QuantDesk® Machine Learning Forecast
for the Week of November 20th, 2017
Integrating Two Uncorrelated “Opinions” Into A Single Decision
by Erez Katz, CEO and Co-founder of Lucena Research.
We often find ourselves in a quandary when faced with making critical decisions. Imagine you were offered an attractive job opportunity in another country while you were quite content with your current situation. Rather than attempting to decide on your own, you would likely consult trusted friends and family. Inherently, your main goal is to validate your gut instinct by gaining consensus from your trusted “advisors.” Furthermore, your confidence in a decision would increase substantially if the backgrounds and motivations of your advisors vary. Hence, getting a consensus from family members alone would not be as convincing as a consensus from family, co-workers, and even total strangers combined.
Similarly, with an investment decision, when you combine your personal insight with additional “machine learning experts” (otherwise known as uncorrelated models) your confidence in your decision would be higher and empirically your decision would most likely be proven more accurate. Many of our deep-value bottom-up customers find machine learning and quantitative analysis an added layer of opinions geared to validate and enhance their traditional research.
Today, I want to show you how easy it is to identify uncorrelated machine learning experts and integrate their opinions into buy and sell decisions on QuantDesk®.
We will implement the following three easy steps:
- Create a multi-factor scan (“Expert 1”) using fundamental factors only.
- Create a multi-factor scan (“Expert 2”) using technical factors only.
- Conduct a backtest that only selects stocks when both “Expert 1” and “Expert 2” agree.
Step 1: Creating A Multi-Factor Scan (Expert 1) Using Fundamental Factors.
Using QuantDesk Event Analyzer Wizard this can be accomplished via the following steps:
|First, select Wizard and name your scan.||Step 1: Identify the universe from which to select your portfolio’s positions.|
|Step 2: Selecting the group of factors to consider in the scan.||Step 3: Identify the period in which to train the model and how many factors should be considered.|
That’s It!: The machine now goes to work and applies a unique feature selection algorithm to identify which fundamental factors, if used together in a scan, will produce stocks geared to consistently outperform the benchmark (SP 500) 21-days later.
Step 2: Creating A Multi-Factor Scan (Expert 2) Using Technical Factors.
Repeat the very same selection screens as in Step 1 but instead of selecting Fundamental Factors in step 2, select Technical Factors.
Step 3: Backtesting A Portfolio Composed Of Expert 1 And Expert 2 Agreeing On Stock Selection.
The backtest simulates running both scans together on the same day and only entering positions that are agreed upon by both scans (fundamental factors based and technical factors based).
As can be seen, the performance of our strategy handsomely outperforms the benchmark (S&P 500 index). The entire backtest produced much fewer transactions as there are fewer instances in which both experts agree. However, we can clearly see higher accuracy over time, as 82.61% of the transactions were successful. By combining two uncorrelated scans we’ve achieved less frequent transactions but with much higher returns accuracy than running each scan alone.
QuantDesk® is a powerful and flexible platform. Our most recent Wizard interface addition enables non-technical users with advanced machine-learning capabilities. What’s even more exciting is QuantDesk’s ability to quickly identify if a data source is predictive and adds value as an independent “expert.” For example, we can validate social media sentiment data with corporate actions data and location-based signals as uncorrelated models and decide which expert best contributes to a higher probability of future returns.
If you are interested in a trial or would like to see a demo of QuantDesk. Please reach out to us at by selecting the [Request a Trial] on our home page: www.lucenaresearch.com
Come and Meet us in Boston
Leveraging Central Bank and Corporate Communications with Machine Learning for Investment Decision Making
In this presentation, leaders from two emerging FinTech firms -- Prattle and Lucena Research -- show how they have joined forces 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. These signals include macroeconomic and foreign exchange pairs derived from central bank communications, and equity signals based on 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. This presentation will showcase how Lucena’s technologies separate actionable intelligence from noise.
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 has been forward traded since 2014 and to date it has enjoyed remarkably low volatility and boasts an impressive return of 52.14%, low volatility as expressed by its max-drawdown of only 6.16%, and a Sharpe of 1.97! (You can see a more detailed view of Tiebreaker’s performance below in this newsletter.)
BlackDog – Lucena’s Risk Parity - YTD return of 19.17 % vs. benchmark of 13.28%
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.
Utilities - Large-Cap Based Actively Managed - YTD return of 51.28% vs. 17.61% 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.
Industrials - Large-Cap Based Actively Managed - YTD Return of 20.06% vs. benchmark of 10.75%
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).
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.
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).
Model Tiebreaker, Lucena's Active Long/Short US Equities Strategy:
Model BlackDog 2X: Lucena's Tactical Asset Allocation Strategy:
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: firstname.lastname@example.org 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: email@example.com
Have a great week!
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.