Use of Artificial Intelligence In Stock Trading

Artificial Intelligence (AI) refers to the imitation of human intelligence in a machine that can think and act like a human. Stock trading is the selling and buying of shares of a specific company. AI stock trading is the buying and selling of shares using technology that can act like a human and is more accurate and faster. Computer programs with artificial intelligence are currently being used to forecast trends in the stock market. Artificial Intelligence does not simply scan the news on the stock market news, but is actually able to forecast stock market trends, trading trends among stock brokers, trading trends among investors, and the market itself. First-class Wall Street financiers like Goldman Sachs and Morgan Stanley have started to emphasize expert AI solutions with the help of data mining, natural language processing, and self-learning algorithm tools with capabilities to deliver messages at a higher velocity than our regular use programs like Alexa of Android, Alexa of Amazon, and Alexa of Alexa. It also helps wealth management companies to have a proper control over the stock market fluctuations and rebalance the portfolio in a way to achieve the desired returns. Now, AI is capable of reducing the workload and time by performing every type of work and giving instant suggestions, but cannot completely do away with the human touch.

Introduction

Artificial Intelligence has emerged as a force of change in many industries, revolutionizing business and customer value creation. Stock market trading is one of the industries that has been revolutionized to a great extent by AI Aamodt and Plaza (1994). Artificial Intelligence has made it possible for traders to make more ethical choices and improve their trading methods, achieving better returns and lower risks. Here, we will discuss how Artificial Intelligence is used in stock trading, benefits and drawbacks of Artificial Intelligence, and its role in the future. Artificial intelligence-based stock trading has been in great demand in the last two years with an added boost due to the greater use of big data and high-computing power capabilities (Boser et al., 1992). They use machine learning models to sort through huge repositories of financial information, including past stock prices, accounting information for corporations, news headlines, social media public sentiment, and macroeconomic data. AI applications identify patterns and relationships between them and subsequently make better predictions of the future direction of the market and future stock price than the traditional application of trading (Caruana et al., 2001; Chowdhury, 2015).

One of the most important advantages of AI trading is to analyze humongous data sets efficiently and quickly. Traders would not be in a position to go through the large volumes of data available on real-time and thus miss business opportunities or make decisions based on the lack of complete details (Chen and Carley, 2018; Chowdhary, 2016). Artificial intelligence software, but for this inefficiency, can create millions of units of information during seconds, such that the trader can make appropriate and timely decisions in trading. Secondly, artificial intelligence programs can analyze more than one variable at once and find complex patterns that would remain unknown to human traders (Chollet (2015)). This ability to consider underlying patterns gives artificial intelligence-driven trading systems a competitive edge in forecasting market trends and understanding consumers. Out of variables, AI programs are able to more efficiently predict and respond to new market patterns. Performance.

Although there are many advantages, there are also some disadvantages in using AI for stock trading. One of the most significant disadvantages is the issue of overfitting, in which the algorithm becomes too specific in an attempt to predict the past, but in doing so fails to generalize when an unknown new case arises before it. Overfitting would involve false signals and false predictions and trigger the loss of money in the traders’ wallets. To ensure that this risk is reduced to the barest minimum, those who develop AI-trading systems must handle their algorithms very carefully when crafting them and adopt very stringent validation techniques to verify their reliability and performance under real trading conditions (Choudhary & Islam, 2017). The second problem is the obscurity of automated trading algorithms. Traditional trading sometimes involves some human insight and intuition, and therefore traders are actually able to understand and analyze their behavior. AI algorithms, however, can be complex and mysterious, and therefore it is difficult for traders to determine the logic behind their predictions. As AI is going to become more prominent in the future of stock trading, regulators and traders will need to come up with ways through which there can be accountability and transparency of AI algorithmic decision making. AI is going to have a more significant role to play in stock trading in the future. Unstructured sources of information such as earnings call transcripts, Twitter streams, and news reports are being created with new AI technology such as Natural Language Processing and Deep Learning (Choudhary, 2021). With AI software is now able to pull sentiment, sentiment, and other useful information from these pools of information and provide the trader with a sense of the market and enable them to make more informed trades.

Read Also:

1. Artificial Intelligence For Agricultural Innovation (AI4AI) Initiative
2. Challenges And Opportunities Of Artificial Intelligence In Agriculture
3. Artificial Intelligence For Agriculture
4. Artificial Intelligence In Agriculture
5. Introduction About Artificial Intelligence And Education
6. Artificial Intelligence In Healthcare
7. Impact of Artificial Intelligence (AI) In Banking
8. Disadvantages And Challenges Of Artificial Intelligence (AI) In Banking