Deep Learning for Finance 作者:Sofien Kaabar 出版社:O'Reilly Media, Inc. 副标题:Creating Machine & Deep Learning Models for Trading in Python 出版年:2024-1 页数:350 装帧:Paperback ISBN:9781098148393 豆瓣评分 评价人数不足 评价: 写笔记
What financial application areas are of interest to DL community? Response: Financial text mining, Algo-trading, risk assessments, sentiment analysis, portfolio management and fraud detection are among the most studied areas of finance research. (Please check Figure 8) How mature is the existing res...
Deep learning for financial applications : A survey Applied Soft Computing(IF7.2)Pub Date : 2020-05-11, DOI:10.1016/j.asoc.2020.106384 Ahmet Murat Ozbayoglu , Mehmet Ugur Gudelek , Omer Berat Sezer Computational intelligence in finance has been a very popular topic for both academia and financi...
Explore the details behind reinforcement learning and see how it’s used in trading Understand how to interpret performance evaluation metrics Examine technical analysis and learn how it works in financial markets Create technical indicators in Python and combine them with ML models for optimization Eval...
The surge of online transactions has increased the rate of fraudulent activities too. With Deep Learning algorithms being excellent at detecting frauds, financial security is being achieved simultaneously. Robo Advisory Robo-advisory is nothing but the algorithms at play for advising the clients with re...
内容提示: Applied Soft Computing Journal 93 (2020) 106384Contents lists available at ScienceDirectAppliedSoftComputingJournaljournal homepage: www.elsevier.com/locate/asocReview articleDeeplearningforfinancialapplications:AsurveyAhmet Murat Ozbayoglu∗ , Mehmet Ugur Gudelek, Omer Berat SezerDepartment of ...
计算机视觉的深度特征学习与自适应 Deep Feature Learning and Adaptation for Computer Vision 热度: DeepLearningforFinancialApplications:ASurvey AhmetMuratOzbayoglu a ,MehmetUgurGudelek a ,OmerBeratSezer a a DepartmentofComputerEngineering,TOBBUniversityofEconomicsandTechnology,Ankara,Turkey ...
financial market prices. To address this shortfall, in the current study, we will use tree-based ensemble models such as random forest and LightGBM compared with 12 deep-learning models and two other machine-learning models (KNN and SVR) to forecast daily crude oil and precious metals market ...
The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stoc...
We explore the use of deep learning hierarchical models for problems in financial prediction and classification. Financial prediction problems - such as those presented in designing and pricing securities, constructing portfolios, and risk management - often involve large data sets with complex data inter...