1. Big Data Science in Finance
Authors: Aldridge, Irene & Avellaneda, Marco
Price: $77.58
In the evolving landscape of finance, understanding how to leverage big data is more critical than ever. “Big Data Science in Finance” delves into the integration of data science within the financial sector. This book provides readers with practical insights into how data analytics can optimize financial strategies, enhance risk management, and drive decision-making processes. It’s a comprehensive guide that lays a strong foundation for anyone looking to make data-driven decisions in finance.
2. Big Data in Finance: Theory and Empirics
Authors: Bhattacharya, Rajeev R
Price: $182.45
“Big Data in Finance: Theory and Empirics” is an essential read for both students and practitioners who aspire to dive deep into the theoretical underpinnings of big data applications in finance. This book discusses empirical evidence and a variety of models that demonstrate how big data can be harnessed to improve financial forecasting and risk assessment. A must-read for anyone interested in understanding the complex interaction between big data and financial principles.
3. Big Data in Finance: Transforming the Financial Landscape: Volume 2
Authors: Alareeni, Bahaaeddin
Price: $208.99
This second volume of the “Big Data in Finance” series offers an advanced exploration into how big data is reshaping financial markets. Alareeni provides case studies and in-depth analyses showcasing the transformation driven by big data analytics. This volume is perfect for professionals looking to expand their knowledge about the methodologies and innovations that are setting new standards in financial performance.
4. Big Data in Finance: Transforming the Financial Landscape: Volume 1
Authors: Alareeni, Bahaaeddin
Price: $208.99
The first volume sets the stage for understanding how big data influences finance. With contributions from leading experts, this book compiles insights on how firms utilize data analytics, machine learning, and technology to enhance their operational strategies. It is an invaluable resource for those aiming to grasp the foundational elements that make data a cornerstone of modern finance.
5. Big Data and Machine Learning in Quantitative Investment
Authors: Guida, Tony
Price: $36.99
Guida’s “Big Data and Machine Learning in Quantitative Investment” is an exceptional guide for financial analysts and quantitative investors keen on integrating advanced data techniques into their practices. The book emphasizes practical applications of machine learning in crafting robust investment strategies, making it a great resource for professionals looking to stay ahead in a competitive landscape.
6. Big Data in Finance: Harnessing Data Analytics for Competitive Advantage
Authors: Tatto, Alfonso Cahero
Price: $15.00
This enlightening book highlights how organizations can utilize data analytics to gain a competitive edge in the financial market. Offering readers practical insights and real-world examples, Tatto emphasizes the importance of data-driven decisions in enhancing overall business performance. Perfect for finance professionals eager to implement data analytics for better strategic initiatives.
7. Big Data and Artificial Intelligence in Digital Finance
Authors: Soldatos, John & Kyriazis, Dimosthenis
Price: $51.39
This book explores the intersection of big data, AI, and digital finance, discussing how these technologies can enhance personalization and trust in financial services. Soldatos and Kyriazis provide insights into ethical considerations and the future landscape of digital finance, making it an essential read for fintech enthusiasts and professionals committed to innovation.
8. Stochastic Modelling of Big Data in Finance
Authors: Swishchuk, Anatoliy
Price: $76.00
Swishchuk’s book provides a profound understanding of stochastic modeling within the context of big data in finance. This specialized text breaks down complex theoretical constructs and applies them to real-life financial scenarios—ideal for advanced students and professionals seeking to refine their analytical techniques and deepen their comprehension of stochastic processes.
