Algorithmic trading and quantitative strategies. (2019)

• Record Type: Book
• Further Information: Note: Raja Velu, Maxence Hardy, Daniel Nehren.
• Authors: Velu, Raja; Hardy, Maxence; Nehren, Daniel
• Contents: I Introduction to Trading 1. Trading Fundamentals A Brief History of Stock Trading Market Structure and Trading Venues: A Review Equity Markets Participants Watering Holes of Equity Markets The Mechanics of Trading How Double Auction Markets Work The Open Auction Continuous Trading The Closing Auction Taxonomy of Data Used in Algorithmic Trading Reference Data Market Data Market Data Derived Statistics Fundamental Data and Other Datasets Market Microstructure: Economic Fundamentals of Trading Liquidity and Market Making II Foundations: Basic Models and Empirics 2. Univariate Time Series Models Trades and Quotes Data and their Aggregation: From Point Processes to Discrete Time Series Trading Decisions as Short-Term Forecast Decisions Stochastic Processes: Some Properties Some Descriptive Tools and their Properties Time Series Models for Aggregated Data: Modeling the Mean Key Steps for Model Building Testing for Nonstationary (Unit Root) in ARIMA Models: To Difference or Not To Forecasting for ARIMA Processes Stylized Models for Asset Returns Time Series Models for Aggregated Data: Modeling the Variance Stylized Models for Variance of Asset Returns Exercises 3. Multivariate Time Series Models Multivariate Regression Dimension-Reduction Methods Multiple Time Series Modeling Co-integration, Co-movement and Commonality in Multiple Time Series Applications in Finance Multivariate GARCH Models Illustrative Examples Exercises 4. Advanced Topics State-Space Modeling Regime Switching and Change-Point Models A Model for Volume-Volatility Relationship Models for Point Processes Stylized Models for High Frequency Financial Data Models for Multiple Assets: High Frequency Context Analysis of Time Aggregated Data Realized Volatility and Econometric Models Volatility and Price Bar Data Analytics from Machine Learning Literature Neural Networks Reinforcement Learning Multiple Indicators and Boosting Methods Exercises III Trading Algorithms 5. Statistical Trading Strategies and Back-Testing Introduction to Trading Strategies: Origin and History Evaluation of Strategies: Various Measures Trading Rules for Time Aggregated Data Filter Rules Moving Average Variants and Oscillators Patterns Discovery via Non-Parametric Smoothing Methods A Decomposition Algorithm Fair Value Models Back-Testing and Data Snooping: In-Sample and Out-of-Sample Performance Evaluation Pairs Trading Distance-Based Algorithms Co-Integration Some General Comments Practical Considerations Cross-Sectional Momentum Strategies Extraneous Signals: Trading Volume, Volatility, etc Filter Rules Based on Return and Volume An Illustrative Example Trading in Multiple Markets Other Topics: Trade Size, etc Machine Learning Methods in Trading Exercises 6. Dynamic Portfolio Management and Trading Strategies Introduction to Modern Portfolio Theory Mean-Variance Portfolio Theory Multifactor Models Tests Related to CAPM and APT An Illustrative Example Implications for Investing Statistical Underpinnings Portfolio Allocation Using Regularization Portfolio Strategies: Some General Findings Dynamic Portfolio Selection Portfolio Tracking and Rebalancing Transaction Costs, Shorting and Liquidity Constraints Portfolio Trading Strategies Exercises 7. News Analytics: From Market Attention and Sentiment to Trading Introduction to News Analytics: Behavioral Finance and Investor Cognitive Biases Automated News Analysis and Market Sentiment News Analytics and Applications to Trading Discussion / Future of Social Media and News in Algorithmic Trading IV Execution Algorithms 8. Modeling Trade Data Normalizing Analytics Order Size Normalization: ADV Time-Scale Normalization: Characteristic Time Intraday Return Normalization: Mid-Quote Volatility Other Microstructure Normalization Intraday Normalization: Profiles Remainder (of the Day) Volume Auctions Volume Microstructure Signals Limit Order Book (LOB): Studying Its Dynamics LOB Construction and Key Descriptives Modeling LOB Dynamics Models Based on Hawkes Process Models for Hidden Liquidity Modeling LOB: Some Concluding Thoughts 9. Market Impact Models Introduction What is Market Impact Modeling Transaction Costs Historical Review of Market Impact Research Some Stylized Models Price Impact in the High Frequency Setting Models Based on LOB Empirical Estimation of Transaction Costs Review of Select Empirical Studies 10. Execution Strategies Execution Benchmarks: Practitioner’s View Evolution of Execution Strategies Layers of an Execution Strategy Scheduling Layer Order Placement </P&
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2019
• Extent: 1 online resource, illustrations (colour)
• Subjects: 332.60285; Finance -- Data processing; Finance -- Computer programs; Investment analysis; Electronic trading of securities
• Languages: English
• ISBNs: 9781498737210; 9781498737197; 9781498737203
• Related ISBNs: 9781498737166
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.