Artificial Intelligence for Financial Markets: The Polymodel Approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing AG
2022
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | Financial Mathematics and Fintech Series
|
| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (182 Seiten) |
| ISBN: | 9783030973193 |
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| 245 | 1 | 0 | |a Artificial Intelligence for Financial Markets |b The Polymodel Approach |
| 250 | |a 1st ed | ||
| 264 | 1 | |a Cham |b Springer International Publishing AG |c 2022 | |
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| 505 | 8 | |a Intro -- Foreword -- Preface -- Contents -- Chapter 1: Introduction -- 1.1 Financial Market Predictions: A Concise Literature Review -- 1.2 A Simple Model of Portfolio Returns -- 1.3 Plan of the Book -- References -- Chapter 2: Polymodel Theory: An Overview -- 2.1 Introduction -- 2.2 Mathematical Formulation -- 2.3 Epistemological Foundations -- 2.3.1 A Statistical Perspectivism -- 2.3.2 A Phenomenological Approach -- 2.4 Comparison of Polymodels to Multivariate Models -- 2.4.1 Reducing Overfitting -- 2.4.2 Increasing Precision -- 2.4.3 Increasing Robustness -- 2.5 Considerations Raised by Polymodels -- 2.5.1 Aggregation of Predictions -- 2.5.2 Number of Variables Per Elementary Model -- 2.6 Conclusions -- References -- Chapter 3: Estimation Method: The Linear Non-Linear Mixed Model -- 3.1 Introduction -- 3.2 Presentation of the LNLM Model -- 3.2.1 Definition -- 3.2.2 Fitting Procedure -- 3.3 Evaluation Methodology -- 3.4 Results -- 3.4.1 For 126 Observations (Tables 3.1 and 3.2) -- 3.4.2 For 252 Observations (Tables 3.3 and 3.4) -- 3.4.3 For 756 Observations (Tables 3.5 and 3.6) -- 3.4.4 For 1,260 Observations (Tables 3.7 and 3.8) -- 3.4.5 Computation Time (Table 3.9) -- 3.4.6 Interpretations -- 3.5 Conclusions -- References -- Chapter 4: Predictions of Market Returns -- 4.1 Introduction -- 4.2 Data -- 4.3 Systemic Risk Indicator -- 4.3.1 Model Estimation -- 4.3.2 Systemic Risk Indicator Definition -- 4.3.3 Primary Analysis -- 4.3.4 Roots of the Predictive Power -- 4.4 Trading Strategy -- 4.4.1 Methodology -- 4.4.2 Results -- 4.5 Robustness Tests -- 4.5.1 Sensitivity to the Noise Filter -- 4.5.2 Sensitivity to Future Returns Windows -- 4.5.3 Sensitivity to Half-life -- 4.5.4 Sensitivity to Rolling Window Length -- 4.5.5 Asset-Class Specific Performances -- 4.5.6 Sensitivity to Trading Strategy -- 4.6 Conclusions -- References | |
| 505 | 8 | |a Chapter 5: Predictions of Industry Returns -- 5.1 Introduction -- 5.2 Data -- 5.2.1 Summary Statistics -- 5.3 Methodology -- 5.3.1 Measuring Antifragility -- 5.3.2 Predicting Industry Returns -- 5.4 Results -- 5.4.1 Long/Short Trading Strategy -- 5.4.2 Regressions on Future Returns -- 5.4.3 Comparisons to Classical Factors -- 5.4.4 Separating Concavity and Convexity -- 5.4.5 Separating Positive and Negative Market Returns -- 5.5 Robustness Tests -- 5.5.1 Stability of the Signal -- 5.5.2 Sensitivity to Hyper-Parameters -- 5.6 Conclusions -- References -- Chapter 6: Predictions of Specific Returns -- 6.1 Introduction -- 6.2 Methodological Foundations -- 6.2.1 Data -- 6.2.2 Time-series Predictions, Cross-sectional Portfolio Construction -- 6.2.3 Subtracting the Average Return -- 6.3 Predictions Selection -- 6.3.1 Root Mean Squared Error Filter Evaluation -- 6.3.2 p-value Filter Evaluation -- 6.3.3 Bayesian Information Criterion Filter Evaluation -- 6.3.4 Selection Using Dynamic Optimal Filtering -- 6.4 Aggregation of Predictions -- 6.4.1 Prediction Aggregation Using Bayesian Model Averaging -- 6.4.2 Prediction Aggregation