Artificial Intelligence for Financial Markets
Springer International Publishing (Verlag)
978-3-030-97318-6 (ISBN)
The first two chapters compare the technique with other regression alternatives and introduces an estimation method which regularizes a polynomial regression using cross-validation. The rest of the book applies these ideas to financial markets. Certain equity return components are predicted using polymodels in very different ways, and a genetic algorithm is describedwhich combines these different predictions into a single portfolio, aiming to optimize the portfolio returns net of transaction costs. Addressed to investors at all levels of experience this book will also be of interest to both seasoned and non-seasoned statisticians.
lt;b>Thomas Barrau is a Senior Quantitative Researcher working in the hedge fund AXAInvestment Managers Chorus Ltd. He is working on the development of an Equity MarketNeutral portfolio, from the creation of quantitative trading strategies to the portfolioconstruction. Prior to this, he worked at Societe Generale as banker and financial advisorto small businesses, and as CFO in an aerospace company. He holds a PhD in AppliedMathematics from Paris 1 Pantheon-Sorbonne University. Previously, he validated withhonors three different Masters of Science from Aix-Marseille School of Economics,Ca'Foscari University of Venice and Poitiers IAE.
Raphael Douady is a French mathematician and economist specializing in data science, financial mathematics and chaos theory at the University of Paris I-Panthéon-Sorbonne. He formerly held the Frey Chair of quantitative finance at Stony Brook University and was academic director of the French Laboratory of Excellence on Financial Regulation. He earned his PhD in Hamiltonian dynamics and has more than 25 years of experience in the financial industry. He has particular interest in researching portfolio risks, for which he has developed especially suited powerful nonlinear statistical and data science models, as well as macroeconomics and systemic risk. He founded fin tech firms Riskdata (risk management for the buyside) and Datacore (quantitative portfolio of ETFs) and is Chief Science Officer of NM Fin tech (numerical methods for fixed income trading in China).
1. Introduction.- 2. Polymodel Theory: An Overview.- 3. Estimation Method: the Linear Non-Linear Mixed Model.- 4. Predictions of Market Returns.- 5. Predictions of Industry Returns.- 6. Predictions of Specific Returns.- 7. Genetic Algorithm-Based Combination of Predictions.- 8. Conclusions.- 9. Appendix.
| Erscheinungsdatum | 11.05.2022 |
|---|---|
| Reihe/Serie | Financial Mathematics and Fintech |
| Zusatzinfo | XIV, 172 p. 87 illus., 58 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 386 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Wirtschaft | |
| Schlagworte | AI for finance • Antifragility • crisis prediction • Cross section of stock returns • Extreme risk • Factor Analysis • Genetic algorithms • High dimensional modeling • machine learning • Nonlinear statistics • Polymodels • Portfolio Management • Quantitative Strategies • systemic risk |
| ISBN-10 | 3-030-97318-2 / 3030973182 |
| ISBN-13 | 978-3-030-97318-6 / 9783030973186 |
| Zustand | Neuware |
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