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· 분류 : 외국도서 > 경제경영 > 투자/증권 > 일반
· ISBN : 9781119601791
· 쪽수 : 416쪽
· 출판일 : 2020-07-21
목차
Preface
Part I - Introduction And Theory
Chapter 1. Alternative data – the lay of the land
1.1. Introduction
1.2. What is “Alternative Data”?
1.3. Segmentation of alternative data
1.4. The Many Vs of Big Data
1.5. Why alternative data?
1.6. Who is using alternative data?
1.7. Capacity of a strategy and alternative data
1.8. Alternative data dimensions
1.9. Who are the alternative data vendors?
1.10. Usage of alternative datasets on the buy side
1.11. Conclusion
Chapter 2. The value of alternative data
2.1. Introduction
2.2. The Decay of Investment Value
2.3. Data markets
2.4. The Monetary Value of Data (Part I)
2.5. Evaluating (alternative) data strategies with and without backtesting
2.5.1. Systematic investors
2.5.2. Discretionary Investors
2.5.3. Risk Managers
2.6. The Monetary Value of Data (Part II)
2.6.1. The buyer’s perspective
2.6.2. The seller’s perspective
2.7. The advantages of maturing alternative datasets
2.8. Summary
Chapter 3. Alternative data risks and challenges
3.1. Legal Aspects of Data
3.2. Risks of using alternative data
3.3. Challenges of using alternative data
3.3.1. Entity Matching
3.3.2. Missing Data
3.3.3. Structuring the data
3.3.4. Treatment of outliers
3.4. Aggregating the data
3.5. Summary
Chapter 4. Machine learning techniques
4.1. Introduction
4.2. Machine Learning – definitions and techniques
4.2.1. Bias, Variance and Noise
4.2.2. Cross-Validation
4.2.3. Introducing machine learning
4.2.4. Popular supervised machine learning techniques
4.2.5. Clustering based unsupervised machine learning techniques
4.2.6. Other unsupervised machine learning techniques
4.2.7. Machine learning libraries
4.2.8. Neutral networks and deep learning
4.2.9. Gaussian Processes
4.3. Which technique to choose?
4.4. Assumptions and Limitations of the Machine Learning Techniques
4.5. Structuring images
4.6. Natural Language Processing (NLP)
4.7. Summary
Chapter 5. The Processes behind the Use of Alternative Data
5.1. Introduction
5.2. Steps in the alternative data journey
5.2.1. Step 1 – Set up a vision and strategy
5.2.2. Step 2 – Identify the Appropriate Datasets
5.2.3. Step 3 – Perform due diligence on vendors
5.2.4. Step 4 – Pre-assess risks
5.2.5. Step 5 – Pre-assess the existence of signals
5.2.6. Step 6 - Data on-boarding
5.2.7. Step 7 – Data pre-processing
5.2.8. Step 8 – Signal extraction
5.2.9. Step 9 – Implementation (or deployment in production)
5.2.10. Maintenance process
5.3. Structuring teams to use alternative data
5.4. Data Vendors
5.5. Summary
Chapter 6. Factor Investing
6.1. Introduction
6.2. The CAPM
6.3. Factor Models
6.3.1. The Arbitrage Pricing Theory
6.3.2. The Fama-French 3-Factor Model
6.3.3. The Carhart Model
6.3.4. Other Approaches (Data Mining)
6.4. The difference between cross sectional and time series trading approaches
6.5. Why factor investing?
6.6. Smart beta indices using alternative data inputs
6.7. ESG factors
6.8. Direct and Indirect Prediction
6.9. Summary
Part II - Practical Applications
Chapter 7. Missing Data - Background
7.1. Introduction
7.2. Missing Data Classification
7.2.1. Missing Data Treatments
7.2.1.1. Deletion
7.2.1.2. Replacement
7.2.1.3. Predictive Imputation
7.3. Literature overview of missing data treatments
7.3.1. Luengo et al. (2012)
7.3.2. Garcia-Laencina et al. (2010)
7.3.3. Grzymala-Busse et al. (2000)
7.3.4. Zou et al (2005)
7.3.5. Jerez et al (2010)
7.3.6. Farhangfar et al. (2008)
7.3.7. Kang et al (2013)
7.3.8. Summary
Chapter 8. Missing Data – Case Studies
8.1. Introduction
8.2. Case Study – Imputing missing values in multivariate Credit Default Swap time series
8.2.1. Missing data classification
8.2.2. Imputation Metrics
8.2.3. CDS data and test data generation
8.2.4. Multiple imputation methods
8.2.4.1. The MVN Case
8.2.4.2. Expectation maximisation (EM) procedure
8.2.5. Deterministic and EOF based techniques
8.2.5.1. Brief recap of Singular Value Decomposition (SVD)
8.2.5.2. Data interpolation with empirical orthogonal functions (DINEOF)
8.2.5.3. Multiple singular spectral analysis (MSSA)
8.2.6. Results
8.3. Case Study – Satellite Images
8.4. Summary
8.5. Appendix: General description of the MICE procedure
8.6. Appendix: Software libraries used in this chapter
8.6.1.1. MICE
8.6.1.2. Amelia II
8.6.1.3. MissForest: Random Forest imputation
8.6.1.4. DINEOF
8.6.1.5. MSSA
Chapter 9. Outliers (Anomalies)
