I posted this article in Artificial Intelligence in Plain English and wanted to repost it here! Comment below and share your thoughts.

There are 100 excuses people use for why they fail to make money in the stock market. Most of it comes down to gambling – treating stock prices like points in a video game. Some of it is also a lack of education – people don't understand that a stock represents a company, and that its price goes up when the company does well.

These excuses no longer hold up anymore. Not only have I made a tool that democratized access to financial knowledge, I've also made a time machine to test out different investing ideas.

And I made it free. Let me show you how you can use it to extract real-world insights.

For a detailed technical article on how the AI in NexusTrade works, check out the following article:

A two-step process: identification and testing

This time-machine is a free investing tool makes it easy for everybody to make more money in the stock market. The process is simple and straightforward:

1. AI is used to help identify fundamentally strong stocks
2. A user creates a trading strategy using those stocks
3. A time machine is used to see how well that strategy performed in the past

Finding fundamentally strong stocks is the first step. It starts by having an idea. For example, because stocks are businesses and businesses that do well tend to have higher stock prices, you might want to try to look for stocks with high gross profit margins or higher incomes.

Then, after you've identified some stocks, you want to test it out and see how well your theory performs across time.

For example, let's say you believe in Mr. Wonderful (Kevin O'Leary's) philosophy – cash flow is king.

Pic: Using AI to find the 5 tech companies with the highest increase in free cash flow

You can find stocks in any industry that have increased their free cash flow during a certain period. Then, you can see how it performs after that period to see if your idea holds real weight.

To be more precise, I typed the following into the chat:

Find me the 5 tech companies with the highest raw increase in free cash flow (not percent change) from 2016 to 2020. Sort by increase in free cash flow descending

The AI then fetches the companies that correspond to my request. Then, I can follow-up with creating a portfolio.

Create a portfolio with $10,000 with buy and hold for these 5 stocks

After creating a portfolio, I can use the time machine to test out my strategies. This process is called backtesting. Because I fetch stocks from 2016 to 2020, I didn't want to bias my analysis with future data, a common problem called lookahead bias. Thus, I decided to perform backtests afterwards to reduce the chances of this happening.

For my analysis, I did backtests for the following years: - 2021 – Jan 1st 2021 to Jan 1st 2022 - 2022 – Jan 1st 2022 to Jan 1st 2023 - 2023 – Jan 1st 2023 to Jan 1st 2024 - YTD (year-to-date) – Jan 1st 2024 to Oct 6th 2024 - Entire period – Jan 1st 2021 to Oct 6th 2024

Pic: Backtests for this collection of stocks

This portfolio generally outperformed the market, achieving gains of 45% in 2021 (versus the market's 27%) and 44% in 2023 (compared to 24%). It underperformed in 2022, falling by 31% (market decline was 20%). Year-to-date, it trails slightly, earning 16% (compared to 22%). Overall, the portfolio has outpaced the market, with a total return of 45%, compared to the S&P 500's 28%

While it's interesting to see that this particular strategy outperformed the broader market, what's more useful is how easy it was to extract these insights, and how easy it would be to iterate and improve on them.

For example, instead of our fixed approach which looked for an increase in free cash flow from 2016 to 2020, we could do a rolling window approach— finding the stocks with the highest increase in free cash flow one year, and then re-fetching the stocks for the next year. This would allow us to better see if the increase in free cash flow is a reliable indicator of future stock prices.

Or, we can use other trading rules and indicators, such as relative strength index (RSI), moving averages, or fundamental metrics such as net income or gross profit margin.

With this approach, we can very easily find real patterns in the data and make better financial decisions.

And one of the most useful parts is that these insights are fully shareable!

Pic: Sharing a conversation is as easy as clicking a button

If you found something intriguing, you can share a link of the conversation to a friend with the click of a button. For example, if you wanted to read the exact chat that I had with the AI, you can do so by clicking this link.

Pic: Sharing a conversation makes it easy for you to collaborate on a strategy with a friend

You can also continue the conversation from where someone left off, allowing your friends to dive deeper into any insights you share with them. This approach allows you to inform your friends and family to make better financial decisions.

Concluding Thoughts

Artificial intelligence gave retail investors access to advanced financial analysis tools. Saying "if I only knew this stock would go up" is no longer a valid excuse.

There is no reason for you to not make better financial decisions using AI. You can now perform research, test ideas, and make more informed decisions.

None of this is theoretical – it is real-life. Anybody can do it, and yes, that means you too. Despite all of the jargon you might read, it's actually a lot simpler than you think, and what do you have to lose by trying out a free tool?

Or, you can sit back and miss the next big stock rally as you gamble away your life savings on poor stock choices.

The choice is yours.