Using Added Value Averaging -- 6.4.3 Uncertainty of Aggregate Predictions -- 6.5 Trading Strategy -- 6.5.1 Methodology -- 6.5.2 Performance -- 6.5.3 Significance of the Methods -- 6.6 Robustness Tests -- 6.6.1 Correlations with Standard Factors -- 6.6.2 Sensitivity to the Filter Regression Window -- 6.6.3 Stability of the Performance -- 6.6.4 Sensitivity to the Believability Measure Window -- 6.6.5 Sensitivity to the Originality Measure Window -- 6.6.6 Unaccounted Parameter Sensitivities -- 6.7 Conclusions -- References -- Chapter 7: Genetic Algorithm-Based Combination of Predictions -- 7.1 Introduction -- 7.2 Analysis of Strategies -- 7.2.1 Predictions of Market Returns -- 7.2.2 Predictions of Industry Returns | |
| 505 | 8 | |a 7.2.3 Predictions of Specific Returns -- 7.2.4 Correlation Analysis -- 7.3 Risk Parity Combination -- 7.3.1 Introducing Risk Parity -- 7.3.2 Transaction Costs Matter -- 7.3.3 Ex-ante Optimal Reduction of the Transaction Costs -- 7.4 Genetic Algorithm-Based Combinations -- 7.4.1 Methodology -- 7.4.2 Results -- 7.5 Robustness Tests -- 7.5.1 Mutation Probability -- 7.5.2 Number of Chromosomes -- 7.5.3 Number of Epochs -- 7.5.4 Seed -- 7.5.5 Optimal Trading Rate Window -- 7.6 Conclusions -- References -- Chapter 8: Conclusions -- Appendix -- Representative RMSE Distributions per Stock Index -- Market Timed Portfolio and Systemic Risk Indicator per Stock Index -- Industry Buckets Summary Statistics -- AFI Scores per Industry over Time | |
| 650 | 4 | |a Artificial intelligence-Financial applications | |
| 700 | 1 | |a Douady, Raphael |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Barrau, Thomas |t Artificial Intelligence for Financial Markets |d Cham : Springer International Publishing AG,c2022 |z 9783030973186 |
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Datensatz im Suchindex
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| author | Barrau, Thomas |
| author_facet | Barrau, Thomas |
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| author_sort | Barrau, Thomas |
| author_variant | t b tb |
| building | Verbundindex |
| bvnumber | BV049019540 |
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| contents | Intro -- Foreword -- Preface -- Contents -- Chapter 1: Introduction -- 1.1 Financial Market Predictions: A Concise Literature Review -- 1.2 A Simple Model of Portfolio Returns -- 1.3 Plan of the Book -- References -- Chapter 2: Polymodel Theory: An Overview -- 2.1 Introduction -- 2.2 Mathematical Formulation -- 2.3 Epistemological Foundations -- 2.3.1 A Statistical Perspectivism -- 2.3.2 A Phenomenological Approach -- 2.4 Comparison of Polymodels to Multivariate Models -- 2.4.1 Reducing Overfitting -- 2.4.2 Increasing Precision -- 2.4.3 Increasing Robustness -- 2.5 Considerations Raised by Polymodels -- 2.5.1 Aggregation of Predictions -- 2.5.2 Number of Variables Per Elementary Model -- 2.6 Conclusions -- References -- Chapter 3: Estimation Method: The Linear Non-Linear Mixed Model -- 3.1 Introduction -- 3.2 Presentation of the LNLM Model -- 3.2.1 Definition -- 3.2.2 Fitting Procedure -- 3.3 Evaluation Methodology -- 3.4 Results -- 3.4.1 For 126 Observations (Tables 3.1 and 3.2) -- 3.4.2 For 252 Observations (Tables 3.3 and 3.4) -- 3.4.3 For 756 Observations (Tables 3.5 and 3.6) -- 3.4.4 For 1,260 Observations (Tables 3.7 and 3.8) -- 3.4.5 Computation Time (Table 3.9) -- 3.4.6 Interpretations -- 3.5 Conclusions -- References -- Chapter 4: Predictions of Market Returns -- 4.1 Introduction -- 4.2 Data -- 4.3 Systemic Risk Indicator -- 4.3.1 Model Estimation -- 4.3.2 Systemic Risk Indicator Definition -- 4.3.3 Primary Analysis -- 4.3.4 Roots of the Predictive Power -- 4.4 Trading Strategy -- 4.4.1 Methodology -- 4.4.2 Results -- 4.5 Robustness Tests -- 4.5.1 Sensitivity to the Noise Filter -- 4.5.2 Sensitivity to Future Returns Windows -- 4.5.3 Sensitivity to Half-life -- 4.5.4 Sensitivity to Rolling Window Length -- 4.5.5 Asset-Class Specific Performances -- 4.5.6 Sensitivity to Trading Strategy -- 4.6 Conclusions -- References Chapter 5: Predictions of Industry Returns -- 5.1 Introduction -- 5.2 Data -- 5.2.1 Summary Statistics -- 5.3 Methodology -- 5.3.1 Measuring Antifragility -- 5.3.2 Predicting Industry Returns -- 5.4 Results -- 5.4.1 Long/Short Trading Strategy -- 5.4.2 Regressions on Future Returns -- 5.4.3 Comparisons to Classical Factors -- 5.4.4 Separating Concavity and Convexity -- 5.4.5 Separating Positive and Negative Market Returns -- 5.5 Robustness Tests -- 5.5.1 Stability of the Signal -- 5.5.2 Sensitivity to Hyper-Parameters -- 5.6 Conclusions -- References -- Chapter 6: Predictions of Specific Returns -- 6.1 Introduction -- 6.2 Methodological Foundations -- 6.2.1 Data -- 6.2.2 Time-series Predictions, Cross-sectional Portfolio Construction -- 6.2.3 Subtracting the Average Return -- 6.3 Predictions Selection -- 6.3.1 Root Mean Squared Error Filter Evaluation -- 6.3.2 p-value Filter Evaluation -- 6.3.3 Bayesian Information Criterion Filter Evaluation -- 6.3.4 Selection Using Dynamic Optimal Filtering -- 6.4 Aggregation of Predictions -- 6.4.1 Prediction Aggregation Using Bayesian Model Averaging -- 6.4.2 Prediction Aggregation Using Added Value Averaging -- 6.4.3 Uncertainty of Aggregate Predictions -- 6.5 Trading Strategy -- 6.5.1 Methodology -- 6.5.2 Performance -- 6.5.3 Significance of the Methods -- 6.6 Robustness Tests -- 6.6.1 Correlations with Standard Factors -- 6.6.2 Sensitivity to the Filter Regression Window -- 6.6.3 Stability of the Performance -- 6.6.4 Sensitivity to the Believability Measure Window -- 6.6.5 Sensitivity to the Originality Measure Window -- 6.6.6 Unaccounted Parameter Sensitivities -- 6.7 Conclusions -- References -- Chapter 7: Genetic Algorithm-Based Combination of Predictions -- 7.1 Introduction -- 7.2 Analysis of Strategies -- 7.2.1 Predictions of Market Returns -- 7.2.2 Predictions of Industry Returns 7.2.3 Predictions of Specific Returns -- 7.2.4 Correlation Analysis -- 7.3 Risk Parity Combination -- 7.3.1 Introducing Risk Parity -- 7.3.2 Transaction Costs Matter -- 7.3.3 Ex-ante Optimal Reduction of the Transaction Costs -- 7.4 Genetic Algorithm-Based Combinations -- 7.4.1 Methodology -- 7.4.2 Results -- 7.5 Robustness Tests -- 7.5.1 Mutation Probability -- 7.5.2 Number of Chromosomes -- 7.5.3 Number of Epochs -- 7.5.4 Seed -- 7.5.5 Optimal Trading Rate Window -- 7.6 Conclusions -- References -- Chapter 8: Conclusions -- Appendix -- Representative RMSE Distributions per Stock Index -- Market Timed Portfolio and Systemic Risk Indicator per Stock Index -- Industry Buckets Summary Statistics -- AFI Scores per Industry over Time |
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| dewey-full | 332.6015195 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.6015195 |
| dewey-search | 332.6015195 |
| dewey-sort | 3332.6015195 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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| id | DE-604.BV049019540 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T22:13:39Z |
| indexdate | 2024-07-10T09:52:58Z |
| institution | BVB |
| isbn | 9783030973193 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034282447 |
| oclc_num | 1323245489 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (182 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer International Publishing AG |
