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The Big R-Book
Introduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Bo...
Maslowian Portfolio Theory
Written from the perspective of a financial investor, this account supports Behavioral Portfolio Theory, draws attention to the importance of asset-liability matching, and offers a natural framework for investor-adviser dialogue and mathematical portfolio optimization. In this system, investment goals--and not investor psychology--drive investment advice; "risk" depends on the investment objective and may be different in each sub-portfolio. This comprehensive book presents an extensive overview of existing portfolio theories and behavioral finance, and introduces new theories and its practical applications.
Python Programming
Maintaining a practical perspective, Python Programming: A Practical Approach acquaints you with the wonderful world of programming. The book is a starting point for those who want to learn Python programming. The backbone of any programming, which is the data structure and components such as strings, lists, etc., have been illustrated with many examples and enough practice problems to instill a level of self-confidence in the reader. Drawing on knowledge gained directly from teaching Computer Science as a subject and working on a wide range of projects related to ML, AI, deep learning, and blockchain, the authors have tried their best to present the necessary skills for a Python programmer....
Finance and the Behavioral Prospect
This book explains how investor behavior, from mental accounting to the combustible interplay of hope and fear, affects financial economics. The transformation of portfolio theory begins with the identification of anomalies. Gaps in perception and behavioral departures from rationality spur momentum, irrational exuberance, and speculative bubbles. Behavioral accounting undermines the rational premises of mathematical finance. Assets and portfolios are imbued with “affect.” Positive and negative emotions warp investment decisions. Whether hedging against intertemporal changes in their ability to bear risk or climbing a psychological hierarchy of needs, investors arrange their portfolios and financial affairs according to emotions and perceptions. Risk aversion and life-cycle theories of consumption provide possible solutions to the equity premium puzzle, an iconic financial mystery. Prospect theory has questioned the cogency of the efficient capital markets hypothesis. Behavioral portfolio theory arises from a psychological account of security, potential, and aspiration.
Investor Behavior
WINNER, Business: Personal Finance/Investing, 2015 USA Best Book Awards FINALIST, Business: Reference, 2015 USA Best Book Awards Investor Behavior provides readers with a comprehensive understanding and the latest research in the area of behavioral finance and investor decision making. Blending contributions from noted academics and experienced practitioners, this 30-chapter book will provide investment professionals with insights on how to understand and manage client behavior; a framework for interpreting financial market activity; and an in-depth understanding of this important new field of investment research. The book should also be of interest to academics, investors, and students. The...
The Econometrics of Financial Markets
The past twenty years have seen an extraordinary growth in the use of quantitative methods in financial markets. Finance professionals now routinely use sophisticated statistical techniques in portfolio management, proprietary trading, risk management, financial consulting, and securities regulation. This graduate-level textbook is intended for PhD students, advanced MBA students, and industry professionals interested in the econometrics of financial modeling. The book covers the entire spectrum of empirical finance, including: the predictability of asset returns, tests of the Random Walk Hypothesis, the microstructure of securities markets, event analysis, the Capital Asset Pricing Model an...
Harry Markowitz
Who is Harry Markowitz An American economist named Harry Max Markowitz was awarded the John von Neumann Theory Prize in 1989 and the Nobel Memorial Prize in Economic Sciences in 1990. He was also a recipient of both of these honors. How you will benefit (I) Insights about the following: Chapter 1: Harry Markowitz Chapter 2: Robert C. Merton Chapter 3: Capital asset pricing model Chapter 4: Merton Miller Chapter 5: William F. Sharpe Chapter 6: Modern portfolio theory Chapter 7: SIMSCRIPT Chapter 8: Roger G. Ibbotson Chapter 9: Diversification (finance) Chapter 10: Leonid Hurwicz Chapter 11: Post-modern portfolio theory Chapter 12: Finance Chapter 13: Portfolio manager Chapter 14: Andrew Lo Chapter 15: Maslowian portfolio theory Chapter 16: Portfolio optimization Chapter 17: Quantitative analysis (finance) Chapter 18: Downside risk Chapter 19: Mathematical finance Chapter 20: Index Fund Advisors Chapter 21: Philippe De Brouwer Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information about Harry Markowitz.
Multiple Stopping Problems
This book presents the theory of rational decisions involving the selection of stopping times in observed discrete-time stochastic processes, both by single and multiple decision-makers. Readers will become acquainted with the models, strategies, and applications of these models. It begins with an examination of selected models framed as stochastic optimization challenges, emphasizing the critical role of optimal stopping times in sequential statistical procedures. The authors go on to explore models featuring multiple stopping and shares on leading applications, particularly focusing on change point detection, selection problems, and the nuances of behavioral ecology. In the following chapt...
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