Investment portfolio management and forecasting the return on assets based on artificial intelligence methods (neural analysis and genetic algorithm)
- DOI
- 10.2991/mtde-19.2019.54How to use a DOI?
- Keywords
- digital technologies, neural networks, genetic algorithm, investment portfolio management
- Abstract
The purpose of the study is the methodology of construction and management of the investment portfolio. Due to the fact that the transition to market economy in Russia has not been made until the end of the XX century, the Russian equity market is still emerging, distinguished by a high degree of volatility and a relatively short time horizon of historical information. Taking into account the crises that have happened in our country over the period from 1991 to 2019 (economic crisis of 1998, financial and economic crisis of 2008, and monetary crisis of 2014), the Russian equity market is characterized by instability and therefore requires a deeper analysis. In this regard, this study appears to be relevant, as it discusses the models for constructing an investment portfolio, as well as its practical realization and software implementation in MS Excel. The paper also reviews the methods of artificial intelligence (neural networks and genetic algorithm), on the basis of which the model of forecasting the return on assets is built.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - A.M. Sunchalin AU - V.A. Ivanyuk AU - A.L. Sunchalina PY - 2019/05 DA - 2019/05 TI - Investment portfolio management and forecasting the return on assets based on artificial intelligence methods (neural analysis and genetic algorithm) BT - Proceedings of the 1st International Scientific Conference "Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth" (MTDE 2019) PB - Atlantis Press SP - 281 EP - 287 SN - 2352-5428 UR - https://doi.org/10.2991/mtde-19.2019.54 DO - 10.2991/mtde-19.2019.54 ID - Sunchalin2019/05 ER -