, we need to give credit where it’s due, as most deep value fundamental managers have been dead right for the past 9 years. Indeed, since the financial crisis there has been a constant flow of money away from active management to traditional buy and hold instruments. However, many portfolio managers recognize that markets can’t continue to climb forever and are growing nervous that our bull market is running out of steam. Many still hold a strong conviction in their portfolios but are seeking protection from an inevitable pullback. In addition, traditional analysts are finally recognizing that there is a big data evolution unfolding right in front of them and are looking for ways to incorporate data science into their traditional research.

Lucena’s Solution

Lucena’s Hedge Finder is geared to protect a portfolio from major pullbacks by introducing additional assets that together form an anti-correlation trend line against the portfolio’s core holdings’ trend line. The technology underneath Lucena’s Hedge Finder is a sophisticated machine learning pattern-matching technology, but QuantDesk makes it easy to deploy by simply following a few short wizard screens.


For today’s demonstration, I have arbitrarily constructed a portfolio of some of the most widely held stocks from the Dow 30.

Image 1: Bottom-up portfolio from the Dow -30.

Using Lucena’s Hedge Finder wizard we can now identify a set of additional constituents from the S&P 500 that when added to the portfolio will preserve its trend line while reducing its volatility.

Image 2: Hedge wizard summary screen. The wizard is an easy to follow set of screens that allows the user to configure the hedger with ease before execution.

What’s exciting about the Hedge Finder wizard (and for that matter, all of QuantDesk’s wizards) is that it gathers only the basic information and upon execution it forms a deep grid search in order to identify which configuration produces the best outcome. For example, rather than having the user select the look-back period (the period that the machine gathers historical information from in order to make its predictions, also called the training period), the machine tries a sequence of look-back periods in order to identify which works best.

Image 3: Hedge Finder wizard output screen. The blue line represents the original portfolio while the orange line represents the hedged portfolio. To the right of the vertical line is the before and after portfolio’s projected performance cone for the upcoming month. On the right, you can analyze the before and after statistics. As you can see the areas highlighted in orange are the metrics that the Hedge Finder was able to improve. Specifically, higher Sharpe, lower volatility and even a slightly better return during the look-back period.

Next, QuantDesk makes it easy to validate if the hedging method works out-of-sample on time periods that have not been analyzed in the past. Again, the Hedge Finder backtest wizard makes it extremely easy to interface with where the user can enter the absolute minimum information, such as the period of time he/she wishes the backtest to cover. The results are then displayed in a comprehensive performance report for additional analysis.

Image 4: Hedge Finder wizard backtest summary. The wizard is an easy to follow set of screens that capture basic backtest configurations before execution.
Image 5: Hedge Finder wizard backtest outcome. Past performance is no guarantee of future returns. The backtest simulates rolling back time and demonstrating how the Hedge Finder actually works over an extended timeframe in the past. QuantDesk features a comprehensive backtest performance report. In this example, you can see how our hedge portfolio performed against its benchmark $SPXTR (the SP total return benchmark). In addition, you can see in the scatter plot chart (marked in green on the bottom left) how the hedged portfolio indicates lower volatility for a higher return against the benchmark.

Finally, it takes only one click to convert the backtest into a forward-traded live strategy.

Image 6: Creating a live forward-traded strategy based on the same execution rules of a backtest.


QuantDesk® Hedge Finder enables the protection of any portfolio without disturbing its core holdings. The easy user interface can enable a non-technical portfolio manager with quantitative research for a fraction of the usual cost and time compared to other research alternatives.

Would you’d like to put your portfolio to the test? Feel free to reach out to us in confidence and we will gladly walk you through process. info@lucenaresearch.com

Come and Meet us in Boston this Tuesday!

Leveraging Central Bank and Corporate Communications with Machine Learning for Investment Decision Making

Presentors: Location:
  • Evan Schnidman, Ph.D. (Prattle)
  • Erez Katz (Lucena Research)
  • Tucker Balch, Ph.D. (Lucena Research)
  • 5:00 to 7:00PM
  • November 28, 2017
  • MCLE New England 10 Winter Place,
  • Boston, MA 02108

In this presentation, leaders from two emerging FinTech firms -- Prattle and Lucena Research -- show how they have joined forces on the Nasdaq Analytics Hub to advance predictive analytics with machine learning. Prattle and Lucena will review how they have created unique and effective investment signals for various asset types. In addition, you will learn more about the Nasdaq’s Analytics Hub and its unique capabilities.

