love.
These two letters are what most people know about artificial intelligence.Others became experts by download the app Use AI to generate answers to questions.
This is an example of how a little knowledge can be dangerous. The AI’s answer depends on the question. If you’re writing an essay for an introductory-level college course, almost all of the questions (called prompts) will be answered satisfactorily.
But if you want to know what your abnormal blood test readings mean, bad prompts can lead to bad answers. Doctors consider the results of other tests to contextualize abnormal results. Failure to do so may lead to incorrect answers and dangerous diagnoses.
One day, AI should be able to take into account all the different variables doctors consider. But when the day comes, you may want to see a real doctor to check your results. This is because humans may encounter anomalies in their experience that AI cannot explain.
Analyzing stock prices is not as important as interpreting medical results. However, there are some notable similarities and potential applications that can be used to trade for profit today.
Traditional Strategies and Future AI Models
Stock market data often contains anomalies. In more technical terms, these are statistical outliers.
Many traders believe that most of their profits come from outliers. Testing confirms that for many strategies.
You can also ask the AI to identify outliers. But it may not be useful by itself. It’s not a strategy to trade when there are anomalies in your data.
Your investment strategy should be based on sound logic. There must be a reason why outliers are important. Otherwise, you’re just trading statistical noise. In the long run, you will almost certainly incur losses.
Traditional trading strategies are based on past market behavior. You may buy undervalued companies because some have made big profits in the past. Alternatively, you may trade based on moving averages, which provide profitable signals in the long run.
These strategies are based on historical probabilities. AI strategies differ in that they predict the future and trading decisions are based on these implicit probabilities.
Bringing AI to the stock market will be a challenge. But it offers great possibilities.
I’ve been experimenting with AI models for the past few months. I would like to share the possibilities I found there…
Discover hidden patterns with AI
To create an AI model, start with a history of what happened in the market. Perhaps you’re looking for a time in the past when the data showed similar price movements over the past month. Then use these examples to find your target price.
This is very different from the traditional model. Previously you had to define a model. Perhaps I should have said, “Show me what happens when the price rises above his 50-day moving average.” We then created a database of those transactions and analyzed the results.
Next, let’s take a look at recent price trends. Perhaps one stock has had 15 up days in the last 20 trading days. Another stock has fallen for 14 straight days. The third shows price movements back and forth and no net progress for 10 days.
AI models can find these patterns in individual symbols. You can examine past results when similar patterns have been deployed.
The difference is that AI models are not limited to visibly definable signals. If set up correctly, the AI will find hidden patterns. Test these patterns and identify if they are statistically significant.
This model may identify dozens of potential opportunities and present options. Alternatively, the model may weigh each opportunity based on history and provide a single forecast.
This is, hopefully, a promising change in how we trade. Done badly, it can quickly become another way to lose money.
If you’ve been following me for a while, you know I take innovation seriously, especially when it comes to improving our trading system and keeping it adaptable to the market. I am always looking for the latest ways to sharpen my edge as a trader.
Naturally, I’m testing an entirely new AI strategy. trade room Work now to diversify our ever-growing collection of profitable trading strategies.
I get very excited when new projects like this show promise. However, it is also important to test new technologies carefully. And by testing in public just like you do in a live room, a wider audience can determine how well it works in real time.
Click here for more information on how to access my latest research and other proven strategies in the trade room.
nice to meet you, Michael Carr Editor, exact profit
(From CNBC: interview With Treasury Secretary Janet Yellen. )
Treasury Secretary Janet Yellen sat down with CNBC’s Ross Sorkin. yesterdayand she had some interesting things to say about commercial real estate.
“Well, I think there is a problem with commercial real estate.
So-so!
I don’t know if Secretary Yellen is reading it. The Banyan Edge, but I’d like to point out that Mike Carr wrote about this issue three weeks ago.He then followed up with me about it banyan edge podcast.
I would like to believe that my country’s treasurers are better, or at least more knowledgeable. be familiar with We care more about data than we do. It really doesn’t seem like it.
Mike practically covered all of her major points week before she does.
And what exactly are those problems?
Bank owns commercial debt
Banks are the primary owners of commercial real estate debt. And some of the properties, such as office towers, that back that debt are at risk of significant price drops as tenants renegotiate leases and reduce square footage.
Let’s play with these numbers.
A typical loan-to-value ratio for commercial real estate is around 80%. So for a $10 million typical building, $8 million of it is loan-financed and capital is only $2 million.
Now, in the normal world, it’s no big deal. Loan holders have a large capital buffer.
Prices would need to fall more than 20% for loans to subside. That means the property will be worth less than the debt used to finance it. And such declines rarely occur in high-quality real estate.
The problem is that this is not normal time. After more than a decade of low interest rates, we entered the pandemic with very high commercial real estate prices.
Then the pandemic hit. Demand for rental offices has decreased as remote work has become more acceptable. If that wasn’t bad enough, financing costs also rose through the ceiling, making real estate much more expensive to hold.
Suddenly the 20% buffer doesn’t seem large enough. Mike mentioned a famous San Francisco building that just sold for 78% less than its pre-pandemic price.
It’s a disaster.
But the good news is that you don’t have to just smirk. Currently, Mike is working on adapting his AI to his trading strategy, especially in his trading room. Want to know more about how Mike trades and invests?
Learn more about.
nice to meet you,
Charles Sizemore Editor-in-chief, The Banyan Edge