1. Why does the world need the VecViz charting framework, or VecViz’s Vector Model or V-Score?
VecViz Charting Framework:
To our knowledge, VecViz’s Vector Strength Histogram is the only charting framework that (a) provides any explanation for how forward prices could come to occur, and (b) does so for a broad spectrum of price points to the upside and downside.
The explanation that the Vector Strength Histogram provides consists, at a minimum, of the Vector Sets that terminate at the prices on the forward price spectrum. Each Vector Set depicts a trajectory. VecViz clearly identifies the trajectories in terms of the tops and bottoms from which the Vector Sets originate. Furthermore, VecViz scores each Vector Set in terms of its expected influence upon the ticker, or Vector Strength.
Once a user knows the “strongest” trajectory consistent with a given price of interest they are likely to ask, “Is the ticker’s outlook consistent with that trajectory (and the specific Vector Set level within the trajectory closest to the given price)?”. VecViz believes that exploring such questions is beneficial to risk and opportunity cognition.
Brief, ticker relevant “VecEvent” narrative elements can further enhance a user’s understanding of Vector Set trajectories. VecViz can systematically tag VecEvents to a ticker’s Vector Set. VecViz is the only charting framework to our knowledge that can embed relevant narrative elements into a chart. By utilizing both trajectory and narrative, the VecViz framework makes cognition of investment risk and opportunity more accessible to both quant and non-quant investors.
Vector Model of Forward Price Probability:
The Vector Model provides investors with volatility forecasts that reflect the jumpy, clustering, and price dependent behavior of realized and option implied volatility in the financial markets. Such forecasts were previously available only via Heston Volatility or some flavor of Rough Volatility, models most investors would struggle to comprehend.
Furthermore, very few trading or charting platforms make even deterministic “Sigma” volatility (i.e., the square root of the variance of returns, the stuff of Stats 101 or Finance 101 courses), available to their users. The Sigma approach to volatility was, afterall, good enough to garner the Nobel prize for the Black Scholes option pricing formula, and the VIX, which is the sigma based volatility implied by near the money S&P500 options prices, is widely quoted. Yet, despite this dearth of information on volatility, options market activity is fairly broad based and robust. VecViz presents Sigma based volatility estimates alongside nearly all Vector Model based volatility estimates.
V-Score of expected forward relative price performance:
Based on inputs not available elsewhere, VecViz’s V-Score may provide some investors incremental information on the relative price performance prospects for a large group of tickers. However, given the constantly growing list of providers of quantitative stock picking models this is at best a tertiary reason to offer the V-Score. VecViz does not claim to have “the best” or even a top ranking stock picking model, though we will constantly report on its performance (as we do with nearly all other VecViz analytics).
The primary purpose of the V-Score is to pre-empt the natural question “are these charts telling me to buy or sell?” in a clear, efficient, objective, thorough manner. With that question answered before they ask it, we expect VecViz users will have more energy and impetus to engage with the “V-Score Criteria Closest Comparables” chart and the Vector Strength Histogram, if only to interrogate the V-Score. In so doing we believe they will improve their cognition of risk and opportunity, and more likely than not arrive at their own conclusions about buying and selling.
The secondary reason we offer the V-Score it is to complement it with the aforementioned “V-Score Criteria Closest Comparables” chart. Here we identify the closest matches to the ticker of interest on the basis of the V-Score criteria. We identify the best match from among top quintile performing ticker-model dates and the best match from the bottom quintile performing ticker-model dates for the selected time horizon. The “Really, is that so?” reaction these closest comparables often draw can prompt users to consider the ticker under consideration from a new perspective, and to engage with the Vector Strength Histogram, as all the V-Score criteria are drawn from it.
2. How does the VecViz charting framework make cognition of investment risk and opportunity more accessible?
Cognition, as defined by Oxford Languages is “the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses“. VecViz delivers cognition consistent with this definition in several respects.
