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Titlebook: Alternative Data and Artificial Intelligence Techniques; Applications in Inve Qingquan Tony Zhang,Beibei Li,Danxia Xie Book 2022 The Editor

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發(fā)表于 2025-3-21 17:08:21 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Alternative Data and Artificial Intelligence Techniques
期刊簡稱Applications in Inve
影響因子2023Qingquan Tony Zhang,Beibei Li,Danxia Xie
視頻videohttp://file.papertrans.cn/155/154043/154043.mp4
發(fā)行地址Introduces alternative data utilized in state-of-art portfolio management and risk evaluation.Includes multiple use cases to illustrate the powerfulness of alternative data to study anomalies.Covers t
學科分類Palgrave Studies in Risk and Insurance
圖書封面Titlebook: Alternative Data and Artificial Intelligence Techniques; Applications in Inve Qingquan Tony Zhang,Beibei Li,Danxia Xie Book 2022 The Editor
影響因子.This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation..
Pindex Book 2022
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Smart Beta and Risk Factors Based on IoTsI with data for training algorithms. The combination of IoT and AI generates and collects massive data, and stores it in device terminals, edge terminals, or on the cloud. Then, the data can be intelligently analyzed through machine learning, so as to realize the digitalization and intelligent conne
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Fraud and Deception Detection: Text-Based Data Analyticsbility of data. This chapter discusses how to imitate and detect similar applications and how to identify fake reviews by machine learning and various statistical methods using deceptive applications and fake reviews as examples.
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Machine Learning Technique in Trading: A Case Study in the EURUSD Markethree supervised learning classification techniques (K-Nearest Neighbors, Support Vector Machines, and Random Forests) in the problem of one day ahead directional prediction of the EURUSD exchange rate with autoregressive terms as inputs. The performance of said machine learning models was benchmarke
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