Using AI in stock trading is not a recent phenomenon in the world. In the past, only financially viable large organizations were able to take advantage of it. The only reason to trade on the stock market is to make money. In decision-making, whether to sell and/or buy stocks, there is no emotional aspect to take into account. Every time humans have to make a choice involving emotions such as greed, fear, etc., they make the wrong choice and bear losses (Choudhary, 2012). Machines make quick decisions with complete accuracy only as a function of tangible factors such as price fluctuations, macroeconomic statistics, news of related listed companies, and government decisions without involving any emotions in the context. Coalition, a UK research firm stated that on Wall Street, 45% of revenues from stock-trading are triggered by artificial intelligence decisions.
Artificial intelligence can reduce risk by studying market trends. It generates new recommendations and can compile excellent portfolios by examining massive amounts of data. It can perform voice recognition, read notes in any other format, identify duplicate copies of data and thus always adhere to risk assessment requirements. AI is being used by many organizations to create an intelligence platform that creates unique models when it works on unique sets of data. For example, “Trade Schedule” is an intelligent tool that is being used by traders to determine the buying and selling times of specific stocks on various stock exchanges in Asia. “Aidiya” is another intelligent tool that is being used in Hong Kong to create hedge funds without human intervention. Trained humans are used to examine certain non-corporeal elements such as emotions and sentiments. Risky trades are widely known and can be developed by advanced AI as well as deep learning (Choudhary, 2017).
Stock trading has been significantly influenced by AI, which forces investors to make portfolio decisions in a different way and hold their assets in a different manner. Stock investors are able to analyze massive volumes of data quickly and accurately with machine learning processes and AI algorithms to make better investment decisions. This has made stock trading more efficient, reducing transaction costs and increasing profit margins. The most important advantage of AI in stock trading is probably the ability to analyze huge volumes of data in real-time (Choudhary, 2021). Computer software can monitor various sources of data, including financial news, media coverage, pure opinions, and market trends, with a view to making conclusions about patterns and drawing conclusions from relevant information. This will help investors react immediately to activity in the market, such as changes in stock prices or unusual activities, and make necessary changes to their investment policy accordingly.
With the use of AI, investors can gain an advantage and make the most optimal trading decisions. In addition, computerized trading systems are designed to learn and improve from historical data to enhance their decision-making capabilities. Using machine learning algorithms, systems can analyze trends and patterns in stock price and market activity and the performance of individual trading strategies. Therefore, AI models have the opportunity to learn how to improve their predictions and recommendations as time goes by and improve to higher accuracy rates with higher trading results. The impact of AI in stock trading is not only limited to decision-making and analysis.
The execution of trading has also been automated using AI algorithms, providing speed and efficiency in the market. High-frequency trading (HFT), for example, relies primarily on AI algorithms that trade at very high frequencies, typically in milliseconds. This allows HFT firms to capitalize on the smallest inefficiencies in the market and make money by trading rapidly and automatically (Choudhary, 2022). Additionally, AI has also reduced transaction costs for investors. With the automation of trading operations and no human involvement, AI systems have reduced the cost of human error or emotional bias. These systems are able to make trades accurately and eliminate errors caused by humans, and therefore, result in higher accuracy and lower transaction costs. While it has many benefits, it should be noted that the advent of AI in stock trading is not problem-free. The biggest problem is that AI models can be biased. An AI model trained on poor or biased data will likely end up with discriminatory tendencies or remain ignorant of larger market signals. There needs to be a lot of data to train an AI model that is representative and has enough diversity so that such biases are minimized.
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