Skip to main content

JEPQ vs. QQQ: Finding Signals in the Noise


As a first-time investor, researching on what financial instruments to invest in, I narrowed my options to two technology ETFs, JEPQ and QQQ. QQQ tracks the NASDAQ 100 index, meaning its fluctuations are tied to that of the NASDAQ 100. JEPQ tracks some of the top stocks in the NASDAQ 100, and sells covered calls to generate monthly dividends for investors. On paper, it seems like JEPQ is for more conservative investors, capping the potential gains with the calls, but also insuring against sharp falls. QQQ, on the other hand, is subject to the whims of the market, with the potential for large gains, but also the risk of steep falls. I wanted to leverage my expertise with data, and see if there were any patterns or trends that I could spot, that would give me a better chance of having success with my investments. For this, I used the daily price data for JEPQ and QQQ from May 4th, 2022, to December 25th, 2025.

If I invested on Day 1, what would be my total return on Day x?

To answer this question, I took the cumulative returns of JEPQ and QQQ. Looking at the big picture, we can see that despite JEPQ holding high-performance stocks and paying out dividends, QQQ shows a greater increase in returns over a longer-term period.



This is because selling options to generate short term income caps the upside of JEPQ. In a bull market, QQQ captures 100% of the increase, allowing its growth to compound. JEPQ hits its call strike on those specific days, meaning that despite creating immediate income by selling the shares through calls, the long-term growth of the fund is hindered by the necessity of fulfilling the calls. I also found that both funds have a strong negative correlation with volatility (VXN) spikes. When market risk increases, both QQQ and JEPQ take a hit. The presence of a covered call does not allow JEPQ to completely escape the loss-making trend. However, JEPQ does protect against the high losses that QQQ can face. When calculating the capture ratios, I found that JEPQ captures about 66.8% of QQQ’s upside on good days, and 69.1% of QQQ's downside on bad days. This shows that while JEPQ may not give the same upside that QQQ does, it also does not completely sink to the lows that QQQ can.

Now that I found how each fund performed over a long-term period, I wanted to figure out when to invest.

So, I decided to try and identify some more granular trends. I used the QQQ data, because it is a superset for JEPQ, and as such, can also serve as a proxy for the trends observed in the JEPQ fund. I applied a 63-day centered moving average (roughly one trading quarter) to smooth out the daily noise. By finding the local maxima and minima of the smoothed curve, I divided the timeline into distinct Market Expansions and Market Contractions.



Once
the phases were mapped, I calculated the exact slope (the rate of change) for both QQQ and JEPQ between those turning points. From this, I found that during a Market Expansion, QQQ accelerates aggressively. Its slope is incredibly steep. JEPQ steadily climbs alongside it, but at a visibly flatter trajectory. During a Market Contraction, QQQ's slope plummets sharply. JEPQ also declines, but its slope is noticeably less severe, which validates the previous conclusions that we found.



Overall, QQQ is best for long term investors. Because of its inexorable rise, investing in QQQ will eventually lead to returns, despite the fluctuations observed in the short term. JEPQ is more suitable for conservative investors. The potential gains are capped, and the losses are ameliorated by the dividend from the call option. The monthly dividends also ensure steady cash flow.
To build upon this research, given the observed correlation between the prices and the VXN level, I would like to try to create a trading model to predict the state of the VXN and whether to enter or exit the market based on the guidance of the model.

Comments

  1. Very insightful, as validating with data what is known about JEPQ. The expansion and contraction periods identification and comparison was interesting. Looking forward to the next iteration.

    ReplyDelete
  2. Very cool insights. Looking to learn more from your upcoming models.

    ReplyDelete

Post a Comment

Popular posts from this blog

Gone Too Soon: The Story Of Dražen Petrović

In the 1989-90 season, the Portland Trail Blazers bought out Dražen Petrović’s contract with Real Madrid and convinced him to join the NBA. This would mark the start of a trailblazing career that was tragically cut short.        Dražen Petrović was born in Šibenik, Croatia on the 22 nd of October, 1964. At the age of 15, he was already in the first team of his hometown club, and by the age of 18, Petrović had blossomed into a star for Šibenik. After serving in the military for a year, he moved to Cibona in 1984, where he would play till 1988. At Cibona, Petrović shined. He once scored 112 points in a Yugoslavian League game ( 40/60 FG, 10/20 3Pts, 22/22 FT), which is possibly the most efficient performance in any European league ever. He averaged 37.7 points in the Yugoslavian first division and 33.8 points in European competitions in his 4 years at Cibona, cementing his status as a European star. In 1988, at the age of 23, he moved to Real Madrid, where he stayed ...

An Analysis of Car and Driver Impact on Formula 1 Success - Part 2

  In the previous part of this project, I looked at the variables I was using, and some of the trends that I identified through a preliminary analysis. Part 2 of this project is dedicated to:  - The research questions I formulated  - The statistical analyses that I used for each question  - The interpretation of my analysis  - What conclusions I was able to draw to answer each research question Research Questions Based on my preliminary analysis of the variables that I was working with, I came up with more questions that I was interested in exploring, in addition to my original goal of figuring out whether the car or driver was more crucial to Formula 1 success. One of the first things that piqued my interest was how the different points systems affected overall scoring. While it was immediately clear that the change in point scoring systems from 10 points for a win to 25 points for a win resulted in drastic changes to the point totals, my hypothesis was ...

The Evolution Of Moreyball

Most casual NBA fans know the term ‘Moreyball’ from watching the Houston Rockets. It gets it name from the Rockets’ GM, Daryl Morey, who helped turn Houston from also-rans into consistent championship contenders. But most people don’t know the origins of Moreyball and how it evolved into its current form. Moreyball has its roots in analytics, consistent with Morey’s status as a co-founder of the MIT Sloan Sports Analytics Conference. The essence of the style is to take analytics-friendly shots (3 pointers and shots at the rim) while reducing the amount of non-analytical friendly shots (the long two or mid-range shot). This is mainly achieved by surrounding a big man with four shooters, ensuring that both 3-pointers as well as shots at the rim are taken. One of the first iterations of Moreyball was showcased by the 2009 Orlando Magic, who surrounded star center Dwight Howard with four shooters. This novel idea helped the Magic reach the 2009 NBA Finals, where they would go on to los...