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AI in Stock Market Prediction

AI in Stock Market Prediction 

AI in Stock Market Prediction : Many new technological developments will strengthen the hands of investors while making predictions about stocks.

Although these are generally referred to as artificial intelligence-supported analysis tools, they contain different sub-headings. 

Some of these are Machine Learning, which can learn from inputs and have the ability to improve itself, and Deep Learning for the formulation of vital statistics in line with the information obtained(mined) from Big Data. 

Deep learning can successfully learn from large amounts of unlabeled/unsupervised data, and it is often used to find meaningful impressions and patterns from big data. 

In its simplest definition, deep learning is expressed as applying machine learning methods to big data. 

Financial forecasting issues – such as designing and pricing securities, portfolio building, risk management – often require large data sets with complex data interactions, making it challenging to create an entire economic model. 

When deep learning methods are applied to these problems, more beneficial results can be obtained than standard finance methods. In particular, Machine learning can detect data interactions invisible to any existing financial, economic theory, at least for the time being, and use them effectively. 

RNN is a type of deep learning architecture called recursive neural networks. It is a deep learning method that has been used frequently in stock market applications.[1] 

Behavior Analysis 

While making a forecast about the stock market to be more accurate, traders apply technical analysis and statistics to their predictions without understanding the trading ecosystem, and human behavior predictions will more likely fail. 

Estimating stakeholders’ behavior would be the same as understanding the company’s stoke movement. For example, it is observed that the changes in the client’s thoughts about the company have a direct effect on the value of the firm in the stock market.[2] 

Anticipating these possible changes or understanding the movement’s direction as soon as possible strengthens the investor’s hand. 

This method was discussed by Richard Thaler as behavioral finance and earned him a Nobel in economics in 2017. 

Social media and marketing channels are among the places that use behavioral analysis the most today. For example, at the beginning of 2018, a scandal emerged by Cambridge-Analytica that the American elections were guided by Facebook data analysis[3]. 

Behind this scandal, some algorithms analyzed people’s behavior and how they would react to what event. Likewise, as soon as they enter a shopping site, potential customers will be followed and directed to which product and how to make personalized campaigns begin to work. 

All these analyzes consist of the application of theories under behavioral science with artificial intelligence. The behavioral analysis looks at historical data and analyzes how investors will take a current situation. 

Of course, a highly experienced investment expert can predict, based on experience, in which situation investors will behave, for example, panic or behave in cold blood. However, the behavioral analysis offers more than that and returns strictly numerical results for the given stock.

Analysis of Environmental Factors 

Interviews or tweets made by people who have an impact on the markets affect share prices. In order to prevent such manipulations, it is necessary to benefit from deep learning. Many bots are filtering useful tweets and try to get helpful information from them. 

In 2017 when cryptocurrencies got mainstream, and there were many new altcoins in the ecosystem. 

John McAfee, the founder of software company McAfee Associates, shared his positive thoughts about one coin every day (Coin of the day). It was not difficult for the algorithm to analyze the text and understand which coin was mentioned[4]. 

That is a basic ML implementation to the stock market; it analyzed the tweets and immediately gave the purchase order. Like Elon Musk, many other figures in Twitter manipulate the stock market. 

Possible uses of artificial intelligence tools: • If the stock’s latest price is below the 100-day price averages, buy 100 lots from the stock and close the position when you make 1% profit from the purchase price. 

Artificial intelligence will now monitor 100 stocks on your behalf 24 hours a day, 7 days a week, make purchases as soon as the positions match your commands take place, and close the position when making 1% profit. 

These processes will be done automatically by the computer in line with the data and conditions we provide to it. For artificial intelligence to benefit us, it must experience with time and have the ability to judge and make decisions by learning from its experiences. 

If we command our system to follow the news in the world, collect data from the markets, analyze it and make a rational decision according to the results; • Do an emotional analysis of all Twitter messages and economy newspaper news about Tesla, and if the result is positive, set a buy order. 

Our program will start buying and selling stocks in light of all this information. Now we have an artificial intelligence robot fed by the continuous flow of data from the outside world while we sleep and enriches you by trading according to this data.

Written by Göktug Önyer

Edited by Alexander Fleiss

AI Investing


[1] Mittal, A. (2019). Understanding RNN and LSTM. [online] Medium. Available at: 

[2] Fornell, Claes, Sunil Mithas, Forrest V. Morgeson, and M.S. Krishnan. 2006. “Customer Satisfaction and Stock Prices: High Returns, Low Risk.” Journal of Marketing 70 (1): 3–14. 

[3] Graham-Harrison, E. and Cadwalladr, C. (2018). Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach. [online] the Guardian. Available at: 

[4] (n.d.). Antivirus software pioneer McAfee charged by U.S. with cryptocurrency fraud By Reuters. [online] Available at: [Accessed 2 Apr. 2021]. Göktug Önyer Friday, 2 April 2021

AI in Stock Market Prediction