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How AI Predicted This 100% Stock Spike

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Written by Timothy Sykes
Updated 1/10/2024 4 min read

At the core of my trading success lies a single principle.

This principle gave me the power to build food banks, animal shelters, and schools through my non-profit Karmagawa.

It helped me see the world (over 130 countries and counting) from the freedom I’ve attained.

It led to the financial security that now blesses my family’s future.

And it generated the resources to help hundreds of students attain that same independence by giving them the tools and knowledge to build their success upon that principle.

I’m talking about discipline.

Discipline underpins my 7-Step Framework for picking high-profit penny stocks. Through discipline, you unleash the full potential of the ABCD pattern. And building watchlists with discipline will let you spend more time focusing on potential winners and less time on the duds.

Unfortunately, there’s only so much time in the day. Human capacity limits our ability to apply frameworks and analyze patterns to find stock trades that pay regardless of our discipline.

Scaling Trading Discipline

© Millionaire Media, LLC

After several months of research and over a million dollars of development, my team has built a tool that harnesses the incredible power of AI to apply the same discipline that has minted so many millionaires over the past 20 years.

And it does it at scale.

This tool is already helping a new generation of students build the foundation to power their dreams. (Learn more here).

It’s called XGPT, and every day, it scans the entire stock market, looking for six powerful attributes that can lead to big wins.

When it finds the perfect conditions for a trade, it generates an alert and writes up a full report that breaks down the opportunity, how to play it, and – critically – how much trading capital you should allocate to the trade.

And just a few days ago, it alerted my students to a real winner.

Shortly after noon last Friday, XGPT noted that conditions were ripe for an explosive move in Elevation Oncology, Inc. (NASDAQ: ELEV).

It found positive news sentiment based on a press release earlier that day. It identified a breakout from crucial support. Plus, it knew ELEV was part of the hot biotech sector.

Moreover, the trading instructions were precise and on point:

“Our strategy involves entering a position at $0.9 per share and looking for a profitable exit approximately at $1.133 per share. To minimize potential loss, we’ve defined a stop price at around $0.762 per share.”

By 10:00 the following Monday, the stock was already on the move.

It traded through $0.90, then past the $1.13 per share target to over $2.00 before settling in at $1.81 for an astonishing 100% move above the target price – a trade that paid off big for several of my students.

 

This is just one example of the many successes we’re already seeing from this remarkable technology.

WATCH THIS FREE PRESENTATION NOW.

That way, you, too, can scale the trading discipline of AI and start building the same independence that so many of my students have built for themselves.


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Timothy Sykes

Tim Sykes is a penny stock trader and teacher who became a self-made millionaire by the age of 22 by trading $12,415 of bar mitzvah money. After becoming disenchanted with the hedge fund world, he established the Tim Sykes Trading Challenge to teach aspiring traders how to follow his trading strategies. He’s been featured in a variety of media outlets including CNN, Larry King, Steve Harvey, Forbes, Men’s Journal, and more. He’s also an active philanthropist and environmental activist, a co-founder of Karmagawa, and has donated millions of dollars to charity. Read More

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* Results are not typical and will vary from person to person. Making money trading stocks takes time, dedication, and hard work. There are inherent risks involved with investing in the stock market, including the loss of your investment. Past performance in the market is not indicative of future results. Any investment is at your own risk. See Terms of Service here

The available research on day trading suggests that most active traders lose money. Fees and overtrading are major contributors to these losses.

A 2000 study called “Trading is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors” evaluated 66,465 U.S. households that held stocks from 1991 to 1996. The households that traded most averaged an 11.4% annual return during a period where the overall market gained 17.9%. These lower returns were attributed to overconfidence.

A 2014 paper (revised 2019) titled “Learning Fast or Slow?” analyzed the complete transaction history of the Taiwan Stock Exchange between 1992 and 2006. It looked at the ongoing performance of day traders in this sample, and found that 97% of day traders can expect to lose money from trading, and more than 90% of all day trading volume can be traced to investors who predictably lose money. Additionally, it tied the behavior of gamblers and drivers who get more speeding tickets to overtrading, and cited studies showing that legalized gambling has an inverse effect on trading volume.

A 2019 research study (revised 2020) called “Day Trading for a Living?” observed 19,646 Brazilian futures contract traders who started day trading from 2013 to 2015, and recorded two years of their trading activity. The study authors found that 97% of traders with more than 300 days actively trading lost money, and only 1.1% earned more than the Brazilian minimum wage ($16 USD per day). They hypothesized that the greater returns shown in previous studies did not differentiate between frequent day traders and those who traded rarely, and that more frequent trading activity decreases the chance of profitability.

These studies show the wide variance of the available data on day trading profitability. One thing that seems clear from the research is that most day traders lose money .

Millionaire Media 66 W Flagler St. Ste. 900 Miami, FL 33130 United States (205) 851-0506 This is for information purposes only as Millionaire Media LLC nor Timothy Sykes is registered as a securities broker-dealer or an investment adviser. No information herein is intended as securities brokerage, investment, tax, accounting or legal advice, as an offer or solicitation of an offer to sell or buy, or as an endorsement, recommendation or sponsorship of any company, security or fund. Millionaire Media LLC and Timothy Sykes cannot and does not assess, verify or guarantee the adequacy, accuracy or completeness of any information, the suitability or profitability of any particular investment, or the potential value of any investment or informational source. The reader bears responsibility for his/her own investment research and decisions, should seek the advice of a qualified securities professional before making any investment, and investigate and fully understand any and all risks before investing. Millionaire Media LLC and Timothy Sykes in no way warrants the solvency, financial condition, or investment advisability of any of the securities mentioned in communications or websites. In addition, Millionaire Media LLC and Timothy Sykes accepts no liability whatsoever for any direct or consequential loss arising from any use of this information. This information is not intended to be used as the sole basis of any investment decision, nor should it be construed as advice designed to meet the investment needs of any particular investor. Past performance is not necessarily indicative of future returns.

Citations for Disclaimer

Barber, Brad M. and Odean, Terrance, Trading is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors. Available at SSRN: “Day Trading for a Living?”

Barber, Brad M. and Lee, Yi-Tsung and Liu, Yu-Jane and Odean, Terrance and Zhang, Ke, Learning Fast or Slow? (May 28, 2019). Forthcoming: Review of Asset Pricing Studies, Available at SSRN: “https://ssrn.com/abstract=2535636”

Chague, Fernando and De-Losso, Rodrigo and Giovannetti, Bruno, Day Trading for a Living? (June 11, 2020). Available at SSRN: “https://ssrn.com/abstract=3423101”