Public mood – driven asset allocation : the importance of financial sentiment in portfolio management
The study of the impact of investor sentiment on stock returns has gained increasing momentum in the past few years. It has been widely accepted that public mood is correlated with financial markets. However, only a few studies discussed how the public mood would affect one of the fundamental proble...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
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2020
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| Online Access: | https://hdl.handle.net/10356/141798 |
| _version_ | 1811696853993914368 |
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| author | Malandri, Lorenzo Xing, Frank Z. Orsenigo, Carlotta Vercellis, Carlo Cambria, Erik |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Malandri, Lorenzo Xing, Frank Z. Orsenigo, Carlotta Vercellis, Carlo Cambria, Erik |
| author_sort | Malandri, Lorenzo |
| collection | NTU |
| description | The study of the impact of investor sentiment on stock returns has gained increasing momentum in the past few years. It has been widely accepted that public mood is correlated with financial markets. However, only a few studies discussed how the public mood would affect one of the fundamental problems of computational finance: portfolio management. In this study, we use public financial sentiment and historical prices collected from the New York Stock Exchange (NYSE) to train multiple machine learning models for automatic wealth allocation across a set of assets. Unlike previous studies which set as target variable the asset prices in the portfolio, the variable to predict here is represented by the best asset allocation strategy ex post. Experiments performed on five portfolios show that long short-term memory networks are superior to multi-layer perceptron and random forests producing, in the period under analysis, an average increase in the revenue across the portfolios ranging between 5% (without financial mood) and 19% (with financial mood) compared to the equal-weighted portfolio. Results show that our all-in-one and end-to-end approach for automatic portfolio selection outperforms the equal-weighted portfolio. Moreover, when using long short-term memory networks, the employment of sentiment data in addition to lagged data leads to greater returns for all the five portfolios under evaluation. Finally, we find that among the employed machine learning algorithms, long short-term memory networks are better suited for learning the impact of public mood on financial time series. |
| first_indexed | 2024-10-01T07:45:58Z |
| format | Journal Article |
| id | ntu-10356/141798 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:45:58Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1417982020-06-11T00:42:14Z Public mood – driven asset allocation : the importance of financial sentiment in portfolio management Malandri, Lorenzo Xing, Frank Z. Orsenigo, Carlotta Vercellis, Carlo Cambria, Erik School of Computer Science and Engineering Engineering::Computer science and engineering Portfolio Allocation Sentiment Analysis The study of the impact of investor sentiment on stock returns has gained increasing momentum in the past few years. It has been widely accepted that public mood is correlated with financial markets. However, only a few studies discussed how the public mood would affect one of the fundamental problems of computational finance: portfolio management. In this study, we use public financial sentiment and historical prices collected from the New York Stock Exchange (NYSE) to train multiple machine learning models for automatic wealth allocation across a set of assets. Unlike previous studies which set as target variable the asset prices in the portfolio, the variable to predict here is represented by the best asset allocation strategy ex post. Experiments performed on five portfolios show that long short-term memory networks are superior to multi-layer perceptron and random forests producing, in the period under analysis, an average increase in the revenue across the portfolios ranging between 5% (without financial mood) and 19% (with financial mood) compared to the equal-weighted portfolio. Results show that our all-in-one and end-to-end approach for automatic portfolio selection outperforms the equal-weighted portfolio. Moreover, when using long short-term memory networks, the employment of sentiment data in addition to lagged data leads to greater returns for all the five portfolios under evaluation. Finally, we find that among the employed machine learning algorithms, long short-term memory networks are better suited for learning the impact of public mood on financial time series. 2020-06-11T00:42:14Z 2020-06-11T00:42:14Z 2018 Journal Article Malandri, L., Xing, F. Z., Orsenigo, C., Vercellis, C., & Cambria, E. (2018). Public mood – driven asset allocation : the importance of financial sentiment in portfolio management. Cognitive Computation, 10(6), 1167-1176. doi:10.1007/s12559-018-9609-2 1866-9956 https://hdl.handle.net/10356/141798 10.1007/s12559-018-9609-2 2-s2.0-85057550141 6 10 1167 1176 en Cognitive Computation © 2018 Springer Science+Business Media, LLC, part of Springer Nature. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Portfolio Allocation Sentiment Analysis Malandri, Lorenzo Xing, Frank Z. Orsenigo, Carlotta Vercellis, Carlo Cambria, Erik Public mood – driven asset allocation : the importance of financial sentiment in portfolio management |
| title | Public mood – driven asset allocation : the importance of financial sentiment in portfolio management |
| title_full | Public mood – driven asset allocation : the importance of financial sentiment in portfolio management |
| title_fullStr | Public mood – driven asset allocation : the importance of financial sentiment in portfolio management |
| title_full_unstemmed | Public mood – driven asset allocation : the importance of financial sentiment in portfolio management |
| title_short | Public mood – driven asset allocation : the importance of financial sentiment in portfolio management |
| title_sort | public mood driven asset allocation the importance of financial sentiment in portfolio management |
| topic | Engineering::Computer science and engineering Portfolio Allocation Sentiment Analysis |
| url | https://hdl.handle.net/10356/141798 |
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