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COMPUTATIONAL ECONOMICS / FINANCE
[ ECONOMICS AND FINANCE ]
COMPUTATIONAL ECONOMICS / FINANCE
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Textbook ALGORITHMIC FINANCE
A Companion to Data Science
by Hian Ann & Christopher Ting (Hiroshima University, Japan) This book champions a fundamental principle of science — objective reproducibility of evidence independently by others. From a companion web site, readers can download many easy-to-understand Python programs and real-world data. Independently, readers can draw for themselves the figures in the book. Even so, readers are encouraged to run the statistical tests described as examples to verify their own results against what the book claims.
This book covers some topics that are seldom discussed in other textbooks. They include the methods to adjust for dividend payment and stock splits, how to reproduce a stock market index such as Nikkei 225 index, and so on. By running the Python programs provided, readers can verify their results against the data published by free data resources such as Yahoo! finance. Though practical, this book provides detailed proofs of propositions such as why certain estimators are unbiased, how the ubiquitous normal distribution is derived from the first principles, and so on. Contents: Introduction; Cross-Sectional Data Analysis; Comparative Data Analysis; Price and Returns; Log Return and Random Walk; Stock Market Indexes and ETF; Non-Equity Indexes; Linear Regression; Event Study; Pair Trading. Readership: Advanced undergraduate and graduate students, researchers and practitioners in the fields of finance and quantitative finance, data scientists who are learning a new application domain.
450pp May 2022 978-981-123-830-7 US$148 £130 MODELING AND ADVANCED TECHNIQUES IN MODERN ECONOMICS
edited by Çagˇdaş Hakan Aladağ (Hacettepe University, Turkey) & Nihan Potas (Ankara Haci Bayram Veli University, Turkey) In the modern world, data is a vital asset for any organization, regardless of industry or size. The world is built upon data. However, data without knowledge is useless. The aim of this book, briefly, is to introduce new approaches that can be used to shape and forecast the future by combining the two disciplines of Statistics and Economics. Readers of Modeling and Advanced Techniques in Modern Economics can find valuable information from a diverse group of experts on topics such as finance, econometric models, stochastic financial models and machine learning, and application of models to financial and macroeconomic data.
Featured Contents: Smart Growth Developments of Europe Union Members by Europe 2020 Strategies (şahika Gökmen and Johan Lyhagen); Spatial Regression Model Specification and Measurement Errors (Anıl Eralp and Rukiye Dağalp); Determining the Harmonic Fluctuations in Food Inflation (Yılmaz Akdi, Kamil Demirberk Ünlü, Cem Baş, Yunus Emre Karamanoğlu); Nonlinear and Chaotic Time Series Analysis (Baki Ünal and Çağdaş Hakan Aladağ); and others. Readership: The book is primarily aimed at academics, researchers, and postgraduate students interested in new economic perspectives with modeling and advanced techniques. A secondary audience involves practitioners and policymakers from diverse related fields.
