Except for the previous eighteen months, in the last twenty years stock pickers were lost in the sauce, drowning and losing big money with their failed predictions. Most didn’t even beat the overall market. A few exceptions are not worth writing about; having lost on average 17.9% per year of their clients’ money. This year it’s different. The S&P 500 and other stock indexes set a record, the best performance in decades.
According to G-101 SPM AI algorithm data, it is unlikely these successes will prevail over the long term. History tends to favor the loser. Indeed, beating the market without rigging it or other malevolent schemes had proven elusive. It’s been said in many versions that a chimp could pick stocks as well as experts.
The underlying conclusions are simple: even though the stock market tends to rise over the long term, no one knows what it will do day to day. It’s fruitless for most people to outthink the overall market.
HISTORY:
Since 2001 to 2023, most active managers haven’t made the right calls:
93 percent of the time over 20 years active managers were wrong.
90 percent of the time over 10 years active managers were wrong.
73 percent of the time over five years active managers were wrong.
72 percent of the time over one-year active managers were wrong.
Why does the investing public tolerate such harmful performance?
Outsmarting the stock market without it being rigged requires a new means of critical thinking with a full-range and semblance of linkable data. It’s no longer exclusively technical and fundamental analysis but a combination of human factors; applications to creates systems and devices that are safer, more intuitive, and more effective for accomplishing their given tasks by the people who are meant to use them. Simply put, human factors strive to make technology work better with humans. We are at that crossroads whereby investing is no longer an art like gambling on sporting events but a science.
When dealing with “intangibles” i.e. common stocks, technical and fundamental analysis are not good enough to properly influence and safeguard investment decisions. Classic Wall Street thinking is a lost and expensive art. Today, a new way of processing information is changing the investing landscape. Quantitative and qualitative research methods, user-centered design methodologies, interpersonal and problem-solving techniques are the human factors necessary to accurately evaluate and instantly apply information in making investment decisions.
G-101 SPM series A1.4 algorithm utilizes 129 subsets to analyze, evaluate, and interpret information to make judgments and decisions relating to intangible investments. As a performance-driven platform the algorithm evaluates current and historical records; and is structured as a digital monitoring surveillance tool to solve and deter misinformation within a managed database to offer "best guess" solutions. Its main engine is the Primary Volume Locator (PVL) which continuously tracks cash flows of the S&P 500 Index ($SPX) components to establish a directional control grid. Once G-101 SPM is locked into that variable, the platform searches for stock anomalies and imbalances between supply and demand which leads to the increase or decrease in a stock’s value.
At length, the individual stocks are selected from fundamental and technical data and other relevant information. Once the complete data package is analyzed and evaluated under Phase One review, the algorithm considers alternative points of interpretation and draws proprietary conclusions. Under Phase Two it reflects and learns from the process. These characteristics when taken as a whole are applied to a tag system based on the subjective probability as a “best guess” conclusion. The higher the SPM tag the more likely its success. +SPM 100 means the data is 100% reliable.
Note: At no time has the G-101 SPM series A1.4 algorithm presented a perfect tag. (The highest tag during accumulative beta testing of 30,000 stocks was +SPM 92.62.)
As for the NOW:
Our MISSION STATEMENT is to prove that non-human
intervention by an analytical chatbot is
far superior to the other kind.
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