QuantDesk® Machine Learning Forecast

for the Week of March 26th, 2018

When One Machine Trains Another

Erez Katz writes about Betting On US Defense Stocks

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

Last year, Tucker and I were invited to visit Bob Pisani from CNBC. At the time there was a big brouhaha surrounding Black Rock replacing a few senior financial analysts with machines powered by AI. I wrote an article with the intent of putting the role of artificial intelligence in finance in perspective. The premise of the article was that except for high frequency trading, where speed takes a higher priority over deterministic best solutions, machines are many decades away from replicating human cognitive reasoning, intuition or intellect.

My article hit someone’s radar at CBNC and I was invited to speak with Bob Pisani at his office at the New York Stock Exchange. As Dr. Tucker Balch (Lucena’s co-founder and chief scientist) and I passed through security, we met Pisani in the hallway rushing back to his office. I stopped him to introduce ourselves and was welcomed with a somewhat hesitant response as if he regretted that he invited us. Reluctantly, he pointed the way to his office overlooking the NYSE floor, and told us that we have 15 minutes. As the conversation unfolded and the discussion extended to more than an hour, I found Pisani to be bright, quick on his feet, and well informed in advanced machine learning concepts and we had a lovely conversation about the role of AI in our lives. Mr. Pisani made a few statements that made it seem like he agrees with those who believe in technological singularity, where advancements in AI are accelerating so remarkably that it will soon threaten the very fabric of society and the human race. At the time my opinion was a bit more reserved as to the extent of the impact machine learning can make on our lives. I remember vividly that we ended the conversation with Tucker stating that in order to make machines capable of exceeding humans intellect, we need to understand the underlying mechanics of the intangibles of human’s intellect (like feelings, instincts, intuition, and cognitive reasoning). In other words, machines are constrained by the limitations of the humans who create them. Since that meeting, my opinion on the possibility of machines exceeding the limits of human intelligence had changed.

Image 1: Bob Pisani, CNBC news correspondent.

Generative Adversarial Networks (GANs)

Generative adversarial networks (GANs) are deep neural net architectures comprised of two nets, competing against each other. In order to understand why GANs is such an exciting concept with such far reaching implications, we first need to understand conceptually how neural networks “learn.”

I will attempt to stay abstract here and shy away from actual math, but for those of you who wish to learn more, I highly recommend checking out the “Gradient Descent, how neural networks learn” by 3blue1Brown.

Neural networks learn by iteratively attempting to minimize some measure of error, typically defined as an error function. The error function measures how off the model’s output is compared to the desired (correct) output as exemplified by a sample labeled data. For example, we can feed a model with lots of examples of a stock price relative to its 21-day simple moving average, where each input example is paired with a labeled output of how the price of the stock changed five days later.

Image 2: Sample input and labeled output. The last row asks the model to predict the 5-day price change based on the sample data.

We then ask the machine to predict a price move five days later based on new unseen data (see the row with the cell marked in yellow). Based on how off the machine’s prediction was vs. what actually transpired, we ask the model to adjust its variables (normally measured by weights and biases) in order to minimize the error. The mechanics of how this really works is fascinating and beyond the scope of this article, but it is clear that the machine can only learn based on sample data that is provided by humans. Generative Adversarial Networks enables two neural nets feeding against each other. One generates sample data (“generator”) and the other tries to discriminate between valid label data and fake label data (“discriminator”). The process in which two networks play against each other forces the generator to get smarter and more sophisticated as it tries to fool the discriminator by providing data that is seemingly valid, while the discriminator gets smarter over time by being able to sharpen its algorithm and disseminate between fake data and valid data. This is a great example of machine intelligence without humans. The machines are able to train each other and achieve results that are beyond the limitations of a human being.

Today, GANs are mainly used for creating new concepts in imagery and in electronic gaming. However, as this technology continues to evolve, we will be able to see new innovative models spanning just about any vertical (including finance) by which the machine can create models that far exceeds the limitations of human imagination.

Strategies Update

As in the past, we will provide weekly updates on how the model portfolios and the theme-based strategies we cover in this newsletter are performing.

Tiebreaker – Lucena’s Long/Short Equity Strategy - YTD return of -0.64% vs. benchmark of 0.43%
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 50.65%, low volatility as expressed by its max-drawdown of only 6.16%, and a Sharpe of 1.74! (You can see a more detailed view of Tiebreaker’s performance below in this newsletter.)

BlackDog – Lucena’s Risk Parity - YTD return of -6.40% vs. benchmark of -2.44%

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.

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 3: 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 4: 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:
Forecaster
QuantDesk Overview

Analysis

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 5: 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:

12 Month Performance BlackDog and Tiebreaker
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:

12 Month Performance BlackDog and Tiebreaker
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.

Appendix

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.

Specifically:

  • 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

erez@lucenaresearch.com


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.