The stock market constantly evolves and generates a wealth of bids, calls, and puts data. Data scientists have found that market trends can be predicted in seconds by using big data mining techniques and machine learning strategies. The algorithms used in stock prediction systems were initially used for scientific research in fields such as genetics, astronomy, and quantum physics. However, scientists soon discovered that these algorithms could be applied to stock markets because the area produces vast amounts of data and follows a specific pattern. The most widely used techniques in stock market forecasting include Genetic Algorithms (GA) and Artificial Neural Networks (ANN). The use of ANN methods for action prediction has mainly proven successful.

ANNs predict future lows by analyzing the low price and time. They lagged, while future highs are expected using lagged highs. These predictions are then used to determine stopping prices for buying and selling. The benefits of using a stock prediction system to predict stock market trends are challenging and risky. There are many factors to consider: physical, psychological, and behavioral. However, through the use of algorithms and data science, predictions have been improved. The following are some benefits of using inventory forecasting systems: Using ANN systems that use a classification approach instead of a traditional quantitative output approach gives better prediction reliability. Certain types of data previously could not be collected or processed, such as B. This unstructured text data relates to news reports or public opinion. Using big data techniques makes it possible to track people’s values, beliefs, and behavior patterns while making predictions; This means that forecasts are not based solely on technical or numerical data. Algorithms help to process large amounts of temporary data quickly. This means that predicting future market events requires a reliable and fast system. Algorithms offer this advantage. Algorithms can use pre-processed data, reducing data storage space. And speed up calculations.