GREENMARK 101 (“G101”) functions within the limits of asymptotic analysis to define restrained behavior and solve a problem within a sequence of unambiguous instructions. A tall order when dealing with intangible factors of fluctuating common stock prices where seller supply meets buyer demand. There is no clean equation that reveals how a stock price will behave, until now. Fundamental and technical analysis are somewhat resolvable, but market sentiment is where assets managers have difficulty comprehending. G101 has the answer to the overall attitude of investors toward a particular security or financial markets. By assessing the tone of a market, its crowd psychology, as revealed through the activity and price movement of the securities traded in that market. Our big data machine learning tools are capable to be reflective and able to calibrate market sentiment within a subjective probability factor far more reliable than any system in current use.
What would an asset manager pay this year and every year to be right 87% of the time?
For the last three years, we have been applying our nontraditional data to interpret patterns, inferences, and insights in hemp centric issues, i.e. crop production, extraction systems, capital deployment, business models, etc. We amassed data because it did not exist. Statistics about hemp and its infamous cousin marijuana, which were illegal for most of the 20th century, was confined to political issues with scant reference to the science and economics behind the plant. To effectively address the need for reliable information our research and analysis were designed to gather alternative data. By applying machine learning algorithms, we were able to gather big data, and use learning tools to farm enormous data to “clean” information that would be useable, ensuring fact validity and reliability.
The collection and use of a massive amount of information that is unavailable through conventional techniques can be autonomously recovered and functional with application software. The intent is to accurately provide a superior advantage to “best guess” answers to unorthodox or traditionally unanswerable questions. The sheer diversity of data can instantly predict macro trends and future analytics that are time reflective. The addictive nature of the data offers a high-frequency resolution to multi-level questions and augments human intelligence, not replacing it.
Assembling alternative data – facts and figures compiled by sources other than company reports, government agencies and the like - will generate $1.7 billion in sales this year, according to AlternativeData.org, an association of analysts and engineers. Currently, the acquisition of large data sets or hiring personnel to handle them are not available except for large, well-funded money managers.
Applying a unique four-tier platform, we possess the scaffolding and means to collect alternative data without the high cost and complexities normally associated with machine learning algorithms.
To demonstrate the effectiveness of G101, we plan to offer the algorithm free of charge to the top 50 Assets Managers for a period of 90 days to present “forward market sentiment" on:
Pre-market daily trends of the S&P 500 index;
Buy, sell or short perimeters of at least 100 common stocks publicly trading, including dates and times of exit strategies,
Tracking investment decisions of the top 20 investment firms, against G101 performance.
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