Prattle signals include macroeconomic and foreign exchange signals derived from central bank communications, as well as equity signals derived from corporate earnings communications.The process begins with a central bank official’s speech, or a CEO’s earnings call. These public communications are analyzed by Prattle’s proprietary natural language processing algorithms to yield quantitative sentiment scores. Lucena then converts the scores into actionable intelligence for specific investment in markets, sectors, currencies, commodities and individual stocks. Ultimately the data is then offered through intelligent daily data feeds on the Nasdaq Analytics Hub.

This presentation will showcase how Lucena has partnered with Nasdaq to separate actionable intelligence from noise.

Strategies Update

As in past weeks, I want to briefly update you on how the model portfolios and the theme-based strategies we covered recently are performing.

Tiebreaker – Lucena’s Long/Short Equity Strategy - YTD return of 14.76% vs. benchmark of -4.54%
Image 1: Tiebreaker YTD– benchmark is VMNIX (Vanguard Market Neutral Fund Institutional Shares)
Past performance is no guarantee of future returns.

Tiebreaker has been forward traded since 2014 and to date it has enjoyed remarkably low volatility and boasts an impressive return of 52.39%, low volatility as expressed by its max-drawdown of only 6.16%, and a Sharpe of 1.99! (You can see a more detailed view of Tiebreaker’s performance below in this newsletter.)

BlackDog – Lucena’s Risk Parity - YTD return of 20.33 % vs. benchmark of 14.56%

We have recently developed a sophisticated multi-sleeve optimization engine set to provide the most suitable asset allocation for a given risk profile, while respecting multi-level allocation restriction rules.

Essentially, we strive to obtain an optimal decision while taking into consideration the trade-offs between two or more conflicting objectives. For example, if you consider a wide universe of constituents, we can find a subset selection and their respective allocations to satisfy the following:

  • Maximizing Sharpe
  • Widely diversified portfolio with certain allocation restrictions across certain asset classes, market sectors and growth/value classifications
  • Restricting volatility
  • Minimizing turnover

We can also determine the proper rebalance frequency and validate the recommended methodology with a comprehensive backtest.

Image 2: BlackDog YTD– benchmark is AQR’s Risk Parity Fund Class B
Past performance is no guarantee of future returns.

Utilities - Large-Cap Based Actively Managed - YTD return of 51.00% vs. 17.76% of the benchmark!!!

I wrote about utilities last year in an attempt to demonstrate how Lucena’s technology can be deployed to identify fixed income alternatives. Since November 2016 we have been tracking our utilities portfolio, and it has been performing exceptionally well in both total return and low volatility -- well ahead of the S&P and its benchmark, the XLU.

Image 3: Utilities based strategy– captured since November of 2016. Benchmark is XLU – Utilities select sector SPDR
Past performance is no guarantee of future returns.

Industrials - Large-Cap Based Actively Managed - YTD Return of 21.71% vs. benchmark of 12.18%

I wrote about an industrial-centric portfolio in January this year. This portfolio was designed to anticipate the administration’s strong desire to invest in infrastructure. The portfolio identifies a well-diversified industrial stock set to track and outperform the XLI (its benchmark).

Image 4: Industrials-based strategy– captured since January 27, 2017 (covered during that week’s newsletter). Benchmark is XLI – Industrials select sector SPDR ETF.
Past performance is no guarantee of future returns.

Forecasting the Top 10 Positions in the S&P

Lucena’s Forecaster uses a predetermined set of 10 factors that are selected from a large set of over 500. Self-adjusting to the most recent data, we apply a genetic algorithm (GA) process that runs over the weekend to identify the most predictive set of factors based on which our price forecasts are assessed. These factors (together called a “model”) are used to forecast the price and its corresponding confidence score of every stock in the S&P. Our machine-learning algorithm travels back in time over a look-back period (or a training period) and searches for historical states in which the underlying equities were similar to their current state. By assessing how prices moved forward in the past, we anticipate their projected price change and forecast their volatility.

The charts below represent the new model and the top 10 positions assessed by Lucena’s Price Forecaster.

Image 5: Default model for the coming week.

The top 10 forecast chart below delineates the ten positions in the S&P with the highest projected market-relative return combined with their highest confidence score.

Image 6: Forecasting the top 10 position in the S&P 500 for the coming week. The yellow stars (0 stars meaning poorest and 5 stars meaning strongest) represent the confidence score based on the forecasted volatility, while the blue stars represent backtest scoring as to how successful the machine was in forecasting the underlying asset over the lookback period -- in our case, the last 3 months.

To view a brief video of all the major functions of QuantDesk, please click on the following link:
QuantDesk Overview


The table below presents the trailing 12-month performance and a YTD comparison between the two model strategies we cover in this newsletter (BlackDog and Tiebreaker), as well as the two ETFs representing the major US indexes (the DOW and the S&P).

12 Month Performance BlackDog and Tiebreaker
Image 8: Last week’s changes, trailing 12 months, and year-to-date gains/losses.
Past performance is no guarantee of future returns.

Model Tiebreaker, Lucena's Active Long/Short US Equities Strategy:

Active Long/Short US Equities Strategy
Tiebreaker: Paper trading model portfolio performance compared to Vanguard Market Neutral Fund since 9/1/2014. Past performance is no guarantee of future returns.

Model BlackDog 2X: Lucena's Tactical Asset Allocation Strategy:

model portfolio performance compared to the SPY and Vanguard Balanced Index Fund
BlackDog: Paper trading model portfolio performance compared to the SPY and Vanguard Balanced Index Fund since 4/1/2014.
Past performance is no guarantee of future returns.


For those of you unfamiliar with BlackDog and Tiebreaker, here is a brief overview: BlackDog and Tiebreaker are two out of an assortment of model strategies that we offer our clients. Our team of quants is constantly on the hunt for innovative investment ideas. Lucena’s model portfolios are a byproduct of some of our best research, packaged into consumable model-portfolios. The performance stats and charts presented here are a reflection of paper traded portfolios on our platform, QuantDesk®. Actual performance of our clients’ portfolios may vary as it is subject to slippage and the manager’s discretionary implementation. We will be happy to facilitate an introduction with one of our clients for those of you interested in reviewing live brokerage accounts that track our model portfolios.

Tiebreaker: Tiebreaker is an actively managed long/short equity strategy. It invests in equities from the S&P 500 and Russell 1000 and is rebalanced bi-weekly using Lucena’s Forecaster, Optimizer and Hedger. Tiebreaker splits its cash evenly between its core and hedge holdings, and its hedge positions consist of long and short equities. Tiebreaker has been able to avoid major market drawdowns while still taking full advantage of subsequent run-ups. Tiebreaker is able to adjust its long/short exposure based on idiosyncratic volatility and risk. Lucena’s Hedge Finder is primarily responsible for driving this long/short exposure tilt.

Tiebreaker Model Portfolio Performance Calculation Methodology Tiebreaker's model portfolio’s performance is a paper trading simulation and it assumes opening account balance of $1,000,000 cash. Tiebreaker started to paper trade on April 28, 2014 as a cash neutral and Bata neutral strategy. However, it was substantially modified to its current dynamic mode on 9/1/2014. Trade execution and return figures assume positions are opened at the 11:00AM EST price quoted by the primary exchange on which the security is traded and unless a stop is triggered, the positions are closed at the 4:00PM EST price quoted by the primary exchange on which the security is traded. In the case of a stop loss, a trailing 5% stop loss is imposed and is measured from the intra-week high (in the case of longs) and low (in the case of shorts). If the stop loss was triggered, an exit from the position 5% below, in the case of longs, and 5% above, in the case of shorts. Tiebreaker assesses the price at which the position is exited with the following modification: prior to March 1st, 2016, at times but not at all times, if, in consultation with a client executing the strategy, it is found that the client received a less favorable price in closing out a position when a stop loss is triggered, the less favorable price is used in determining the exit price. On September 28, 2016 we have applied new allocation algorithms to Tiebreaker and modified its rebalancing sequence to be every two weeks (10 trading days). Since March 1st, 2016, all trades are conducted automatically with no modifications based on the guidelines outlined herein. No manual modifications have been made to the gain stop prices. In instances where a position gaps through the trigger price, the initial open gapped trading price is utilized. Transaction costs are calculated as the larger of 6.95 per trade or $0.0035 * number of shares trades.

