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 »
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 »
Posted this video on X/Twitter under @vecvizanalytics pre-market on the morning of 5/19/25 with the comment $TLT as of the 5/16/25 close, reviewed using systematic channel analysis, machine learning, and narrative in vide below. Pre-market on 5/19 TLT is at the 0.382 level of Nov2019/ Dec2023/Jan2025 anchored vs10. #stocks #analytics #quantfinance #charts We intend to
TLT as of 5/16/25, followed by methodology Explanations Read More »
Posted this video on X/Twitter under @vecvizanalytics on the morning of 5/16/25 with the comment “Do you agree that 593 is likely tough resistance?”. We intend to post case studies on X/Twitter first, to establish a “time-stamp”, then add the content here. If you are interested in methodological linkages to established quant finance principles, a
SPY at the 5/15/25 close, Followed by methodology Explanations Read More »
Posted this video on X/Twitter under @vecvizanalytics pre-market on the morning of 5/15/25 with the comment “$UNH chart looks like TDG circa March 2020 as per Vector Model chart shape metrics. That would be a good thing bulls over 3m-6m. Conversely, if UNH has truly “leveled down”, we show the case for settling in at
UNH at the 5/14/25 close, followed by methodology Explanations Read More »
Posted this video on X/Twitter under @vecvizanalytics on the afternoon of 5/13/25 with the comment “$NVDA strong today. Video below explores the near /intermediate term upside. Shows potential gap in resistance between $135 and $141, but Vector Model indicates absent an unusual bullish lift or catalyst getting past $143 probably takes a month.” We intend to
NVDA as of 5/12/25 close, followed by methodology Explanations 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