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: |
What Is High-Frequency Trading? |
Why High-Frequency Trading Is Important |
Major High-Frequency Trading Firms in the U.S |