First, VecViz is a highly visual, interactive dashboard that can engage users on the subject of investment risk and opportunity visually, through narrative, and even tactically via the tooltip and filter functionality.
Second, similar to how a close reader can identify the events in a novel that foreshadow the ending, VecViz identifies every trajectory resulting from major price chart tops and bottoms that points to the current price and typically a broad range of prices above and below it as well. With the strongest Vector Set that best envelops a given price and the associated VecDates and VecEvents identified, you can consider how that price was “foreshadowed” by that Vector Set.
Third, VecViz can help encourage dialogue, a critical path to cognition and understanding. That dialogue could be internal, within an investor, as they consider the forward relevance of a Vector Set best supporting their broker’s price target for a stock, or a V-Score or V-Score criteria closest comparable that challenges their prior opinion of a ticker. Alternatively, that dialogue could be among a team of investors, particularly if they have populated VecViz with VecEvents authored by their own analysts.
3. What are “Support” and “Resistance” and how does VecViz incorporate them into its investment analytics?
Most definitions of Support and Resistance available on the internet we are aware of define Support and Resistance as zones of buying and selling pressure, typically identified with horizontal lines at tops or bottoms or with trend lines or Fibonacci channels connecting tops or bottoms.
From a mechanical, processing of numbers perspective, VecViz relies on tops and bottoms as well, constructing three tiers of Fibonacci channels from them. What sets VecViz apart from other charting providers is that it identifies nearly every conceivable channel set resulting from a ticker’s major tops and bottoms for the user, scores the support and resistance each is likely to represent, and ranks the channels accordingly. We call that scoring “Vector Strength”.
Cumulative Vector Strength between the current price and any forward price is the support or resistance to that price, and is depicted for the user on the Vector Strength Histogram. The Vector Model of price probability utilizes machine learning to define the likelihood of forward price movement scaled in terms of Vector Strength traversed in terms of a collection of Vector Strength Histogram related “chart shape” metrics for the associated ticker -model dates. All VecViz investment analytics relate either to the Vector Model or the chart shape metric inputs, and are thus “Support and Resistance based”.
However, VecViz encourages its users to apply conceptually richer definitions of “Support” and “Resistance” that it believes allow the Vector Strength Histogram to further enhance cognition of investment risk and opportunity. Tops and bottoms in these definitions of Support and Resistance reflect changes in the net balance of forces, or “Vectors” acting upon a ticker, and the channels they anchor represent net vector balance trajectories. For more on these concepts, read this blog.
4. How is Vector Strength calculated and how is it used?
Vector Strength is calculated using the criteria listed below, each of which is visible on a ticker’s price chart. While we are going to keep the specifics of the function that applies them proprietary while our patent application is pending, we can share the directionality of their impact, which we expect you will find to be fairly intuitive:
a) VecDate proximity to the Model Date (closer proximity => greater Vector Strength)
b) Vector price level proximity to the Model Date price (closer proximity => greater Vector Strength)
c) the # of tops and bottoms the Vectors of the Vector Set touched from inception to the Model Date, including minor tops and bottoms (more touches => greater Vector Strength)
Vector Strength is used by VecViz to construct the Vector Strength Histogram from which you can identify the strongest Vectors associated with a typically broad spectrum of prices to the upside and downside. See FAQ #2 above for more detail.
For more on Vector Strength from a conceptual perspective, please see this blog post.
5. How are the Vector Model price probability percentiles and related analytics calculated?
Forward price movement scaled in terms of cumulative Vector Strength is what Vector Model AI is trained to predict, on the basis of vector chart shape profile. The forecast for a ticker, based on its chart shape is then applied to its distribution of vector strength to arrive at price probability distribution.
The Vector Chart Shape profile, uses some, but not all of the features displayed on the V-Score spider chart that compares the selected ticker with the closest matching ticker-model dates among top quintile and bottom quintile performers for the selected time horizon. It also uses a some additional features.
