ABSTRACT
High-frequency stochastic data analysis and prediction are challenging and exciting problems if we aim to maintain high level of accuracy. The stock market dataset is selected randomly for the experimental investigation of the study. Historical datasets of a few stock markets have been collected and used for this purpose. The model is trained, and the results are compared with the real data. In the past few years, specialists have tried to build computationally efficient techniques and algorithms, which predict and capture the nature of the stock market accurately. This chapter presents a comparative analysis of the literature on applications of machine learning tools on the financial market dataset. This chapter provides a comparative and brief study of some relevant existing tools and techniques used in financial market analysis. The main objective of this chapter is to provide a comparative study of novel and appropriate methods of stock market prediction. A brief explanation of advanced and recent tools and techniques available for the analysis and a generalized and fundamental model in R-language for the stock market analysis and prediction are also provided in this chapter. In addition, this chapter presents a review of significant challenges and futuristic challenges of the field.
