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MATHEMATICAL / QUANTITATIVE FINANCE MONEY AND BANKING / INVESTMENTS / FINANCIAL MARKETS AND INSTITUTIONS
MATHEMATICAL / QUANTITATIVE FINANCE
Textbook MODERN EQUITY INVESTING STRATEGIES
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by Anatoly B Schmidt (New York University, USA) This book will satisfy the demand among college majors in Finance and Financial Engineering, and mathematicallyversed practitioners for description of both the classical approaches to equity investing and new investment strategies scattered in the periodic literature. Besides the major portfolio management theories (mean variance theory, CAPM, and APT), the book addresses several important topics: portfolio diversification, optimal ESG portfolios, factor models (smart betas), robust portfolio optimization, risk-based asset allocation, statistical arbitrage, alternative data based investing, back-testing of trading strategies, modern market microstructure, algorithmic trading, and agent-based modeling of financial markets. The book also includes the basic elements of time series analysis in the Appendix for self-contained presentation of the material. Contents: Modern Equity Markets: Traders, Orders, and Structures; Models of Dealer Markets; Models of Limit-Order Markets; Market Dynamics: Efficient Market Hypothesis and Dynamics of Returns; Price Volatility; Agent-Based Modeling of Financial Markets; Portfolio Management: Mean Variance Theory; Portfolio Optimization; Risk-Based Asset Allocation; Factor Models; Active Trading Strategies: Technical Analysis Based Strategies; Arbitrage Strategies; News and Sentiment Based Strategies; Back-Testing of Trading Strategies; Execution Strategies; Appendices: Probability Distributions; Elements of Time Series Analysis. Readership: Advanced undergraduate and graduate students, researchers, and practitioners.
300pp Oct 2021 978-981-123-949-6 US$88 £75
World Scientific Series on Financial Data Analytics - Vol 1
RISK ANALYTICS
From Concept to Deployment
by Edward H K Ng (Singapore Management University, Singapore) Modeling was done using software with output codes not readily processed by databases. Data have to be manually extracted and run on the models with results input into the databases manually again. With the increasing acceptance of open source languages, database vendors have seen the value of integrating modeling capabilities into their products. That has made it possible to insert models developed using R, Python or other languages directly into SQL scripts used for database transactions. As R or Python are free, there is no additional cost involved. Nevertheless, deploying solutions developed to automate the process remains a challenge. While not comprehensive in dealing with all facets of risks, the author with his wealth of consulting experience, aims to contribute to the development of risk professionals who will be able to progress beyond theories and concepts to create solutions that can support planning and automated decision-making. Contents: Introduction; Risk Typology and Data Implications; Risk Analytics Landscape; Embedded R; Data Audit; Data Warehousing; Analytical Data Sphere; Risks in Financial Institutions; Common Risk Models and Analytics; Internal Rating System; Deployment; Through the Cycle (TTC) Updating; Desktop Analytics; Resources. Readership: Risk management professionals, bankers, compliance officers, modelers and students.
