March 2, 2026 What do the 12 strategies we developed this summer and blogged about here say as we head into March 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 […]
VecViz vs Sigma Based MVO Ticker Weights at 2/28/2026 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 »
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 »
December 2, 2025 Correlation based on historic returns is arbitrarily calculated and ignores price context How long should the lookback window be? What periodicity of returns should be used? Such questions confront anyone using Pearson correlation of historic returns to estimate portfolio risk. Results can vary significantly depending on the choices made. What doesn’t matter
Price and Return Aware Correlation via VecViz Fingerprint Read More »
TL/DR: Exposure weightings based upon the ratio of Vector Model based “Expected Body” metrics to corresponding Sigma implied “Expected Body” metrics, have generated positive alpha over the highly volatile prior ~90 days spanning 2/18/25 thru 5/14/25, as they have for our overall 3+ year out of sample period, before accounting for transaction costs, financing, and
Expected Body Performance Highlights: 5/15/25 Read More »
Key issues confronting anyone reviewing a ticker’s merits as an investment, particularly one they are unfamiliar with: Vector Sets systematically select and frame the price history that matters most, given the most recent price. Vector Sets are the channels that VecViz systematically identifies from all combinations of tops and bottoms and then ranks according to