| record_format | marc |
| series2 | Financial Mathematics and Fintech Series |
| spelling | Barrau, Thomas Verfasser aut Artificial Intelligence for Financial Markets The Polymodel Approach 1st ed Cham Springer International Publishing AG 2022 ©2022 1 Online-Ressource (182 Seiten) txt rdacontent c rdamedia cr rdacarrier Financial Mathematics and Fintech Series Description based on publisher supplied metadata and other sources Intro -- Foreword -- Preface -- Contents -- Chapter 1: Introduction -- 1.1 Financial Market Predictions: A Concise Literature Review -- 1.2 A Simple Model of Portfolio Returns -- 1.3 Plan of the Book -- References -- Chapter 2: Polymodel Theory: An Overview -- 2.1 Introduction -- 2.2 Mathematical Formulation -- 2.3 Epistemological Foundations -- 2.3.1 A Statistical Perspectivism -- 2.3.2 A Phenomenological Approach -- 2.4 Comparison of Polymodels to Multivariate Models -- 2.4.1 Reducing Overfitting -- 2.4.2 Increasing Precision -- 2.4.3 Increasing Robustness -- 2.5 Considerations Raised by Polymodels -- 2.5.1 Aggregation of Predictions -- 2.5.2 Number of Variables Per Elementary Model -- 2.6 Conclusions -- References -- Chapter 3: Estimation Method: The Linear Non-Linear Mixed Model -- 3.1 Introduction -- 3.2 Presentation of the LNLM Model -- 3.2.1 Definition -- 3.2.2 Fitting Procedure -- 3.3 Evaluation Methodology -- 3.4 Results -- 3.4.1 For 126 Observations (Tables 3.1 and 3.2) -- 3.4.2 For 252 Observations (Tables 3.3 and 3.4) -- 3.4.3 For 756 Observations (Tables 3.5 and 3.6) -- 3.4.4 For 1,260 Observations (Tables 3.7 and 3.8) -- 3.4.5 Computation Time (Table 3.9) -- 3.4.6 Interpretations -- 3.5 Conclusions -- References -- Chapter 4: Predictions of Market Returns -- 4.1 Introduction -- 4.2 Data -- 4.3 Systemic Risk Indicator -- 4.3.1 Model Estimation -- 4.3.2 Systemic Risk Indicator Definition -- 4.3.3 Primary Analysis -- 4.3.4 Roots of the Predictive Power -- 4.4 Trading Strategy -- 4.4.1 Methodology -- 4.4.2 Results -- 4.5 Robustness Tests -- 4.5.1 Sensitivity to the Noise Filter -- 4.5.2 Sensitivity to Future Returns Windows -- 4.5.3 Sensitivity to Half-life -- 4.5.4 Sensitivity to Rolling Window Length -- 4.5.5 Asset-Class Specific Performances -- 4.5.6 Sensitivity to Trading Strategy -- 4.6 Conclusions -- References Chapter 5: Predictions of Industry Returns -- 5.1 Introduction -- 5.2 Data -- 5.2.1 Summary Statistics -- 5.3 Methodology -- 5.3.1 Measuring Antifragility -- 5.3.2 Predicting Industry Returns -- 5.4 Results -- 5.4.1 Long/Short Trading Strategy -- 5.4.2 Regressions on Future Returns -- 5.4.3 Comparisons to Classical Factors -- 5.4.4 Separating Concavity and Convexity -- 5.4.5 Separating Positive and Negative Market Returns -- 5.5 Robustness Tests -- 5.5.1 Stability of the Signal -- 5.5.2 Sensitivity to Hyper-Parameters -- 5.6 Conclusions -- References -- Chapter 6: Predictions of Specific Returns -- 6.1 Introduction -- 6.2 Methodological Foundations -- 6.2.1 Data -- 6.2.2 Time-series Predictions, Cross-sectional Portfolio Construction -- 6.2.3 Subtracting the Average Return -- 6.3 Predictions Selection -- 6.3.1 Root Mean Squared Error Filter Evaluation -- 6.3.2 p-value Filter Evaluation -- 6.3.3 Bayesian Information Criterion Filter Evaluation -- 6.3.4 Selection Using Dynamic Optimal Filtering -- 6.4 Aggregation of Predictions -- 6.4.1 Prediction Aggregation Using Bayesian Model Averaging -- 6.4.2 Prediction Aggregation Using Added Value Averaging -- 6.4.3 Uncertainty of Aggregate Predictions -- 6.5 Trading Strategy -- 6.5.1 Methodology -- 6.5.2 Performance -- 6.5.3 Significance of the Methods -- 6.6 Robustness Tests -- 6.6.1 Correlations with Standard Factors -- 6.6.2 Sensitivity to the Filter Regression Window -- 6.6.3 Stability of the Performance -- 6.6.4 Sensitivity to the Believability Measure Window -- 6.6.5 Sensitivity to the Originality Measure Window -- 6.6.6 Unaccounted Parameter Sensitivities -- 6.7 Conclusions -- References -- Chapter 7: Genetic Algorithm-Based Combination of Predictions -- 7.1 Introduction -- 7.2 Analysis of Strategies -- 7.2.1 Predictions of Market Returns -- 7.2.2 Predictions of Industry Returns 7.2.3 Predictions of Specific Returns -- 7.2.4 Correlation Analysis -- 7.3 Risk Parity Combination -- 7.3.1 Introducing Risk Parity -- 7.3.2 Transaction Costs Matter -- 7.3.3 Ex-ante Optimal Reduction of the Transaction Costs -- 7.4 Genetic Algorithm-Based Combinations -- 7.4.1 Methodology -- 7.4.2 Results -- 7.5 Robustness Tests -- 7.5.1 Mutation Probability -- 7.5.2 Number of Chromosomes -- 7.5.3 Number of Epochs -- 7.5.4 Seed -- 7.5.5 Optimal Trading Rate Window -- 7.6 Conclusions -- References -- Chapter 8: Conclusions -- Appendix -- Representative RMSE Distributions per Stock Index -- Market Timed Portfolio and Systemic Risk Indicator per Stock Index -- Industry Buckets Summary Statistics -- AFI Scores per Industry over Time Artificial intelligence-Financial applications Douady, Raphael Sonstige oth Erscheint auch als Druck-Ausgabe Barrau, Thomas Artificial Intelligence for Financial Markets Cham : Springer International Publishing AG,c2022 9783030973186 |
| spellingShingle | Barrau, Thomas Artificial Intelligence for Financial Markets The Polymodel Approach Intro -- Foreword -- Preface -- Contents -- Chapter 1: Introduction -- 1.1 Financial Market Predictions: A Concise Literature Review -- 1.2 A Simple Model of Portfolio Returns -- 1.3 Plan of the Book -- References -- Chapter 2: Polymodel Theory: An Overview -- 2.1 Introduction -- 2.2 Mathematical Formulation -- 2.3 Epistemological Foundations -- 2.3.1 A Statistical Perspectivism -- 2.3.2 A Phenomenological Approach -- 2.4 Comparison of Polymodels to Multivariate Models -- 2.4.1 Reducing Overfitting -- 2.4.2 Increasing Precision -- 2.4.3 Increasing Robustness -- 2.5 Considerations Raised by Polymodels -- 2.5.1 Aggregation of Predictions -- 2.5.2 Number of Variables Per Elementary Model -- 2.6 Conclusions -- References -- Chapter 3: Estimation Method: The Linear Non-Linear Mixed Model -- 3.1 Introduction -- 3.2 Presentation of the LNLM Model -- 3.2.1 Definition -- 3.2.2 Fitting Procedure -- 3.3 Evaluation Methodology -- 3.4 Results -- 3.4.1 For 126 Observations (Tables 3.1 and 3.2) -- 3.4.2 For 252 Observations (Tables 3.3 and 3.4) -- 3.4.3 For 756 Observations (Tables 3.5 and 3.6) -- 3.4.4 For 1,260 Observations (Tables 3.7 and 3.8) -- 3.4.5 Computation Time (Table 3.9) -- 3.4.6 Interpretations -- 3.5 Conclusions -- References -- Chapter 4: Predictions of Market Returns -- 4.1 Introduction -- 4.2 Data -- 4.3 Systemic Risk Indicator -- 4.3.1 Model Estimation -- 4.3.2 Systemic Risk Indicator Definition -- 4.3.3 Primary Analysis -- 4.3.4 Roots of the Predictive Power -- 4.4 Trading Strategy -- 4.4.1 Methodology -- 4.4.2 Results -- 4.5 Robustness Tests -- 4.5.1 Sensitivity to the Noise Filter -- 4.5.2 Sensitivity to Future Returns Windows -- 4.5.3 Sensitivity to Half-life -- 4.5.4 Sensitivity to Rolling Window Length -- 4.5.5 Asset-Class Specific Performances -- 4.5.6 Sensitivity to Trading Strategy -- 4.6 Conclusions -- References Chapter 5: Predictions of Industry Returns -- 5.1 Introduction -- 5.2 Data -- 5.2.1 Summary Statistics -- 5.3 Methodology -- 