Advanced Classification Technique to Detect the Changes of Regimes in Financial Markets by Hybrid CNN-based Prediction

Abstract

Traders' tactics shift in response to the shifting market circumstances. The statistical features of price fluctuations may be significantly altered by the collective conduct of traders. When some changes in the market eventuate, a "regime shift" takes place. According to the observed directional shifts, this proposed study attempts to define what constitutes between normal and abnormal market regimes in the financial markets. The study begins by using data from ten financial marketplaces. For each call, a time frame in which major events may have led to regime change is chosen. Using the previous returns of all the companies in the index, this study investigates the usage of a CNN with SVM deep learning hybrid to anticipate the index's movement. The experiment findings reveal that this CNN model can successfully extract more generic and useful features than conventional technical indicators and produce more resilient and lucrative financial performance than earlier machine learning techniques. Most of the inability to forecast is due to randomness, and a small amount is due to non-stationarity. There is also a statistical correlation between the legal regimes of various marketplaces. Using this data, it is conceivable to tell the difference between normal regimes and lawful regimes. The results show that the stock market efficiency has never been tested before with such a large data set, and this is a significant step forward for weak-form market efficiency testing.