BlackDog: BlackDog is a paper trading simulation of a tactical asset allocation strategy that utilizes highly liquid ETFs of large cap and fixed income instruments. The portfolio is adjusted approximately once per month based on Lucena’s Optimizer in conjunction with Lucena’s macroeconomic ensemble voting model. Due to BlackDog’s low volatility (half the market in backtesting) we leveraged it 2X. By exposing twice its original cash assets, we take full advantage of its potential returns while maintaining market-relative low volatility and risk. As evidenced by the chart below, BlackDog 2X is substantially ahead of its benchmark (S&P 500).

In the past year, we covered QuantDesk's Forecaster, Back-tester, Optimizer, Hedger and our Event Study. In future briefings, we will keep you up-to-date on how our live portfolios are executing. We will also showcase new technologies and capabilities that we intend to deploy and make available through our premium strategies and QuantDesk® our flagship cloud-based software.
My hope is that those of you who will be following us closely will gain a good understanding of Machine Learning techniques in statistical forecasting and will gain expertise in our suite of offerings and services.


  • Forecaster - Pattern recognition price prediction
  • Optimizer - Portfolio allocation based on risk profile
  • Hedger - Hedge positions to reduce volatility and maximize risk adjusted return
  • Event Analyzer - Identify predictable behavior following a meaningful event
  • Back Tester - Assess an investment strategy through a historical test drive before risking capital

Your comments and questions are important to us and help to drive the content of this weekly briefing. I encourage you to continue to send us your feedback, your portfolios for analysis, or any questions you wish for us to showcase in future briefings.
Send your emails to: info@lucenaresearch.com and we will do our best to address each email received.

Please remember: This sample portfolio and the content delivered in this newsletter are for educational purposes only and NOT as the basis for one's investment strategy. Beyond discounting market impact and not counting transaction costs, there are additional factors that can impact success. Hence, additional professional due diligence and investors' insights should be considered prior to risking capital.

If you have any questions or comments on the above, feel free to contact me: erez@lucenaresearch.com

Have a great week!

Erez Katz Signature


Disclaimer Pertaining to Content Delivered & Investment Advice

This information has been prepared by Lucena Research Inc. and is intended for informational purposes only. This information should not be construed as investment, legal and/or tax advice. Additionally, this content is not intended as an offer to sell or a solicitation of any investment product or service.

Please note: Lucena is a technology company and neither manages funds nor functions as an investment advisor. Do not take the opinions expressed explicitly or implicitly in this communication as investment advice. The opinions expressed are of the author and are based on statistical forecasting on historical data analysis.
Past performance does not guarantee future success. In addition, the assumptions and the historical data based on which opinions are made could be faulty. All results and analyses expressed are hypothetical and are NOT guaranteed. All Trading involves substantial risk. Leverage Trading has large potential reward but also large potential risk. Never trade with money you cannot afford to lose. If you are neither a registered nor a certified investment professional this information is not intended for you. Please consult a registered or a certified investment advisor before risking any capital.
The performance results for active portfolios following the screen presented here will differ from the performance contained in this report for a variety of reasons, including differences related to incurring transaction costs and/or investment advisory fees, as well as differences in the time and price that securities were acquired and disposed of, and differences in the weighting of such securities. The performance results for individuals following the strategy could also differ based on differences in treatment of dividends received, including the amount received and whether and when such dividends were reinvested. Historical performance can be revisited to correct errors or anomalies and ensure it most accurately reflects the performance of the strategy.