QuantDesk® Machine Learning Forecast

for the Week of January 30

Last Week’s Summary:

As the Dow finally crossed the 20,000 level with all major US indexes marking yet another all-time high, Q4 GDP growth slowed down to a year-over-year pace of 1.9%, lower than the 3.5% growth marked in Q3. The bright spot however was in the capital expenditures reading, which measured the third consecutive quarter of improvement. Whether you support president Trump’s policies or not, there is undoubtedly a sense of renewed optimism by corporate America as is evident by the growth in capital expenses.

One sector set to benefit greatly from Trump’s economic agenda is industrials. Infrastructure spending and major projects will most likely dominate Trump’s agenda. Interestingly, putting Americans back to work through major fiscal stimulus is one of the less contentious policies also favored by many democrats. This is a classic case in which a fundamental vision can be greatly enhanced with quantitative means. A hybrid approach to investment where a macro idea is formed through fundamental research, human intuition, and vision is then further refined and enhanced with science. In our case, using big-data and machine learning.

Creating a US Industrials-Centric Strategy
Adopting a top-down approach, we start by inspecting the XLI, the industrial Select Sector SPDR ETF which has outperformed the major indexes in the last 12 months with an impressive 32.59% return. Not surprisingly, approximately half of last year’s return can be attributed to the Trump rally that has taken place since the November 8th election.

Image 1: XLI – The Industrial Select Sector SPDR Fund – 1-year performance – source Google Finance
Past performance is not indicative of future returns.

Today, I’d like to evaluate how dynamically selecting a set of constituents from the XLI universe will set us with a favorable statistical probability to outperform the XLI.

Step 1 – Create an Event Scan / Event Study

An event study is nothing more than a scan or a filter. The only difference from a typical filter is that an event study allows us to inspect the scan in the past. Our aim is to identify what attributes are common to industrial securities in the S&P and Russell 1000 that lead them to consistently outperform XLI in the past. For example, if we can ascertain that industrial stocks with a PE ratio of 12 or below outperformed XLI in 95% of the cases historically, our assumption is that if we select today industrial stocks with a PE ratio below 12, we have a strong statistical probability that they will continue to outperform XLI in the near future. Since we are going to inspect our scan historically, we are not interested in fine-tuning our scan for absolute values (PE of 12) since PE of 12 can be interpreted differently in different market conditions. In order to define a scan that can be applicable in different market regimes we introduced ranked indicators. Ranked indicators allow us to select securities based on their relative values against their peers. PE ratio ranked in the bottom 10% of the Russell 1000 indicate that 90% of the Russell’s constituents have a higher PE relative to the securities selected.

QuantDesk® is able to apply AI & machine learning disciplines in order to identify which factors (from our over 500 in our database) are most suitable to identify statistical XLI outperformance. Below is the event scan that QuantDesk recommended for identifying XLI relative outperformers for the periods 1/1/2010 to 12/31/2012 (the in-sample period). As can be seen, the AI engine came up with a five ranked factors model consisting of both technical and fundamental attributes.

Image 2: XLI outperformers event scan definition. As can be seen the scan determines 5 ranked factors that in combination identified historically which securities are set to outperform XLI.
Past performance is not indicative of future returns.

The image below depicts the results of the scan.

Image 3: Event Scan output for the years 2010 to 2012. As can be seen, the scan has identified 870 events (securities) with an average relative outperformance ahead of XLI of 0.86%. The engine also recommends how many days to optimally hold on to such positions before re-assessing new constituents, 21 days in our case.
Past performance is not indicative of future returns.

Step 2 – Backtesting Out of Sample

Now that we can identify XLI outperformers during the years 2010 to 2012, let’s see if our rules (the event scan definitions) are indeed predictive out of sample. Out of sample is a period during which no prior research was conducted. In other words, it has no overlap with our training period (in our case 2010 to 2012). QuantDesk Backtester is a portfolio construction simulation tool that follows certain rules of entry and exit as if the user lived in the past without any future knowledge.

In our case, we want to simulate trading from 2010 through 1/26/2017. We overlap 2010 through 2012 where we expect to see good results as this is the period in which the machine refined its investment guidelines. But the real question is, what happened to the portfolio after 2012 through the end of January 2017?

Image 4: Backtest simulating running the event scan daily and buying up to 10 positions and holding them for a period of 21 trading days (approximately one calendar month). The backtest indeed showcases handsome outperformance against XLI with a 64% success rate. Also, it is important to note that the backtest has taken into account and is net of slippage and transactions cost.
Past performance is not indicative of future returns.

Step 3 and Conclusion

Now that we have a well-defined scan (Event Study) and a compelling backtest, there is one remaining task left to complete our research: paper trading, also known as forward testing. We now want to assess if the rules that have proven predictive will continue to outperform on a roll forward basis.

With QuantDesk it is as simple as clicking one button by which we create a model portfolio (similar to Tiebreaker and BlackDog) that follow the same rules that guided our backtest but on a perpetual basis. The greatest benefit of Lucena’s offerings is that what would normally take weeks or months to research by multiple quants at most sophisticated funds, would take hours using QuantDesk in conjunction with Lucena’s quants capabilities. Our big-data infrastructure, combined with our in-house machine learning expertise via our quants offer world-class research at a fraction of the time and cost compared to traditional investment research.

If there is an investment idea you’d like to validate, refine, and enhance, we are here ready to help. Feel free to reach out to me and I will gladly make our technology and resources available to help you realize your ideas and put them into practice.

We will keep you posted on our XLI-Opt performance in the coming weeks.

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.

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 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 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 7: 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 1/27/2016.
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 1/27/2016.
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.