QuantDesk® Machine Learning Forecast
for the Week of September 19
The US indexes ended the week flat, as investors await the much anticipated Fed rate decision on Wednesday of this week. As dovish monetary policy in the US and globally is starting to reach its effectiveness limits, investors are growing more concerned over overvalued asset prices, historically low (at times even negative) interest rates and monetary policy running out of dry powder to further simulate economic growth. With the pending election in the US, focus cannot yet shift towards fiscal policy, which makes the next three to six months somewhat vulnerable, as evidenced by the spike in volatility in the US market last week.
The Chicago Board Options Exchange Volatility Index (VIX) spiked to 18.90 early in the week and continued on a wild ride, averaging daily peak to trough volatility of (+-)16.5% to end the week at 15.37.
Image 1: VIX September 12th to September 19th – Source: Google Finance
Past performance is not indicative of future returns.
West Texas Intermediate crude slumped -7.9% to $42.82 per barrel from $46.50 as an oversupplied market is projected by the International Energy Agency to persist into late 2017. This comes in sharp contrast to a statement issued just a month ago of oil markets balancing by the fourth quarter of 2016.
Image 2: USO September 12th to September 19th – Source: Google Finance
Past performance is not indicative of future returns.
Let’s Build a Low Volatility Active Portfolio
This week, as we have witnessed heightened market volatility and renewed flight to quality low volatility assets, I want to showcase an algorithmic approach to constructing a low-vol portfolio. As is evident by academic studies such as Black, Jensen & Scholes (1972), and Fama MacBeth (1973), low volatility stock portfolios often earn higher risk-adjusted returns against their respective cap weighted or average weighted benchmarks. Today I want to show you how, using QuantDesk, you can create your own actively managed portfolio that aims to outperform a popular low-vol ETF, SPLV.
Why SPLV?
SPLV seeks to invest 90% of its assets in the lowest volatility stocks in the S&P. It looks to track the Low Volatility Index and it actively rebalances its underlying assets based on historical volatility. If you look at SPLV’s performance in the last five years and year-to-date, it is apparent that SPLV had a stellar performance during both low volatility and especially during high volatility periods.
Image 3: SPLV vs. SPY twelve month return comparison – Source: Google Finance
Past performance is not indicative of future returns.
There is a high correlation between low volatility stocks and high dividend yield stocks since the latter tend to normalize their prices over time. As high dividend stocks get cheaper they get more desirable due to a more attractive dividend to price payout, which in turn causes the prices to rise again.
SPLV’s year-to-date returns stands at 8.22%, boasting a 2.05% dividend yield. With volatility approximately half of the S&P’s and a Sharpe ratio of 1.17, SPLV is a well suited benchmark to follow. On top of all that, Lucena’s Price Forecaster also confirms an SPLV projected price appreciation of almost 3% in the coming month.
Image 4: SPLV one-month price forecast using QuantDesk forecaster. Forecast volatility is projected at 0.50%
Past performance is not indicative of future returns.
So why look for an SPLV alterative? Can’t we just use SPLV?
There are two reasons:
- SPLV relies on historical volatility, an optimization model that fell apart during the financial crisis of 2008.
- We believe that by applying some of the machine learning disciplines available on QuantDesk®, we will be able to construct a well-hedged stock selection that could outperform SPLV in both total return and risk adjusted return.
Let’s get to work
Step 1: Replicated SPLV with 10 positions: Using Lucena’s portfolio replication technology we can easily identify the top 10 positions that replicate SPLV’s daily returns in the last 12 months. The Portfolio Replicator is a powerful technology that is able to reconstruct a time series chart of an index, a portfolio or an ETF/mutual fund from a predetermined equity basket.
Image 5: SPLV replica with 10 positions. Tracking error stands low at 0.18%
Past performance is not indicative of future returns.
Step 2: Construct a new portfolio: Using Lucena’s portfolio optimization technology, we can apply mean variance optimization (MVO) based on forecasted volatility (vs. historical volatility) in order to apply the proper allocation to our 10 constituents.
Image 6: Optimizing our 10 position based on SPLV replica. Blue represents equal weighted allocation and orange represents our optimized portfolio. As can be seen, through a single push of an optimization button, we were able to improve the lookback return, we lowered the volatility, and handsomely increased the Sharpe from 1.52 to 1.97.
Past performance is not indicative of future returns.
Step 3: Validate through backtesting: So now that we have a process of selecting 10 positions and optimizing their allocations, let’s validate how the process holds over time. We can travel back in time (let’s say 5 years) and repeat the process every month, by which we optimize our 10 positions and evaluate how our portfolio holds against the original SPLV.
Image 7: Backtesting our process over the last 5 years. As can be seen, our strategy (in orange) boasts a Sharpe of 1.35 vs. 1.00 of SPLV (in blue). Given a slightly higher volatility as indicated by the daily std. deviation (0.87% vs. 0.77%) we were able to achieve return of 172.63% vs. that of SPLV of 91.57%.
Step 4: Deploy “live” through forward testing simulation: One of the unique capabilities of QuantDesk is its ability to migrate any backtest to a “live” forward testing/paper traded portfolio. We have done exactly that and we will report on our newly formed portfolio’s performance in the weeks to come.
Conclusion
Using QuantDesk, a portfolio manager can autonomously implement an algorithmic trading idea as follows:
- Apply Lucena’s machine learning disciplines. Namely: Price Forecaster, Portfolio Optimizer, Hedge Finder, Event Analyzer and Portfolio Replicator.
- Backtest the algorithm over time through our comprehensive Backtester and performance attribution reporting.
- Deploy “live” into a forward paper trading process.
Any quant, trader, portfolio manager or analyst can create his/her own active machine-learning-based portfolio without writing a single line of code, and for a small fraction of the time and cost of a full research alternative.
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 8: 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 9/16/2016.
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 9/16/2016.
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.