6. What is “Sigma” and how are Sigma price probability percentiles calculated?
The foundation of Sigma as presented alongside Vector Model output by VecViz is the standard deviation of price based returns that gets discussed in any introductory book on risk or portfolio management. This is the same definition of volatility that is utilized in the Black Scholes option pricing formula.
We present Sigma based on daily log based returns and a lookback period of two years. To enhance Sigma’s accuracy as an indicator of forward volatility we apply a 6 month half-life rate of decay to the weightings applied to the returns used to calculate Sigma.
The resulting standard deviation of returns is converted to probabilities by applying multipliers associated with the standard normal (i.e. Gaussian) distribution (ex: -1.645 for 95% VaR, -2.326 for 99% VaR). The daily standard deviation is used to estimate the probability percentiles for longer time horizons by multiplying it by the square root of the number of trading days in the given horizon (implicitly assuming daily returns are independent and identically distributed). So, for example, the multiplier that converts daily horizon sigma to 1 year horizon sigma is the square root of 252.
7. What are VecEvents and VecDates? How are they determined? How are they linked to Vector Sets?
VecEvents are news events, themes, phenomena that occurred over a specified time period that were influential and relevant to the specified ticker(s), or macro market. At present, VecEvents are sourced internally by VecViz.
VecDates are the dates of the tops and bottoms that anchor a Vector Set. VecEvents are anchored to VectorSets that have VecDates residing within the time span of the VecEvent’s influence.
8. What is the V-Score? How does it relate to the Vector Model?
The V-Score is an indicator of expected relative performance, based entirely on VecViz and Vector Model related parameters and output as processed by an ensemble of machine learning techniques. A preliminary V-Score is calculated for each of the 6 forecast horizons addressed by the Vector Model. These preliminary scores from -2 (lowest quintile) to +2 (highest quintile). The sum of the preliminary V-Scores across the 6 time horizons, is the final V-Score, and it ranges from -12 to +12.
The features that the preliminary V-Scores is trained upon are listed and described in the V-Score Spider Chart. The rungs of the spider chart represent the quintile rank of each feature. Feature percentile values for the selected ticker and the best matching top quintile performer and bottom quintile performer are plotted on these rungs. Some of the features are Vector Model outputs and some are Vector Model inputs or other broader VecViz framework related metrics.
9. What is “Vector_BodyFrcst”, “EUB”, “EDB”?
The Vector Set Study area of the main VecViz dashboard features the option to display the “Vector_BodyFrcst” and the table below it that details prices by date features a row for “EUB” and “EDB”. What are these things?
The “EUB” is the “Expected Up Body” and the EDB is the “Expected Down Body” and together they comprise the “Vector_BodyFrcst”. The term “Body” in these labels refers to the body of the probability distribution, which we define as residing between the 95th percentiles upward and downward (95U and 99D) accorrding to the Vector Model.
The “Expected Body Up”, or EUB, is the probability weighted average value between the model date price and the 95U price according to the Vector Model. It tells you what level to expect over the given forecast horizon if price rises but stays inside the tail of the distribution (95U and upward).
Likewise, the “Expected Body Down”, or EDB, is the probability weighted average value between the model date price and the 95D price according to the Vector Model. It tells you what level to expect over the given forecast horizon if price rises but stays inside the tail of the distribution (95U and upward).
10. What are VaR and OaR?
VaR: Value at Risk. The maximum amount you could lose by being long a ticker as of a specified date in the future at a specified level of probability. If VaR was accurate then losses incurred from being invested would exceed VaR in (1-specified probability)% of all specified dates. Importantly, VaR estimates for specified forward dates beyond the next day do not represent the minimum price forecast for the entire period between the model date and the specified forward date. They apply to the specified forward date only (or the next subsequent trading day if the market is closed that date), and their accuracy as presented on this website is presented on that basis. The VaR levels presented on vecviz.com dashboards are 95D and 99D for the Vector Model (depicted in blue), and Sigma_95D and Sigma_99D for Sigma (depicted in red). We monitor VaR breakage rates for the Vector Model and Sigma in the VaR dashboards.
