標(biāo)題: Titlebook: Artificial Intelligence for Financial Markets; The Polymodel Approa Thomas Barrau,Raphael Douady Book 2022 The Editor(s) (if applicable) an [打印本頁(yè)] 作者: Halcyon 時(shí)間: 2025-3-21 16:48
書目名稱Artificial Intelligence for Financial Markets影響因子(影響力)
書目名稱Artificial Intelligence for Financial Markets影響因子(影響力)學(xué)科排名
書目名稱Artificial Intelligence for Financial Markets網(wǎng)絡(luò)公開度
書目名稱Artificial Intelligence for Financial Markets網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Intelligence for Financial Markets被引頻次
書目名稱Artificial Intelligence for Financial Markets被引頻次學(xué)科排名
書目名稱Artificial Intelligence for Financial Markets年度引用
書目名稱Artificial Intelligence for Financial Markets年度引用學(xué)科排名
書目名稱Artificial Intelligence for Financial Markets讀者反饋
書目名稱Artificial Intelligence for Financial Markets讀者反饋學(xué)科排名
作者: 口味 時(shí)間: 2025-3-21 20:50
Polymodel Theory: An Overview, how polymodels are, in several respects, a superior alternative to classical multivariate regressions estimated with OLS, Ridge and Stepwise techniques; we also present the limits of the method. Although it is a regression technique, we clarify how the polymodels framework is closer to artificial i作者: 心神不寧 時(shí)間: 2025-3-22 00:31
Estimation Method: The Linear Non-Linear Mixed Model,cing overfitting. We show using numerical simulations that the LNLM model is able to successfully detect patterns in noisy data, with an accuracy similar to or better than data-driven modeling alternatives. We find that our algorithm is computationally efficient, an essential characteristic for mach作者: 感染 時(shí)間: 2025-3-22 04:43 作者: temperate 時(shí)間: 2025-3-22 12:25
Predictions of Industry Returns,ing out that the non-linearity of the link between market and industry returns is priced by market participants. It is shown to differ from other well-known industry factors, including downside beta and coskewness factors, which are entirely subsumed by the antifragility factor. A trading strategy d作者: LVAD360 時(shí)間: 2025-3-22 16:04 作者: surmount 時(shí)間: 2025-3-22 17:03 作者: Nebulous 時(shí)間: 2025-3-22 21:36
Conclusions,rns about the limits of Polymodel Theory have been addressed. Returning to the different applications discussed throughout the book, we outline how polymodels successfully contribute to the literature of financial market predictions, thus providing an effective artificial intelligence technique when作者: formula 時(shí)間: 2025-3-23 05:09
2662-7167 tical portfolio construction.Provides new explicit quantitatThis book introduces the novel artificial intelligence technique of polymodels and applies it to the prediction of stock returns. The idea of polymodels is to describe a system by its sensitivities to an environment, and to monitor it, imit作者: confide 時(shí)間: 2025-3-23 08:20
https://doi.org/10.1007/978-3-8349-9782-1d, and we explain how contributing to the model actually coincides with contributing to the literature. We finally develop the plan of the book, which answers each of the different points evocated about the literature of financial market prediction.作者: CARE 時(shí)間: 2025-3-23 12:24 作者: Monotonous 時(shí)間: 2025-3-23 14:05
Die Abgrenzung zu anderen Vertriebsarten-known industry factors, including downside beta and coskewness factors, which are entirely subsumed by the antifragility factor. A trading strategy derived from the factor exhibits a Sharpe ratio of 1.10, and successfully resists various robustness tests.作者: 牽連 時(shí)間: 2025-3-23 21:11 作者: anaerobic 時(shí)間: 2025-3-23 23:35
Introduction,d, and we explain how contributing to the model actually coincides with contributing to the literature. We finally develop the plan of the book, which answers each of the different points evocated about the literature of financial market prediction.作者: 彩色的蠟筆 時(shí)間: 2025-3-24 05:32
