QuantDesk® Machine Learning Forecast

for the Week of January 16

Last Week’s Summary:
The week ended marginally lower for US stocks while December retail sales came below expectations. Although in aggregate, year over year sales came in higher by 0.6%, excluding auto sales and gasoline consumption, retail figures were flat. With the kickoff of the year-end earnings season, the disparity between analysts’ earnings expectations are wider than we have seen recently, a notion geared to disrupt the low volatility environment we’ve become accustomed to. Since the November election, investors have been mostly excited about Trump’s pro-business policies. Lower corporate tax and less regulations combined with infrastructure spending have mostly been welcomed in the investment community. However, as President-elect Trump is about to take office, investors will pay close attention to the implementation of such policies.

Murky Outlook for Oil
Opposing forces are holding oil prices in a delicate balance. Excess production and supply is countered by growing demand, OPEC production cuts and political unrest. While oil production and consumption are at historically high levels, the oil industry is going through a major transformation. In 2016, after oil prices fell as a result of excess production, for the first time in years OPEC seemed committed to curtailing production in order to stabilize prices. The US, on the other hand, the world’s largest oil consumer, is growing more independent of oil imports as active oil rigs continue to come back on-line to pre-2016 levels. In addition, oil producers who held production due to political instability are coming back online with fresh oil supply. These are Iraq, Libya and Nigeria although the latter is still subject to disruption by the Niger Delta Avengers, a militant faction fighting the government for influence over Nigeria’s oil supply. In the short-term oil futures are holding firm slightly above 50 but are subject to disruptions at any time. Today, I want to deploy Lucena’s technology in an attempt to identify short term investment opportunities around crude prices.

One-Month Oil Forecast:
Last week, I made the case for a bearish gold outlook. We created a portfolio with five positions that we’ve identified as highly correlated to gold and projected their one-month price forecast relative to the S&P. If you recall, we projected the portfolio to move lower, and last week the portfolio indeed moved down, in line with our projections.

Image 1: Gold correlated portfolio 1 week price performance. Our bearish projection were realized with a market relative down draft of -1.49%
Past performance is not indicative of future returns.

This week, I followed the same process to project oil prices. I’ve applied two distinct statistical forecasting disciplines in two steps:

1) Identify a basket of 5 positions that are highly correlated to gold.
Using Lucena’s portfolio replication technology, I have identified the following basket that together (as one portfolio or one unit) move along with USO. As expected, the replication engine identified oil related companies varying from oil exploration, to oil retail and technology.

COPConoco Phillips21.4%
MURMurphy Oil Corp12.1%
SLBSchlumberger NV33.3%
XECCimarex Energy Co18.8%

Image 2: Using Lucena’s portfolio replication technology we have identified a basket of 5 positions that move in lockstep with USO oil ETF.
Past performance is not indicative of future results.

2) Forecast the basket’s price trajectory for the next month.
Using Lucena’s machine learning Forecaster, I want to see if there is a consensus of a price trajectory on all of the positions outlined as highly correlated to oil. Examining the output below, we see a mixed outlook as some stocks are projected to appreciate while others are expected to drop. More importantly, the Forecaster suggests that if we proportionally allocate our long/short portfolio based on the Forecaster’s output, our portfolio will enjoy a healthy +1.84% market relative return.

Image 3: Lucena’s Price Forecaster, projecting a one-month price action of our highly correlated to oil basket.
Past performance is not indicative of future results.

As can be seen, the Price Forecaster confidence score is low in two of the long securities due to the high volatility of the underlying constituents (yellow stars). However, whenever the Forecaster’s confidence was low the backtest confidence score was high. Empirically speaking, the forecaster was able to project uptrends fairly effectively judging from the backtest confidence scores (blue stars).

In order to address this mixed bag of longs and shorts, I’ve tasked Lucena’s Portfolio Optimizer to proportionally allocate our $1M initial cash between our constituents based on our Price Forecaster. Using the Price Forecaster as input to the Optimizer enables the mean variance optimizer (MVO) to take into account the projected prices and volatility of the constituents vs. only their historical price average.

Image 4: Lucena’s Portfolio Optimizer, suggests the optimal allocations for maximum risk adjusted return (Sharpe Ratio).
The blue line represents the portfolio before optimization and the orange line represent the optimized portfolio. Please note the 2 longs and 3 short constituents. Also, note the blue cone (presents the projected wide variance) compared to the orange cone which is much smaller representing lower volatility (or higher confidence).
Past performance is not indicative of future results.

We will continue to monitor the above portfolio and report on its progress 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 delineates a 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 6: 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 1/13/2016.
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 1/13/2016.
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.