5.3.1 Measuring Antifragility -- 5.3.2 Predicting Industry Returns -- 5.4 Results -- 5.4.1 Long/Short Trading Strategy -- 5.4.2 Regressions on Future Returns -- 5.4.3 Comparisons to Classical Factors -- 5.4.4 Separating Concavity and Convexity -- 5.4.5 Separating Positive and Negative Market Returns -- 5.5 Robustness Tests -- 5.5.1 Stability of the Signal -- 5.5.2 Sensitivity to Hyper-Parameters -- 5.6 Conclusions -- References -- Chapter 6: Predictions of Specific Returns -- 6.1 Introduction -- 6.2 Methodological Foundations -- 6.2.1 Data -- 6.2.2 Time-series Predictions, Cross-sectional Portfolio Construction -- 6.2.3 Subtracting the Average Return -- 6.3 Predictions Selection -- 6.3.1 Root Mean Squared Error Filter Evaluation -- 6.3.2 p-value Filter Evaluation -- 6.3.3 Bayesian Information Criterion Filter Evaluation -- 6.3.4 Selection Using Dynamic Optimal Filtering -- 6.4 Aggregation of Predictions -- 6.4.1 Prediction Aggregation Using Bayesian Model Averaging -- 6.4.2 Prediction Aggregation Using Added Value Averaging -- 6.4.3 Uncertainty of Aggregate Predictions -- 6.5 Trading Strategy -- 6.5.1 Methodology -- 6.5.2 Performance -- 6.5.3 Significance of the Methods -- 6.6 Robustness Tests -- 6.6.1 Correlations with Standard Factors -- 6.6.2 Sensitivity to the Filter Regression Window -- 6.6.3 Stability of the Performance -- 6.6.4 Sensitivity to the Believability Measure Window -- 6.6.5 Sensitivity to the Originality Measure Window -- 6.6.6 Unaccounted Parameter Sensitivities -- 6.7 Conclusions -- References -- Chapter 7: Genetic Algorithm-Based Combination of Predictions -- 7.1 Introduction -- 7.2 Analysis of Strategies -- 7.2.1 Predictions of Market Returns -- 7.2.2 Predictions of Industry Returns 7.2.3 Predictions of Specific Returns -- 7.2.4 Correlation Analysis -- 7.3 Risk Parity Combination -- 7.3.1 Introducing Risk Parity -- 7.3.2 Transaction Costs Matter -- 7.3.3 Ex-ante Optimal Reduction of the Transaction Costs -- 7.4 Genetic Algorithm-Based Combinations -- 7.4.1 Methodology -- 7.4.2 Results -- 7.5 Robustness Tests -- 7.5.1 Mutation Probability -- 7.5.2 Number of Chromosomes -- 7.5.3 Number of Epochs -- 7.5.4 Seed -- 7.5.5 Optimal Trading Rate Window -- 7.6 Conclusions -- References -- Chapter 8: Conclusions -- Appendix -- Representative RMSE Distributions per Stock Index -- Market Timed Portfolio and Systemic Risk Indicator per Stock Index -- Industry Buckets Summary Statistics -- AFI Scores per Industry over Time Artificial intelligence-Financial applications |
| title | Artificial Intelligence for Financial Markets The Polymodel Approach |
| title_auth | Artificial Intelligence for Financial Markets The Polymodel Approach |
| title_exact_search | Artificial Intelligence for Financial Markets The Polymodel Approach |
| title_exact_search_txtP | Artificial Intelligence for Financial Markets The Polymodel Approach |
| title_full | Artificial Intelligence for Financial Markets The Polymodel Approach |
| title_fullStr | Artificial Intelligence for Financial Markets The Polymodel Approach |
| title_full_unstemmed | Artificial Intelligence for Financial Markets The Polymodel Approach |
| title_short | Artificial Intelligence for Financial Markets |
| title_sort | artificial intelligence for financial markets the polymodel approach |
| title_sub | The Polymodel Approach |
| topic | Artificial intelligence-Financial applications |
| topic_facet | Artificial intelligence-Financial applications |
| work_keys_str_mv | AT barrauthomas artificialintelligenceforfinancialmarketsthepolymodelapproach AT douadyraphael artificialintelligenceforfinancialmarketsthepolymodelapproach |