@article{1461175,
      recid = {1461175},
      author = {Abedin, Mohammad Zoynul, and Hajek, Petr,},
      title = {Novel financial applications of machine learning and deep  learning : algorithms, product modeling, and applications  /},
      pages = {1 online resource},
      abstract = {This book presents the state-of-the-art applications of  machine learning in the finance domain with a focus on  financial product modeling, which aims to advance the model  performance and minimize risk and uncertainty. It provides  both practical and managerial implications of financial and  managerial decision support systems which capture a broad  range of financial data traits. It also serves as a guide  for the implementation of risk-adjusted financial product  pricing systems, while adding a significant supplement to  the financial literacy of the investigated study. The book  covers advanced machine learning techniques, such as  Support Vector Machine, Neural Networks, Random Forest,  K-Nearest Neighbors, Extreme Learning Machine, Deep  Learning Approaches, and their application to finance  datasets. It also leverages real-world financial instances  to practice business product modeling and data analysis.  Software code, such as MATLAB, Python and/or R including  datasets within a broad range of financial domain are  included for more rigorous practice. The book primarily  aims at providing graduate students and researchers with a  roadmap for financial data analysis. It is also intended  for a broad audience, including academics, professional  financial analysts, and policy-makers who are involved in  forecasting, modeling, trading, risk management, economics,  credit risk, and portfolio management.},
      url = {http://library.usi.edu/record/1461175},
      doi = {https://doi.org/10.1007/978-3-031-18552-6},
}