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Intelligent Asset Management

Part of the Socio-Affective Computing Book Series (volume 9)

DOI: 10.1007/978-3-030-30263-4

ISBN: 978-3-030-30262-7

Publisher: Springer International Publishing - Springer Journals

Nov 14, 2019

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Introduction

Xing, Frank; Cambria, Erik; Welsch, Roy

Pages: 1 –8

[This introductory chapter revisits the historical progress of financial news analyticsNews analytics. In particular, the chapter emphasizes the importance and necessity of having asset allocation models for automatic asset management, superseding the first wave of predicting individual asset...
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Literature Review and Preliminaries

Xing, Frank; Cambria, Erik; Welsch, Roy

Pages: 9 –25

[This chapter reviews the text miningText mining approaches employed and the problem formalization of stock market prediction by previous studies. A fine-grained categorization of text source is provided. The basic concepts and preliminaries of asset returns and portfolio optimization techniques...
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Theoretical Underpinnings on Text Mining

Xing, Frank; Cambria, Erik; Welsch, Roy

Pages: 27 –35

[This chapter provides multiple perspectives on the structure of natural language. We try to answer two fascinating questions in this chapter: what kind of information can we extract from human language, and is the extracted information sufficient or effective for financial forecasting? Three...
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Computational Semantics for Asset Correlations

Xing, Frank; Cambria, Erik; Welsch, Roy

Pages: 37 –61

[This chapter explores the possibility to leverage semantic knowledge for robust estimation of correlations among financial assets. A graphical model for high-dimensional stochastic dependenceDependence modelinghigh-dimensional termed a “vine” structure, which is derived from copula theory, is...
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Sentiment Analysis for View Modeling

Xing, Frank; Cambria, Erik; Welsch, Roy

Pages: 63 –96

[This chapter investigates a method to incorporate market sentiment to asset allocation models. In the previous chapter, we experimented with robust mean-variance optimization, which is a static process that finds the status quo optimal portfolio weights and surfs market fluctuations. However, an...
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Storage and Update of Knowledge

Xing, Frank; Cambria, Erik; Welsch, Roy

Pages: 97 –111

[Experience in developing large knowledge-based AI projects suggests a progressive approach: the system needs maintenance to keep pace with demands and accumulation of commonsense knowledge to prevent having to start all over again. Financial asset management is no exception. The balance between...
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Robo-Advisory

Xing, Frank; Cambria, Erik; Welsch, Roy

Pages: 113 –122

[Robo-advisory completes the last missing part of the vision of intelligent asset management—featuring the human-computer interaction process that provides the necessary information for the asset allocation algorithms. In this chapter, we picture the industry by studying companies that do...
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Concluding Remarks

Xing, Frank; Cambria, Erik; Welsch, Roy

Pages: 123 –127

[In this final chapter, we summarize the whole book by reviewing concepts and algorithms proposed, as well as theories derived in this book. An outline of extracting and leveraging different aspects of knowledge from financial texts with the help of NLP techniques is given. We also mention here...

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