Financial Data Analytics: Theory and Application

ISBN-13: 9783030837983 / Angielski / Twarda / 2022 / 370 str.

Sinem Derindere Köseoğlu

Spis treści

PART 1. INTRODUCTION AND ANALYTICS MODELS.- Retraining and Reskilling Financial Participators in the Digital Age.- Basics of Financial Data Analytics.- Predictive Analytics Techniques: Theory and Applications in Finance.- Prescriptive Analytics Techniques: Theory and Applications in Finance.- Forecasting Returns of Crypto Currency - Analyzing Robustness of Auto Regressive and Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNS).- PART 2. MACHINE LEARNING.- Machine Learning in Financial Markets: Dimension Reduction and Support Vector Machine.- Pruned Random Forests for Effective and Efficient Financial Data Analytics.- Foreign Currency Exchange Rate Prediction Using Long Short Term Memory.- Natural Language Processing (NLP) for Exploring Culture in Finance: Theory and Applications.- PART 3. TECHNOLOGY DRIVEN FINANCE.- Financial Networks: A Review of Models and the Use of Network Similarities.- Optimization of Regulatory Economic-Capital Structured Portfolios: Modeling Algorithms, Financial Data Analytics and Reinforcement Machine Learning in Emerging Markets.- Transforming Insurance Business with Data Science.- A General Cyber Hygiene Approach for Financial Analytical Environment.

O autorze

Sinem Derindere Köseoğlu is an associate professor of finance and former professor at Istanbul University (Turkey) and a freelance consultant and trainer. Derindere Köseoğlu has published in various renowned international journals and volumes.

Blurb

​​This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization.

This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. 

Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics.