Featuring the Global ETF Cash Flow (GCF) Index and its linked subsets.
These are the specific subsets within the 128 data package contained in the G-101 SPM series A1.4 algorithm.
Global ETF Cash Flow (GCF) Index GCF Index ST 47 [1].
Primary Volume Locator (PVL) PVL ST 51.
SPM Sentiment Amplifier (SPM-SM) SPM-SM ST 14.
The Series is dedicated to the G-101 SPM series A1.4 algorithm and its 128 parts known as “subsets” of machine learning. The correlation of these subsets function as a tree-like structure of decision rules that input their specific data to predict subjective probability outcomes.
OVERVIEW: The proprietary AI algorithm is an investment predictor that gathers data from 128 preset sources and presents the values as a subjective probability model (SPM) matrix number. The database tracks over 5215 individual stocks on a continuous set-time basis with each one stipulated data stream carrying a "floating" SPM tag. The higher the value the greater the subjective probability of the collective data being accurate and culminates with the issuance of an auxiliary SPM tag.
GCF Index is a first-level data component of G-101 SPM series A1.4 algorithm.
Global ETF Cash flow (GCF) Index is one of three stipulated data sources[2] that form a directional grid. The combined data is sequenced with S&P 500 Index components to continuously track investment cash flows and presents a statistical value, identified as Primary Volume Locator (PVL). A proprietary formula interprets the data and rates its investment characteristics and values.
APPLICATION AND PERFORMANCE STUDY:
Global ETF Cash Flow (GCF) Index projects full-year (2024) values to be $1.5tn (1,2) and set an all-time record (3).
Subjective probability – [3]SPM 91.25 tag
Fixed Income ETF Flows projects full-year (2024) values to be $607bn and be an all-time record year.
Subjective probability – SPM 90.03 tag
CONCLUSION
Stated herein, both questions have been answered and allowed as “knowledge data points” to be included as components in the SPM tag matrix. Once the totality of the tag values is known that assumption is applied to the final questions:
What is the SPM tag value for ‘the particular’ investment?
What is the length of time for its value to enhance or be ruinous?
As expressed, SPM tag values for Global ETF Cash Flows (GCF) Index and Fixed Income ETF Flows are tallied with the other 126 subset tags to produce a net SPM tag as to the condition of the event, its value and its probability as a “best guess” conclusions.
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[1] The AI algorithm is an investment predictor that gathers data from 128 preset sources “subsets” and presents the values as a subjective probability model (SPM) matrix number. Each of the 128 subsets are classified as ST values.
[2] Other data sources are SPM Sentiment Amplifier (SPM-SM), which rates selective opinions and thoughts at a specific point in time, and Primary Volume Locator (PVL).
[3] SPM 91.25 tag means “SPM” Subjective Probability Model, “91.25” likelihood in percentage of the event or issues occurring i.e. 91.25%, and “tag” as to net value designated by the algorithm.
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