QuantDesk® Machine Learning Forecast

for the Week of November 28

Last week marked the third consecutive week of the post-election rally as the major indexes again reached record highs on Friday. Expectations of faster economic growth, lower tax rates, higher government spending, and less regulations are fueling bullish sentiment, although some analysts expressed caution about a market running ahead of itself on pure emotions. Although business-friendly fiscal policy is set to fuel growth, none of the new policies have been put in place yet. In addition, even when such policies are enacted their effect on the economy will not materialize for quite some time (possibly 12 to 18 months).

Volatility, as measured by the VIX, dropped to 12.34, a 7.57% drop since Monday.

Image 1: VIX November 18th to November 25th — Source: Google Finance
Past performance is not indicative of future returns.

Oil prices continue to trade within a narrow range and are subject to speculations on the impending production cut deal between OPEC and non-OPEC members. Although Iran is still the wild card, there seems to be a consensus forming between most OPEC members and the Russians of a cut in production output. The final terms and the extent of this cut are scheduled to be finalized in Vienna on Wednesday, 11/30/2016.

West Texas Intermediate crude ended at 45.96 after dropping 4% on Friday, when Russia and Saudi Arabia cancelled their meeting with non-OPEC producers. The Saudis quickly stated that the market should not read too much into such cancelation since they wish to concentrate on the OPEC meeting first.

Image 2: USO November 18th to November 25th – Source: Google Finance
Past performance is not indicative of future returns.

How Long Will the Rally Last?

I came across an interesting article this weekend on CNBC’s website.
“Cramer’s Charts Predict When the Trump Rally Will Run Out of Steam.”

The article states that by looking at the market direction alongside the VIX, one can tell if the rally is set to continue. Specifically, when the market moves higher while volatility moves lower, one can assume that fear of over-exuberance (as measured by the VIX) has not yet reached a critical mass and consequently, the rally has more room to run. I wanted to take on this theory and validate it on QuantDesk. Specifically, I wanted to use our Event Analyzer to measure the behavior of the S&P 500 index historically following a market rally while the VIX dropped.

First, I need set a benchmark. We all know that the market has been moving higher since the financial crisis of 2008/2009 and thus I wanted to measure the average monthly price move of the S&P (as measured by the SPY ETF) without any qualifying conditions. By marking SPY’s price every day and measuring how it moved 21 trading day later, I can easily gauge its 21-day average price move.

Image 3: SPY 21-day price action since 1/1/2004. As can be seen, there are 3249 trading days averaging 0.69% return on SPY.
Past performance is not indicative of future returns.

The top-ten 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: Event filter definition looking for SPY’s 21-day price action when the VIX moved lower as measured by the 21-day slow stochastic between 0 and 0.25 and the SPY moved higher as measured by the21-day slow stochastic between 0.75 and 1.

As can be seen, since 2004 there were 984 days in which the conditions defined above were met. On the average, 21 days later the SPY moved higher by 0.73% (or 73 BPs). On the surface this seems positive, especially where the margin of error is somewhat reasonable as measured by the standard deviation of the aggregate at 3.06% (see image 5 below). The chart also represents the optimal holding time following the event, which is 16 days.

Image 5: Scan results representing the SPY’s 21-day price action when the VIX moved lower as measures by the 21-day slow stochastic between 0 and 0.25, and the SPY moved higher as measured by the21-day slow stochastic between 0.75 and 1.

However, while looking at the results more carefully we need to see how much additional return the SPY experienced relative to the benchmark. Unfortunately, the 0.73% average return represents a measly 0.04% above the average of 0.69%. When considering slippage and transaction costs, this doesn’t look very compelling.

To prove the point one step further let’s run the same event scan, only this time evaluating an S&P rally with raising volatility. Based on Jim Cramer’s article this normally represents a trend reversal to the downside. This sounds logical as nervous investors (as represented by the raising fear index, the VIX) would tend to take chips off the table and lock in profits causing the market to drop. Let’s see if the data supports the theory.

Running the scan with a slow stochastic greater than 75% for both the S&P and the VIX is depicted in image 6 below.

Image 6: Scan definition representing the SPY’s 21-day price action when the VIX moved higher as measured by the 21-day slow stochastic greater than 0.75, and the SPY moved higher as measured by the 21-day slow stochastic greater than 0.75.

Interestingly, the results of the new scan represent an even more compelling bullishness for the rally. Contrary to Cramer’s theory we see an even greater average 21-day return of 2.09%, well above the 0.69% benchmark. It is important to note, however, that the situation in which there is a sustainable rally alongside a sustainable rise in volatility is less common. There were only 11 cases since 2004 that matched the criteria outlined.

Image 7: Scan results representing the SPY’s 21-day price action when the VIX moved higher as measured by the 21-day slow stochastic greater than 0.75, and the SPY moved higher as measured by the 21-day slow stochastic greater than 0.75.

Conclusion

Looking at the S&P and the VIX trends alone does not indicate any statistical significance on the S&P’s one-month outlook.

Forecasting the Top 10 Positions in the S&P

Lucena’s Forecaster uses a predetermined set of ten factors that are selected from a large set of over 450. 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 8: Default model for the coming week.

The top-ten 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 9: 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:

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Register today to secure your spot! Space is limited.
How to Design and Validate a Systematic Foreign Exchange Strategy

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 11/25/2016.
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 11/25/2016.
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: 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.