Titlebook: Intelligent Asset Management; Frank Xing,Erik Cambria,Roy Welsch Book 2019 Springer Nature Switzerland AG 2019 text mining.market sentimen

军火

landmark

Computational Semantics for Asset Correlations,thod, which is based on an arbitrary vine that at least guarantees robustness of the estimated covariance matrix. The effectiveness of using semantic vines for robust correlation estimation for Markowitz’s asset allocation model on a large scale of assets (up to 50 stocks) is also showed and discussed.

BARGE

Storage and Update of Knowledge,ed. A special effort on adding and updating polarity scores of words with high-level supervision is made. The same idea can be extended to other application domains as well as the curation of concepts or events.

TIGER

Theoretical Underpinnings on Text Mining,guage, which is ideally suited for financial applications and takes into account facts about time. Finally, we present some examples to show that utilization of natural language in business areas has inadvertently followed this structure.

sundowning

严厉谴责

Robo-Advisory,ry as a service. The main body discusses the technical framework of a robo-advisor, how it is different from the traditional financial advisory process, and the latest relevant research about dialog systems and recommendation systems. In the end, we develop the outlook for the future of robo-advisory.

companion

合乎习俗

2509-5706 neral AI techniques to a specific application and business s.This book presents a systematic application of recent advances in artificial intelligence (AI) to the problem of asset management. While natural language processing and text mining techniques, such as semantic representation, sentiment ana

Mettle

https://doi.org/10.1007/978-3-030-30263-4text mining; market sentiment; asset allocation; natural language processing; computational finance

Autobiography