Can artificial intelligence predict the movement of stocks and financial markets?

Stock markets are a very profitable and popular way to grow capital, and it suffices to know that more than 6 trillion dollars are traded daily in forex alone, as stated by the “finance magnates” platform in a recent report.

And if we add to this number what is being traded in other central stock markets, such as Wall Street, London, Tokyo and other markets, the number becomes really huge. In fact, the global stock markets are the factory of capital, and the fortress of global capitalism.

With the tremendous technical development witnessed by our time with the entry of the products of the Fourth Industrial Revolution - including algorithms, artificial intelligence and machine learning - in every field of life, the question that arises is whether artificial intelligence can be used to predict the movement of markets?

The answer is certainly yes, the use of artificial intelligence has increased recently to predict the movement of the stock market, but can blind confidence in the use of this intelligence lead to a loss of capital for traders and investors in different parts of the world?

To what extent can we rely on this technology in predicting the movement of stocks with reliable accuracy?

Sohrab Mokhtari, Kang Kee-yin, and Jin Liu, professors of computer engineering at the University of Florida, US, discussed this in their paper titled "The

," according to Cornell University. Cornell University.

stock market analysis

Before getting into the depth of the study, the researchers discussed the three main analysis methods used by investors around the world to predict the market movement. The following two methods are used to analyze the movement of stocks in the stock market:

Fundamental Analysis:

This financial school attempts to calculate the intrinsic value of a stock based on the company's revenue, profitability, degree of liquidity, and operating efficiency. Ideally, if the intrinsic value is greater than the "last traded price" (LTP), it should be bought, and if the intrinsic value is less than the "last traded price", it should be sold.

Technical Analysis:

This methodology uses historical stock price data using a range of indicators - such as the Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), and the Money Flow Index ( MFI)- It tries to know the market movement through it. If the technical analysis indicates that the stock price will rise, this will lead to a buy order, and if the analysis indicates that the stock price will decrease, it will lead to a sell order.

The study discussed the main analysis methods used by investors around the world to forecast market movement (Getty Images)

There are other models used in forecasting, including:

“Efficient Market Hypothesis” (EHM)

: This hypothesis indicates that the stock price is moving in the direction of general sentiment which is a reaction to the latest economic and political news published in the world.

Adaptive Market Hypothesis (AHM): It

attempts to predict the direction of market movement using theories based on psychology.

Returning to the study that tried to address the problem of predicting the movement of the stock market using artificial intelligence and algorithms based on machine learning, in order to do so, the researchers studied the two main schools used in market movement analysis - the method of technical analysis and the method of fundamental analysis - and the effectiveness of the algorithms based on Machine learning predicts the movement of financial markets using the two methods mentioned.

For this, the previously classified data sets were used to train the prediction algorithms, and the rating scales were used to check the accuracy of the algorithms in the prediction process.

The results showed that the "linear regression" model followed by the researchers predicted the closing price significantly with a narrow margin of error when using the technical analysis method, while when using algorithms to predict the method of fundamental analysis, the model was able to predict the market movement with an accuracy of 76%.

Results

These findings suggest that although AI can predict stock price trends or general sentiment about the movement of financial markets, its accuracy is not sufficient.

Moreover, while the Linear Regression model can predict the closing price with a reasonable margin of error, it cannot accurately predict the same value for the next business day, i.e., this forecasting ability is valid for only one business day.

Thus, this AI-based model cannot be used for long-term investments.

On the other hand, the accuracy of algorithms in predicting whether to buy, sell or hold a stock is not satisfactory enough and can lead to capital loss.

Based on this study, the researchers concluded that artificial intelligence is not yet able to predict the movement of the stock market with a reliable and reassuring accuracy.