Use of Artificial Intelligence in the Stock Market

Technology has advanced, and engineers have created machines that can perform human-like activities. Artificial intelligence is a branch of computer science in which researchers create intelligent computer systems that function and behave like humans. The most typical tasks performed by machines with artificial intelligence are speech recognition, learning, planning, the ability to move and operate objects, and problem solving (Debashish et al. 2016). Artificial intelligence has transformed the commercial world. The programmed machines deliver the final answer in a more reliable and cost-effective manner. The stock market is among the business that has benefited from development of artificial intelligence. This market is non-linear. This factor complicates and makes the process very challenging (Zhao and Wang 2015). Success in this industry requires intelligence in the prediction of the financial market exchange rates and among other factors. Application of traditional forecasting methods in this market is not reliable and can lead to losses. Stock market companies are required to analyse massive data, identify trends and patterns of the market, be updated on any events and factors that can affect the market either positively or negatively. Consistency in successful prediction of the stock market is a gold mine that technologists ventured into for several years. Artificial intelligence provides a reliable prediction approach in the market.

Financial markets rely on data analysis when making decisions. Various artificial intelligence methods originate from different research disciplines like data mining, pattern recognition, and machine learning. These approaches apply during data analysis for the financial markets.

Artificial intelligence simplifies the complications that result from huge volumes of stock market data. Dealers in the market use hybridization of data mining and techniques of the neural network when predicting stocks. Data mining involves the extraction of significant information from big databases. This technology is highly used in the financial market by companies to focus on most relevant information from large depositories which have a great potential. The stock exchange has huge volumes of data. Through artificial intelligence, the dealers can select and distinguish information which is significant in making a successful forecast. The computer systems classify large data bodies that have similar characteristics in a process known as clustering. They produce clusters of data that have common properties. Other useful data mining methods utilized in financial markets include the Principal Component Analysis, Kernel-based Principal Component Analysis, and the Fuzzy Robust Principal Component Analysis (Zhong and Enke 2017). These approaches simplify and rearrange data sets. These processes make the prediction process easy.

Artificial neural networks comprise of parallel computing systems that have many simple interconnected processors. This approach is useful in prediction of stocks. It deals with fuzzy, insufficient and uncertain data that might fluctuate rapidly in a short time. The dealers monitor and separate unreliable data when making predictions in the stock market.

Social networks have become very popular, and they avail significant data for the financial market. The primary factors that matter in the stock exchange are the change in price and the trade volume. Artificial intelligence gathers information of the factors that affect the price, trade volume, unforeseen events that affect the prices of the stocks, public sentiments and any other factors that influence the stock market (Ruan et al., 2005). The information provided is used in predicting the fluctuation of prices of various stocks.

Various artificial intelligence methods are used to detect change patterns and trends in the stock market. The computer systems analyse data of the financial markets to come up with a pattern of changes in prices. The approachhes also provide a trend of the factors that affect the market. Stock exchange companies use the information submitted by the machines to make predictions. Machine systems have been successfully implemented in other financial problems in the stock exchange like an approximation of trends, investment targets, and prediction of bankruptcy and evaluation of credit worthiness of a company (Chen et al., 2016). All this information is vital in making forecasts in the stock exchange market.

Advancement in technology has revolutionized the business industry and the stock market in particular. Companies that invest in financial markets rely on information that enables them to make fruitful and consistent predictions. The dealers need to analyse massive data sets to establish market trends and patterns before making predictions. Artificial intelligence plays a major role in data mining, analysis, and separation of data to allow focus on most significant information. Computer systems also provide information about other factors that are likely to affect the prices of stocks and the trading volume. Provision of such information is essential when making predictions in financial markets.

References

Zhao, L. & Wang, L., 2015. Price Trend Prediction of Stock Market Using Outlier Data Mining Algorithm. 2015 IEEE Fifth International Conference on Big Data and Cloud Computing.

Zhong, X. & Enke, D., 2017. Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, pp.126–139.

Chen, J.-F. et al., 2016. Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks. 2016 7th International Conference on Cloud Computing and Big Data (CCBD).

Ruan, Y., Alfantoukh, L. & Durresi, A., 2015. Exploring Stock Market Using Twitter Trust Network. 2015 IEEE 29th International Conference on Advanced Information Networking and Applications.

Debashish, D., Safa, S.A. & Noraziah, A., 2016. An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique. Indian Journal of Science and Technology, 9(21).