WHEN IN DOUBT GET OUT!
- Greenmark 101
- 21 hours ago
- 5 min read
This should be your subconscious speaking!
Whether it’s going to the store to buy a loaf of bread, picking a forever partner, deciding on open-heart surgery or just making a run-of-the-mill pick, your yes-no switch makes the final decision. Whatever happens after the fact has an element of unintended consequences. You may not think so, but all choices have a material value attached, eventually distilled into a cost that you alone must accept. Without offering a philosophical dissertation, the truth remains that “time is money," and decisions are the conscious choices about how to allocate your finite time and resources, considering more effectively both their monetary value and opportunity cost. It means viewing time as a precious, limited resource that, like money, can be invested, spent, or saved to achieve life goals.
Within this milieu, the stock market is the classic arena when you’re in doubt about an investment, and don’t want fear or greed to work you over.
Do I sell and take the profit?
Do I sell and take the loss?
Do I hold and make more?
The final decision is a money choice with the catalyst being greed or fear.
Greed is good if the understand its potential downsides.
This complex human trait has both positive and negative values. You’re happy with the profit on the trade. The result is powerful force to continue to do the same. Whatever reason to buy the stock in the first place will be repeated, even if it fails in the future. This unconscious drive to repeat behaviors or recreate circumstances will turn greed into fear. However, using sound investment principles that generated the profit in the first place are repeated, greed is good.
Fear is better than greed.
While collective fear can create opportunity, allowing your own personal fear to dictate your actions is dangerous. The "fear of loss" bias can lead to hasty decisions, causing you to sell at market bottoms and lock in losses. The most common mistake is selling your investments during a market panic. You lock in losses that might have been temporary, preventing you from participating in the eventual market recovery. Don’t become so paralyzed by fear that you avoid investing altogether, missing out on the long-term growth opportunities the market provides. Fear can make investors overreact to news headlines and lose sight of their long-term financial goals, leading to impulsive and poorly timed decisions.
Wall Street plays on greed because the pursuit of financial gain is a powerful motivator for both investors and financial professionals, driving market activity and fueling the creation of strategies designed for profit. This pursuit often fosters a culture where rapid wealth accumulation is rewarded, leading to excessive risk-taking, asset bubbles, and market instability, as seen in the movie Wall Street where the character Gordon Gekko famously declared, "Greed is good".
Meanwhile, average investors lose money trading stocks due to a combination of psychological biases, poor risk management, and a lack of knowledge. These factors lead to poor decision-making, such as panic-selling during downturns or chasing hyped stocks, which can erode capital over time.
HOW TO MAKE FEAR AND GREED WORK FOR THE AVERAGE INVESTOR.
While not all brokerage houses make "all the money" on Wall Street, major firms are extremely profitable due to a sophisticated system of fees and financial activities that go far beyond simple trading commissions. Large firms thrive by leveraging their scale, access to capital, in-house trading portfolios, and diverse business lines that profit regardless of whether their clients make or lose money.
Since we can’t beat Wall Street, what about using a few of their tactics?
Prior to the development of the G-101 SPM AI algorithm (a collection of 145 data subsets organized into a single column), it was generally assumed that the equity market, where company shares are traded and safeguarded by anti-fraud regulations, was inherently fair. However, after extensive beta testing over many years, it has been observed that the stock market may not function on an entirely level playing field. This conclusion was reached during our latest iteration of the G-101 SPM AI algorithm when stock-picking accuracy ratings surpassed 85% on the last 2000 transactions.
The algorithm can identify and track stock manipulations through data irregularities; can follow variations in trading patterns of schemers with low-latency data or insider information. These anomalies are predictable due to their repetitive and consistent structure, allowing for anticipation of a stock’s direction. Such predictabilities enable our algorithm to follow guidelines that disclose pattern creations and tendencies. By recognizing collective changes and repetitions between elements in the patterns, the platform is able to rate the reliability of such formations by applying a subjective probability model (SPM) tag value as a “best guess” conclusion score. i.e. A higher SPM tag value indicates a more reliable column of data.
Market predictions are no longer exclusively technical and fundamental analysis.
Outsmarting the stock market, knowing that it's possibly 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 other types of data, more intuitive, stickier. Algorithms and machine learning have taken data exploitation to a level that seemed impossible just a few years ago. The result: We’re at the crossroad whereby investing is no longer an art like gambling, 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 data search platforms, user-centered design methodologies, interpersonal and problem-solving techniques are the non-human factors necessary to accurately evaluate and instantly apply information in making investment decisions.
In conclusion, we cannot alter the rules or control access. Our solution is to adapt and leverage anomalies; that’s the essence of SPM TAGS - 'wave riding after the fact' to profit without fear from momentum created by others – whether long or short – and to exit without greed before the “wave” loses momentum.
OUR LATEST CREATION is --- SPMTAG.COM https://www.spmtag.com/g101-spm-ai-algorithm.
MISSION STATEMENT:
On March 17, 2023, we began our certified beta testing on stocktwits, [ https://stocktwits.com/G101SPM. Click SEARCH (magnifying glass) icon on our landing page to review all trades and related data] an independent platform with audience dynamics, to prove that the G-101 SPM AI algorithm outclasses conventional data fidelity methods. Remarkably, by July 31, 2025, it achieved 3089 stock picks with an 84.11% accuracy rating. A projected rate of 60% was deemed to be an acceptable target, based on research indicating that consistently outperforming the market is challenging for both individual investors and professional fund managers.
During the first year of beta testing, we observed performance changes due to data anomalies associated with stock manipulation. Through the analysis of these data subsets, the characteristics of different types of stock manipulation were identified as they occurred. The specific data spikes following the event indicated notable investment opportunities. This strategy, called "wave riding after the fact," involves trading after an anomaly occurs.
WHAT SPMTAG.COM CAN DO:
Remove the Fear Factor in making trading decisions: Our SPM tag rating system allows instant forecasting of target stocks with predictable anomalies due to their repetitive and consistent structure, allowing for anticipation of a stock’s direction.
Remove the Greed Factor by recognizing collective changes and repetitions between elements in the patterns, the platform is able to rate the reliability of such formations by establishing EXIT points.
“The best information always wins.”
Christopher Netelkos Co-Author of the G-101 SPM AI algorithm