Accounting for Value teaches investors and analysts how to handle accounting in evaluating equity investments. The book's novel approach shows that valuation and accounting are much the same: valuation is actually a matter of accounting for value. Laying aside many of the tools of modern financethe cost-of-capital, the CAPM, and discounted cash flow analysisStephen Penman retur...(展开全部)

Economists generally accept as a given the old adage that there is no accounting for tastes. The author of this text disagrees, and confronts the problem of preferences and values: how they are formed and how they affect our behaviour. He argues that past experiences and social influences form two basic capital stocks: personal and social. He then applies these concepts to asse...(展开全部)

Accounting for Value teaches investors and analysts how to handle accounting in evaluating equity investments. The book's novel approach shows that valuation and accounting are much the same: valuation is actually a matter of accounting for value. Laying aside many of the tools of modern financethe cost-of-capital, the CAPM, and discounted cash flow analysisStephen Penman retur...(展开全部)

Financial data analysts and data scientists who want to use SAS to process and analyze financial data and find hidden patterns and trends from it will find this book useful. Prior exposure to SAS will be helpful but is not mandatory. Some basic understanding of the financial concepts is required.

严玉星，毕业于麦吉尔大学，获金融学博士学位。他有着丰富的教学经验，教授过各类本科学位和研究生学位的金融课程，如金融建模、期权和期货、投资组合理论、定量财务分析、企业融资和金融数据库等。他曾在8所全球知名的大学任教：两所在加拿大，一所在新加坡，5所在美国。 严博士一直活跃于学术研究的前沿，他的研究成果在多个国际学术期刊发表。此外，他还是财务数据方面的专家。在新加坡南洋理工大学任教时，他曾为博士生讲授一门名为“金融数据库入门”的课程。

严玉星，毕业于麦吉尔大学，获金融学博士学位。他有着丰富的教学经验，教授过各类本科学位和研究生学位的金融课程，如金融建模、期权和期货、投资组合理论、定量财务分析、企业融资和金融数据库等。他曾在8所全球知名的大学任教：两所在加拿大，一所在新加坡，5所在美国。 严博士一直活跃于学术研究的前沿，他的研究成果在多个国际学术期刊发表。此外，他还是财务数据方面的专家。在新加坡南洋理工大学任教时，他曾为博士生讲授一门名为“金融数据库入门”的课程。

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways.The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming.

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways.The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming.