QuantDesk® Machine Learning Forecast

for the Week of April 3rd

[slider]Last Week’s Summary
When we founded Lucena and embarked on this incredible journey, we based our business approach on an important key principle: distinguish substance from noise. While many fin-tech start-ups raised capital based on unsustainable growth projections and valuations, we decided to build substance before employing a robust sales and marketing infrastructure. Our seed funding and cash flow were funneled almost exclusively into building meaningful empirical value by employing breakthough machine-learning technology that works.[/slider]

As big data, predictive analytics and machine learning grow popular, the confusion among industry buzzwords continues to grow. It’s incredible to see how many companies profess to be machine-learning experts while providing, at most, statistical analysis or statistical modeling. While this discussion could be controversial, I wanted to shed some light on the main differences between machine learning and big-data analysis or statistical modeling. While both machine learning and statistical modeling rely on historical data to predict the probability of an outcome, machine learning holds an important distinguishing ingredient in the form of inductive reasoning (hence the “Artificial Intelligence” inference). Let’s look at why inductive reasoning is so important for financial modeling.

According to Investopedia, inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has NOT yet encountered. Simply put, the machine-learning technology that Lucena employs is capable of providing answers to questions our users may not have thought of asking. With that in mind, it is equally important to set expectations and distinguish between inductive reasoning and cognitive reasoning — which is the application of senses, sentiment and human-like instincts — into a decision process — still lightyears away from commercial application.

Static models have lost their luster with the advent of big-data technology, its speed of delivery, and its affordability and pervasiveness. The efficient market hypothesis (EMH) asserts that it’s impossible to “beat the market” because the stock market is so efficient that share prices always reflect all relevant information. It’s therefore important for us at Lucena to incorporate capabilities that are not easily accessible by the masses while also enabling an individual’s instinct to have the cognitive final say before applying the machine’s recommendations. When evaluating a machine-learning solution, here are a few good questions you should ask in order to determine if a provider can really back-up its marketing claim:

Dynamic Models vs. Static Models
Ask if the provider supports dynamic models. Dynamic models are designed to morph and accommodate changing conditions in the financial markets. To use the autonomous vehicle as an analogy, a driverless car must accommodate multiple terrains, weather conditions, traffic conditions and environmental surroundings that could drastically change during the course of a route from point A to point B.

Self-Adjusting Models
Ask if the providers’ models self-adjust and how the providers’ models get “smarter” over time.

Backtesting, Cross Validation and Overfitting
Ask how the provider assesses their models for robustness, and how the did the model hold during the financial crisis of 2008/2009, for example. Does the provider incorporate forward testing? What are the in-sample and out-of-sample testing periods? Do they account for slippage and transactions cost? Is their data survivor biased free? (are dead stocks considered historically when they were alive)? As in any emerging industry, there are good honest providers, but at the same time there are others ready to exploit misinformation and confusion. As an educated consumer one should be aware of some of the pitfalls to avoid.

Lucena Webinar Recordings are Available Online:

Last Friday, we had our third and final presentation of how Lucena builds winning strategies. Those of you who missed the webinar or the previous two sessions can find our recordings here.

Lucena Presenting at the 44th MTA Symposium in NYC – April 6 and April 7
Gain expert guidance from the industry’s top performers and best practices in technical analysis at the MTA’s 44th Annual Symposium. The 2017 Symposium will explore the theoretical frameworks popularized in behavioral finance through vigorously validated elements of technical analysis, leaving attendees with insights to navigate uncertain global markets across asset classes.

Strategy’s Update

As in past weeks, I wanted 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 6.27% vs. benchmark of -2.43%
Tiebreaker has been forward traded since 2014 and to date it has enjoyed remarkably low volatility and boasts an impressive return of 40.45%, a low max-drawdown of 6.16%, and a Sharpe of 1.898! (You can see a more detailed view of Tiebreaker’s performance below in this newsletter.)

Image 1: Tiebreaker YTD– benchmark is VMNIX (Vanguard Market Neutral Fund Institutional Shares
Past performance is no guarantee of future returns.

BlackDog – Lucena’s Risk Parity – YTD return of 8.55 % vs. benchmark of 4.50%
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.
In essence, we strive to obtain an optimal decision while taking into consideration the trade-offs between two or more conflicting objectives. For example, 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
  • Restrict volatility
  • Minimize 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 not indicative of future returns.

Utilities – Large-Cap Based Actively Managed – YTD return of 17.43% vs. 6.44% of 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 not indicative of future returns.

Industrials – Large-Cap Based and Actively Managed – YTD Return of 2.03% vs. benchmark of 1.26%
I wrote about an Industrial centric portfolio on 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 not indicative 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: Top 10 performance relative to the S&P 500 total return index. The top 10 are designed as smart alpha geared to outperform the S&P as has been evident in last week’s performance.

Image 6: 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 7: Forecasting the top 10 position in the SPY 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 introduction video of all the major functions of QuantDesk, please click on the following link:


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).

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:

Tiebreaker: Paper trading model portfolio performance compared to the SPY and Vanguard Market Neutral Fund from 9/1/2014 to 3/31/2017.
Past performance is no guarantee of future returns.

Model BlackDog 2X, Lucena’s Tactical Asset Allocation Strategy:

BlackDog: Paper trading model portfolio performance compared to the SPY and Vanguard Balanced Index Fund from 4/1/2014 to 3/31/2017.
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 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 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.

For those of you who are interested in the spreadsheet with all historical forecasts and results, please email me directly and I will gladly send you the data.

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

Have a great week!

Lucena Research brings elite technology to hedge funds, investment professionals and wealth advisors. Our Artificial Intelligence decision support technology enables investment professionals to find market opportunities and to reduce risk in their portfolio.

We employ Machine Learning technology to help our customers exploit market opportunities with precision and scientifically validate their investment strategies before risking capital.

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 not a certified 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 based on historical data analysis. Past performance does not guarantee future success. In addition, the assumptions and the historical data based on which an opinion is 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.