Prediction Markets Using Machine Learning

Abstract

The introduction of a brand-new product line predicting future stock prices is an important part of financial decision-making and investment since stock values fluctuate often. Although the value of the share may reduce due to market movements, there is still a danger of losing money. The stock price and trade volume are affected by these swings, making the forecast even more difficult. There are a wide range of methods and techniques that may be used to anticipate the stock market's behavior, and these methods and techniques can help investors respond more quickly and accurately to know when to purchase or sell the stock therefore a tremendous diversity of strategies have been created. Even though a number of strategies have been developed, none of them reliably anticipate stock prices. Stock price prediction concerns are being solved via data mining and evolutionary strategies. In data mining, the extraction of a large amount of information from a big database is called “mining”. Data mining techniques are aimed to assist investors in uncovering hidden patterns from the historical data that include plausible forecasting capability in the stock market because of the enormous volume of data. Stock market robots have been created by combining predictive analytics with data mining. Prediction models are built using historical data, which helps investors find patterns in the data and anticipate future returns. The evolutionary algorithm, on the other hand, is critical in properly projecting stock values. Evolutionary strategies have been found to outperform other parametric approaches in a number of studies. Evolutionary approaches may be used to improve more formal procedures since they are simple to apply and comprehend. They also do not suffer from the negative consequences of dimensionality.