S&P500 (^GSPC) and 10-year Treasuries will go down in a month’s time, Bloomberg reported in early July, citing a very unorthodox analyst. The forecast in question was delivered by an AI algorithm trained by JPMorgan Chase, one of the world’s top investment banks. This AI is just one of the many cases which highlight the interest that the big players have for the technology. As algorithmic trading becomes the word of the day, the giants seek to ride the AI tide. And at least one of their incentives for doing so is also relevant for retail traders. It sounds simple; in fact, it is a simple truth that greatly pre-dates the era of AI. What is it, you may ask?

It’s just the good old two heads are better than one, that’s just it. Sounds a bit ridiculous, doesn’t it? Well, let us dive deeper into this.

Trust, But Verify

Obviously enough, financial markets emerged long before the current AI boom, and so did the strategies that shape the classical investment dogmas. Equally obvious, these strategies existed at a time when the world was slower than it is today. It was a world of railroads and telephones, not a world of 4G Internet and super-fast high-speed rails.

Today, though, this is very much different.

What has not changed, however, is that, in general, the investors want to be as certain in their decisions as they can, and they have little appetite for uncertainty. At the end of the day, you cannot know for sure what stock A or B will cost in a month, which means that any bet you can is a bit of a gamble. What you can do, however, is try to decrease the uncertainty through statistics and calculations and make sure that losing this gamble would not hurt you as much as it could.

A strategy must also include clearly defined rules for entering and exiting a trade. For example, let’s suppose we are engaging in day trading, making gains on short-term price fluctuations and closing all of our trades by the end of the session. One of the things we could keep an eye for is the behavior of two moving averages – 50-minute and 10-minute. When the 10-minute MA crosses below the 50-minute one, this means we can expect the stock to go up – and this would be our entry point. Similarly, when the 50-minute MA goes on top, we exit the trade.

This, in fact, is the idea behind any given trading strategy, which seeks to provide a framework for decision-making that would work as a bulwark against the market uncertainty. Without a strategy, you might as well make your investment decisions by flipping a coin. With a strategy, you will take your shots based on certain calculations, whether it is about the fundamentals of company A suggesting its stock is currently underpriced, or about the fresh market data indicating that the stock of company B is shooting upwards, and you can make use of the trend. Again, this does not mean that A or B are set to do well. But it does mean that with proper analysis, you are more likely to make a profit than if you picked the stocks randomly.

The problem there, however, is that in some cases, the strategy fails to accurately predict what happens on the market. For these scenarios, most investors have hedging measures in place, making sure that they do not lose too much. But the loss is still there, and nobody likes losing money.

And this is where AI market prediction tools come in. What they do is give the investor a way to cross-reference their own calculations by looking at the AI output and comparing it to their own plans. In terms of pure statistics, this gives them a better measure of certainty in their decisions. If a human analyst utilizing a strategy and an AI, both with known accuracy rates (through assessment of prior performance) converge in their assessment of a specific stock, it is reasonable to expect that the probability of its price following the predicted course is higher than for that on which AI and human analysts disagree.

This may not sound like a lot, but when you take into account the amount of money that is at stake with pretty much any decision made by the big players, it is easy to see why any extra way to decrease the uncertainty is welcome. Paying the quant startup for the predictive AI they trained and increasing your returns through smaller error rates makes more financial sense in the long run than accumulating losses on mistakes that could have been averted.

The same logic can be applied to retail investors, if the service they are looking at is affordable enough. Losing less means winning more, and more winning leads to higher returns. Is that worth the investment? Yes, as long as the expenses on the AI prediction service are less than the returns it generates by decreasing the market uncertainty for you and boosting your decision-making.

And if you think that it’s high time to get an AI-driven second head to aid you in your trading, we have some very good news for you. Below is an example of how to build an algorithm that mitigates risk and maximizes investor returns using signals from I Know First.

I Know First AI For Algorithmic Trading

One of the leaders in AI-driven stock market prediction is I Know First, as Israel-based company that has launched a deep learning-based AI delivering daily forecasts for over 10,500 financial instruments, including ETFs, stocks, and currencies. Deep learning is one of the most advanced kinds of machine learning, one that mimics the way the human brain works. It relies on deep neural networks, which incorporate a number of so-called hidden layers between the input and output ones. This high degree of complexity allows deep neural networks to take on the tasks that a simpler algorithm would not be able to handle.

Trained on a historic dataset covering 15 years of trading, the AI views the markets from a holistic perspective, looking for trading signals in the fresh market data and using those to model the trends and seasonal patterns.

The AI delivers its forecasts as easily interpretable heatmaps with two numeric indicators: signal and predictability. Signal shows how far the current price of the asset is from what the AI thinks is a fair price. A strong positive signal means the stock is underappreciated and can be expected to rise, and a strong negative one suggests an upcoming nosedive. Predictability is an indication of how accurately the algorithm has been predicting the asset in its previous forecasts. It ranges from -1 to 1 and is defined as the Pearson correlation rate between earlier forecasts and actual price movements.

Strategies based on the I Know First AI have been proven to show high returns, beating the market by a wide margin. The predictability indicator allows traders to make sure that they are minimizing their risks by only betting on the most predictable assets.

The algorithm can adapt to periods of market volatility by virtue of incorporating elements of genetic programming in its design. In other words, it keeps tabs on its own performance and updates its predictive models as soon as they start to lose their touch. This also ensures that the prediction accuracy goes up with every new forecast, since the learning cycle is part of every forecast generation.

The AI also draws on chaos theory to account for market volatility. It delivers its forecasts for time horizons ranging from 3 to 365 days, covering short, medium and long-term perspectives, which helps the investors in picking the stocks to long and short. Recent evaluation reports like this one show the accuracy is higher for longer periods, but even for short-term forecasts, it is still around 60%, which makes for a formidable way to double-check your own strategic calculations.

This approach offers a new tool for traders as they seek to beat the market, its capabilities coming in handy to adepts of algorithmic trading and more conventional strategies alike. The AI predictions are based only on objective trading data, with no human emotion or bias coming into play. This ensures that the AI remains objective even at a time when emotions run high, suggesting the optimal investment decisions and helping the investor make the most of the market under any given conditions.