QuantDesk® Machine Learning Forecast

for the Week of August 14th

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Predictive Analytics With Supply Chain Data

Erez Katz writes about Predictive Analytics with Supply Chain Data

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

With the rapid adoption of machine learning technology, alternative data has become essential to maximizing the benefit of quantitative research for investment. As traditional technical and fundamental data have become easily accessible, they have been exploited to a point in which even the most sophisticated machine learning applications are no longer sufficient to create a meaningful advantage. Consequently, fund managers have been on the hunt for alternative data sources to make their investment algorithms shine. Today, I want to discuss how supply chain data can be effective in forecasting asset prices.

What Is Supply Chain?

From Wikipedia: “The flow of goods and services, involves the movement and storage of raw materials, of work-in-process inventory, and of finished goods from point of origin to point of consumption.” Most public companies and in particular companies with inventories of goods, are part of a well-defined web of corporate dependencies.

We see the dupply chain flow from raw materials and sources to the end consumers
Image 1: An example of a supply chain from crude oil to an end product laptop. Source: Wikipedia

So How Can Supply Chain Be Helpful For Investment?

If we could create an elaborate web of all the supply-chain dependencies in the S&P 500, for example, we could study how the performance of one business could have a lagging effect on another. Particularly during earnings season, a business that reports an earnings surprise could indicate what’s to come with dependent companies yet to report their earnings. If Apple, for example, sees a meaningful increase in their laptop sales, the companies who manufacture keyboards for Apple’s laptops are likely to report good numbers as well.

How Can QuantDesk® Be Used To Identify And Exploit Supply Chain Dependencies?

There are two modules in QuantDesk® that have been proven to be most useful:

  1. QuantDesk’s Portfolio Replicator – By replicating the historical time series chart of one company, we can identify other companies with highly correlated performance. That level of correlation is most likely due to a supply chain dependency.
  2. QuantDesk’s Event Analyzer – Once we’ve identified the dependent asset, we can determine what price action of the original asset presents the most meaningful conditions for entering a position in the dependent asset. Moreover, we can identify the relative price move expectancy and the optimal hold time of the dependent asset.

Let’s Look At Apple, For Example:

First, let’s use the portfolio replication engine to see what asset in the S&P 500 is highly correlated to APPL . By inspecting the last two years of Apple’s price data, we’ve identified AVGO (BroadCom) to have the highest correlation. A quick search online yields the following piece of news from Motley Fool.

“Broadcom (NASDAQ:AVGO), which was merged with fellow semiconductor behemoth Avago in 2016, is a $94 billion company by market cap. Broadcom is a major supplier of wireless chips used in the iPhone, and there's been speculation lately that Broadcom and Apple are working together on wireless-charging technology that could be in the iPhone 8, expected to be released later this year. That would be a major positive development for both companies.” 
Image 2: Portfolio replication identifying which asset is mostly correlated to AAPL – AVGO (Broadcom) is identified.

Now that we’ve identified the supply chain relationship between APPL and AVGO, let’s analyze historically what happened to AVGO when APPL’s price moved significantly higher in one day.

The event definition is: APPL price move higher of 2% or more in one day. Specifically, we are looking at the event impact on AVGO: What happened to AVGO after AAPL’s price spike?

Image 3: Event definition: APPL price move higher by 2% or more in one day.

Inspecting AVGO price action after AAPL jumps 2% or more in one day, yields the following results:

Image 4: AVGO price move after AAPL jumps by 2% or more in one day.

Since 2010, there were 474 instances in which AAPL moved by 2% or more. On average, AVGO moved higher a month later by 2.94% above the market (prices are relative to the S&P 500). That’s a pretty compelling reason to buy AVGO if and when AAPL rises by 2% or more in one day. More strikingly, looking at AVGO up to one year later, its price continues to rise relative to the market by an average of 36%!

Image 5: AVGO price move one year after AAPL jumps by 2% or more in one day.

Conclusion

QuantDesk is an affordable, highly visual, and actionable platform that enables a portfolio manager with many of the capabilities of in-house Quants. With QuantDesk, users can identify investable opportunities using statistical forecasting and machine learning. In this particular example, we’ve demonstrated how supply chain dependencies can be detected and exploited for profit.

Strategies Update

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 – Lucena’s Long/Short Equity Strategy - YTD return of 10.11% vs. benchmark of -4.46%
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 46.65%, low volatility as expressed by its max-drawdown of only 6.16%, and a Sharpe of 1.89! (You can see a more detailed view of Tiebreaker’s performance below in this newsletter.)

BlackDog – Lucena’s Risk Parity - YTD return of 13.73 % vs. benchmark of 7.92%

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.

Utilities - Large-Cap Based Actively Managed - YTD return of 36.92% vs. 12.39% 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.

Image 3: Utilities based strategy– captured since November of 2016. Benchmark is XLU – Utilities select sector SPDR Past performance is no guarantee of future returns.

Industrials - Large-Cap Based Actively Managed - YTD Return of 9.97% vs. benchmark of 6.12%

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).

Image 4: Industrials-based strategy– captured since January 27, 2017 (covered during that week’s newsletter). Benchmark is XLI – Industrials select sector SPDR ETF. 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 6: 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 7: 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 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:

Active Long/Short US Equities Strategy
TTiebreaker: 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:

model portfolio performance compared to the SPY and Vanguard Balanced Index Fund
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.