2/2/2026 Our thanks to Jessie Li for her assistance with key elements of the analysis discussed below. TL/DR: They do, despite the fact that volume is not a direct input to any VecViz model. More explicit consideration of volume leads to improvements in baseline VecViz analytic performance, particularly for the V-Score. Volume is a well-regarded […]
Do The Virtues of Volume Extend to VecViz? Read More »
2/2/2026, 9:00am What do the 12 strategies we developed this summer and blogged about here say as we head into February 2026? Before we share that let us first note that the strategies utilize volatility constraints ranging from 10% to 20%, and ticker concentration constraints ranging from 3% to 10%. VecViz has no idea where your risk
VecViz vs Sigma Based MVO Ticker Weights at 1/31/2026 Read More »
We are always wary of the potential for decay in the performance of VecViz’s suite of machine learning-based analytics, especially since we have done zero retraining. Thus, we are somewhat surprised that 2025 was perhaps the strongest consecutive 12 months for individual metric performance.in VecViz’s nearly four year out of sample performance period, Here we
2025 VecViz Analytics Performance Review Read More »
Strong December 2025 performance driven by P/E Ratio and V-Score criteria If purchased at the closing price of 12/10/25, the average of the 19 tickers in our 12/9/ 2025 close-based OpenBB filter returned 7.43% through the 1/12/26 close. This compares favorably with both the SPY, with a price return of 1.10% for the same period,
VecViz on OpenBB Filter: Dec2025 review/ Jan2026 Update Read More »
What do the 12 strategies we developed this summer and blogged about here say as we head into 2026? Before we share that let us first note that the strategies utilize volatility constraints ranging from 10% to 20%, and ticker concentration constraints ranging from 3% to 10%. VecViz has no idea where your risk tolerance
VecViz vs Sigma Based MVO Ticker Weights at 12/31/2025 Read More »
December 10, 2025 With year end approaching and debate as to whether there is an “AI bubble” all over financial media, we wanted to update the idea generation analysis we did using OpenBB’s Workspace back in August using 8/26/25 closing price based analytics (see youtube vid embedded below), and see if it generates any Mag7
Filtering VecViz on the OpenBB Workspace Amidst AI Bubble Concerns Read More »
December 10, 2025 We are approaching year-end amid widespread discussion of a potential “AI Bubble”. Let’s run the OpenBB Workspace facilitated analysis we posted on for AAPL this last summer (video embedded at end of note) for NVDA, to see if there is any unusually large disconnect between the market and fundamental based expectations for
NVDA Px Targets vs. Upside Px Probabilities via OpenBB Read More »
December 2, 2025 Each month we publish a 100+ page report for each individual VecViz volatility and expected return related features so that we can readily answer any of the following common concerns about summary performance stats: Quant Feature Quant Feature Eval Criteria Common Concerns: Expected forward return 1) correlation to forward returns2) interquartile range
December 2, 2025 Regimes help model builders differentiate market behavior into more stationary (and hence predictable) groupings. Regimes are commonly defined in terms of levels or growth rates in macroeconomic variables and market indices. Research on regime based models dates back at least to 1989. Here we discuss a regime framework built around the thesis
Breaking Good: VaR & OaR Breakage Rate Regimes Read More »
December 2, 2025 Correlation measured using trailing returns is volatile and backward looking Many investors measure diversification via historic return correlation, which is itself correlated to volatility. When incorporated into portfolio risk metrics such as VaR, it can encourage selling amidst panics and buying amidst complacent booms. VecEvent based correlation is more stable and very
Quantifying Narrative Similarity with VecEvent Correlation Read More »