It is difficult to predict the performance of the stock market and it involves a certain amount of risk. An accurate prediction could result in a significant amount of profit. The stock market is influenced by a number of factors:

• News, both national and international
• government reports
• Natural disasters

The stock market is constantly evolving and regularly generates a large amount of data on bids, calls, and puts. Data scientists have discovered that using Big Data mining techniques and machine learning strategies, movements in the markets can be predicted in a matter of seconds. Previously, experts used to use various methods to try to predict the stock market; however, with the advent of deep learning and data science, these predictions are faster and more accurate than ever. This significantly increases profits for companies and investors alike.

What are stock prediction systems?

Stock prediction systems are programs that use algorithms to predict future trends in the stock market. The algorithms used in action prediction systems were originally used for scientific research in fields such as genetics, astronomy, and quantum physics.

However, scientists soon discovered that these algorithms can be applied to stock markets, since the field produces huge amounts of data and follows some kind of pattern.

The most widely used techniques in stock market prediction include genetic algorithms (GA) and artificial neural networks (ANN).

The use of ANN methods for stock prediction has been found to be widely successful. ANNs predict future lows by analyzing low prices and time lags, while future highs are predicted using delayed highs. These predictions are then used to determine the stop prices for buying and selling.

Benefits of using a Stock Prediction System

Predicting stock market performance is challenging and risky. There are many factors to consider: physical factors, psychological and behavioral factors. These aspects make stock prices unstable and difficult to predict accurately. However, with the use of algorithms and data science, there have been improvements in predictions. The following are some of the benefits of using stock prediction systems:

• The use of ANN systems, which use a classification approach instead of a traditional quantitative output approach, produces better predictive reliability.
• Certain types of data that previously could not be collected or processed, such as unstructured text data, can be used to make predictions with the help of algorithms. This unstructured text data refers to news or public opinion. The use of Big Data techniques makes it possible to track people’s values, opinions and behavior patterns while making predictions; this means that the predictions are not based solely on technical or numerical data.
• Algorithms help to quickly process large amounts of data that is perishable. In the stock market, conditions change constantly and rapidly. This means that in order to predict future events in the market, a reliable and fast system is needed. Algorithms provide this benefit. Algorithms can use pre-processed data, which reduces data storage space and speeds up computations.