Estimation Method: The Linear Non-Linear Mixed Model,lar to or better than data-driven modeling alternatives. We find that our algorithm is computationally efficient, an essential characteristic for machine learning applications often involving a large number of estimations.作者: Gossamer 時(shí)間: 2025-3-24 09:23 作者: Repatriate 時(shí)間: 2025-3-24 10:45 作者: separate 時(shí)間: 2025-3-24 16:05
Book 2022ls is to describe a system by its sensitivities to an environment, and to monitor it, imitating what a natural brain does spontaneously. In practice this involves running a collection of non-linear univariate models. This very powerful standalone technique has several advantages over traditional mul作者: Coordinate 時(shí)間: 2025-3-24 19:17
Predictions of Market Returns,signal, which is shown to be strongly significant, both from an economic and statistical point of view. Results are robust across different time-periods, for various sets of explanatory variables, and among 12 different stock markets.作者: gentle 時(shí)間: 2025-3-25 00:57
Predictions of Specific Returns,stness tests. Through the implementation of the trading strategy, we propose a method to tackle the problem of the aggregation of the predictions of a polymodel, based on the information added by each elementary model.作者: 暫時(shí)別動(dòng) 時(shí)間: 2025-3-25 03:24 作者: Intuitive 時(shí)間: 2025-3-25 10:10
https://doi.org/10.1007/978-3-8349-9782-1stness tests. Through the implementation of the trading strategy, we propose a method to tackle the problem of the aggregation of the predictions of a polymodel, based on the information added by each elementary model.作者: Epidural-Space 時(shí)間: 2025-3-25 13:52 作者: 受人支配 時(shí)間: 2025-3-25 19:25
Book 2022ing 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 b作者: Myosin 時(shí)間: 2025-3-25 22:28
Polymodel Theory: An Overview, how polymodels are, in several respects, a superior alternative to classical multivariate regressions estimated with OLS, Ridge and Stepwise techniques; we also present the limits of the method. Although it is a regression technique, we clarify how the polymodels framework is closer to artificial intelligence than traditional statistics.作者: 領(lǐng)導(dǎo)權(quán) 時(shí)間: 2025-3-26 03:27 作者: consent 時(shí)間: 2025-3-26 05:43
https://doi.org/10.1007/978-3-030-97319-3AI for finance; Quantitative strategies; Polymodels; Machine learning; Cross section of stock returns; No作者: 大溝 時(shí)間: 2025-3-26 11:13
978-3-030-97321-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: synovium 時(shí)間: 2025-3-26 14:35 作者: cocoon 時(shí)間: 2025-3-26 20:07
Regel: Jeder Erfolg hat Spielregeln how polymodels are, in several respects, a superior alternative to classical multivariate regressions estimated with OLS, Ridge and Stepwise techniques; we also present the limits of the method. Although it is a regression technique, we clarify how the polymodels framework is closer to artificial intelligence than traditional statistics.作者: 自作多情 時(shí)間: 2025-3-26 23:50 作者: figurine 時(shí)間: 2025-3-27 02:42 作者: 透明 時(shí)間: 2025-3-27 08:55 作者: debble 時(shí)間: 2025-3-27 09:31 作者: dissent 時(shí)間: 2025-3-27 17:35 作者: Ruptured-Disk 時(shí)間: 2025-3-27 19:21 作者: ESO 時(shí)間: 2025-3-28 01:18 作者: debouch 時(shí)間: 2025-3-28 02:25 作者: 攝取 時(shí)間: 2025-3-28 10:16 作者: 圓錐體 時(shí)間: 2025-3-28 13:35
Thomas Barrau,Raphael DouadyIntroduces a novel quantitative investment approach that handles highly uncertain markets.Guides the reader step by step towards a very practical portfolio construction.Provides new explicit quantitat作者: overweight 時(shí)間: 2025-3-28 17:24 作者: harbinger 時(shí)間: 2025-3-28 19:49
