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 […]
VecViz provides “Support and Resistance Based Investment Analytics”. Support and Resistance are long standing, popular concepts in technical analysis. However, the definition of neither “Support” nor “Resistance” has ever been well settled, let alone the methodology for identifying and measuring it. In the table below we summarize four papers from academia that focus on Support
Support and Resistance’s Evolving Definition Read More »
Mean Variance Optimization is primarily the concern of institutional investors and quants, but this blog can still be of interest to individual investors who actively consider their personal asset allocation. With month end, quarter end, and fiscal year end approaching, many institutional investment teams are in the late innings of portfolio strategy and asset allocation
Mitigating Common Mean Variance Optimization Process Challenges Read More »
When a recent contact forwarded “(Re-)Imag(in)ing Price Trends” to me this spring, I was greatly heartened. I knew my undertaking in developing VecViz’s Vector Model and V-Score – distilling predictive performance insights from price history charts, was ambitious. I believed that I had done so, but convincing others that it was even theoretically possible was
Comparison can aid definition. In other blog posts we have discussed the approach of VecViz and its Vector Model to price probability. Here we seek to further illuminate that approach through qualitative comparison to a few well-established volatility model archetypes that enjoy significant institutional money manager use. Specifically, we seek to compare the Vector Model
An LLM’s Comparison of VecViz to Established Vol Models Read More »
“Sigma” vol is familiar and easy but has important flaws. “Sigma” type volatility metrics that rely on assumptions of normally, independent, identically distributed returns have many shortcomings when applied to financial markets, including: More realistic, established vol models are challenging to implement. Sigma’s shortcomings have for decades motivated the creation of many flavors of “local
Realistic Vol Estimates through Support, Resistance and Machine Learning Read More »
Vector Set Strength decreases with distance and time Like the wake behind a speedboat, Vector Set Strength generally dissipates with distance and time from the model date price. Therefore, Vector Sets anchored by recent tops and bottoms have greater strength than those formed from tops and bottoms that occurred in the distant past. Likewise, Vector
“Vector Strength” quantifies support and resistance Read More »
In this post we (1) link chart support and resistance lines to the concept of “vectors” from introductory physics, (2) describe the associated zone of influence for the identified vectors, (3) describe how vectors can aid explain-ability of forward prices, (4) introduce the concept of Vector Strength. A ticker’s price reflects the net balance of
Vector Based Support and Resistance Read More »
“Let me warn you, Icarus, to take the middle way, in case the moisture weighs down your wings, if you fly too low, or if you go too high, the sun scorches them. Travel between the extremes.” Ovid, Metamorpheses Similar to Daedelus’ warning to Icarus regarding his wings, VecViz advises you use the Vector Model
“Let me warn you…” of the limitations of VecViz’s Analytics. Read More »