QuantDesk® Machine Learning Forecast
for the Week of August 29
The Jackson Hole Federal Reserve Symposium and the Fed chair Janet Yellen’s remarks on Friday held investors on the sidelines as global markets remained unchanged with the US markets ending the week slightly lower. While the US Federal Reserve hinted of riper conditions for a gradual rate hike, the general consensus remains that the Fed will not act prior to the November elections. The recent spike in the VIX, however, points to investors growing more concerned about unsubstantiated PE multiples. For the time being, as traditional fixed income instruments such as treasuries and government bonds remain unattractive, investors continue to favor stocks and other riskier alternatives.
The VIX spiked higher from 12.79 to 13.65, but expectations are that it will likely continue to exhibit historically low levels until the next earnings season which will coincide with the November elections.
Image 1: VIX Aug 22nd to August 26th – Source: Google Finance
Past performance is not indicative of future returns.
On the oil front, the supply glut continues to pressure Brent and WTI crude even in the face of an unplanned September OPEC meeting to curtail record production in order to stabilize crude prices.
WTI Crude dropped from 48 to 47.65 for the week.
Image 2: USO Aug 22nd to August 26th – Source: Google Finance
Past performance is not indicative of future returns.
Incorporating Orthogonal Machine Learning Disciplines Into A Winning Strategy
Many algorithmic strategies are predicated on a single quantitative discipline. A strategy that is based on technical momentum conditions assumes entry and exit primarily based on single price/volume action of stocks. For example, entering a new positions based on certain stocks exhibiting a golden cross event (an event in which a fast moving average crosses above a slow moving average). With growing exposure to quantitative analysis, traders seek to stay ahead of the curve by incorporating multiple conditions to equity selections. We have demonstrated how Lucena developed sophisticated technology geared to dynamically discovering a selection criteria based on multiple conditions (also called factors) meant to provide the most opportune time for entry and exit in a portfolio (More information on how QuantDesk’s Event Analyzer works). The following illustration represents the process by which we uncovered the optimal factors and their thresholds for a one-month time horizon. As you can imagine, with over 450 factors and unlimited combinations, one needs to employ sophisticated technology in order to identify which factors and their corresponding thresholds should be used. We have described in prior newsletters how QuantDesk® Event Analyzer and our decision tree process help to discover set of factors for predetermined equity baskets and holding timeframes.
Image 3: Iterative discovery process to identify optimal factors and their thresholds for 21-trading-days (one month) returns.
The backtest performance report below is of a strategy called BullPen 20. We share this particular strategy with our QuantDesk subscribers as a way to illustrate how effective event-based investment can be. The backtest simulates rolling back time and buying up to five positions per day and holding them for one month. The selection is made based on a qualified scan which screens the Russell 1000 for stocks with the highest probability to rise relative to the market (S&P 500). The scan definition, its factors, and their thresholds are the product of the process described in Image 3.
Image 4: Bullpen 20 backtest 1/1/2010 through 8/25/2016. Benchmark $SPXTR (S&P adjusted for dividends and splits)
The performance above, although compelling, could be further enhanced by incorporating yet another machine learning discipline. Assume we have 10 positions on a certain daily scan. The strategy is looking for the best five and use a rank factor to hone in on the best 5 positions. For example, we can use Sharpe Ratio as the ranking factor to select the 5 positions with the highest Sharpe. Our empirical testing identified that by incorporating QuantDesk’s Price Forecaster to rank, it produces an even more compelling backtest outcome. QuantDesk’s Price Forecaster takes an equity basket and projects a market relative price forecast combined with a confidence measure. We have described QuantDesk’s Price Forecaster in detail in previous newsletters, as well (More information on how QuantDesk’s Price Forecaster works). We could just as easily use the Price Forecaster to rank the top five positions when the scan produces more than five securities for entry. In other words, we want to select the five positions with the highest combination of price changes and confidence measures.
Having the same backtest above with one modification by which we use the Price Forecaster to rank the top five positions per day produces the following backtest.
Image 5: Bullpen 20 backtest 1/1/2010 through 8/25/2016. Benchmark $SPXTR (S&P adjusted for dividends and splits). This backtest incorporates price forecaster ranking.
It is evident that the Price Forecaster ranking produced higher return of 346.44% vs. 238.20% at the cost of a slightly higher volatility but with an overall compelling risk adjusted return as measured by Sharpe ratio of 1.54 vs. 1.44.
As can be seen, applying orthogonal machine learning disciplines into one strategy can provide compelling results, but one will need the proper platform to enable him/her to do that with ease.
Analysis
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 8/26/2016.
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 8/26/2016.
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 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 Live Interactive Brokers Portfolio Performance
Live performance reports are taken from an interactive brokers account which attempts to follow Tiebreaker’s model closely with the following potential differences:
- Transactions Fees - Performance is net of transactions fees.
- Management Fees - Performance is net of management fees.
- Manager’s discretion – Manager can use own discretion as to final trade executions. For example, employing VWAP (volume weighted average price) and/or manually monitoring exit during stop loss and target gain.
- Hard to borrow and restricted stocks - Hard to borrow, and restricted stocks may be substituted with highly correlated alternatives.
- Dividends, interest or any other credits are reinvested.
- Slippage - Depending liquidity, large block purchases could impact certain stock prices unfavorably.
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. 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: [email protected] 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: [email protected]
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.