Name
Abstract | ||
|---|---|---|
Factor investing is one kind of quantitative investing methodologies for portfolio construction based on factors. Factors with different style are extracted from multiple sources such as market data, fundamental information from financial statements, sentimental information from the Internet, etc. Numerous style factors are defined by Barra model proposed by Morgan Stanley Capital International(MSCI) to explain the return of a portfolio. Multiple factors are usually integrated linearly when being put to use, which ensures the stability of the process of integration and enhances the effectiveness of integrated factors. In this work, we integrate factors by machine learning and deep learning methodologies to explore deeper information among multiple style factors defined by MSCI Barra model. Multi-factors indexes are compiled using Smart Beta Index methodology proposed by MSCI. The results show non-linear integration by deep neural network can enhance the profitability and stability of the index compiled according to the integrated factor. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-22744-9_22 | COMPUTATIONAL SCIENCE - ICCS 2019, PT III |
Keywords | Field | DocType |
Neural networks, Deep learning, Factor investing | Data science,Quantitative investing,Computer science,Portfolio,Profitability index,Artificial intelligence,Deep learning,Market data,Artificial neural network,Distributed computing,The Internet,Smart beta | Conference |
Volume | ISSN | Citations |
11538 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhichen Lu | 1 | 0 | 0.34 |
| Long Wen | 2 | 1 | 1.71 |
| Jiashuai Zhang | 3 | 0 | 0.34 |
| Yingjie Tian | 4 | 807 | 58.32 |