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1.

図書

図書
translated and with commentary by Mark Davis and Alison Etheridge
出版情報: Princeton, N.J. : Princeton University Press, c2006  xv, 188 p. ; 24 cm
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2.

図書

図書
Gewei Ye
出版情報: Hoboken, N.J. : J. Wiley, c2011  xiv, 322 p. ; 24 cm
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目次情報: 続きを見る
Preface
Revenue Models of High-Frequency Trading / Part 1:
High-Frequency Trading and Existing Revenue Models / Chapter 1:
What Is High-Frequency Trading?
Why High-Frequency Trading Is Important
Major High-Frequency Trading Firms in the U.S
Existing Revenue Models of High-Frequency Trading Operations
Categorizing High-Frequency Trading Operations
Conclusion
Roots of High-Frequency Trading in Revenue Models of Investment Management / Chapter 2:
Investing / Revenue Model 1:
Investment Banking / Revenue Model 2:
Market Making / Revenue Model 3:
Trading / Revenue Model 4:
Cash Management / Revenue Model 5:
Mergers and Acquisitions / Revenue Model 6:
Back-office Activities / Revenue Model 7:
Venture Capital / Revenue Model 8:
Creating Your Own Revenue Model
How to Achieve Success: Four Personal Drivers
History and Future of High-Frequency Trading with Investment Management / Chapter 3:
Revenue Models in the Future
Investment Management and Financial Institutions
High-Frequency Trading and Investment Management
Technology Inventions to Drive Financial Inventions
The Ultimate Goal for Models and Financial Inventions
Theoretical Models as Foundation of Computer Algos for High-Frequency Trading / Part 2:
Behavioral Economics Models on Loss Aversion / Chapter 4:
What Is Loss Aversion?
The Locus Effect
Theory and Hypotheses
The Locus Effect on Inertia Equity / Study 1:
Assumption A1 and A2 / Study 2:
General Discussion
Loss Aversion in Option Pricing: Integrating Two Nobel Models / Chapter 5:
Demonstrating Loss Aversion with Computer Algos
Visualizing the Findings
Computer Algos for the Finding
Explaining the Finding with the Black-Scholes Formula
Expanding the Size of Options in Option Pricing / Chapter 6:
The NBA Event
Web Data
Theoretical Analysis
The NBA Event and the Uncertainty Account
Controlled Offline Data
Multinomial Models for Equity Returns / Chapter 7:
Literature Review
A Computational Framework: The MDP Model
Implicit Consumer Decision Theory
Empirical Approaches
Examination of Correlations and a Regression Model / Analysis 1:
Structural Equation Model / Analysis 2:
Contributions of the ICD Theory
More Multinomial Models and Signal Detection Models for Risk Propensity / Chapter 8:
Multinomial Models for Retail Investor Growth
Deriving Implicit Utility Functions
Transforming Likeability Rating Data into Observed Frequencies
Signal Detection Theory (SDT)
Assessing a Fund's Performance with SDT
Assessing Value at Risk with Risk Propensity of SDT for Portfolio Managers
Defining Risk Propensity Surface
Behavioral Economics Models on Fund Switching and Reference Prices / Chapter 9:
What Is VisualFunds for Fund Switching?
Behavioral Factors That Affect Fund Switching
Theory and Predictions
Arbitrary Anchoring on Inertia Equity
Anchor Competition
Double Log Law / Study 3:
A Unique Model of Sentiment Asset Pricing Engine for Portfolio Management / Part 3:
A Sentiment Asset Pricing Model / Chapter 10:
What Is Sentiment Asset Pricing Engine (SAPE)?
Contributions of SAPE
Testing the Effectiveness of SAPE Algos
Primary Users of SAPE
Three Implementations of SAPE
SAPE Extensions: TopTickEngine, FundEngine, PortfolioEngine, and TestEngine
Summary on SAPE
Alternative Assessment Tools of Macro Investor Sentiment
SAPE for Portfolio Management: Effectiveness and Strategies / Chapter 11:
Contributions of SAPE to Portfolio Management
Intraday Evidence of SAPE Effectiveness
Trading Strategies Based on the SAPE Funds
Execution of SAPE Investment Strategies / Case Study 1:
The Trading Process with SAPE / Case Study 2:
Advanced Trading Strategies with SAPE / Case Study 3:
Creating a Successful Fund with SAPE and High-Frequency Trading
New Models of High-Frequency Trading / Part 4:
Derivatives / Chapter 12:
What Is a Derivative?
Mortgage Backed Securities: Linking Major Financial Institutions
Credit Default Swaps
Options and Option Values
The Benefits of Using Options
Profiting with Options
New Profitable Financial Instruments by Writing Options
The Black-Scholes Model As a Special Case of the Binomial Model
Implied Volatility
Volatility Smile
Comparing Volatilities Over Time
Forwards and Futures
Pricing an Interest-Rate Swap with Prospect Theory
The Behavioral Investing Based On Behavioral Economics
Technology Infrastructure for Creating Computer Algos / Chapter 13:
Web Hosting vs. Dedicated Web Servers
Setting Up a Dedicated Web Server
Developing Computer Algos
Jump Starting Algo Development with PHP Programming
Jump Starting Algo Development with Java Programming
Jump Starting Algo Development with C++ Programming
Jump Starting Algo Development with Flex Programming
Jump Starting Algo Development with SQL
Common UNIX/LINUX Commands for Algo Development
Creating Computer Algos for High-Frequency Trading / Chapter 14:
Getting Probability from Z Score
Getting Z Scores from Probability
Algos for the Sharpe Ratio
Computing Net Present Value
Developing a Flex User Interface for Computer Algos
Algos for the Black-Scholes Model
Computing Volatility with the ARCH Formula
Algos for Monte Carlo Simulations
Algos for an Efficient Portfolio Frontier
Algos for Signal Detection Theory (SDT)
Notes
References
About the Author
Index
Preface
Revenue Models of High-Frequency Trading / Part 1:
High-Frequency Trading and Existing Revenue Models / Chapter 1:
3.

図書

図書
Álvaro Cartea, Sebastian Jaimungal, José Penalva
出版情報: Cambridge : Cambridge University Press, 2015  xv, 343 p. ; 26 cm
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