| 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 |