OaR: Opportunity at Risk. The maximum amount of gain you could lose out on over a specified time horizon at a specified level of probability by being uninvested. If OaR was accurate then gains foregone by being uninvested would exceed OaR in (1-specified probability)% of all periods. Importantly, OaR estimates for specified forward dates beyond the next day do not represent the maximum price forecast for the entire period between the model date and the specified forward date. They apply to the specified forward date only (or the next subsequent trading day if the market is closed that date), and their accuracy as presented on this website is presented on that basis. The OaR levels presented on the vecviz.com dashboards are 95U and 99U for the Vector Model (depicted in blue), and Sigma_95U and Sigma_99U for Sigma (depicted in red). We monitor OaR breakage rates for the Vector Model and Sigma in the OaR dashboards.
Please note that we cap VaR at 99.5% losses for stocks.
11. What are ROVBC and ROOBC?
These metrics attempt to capture the impact on investor returns of using the Vector Model instead of Sigma VaR and OaR metrics to size positions, assuming the investor has a fixed risk budget per ticker.
Return on VaR Based Capital (ROVBC) assumes that Sigma earns the return of the ticker and the Vector Model earns a return proportionate to that, where the proportion is the ratio of Sigma VaR / Vector VaR, subject to a cap and floor (we use 300% and 33.33%). So, for example, if Sigma said VaR was 2.00% and the Vector Model said VaR was 4.00%, the Vector Model ROVBC would be half of Sigma’s. Likewise, if the Vector Model said VaR was 1.00% the Vector Model’s ROVBC would be double Sigma’s. Higher ROVBC is preferred to lower ROVBC.
For the Vector Model ROVBC to be higher than Sigma’s it signifies that either (1) Vector Model VaR exceeded Sigma’s VaR (to the downside) and the ticker traded lower, or (2) Sigma VaR exceeded the Vector Model’s VaR (to the downside) and the ticker traded higher.
Return on OaR Based Capital (ROOBC) assumes that Sigma earns the negative of the return of the underlying (i.e. underlying return multiplied by negative one) and the Vector Model earns a return proportionate to that, where the proportion is the ratio of Sigma OaR / Vector OaR, subject to a cap and floor (we use 300% and 33.33%). So, for example, if Sigma said OaR was 2.00% and the Vector Model said OaR was 4.00%, the Vector Model ROOBC would be half of Sigma’s. Likewise, if the Vector Model said OaR was 1.00% the Vector Model’s ROOBC would be double Sigma’s. Higher ROOBC is preferred to lower ROOBC.
For the Vector Model ROOBC to be higher than Sigma’s it signifies that either (1) Vector Model OaR exceeded Sigma’s OaR (to the upside) and the ticker traded higher, or (2) Sigma VaR exceeded the Vector Model’s OaR (to the upside) and the ticker traded lower.
12. What is a “Vector Set” and why is it a good foundation for the Vector Model?
A Vector Set is a price channel anchored by at least one major top and at least one major bottom. It comprises 15 lines or Vectors, divided between a “core” section, a “leveled up” section, and a “leveled down” section. The Vectors are spaced using Fibonacci ratios that may be familiar from technical analysis.
From a “price reflects the balance of forces acting upon it” / Vector perspective, it represents the trajectory of net vector accumulation, as tops and bottoms represent changes in the net vector balance, presumably at least in part due to introduction of new vectors. The VecEvents that are tagged to a Vector Set can accelerate assessment on what those vectors may have been.
From a time series analysis perspective, the structure of each Vector Set is designed to capture several common behaviors of securities prices, including (1) mean reversion through the concentration of lines at the center of each section (0.382, 0.5, 0.618), (2) jump behavior via the leveled up and leveled down sections, and (3) drift and momentum via sloped vector sets.