9.1. Introduction
9.2. Outliers definition, classification, and approaches to detection
9.3. Temporal structure
9.4. Global vs local outliers, point anomalies and micro-clusters
9.5. Outlier detection problem setup
9.6. Comparative evaluation of outlier detection algorithms
9.7. Approaches to outlier explanation
9.7.1. Micenkova et al.
9.7.2. Duan et al.
9.7.3. Angiulli et al.
9.8. Case Study - outlier detection on Fed communications index
9.9. Summary
9.10. Appendix
9.10.1. Model-based techniques
9.10.2. Distance-based techniques
9.10.3. Density-based Techniques
9.10.4. Heuristics-based approaches
Chapter 10. Automotive Fundamental Data
10.1. Introduction
10.2. Data
10.3. Approach 1 – Indirect Approach
10.3.1. The Steps Followed
10.3.2. Stage 1
10.3.2.1. Process
10.3.2.2. Example
10.3.2.3. Clairvoyance
10.3.2.4. Ranking factors used
10.3.2.5. Supporting statistics
10.3.2.6. Other info
10.3.2.7. Results
10.4. Approach 2 – Direct Approach
10.4.1. The Data
10.4.2. Factor Generation
10.4.3. Factor performance
10.4.4. Detailed factor results
10.4.4.1. revenues_sales_prev_3m_sum_prev_1m_pct_change – monthly change in quarterly sales volume
10.4.4.2. ww_market_share_prev_1m_pct_change – monthly change in worldwide market share
10.4.4.3. usa_sales_volume_prev_12m_sum_prev_3m_pct_change – quarterly change in yearly US sales volume
10.4.4.4. Factor correlations
10.4.5. Notes
10.5. Gaussian Processes Example
10.6. Summary
10.7. Appendix
10.7.1. List of companies
10.7.2. Description of financial statement items
10.7.3. Ratios used
10.7.4. IHS Markit data features
10.7.5. Reporting delays by country
Chapter 11. Survey and crowdsourced data
11.1. Introduction
11.2. Survey Data as Alternative Data
11.3. About Grapedata
11.4. The Product
11.5. Case Studies
11.5.1. Case Study – Company Event Study (Pooled Survey)
11.5.2. Case Study – Oil & Gas Production (Q&A Survey)
11.6. Technical considerations
11.7. Alpha capture data
11.8. Crowdsourcing analyst estimates
11.9. Summary
11.10. Appendix
Chapter 12. Purchasing Managers’ Index
12.1. Introduction
12.2. PMI Performance
12.3. Nowcasting GDP Growth
12.4. Impacts on financial markets
12.5. Summary
Chapter 13. Satellite imagery and aerial photography
13.1. Introduction
13.2. Forecasting US export growth
13.3. Car counts and earnings per share for retailers
13.4. Measuring Chinese PMI manufacturing with satellite data
13.5. Summary
Chapter 14. Location data
14.1. Introduction
14.2. Shipping data to track crude oil supplies
14.3. Mobile phone location data to understand retail activity
14.3.1. Trading REIT ETF using mobile phone location data
14.3.2. Estimating earnings per share with mobile phone location data
14.4. Taxi ride data and New York Fed meetings
14.5. Corporate jet location data and M&A
14.6. Summary
Chapter 15. Text, web, social media and news
15.1. Introduction
15.2. Collecting web data
15.3. Social media
15.3.1. Hedonmeter Index
15.3.2. Using Twitter data to help forecast US change in nonfarm payrolls
15.3.3. Twitter data to forecast stock market reaction to FOMC
15.3.4. Liquidity and sentiment from social media
15.4. News
15.4.1. Machine readable news to trade FX and understand FX volatility
15.4.2. Federal Reserve communications and US Treasury yields
15.5. Other web sources
15.5.1. Measuring consumer price inflation
15.6. Summary
Chapter 16. Investor attention
16.1. Introduction
16.2. Readership of payrolls to measure investor attention
16.3. Google Trends data to measure market themes
16.4. Investopedia search data to measure investor anxiety
16.5. Using Wikipedia to understand price action in cryptocurrencies
16.6. Online attention for countries to inform EMFX trading
16.7. Summary
Chapter 17. Consumer transactions
17.1. Introduction
17.2. Credit and debit card transaction data
17.3. Consumer receipts
17.4. Summary
Chapter 18. Government, industrial and corporate data
18.1. Introduction
18.2. Using innovation measures to trade equities
18.3. Quantifying currency crisis risk
18.4. Modelling central bank invention in currency markets
18.5. Summary
Chapter 19. Market data
19.1. Introduction
19.2. Relationship between institutional FX flow data and FX spot
19.3. Understanding liquidity using high frequency FX data
19.4. Summary
Chapter 20. Alternative data in private markets
20.1. Introduction
20.2. Defining private equity and venture capital firms
20.3. Private equity datasets
20.4. Understanding the performance of private firms
20.5. Summary
Chapter 21. Conclusions
21.1. Some last words
Bibliography
About the Authors
Index