Machine Learning for Financial Risk Management with Python — Algorithms for Modeling Risk: https://t.co/oBePOF3nrh https://t.co/Lc628Cvnjl

Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk: Karasan, Abdullah: 9781492085256: Amazon.com: Books

Source: https://www.amazon.com/Machine-Learning-Financial-Management-Python/dp/1492085251?&_encoding=UTF8&tag=kirkdborne-20&linkCode=ur2&linkId=e715c5d063675c18ee7236dea0e01285&camp=1789&creative=9325 The Financial Stability Board (2017) stresses the validity of this fact:

Many applications, or use “cases,” of AI and machine learning already exist. The adoption of these use cases has been driven by both supply factors, such as technological advances and the availability of financial sector data and infrastructure, and by demand factors, such as profitability needs, competition with other firms, and the demands of financial regulation.

As a subbranch of financial modeling, financial risk management has been evolving with the adoption of AI in parallel with its ever-growing role in the financial decision-making process. In his celebrated book, Bostrom (2014) denotes that there are two important revolutions in the history of mankind: the Agricultural Revolution and the Industrial Revolution. These two revolutions have had such a profound impact that any third revolution of similar magnitude would double the size of the world economy in two weeks. Even more strikingly, if the third revolution were accomplished by AI, the impact would be way more profound.

So expectations are sky-high for AI applications shaping financial risk management at an unprecedented scale by making use of big data and understanding the complex structure of risk processes.

With this study, I aim to fill the void about machine learning-based applications in finance so that predictive and measurement performance of financial models can be improved. Parametric models suffer from issues of low variance and high bias; machine learning models, with their flexibility, can address this problem. Moreover, a common problem in finance is that changing distribution of the data always poses a threat to the reliability of the model result, but machine learning models can adapt themselves to changing patterns in a way that models fit better. So there is a huge need and demand for applicable machine learning models in finance, and what mainly distinguish this book is the inclusion of brand-new machine learning-based modeling approaches in financial risk management.

In a nutshell, this book aims to shift the current landscape of financial risk management, which is heavily based on the parametric models. The main motivation for this shift is recent developments in highly accurate financial models based on machine learning models. Thus, this book is intended for those who have some initial knowledge of about finance and machine learning in the sense that I just provide brief explanations on these topics.

Consequently, the targeted audience of the book includes, but is not limited to, financial risk analysts, financial engineers, risk associates, risk modelers, model validators, quant risk analysts, portfolio analysis, and those who are interested in finance and data science.

In light of the background of the targeted audience, having an introductory level of finance and data science knowledge will enable you to benefit most from the book. It does not, however, mean that people from different backgrounds cannot follow the book topics. Rather, readers from different backgrounds can grasp the concepts as long as they spend enough time and refer to some other finance and data science books along with this one.