In my previous analysis , I explored the structural trade-offs between pure growth equities via QQQ and options-derived income via JEPQ. While static back tests provided valuable insights into performance across historical market phases, I wanted to create a more rigorous quantitative analysis, taking into account the non-stationarity of financial markets. To this end, I developed an unsupervised 3-state Gaussian Hidden Markov Model (HMM) that dynamically identifies market regimes in real-time and allocates capital among QQQ, JEPQ, and Risk-Free Cash. 1. Methodology & Unsupervised State Discovery Feature Engineering & Emission Selection Rather than relying on ad-hoc technical indicators, the HMM relies on two mathematical features observed at daily frequency: 1. QQQ Daily Log Returns: 2. QQQ 5-Day Realized Volatility: These emissions capture both price momentum and local volatility clustering. Model Mechanics & Expect...
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 4 th , 2022, to December 25 th , 2025. If I invested on Day 1, what would be my total return on Day x ? To an...