Parallel Programming for Portfolio Optimization: A Robo-Advisor Prototype using Genetic Algorithms with Recurrent Neural Networks

Muganda, Brian Wesley; Kasamani, Bernard Shibwabo

doi:10.1109/iccns58795.2023.10193396

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…In addition to the mainstream research on the identification of robo-advisory acceptance factors, there are attempts in the literature to construct prediction models in this area [ 42 ]. In particular, models related to asset price forecasting and investment portfolio optimization were presented [ 43 – 47 ]. The proposed models are used to enhance forecasting accuracy and efficiency in robo-advisory.…”

Section: Introductionmentioning

confidence: 99%

Prediction of robo-advisory acceptance in banking services using tree-based algorithms

Orzeszko,

Piotrowski

2024

PLoS ONE

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The banking sector is increasingly recognising the need to implement robo-advisory. The introduction of this service may lead to increased efficiency of banks, improved quality of customer service, and a strengthened image of banks as innovative institutions. Robo-advisory uses data relating to customers, their behaviors and preferences obtained by banks from various communication channels. In the research carried out in the work, an attempt was made to obtain an answer to the question whether the data collected by banks can also be used to determine the degree of consumer interest in this type of service. This is important because the identification of customers interested in the service will allow banks to direct a properly prepared message to a selected group of addressees, increasing the effectiveness of their promotional activities. The aim of the article is to construct and examine the effectiveness of predictive models of consumer acceptance of robo-advisory services provided by banks. Based on the authors’ survey on the use of artificial intelligence technology in the banking sector in Poland, in this article we construct tree-based models to predict customers’ attitudes towards using robo-advisory in banking services using, as predictors, their socio-demographic characteristics, behaviours and attitudes towards modern digital technologies, experience in using banking services, as well as trust towards banks. In our study, we use selected machine learning algorithms, including a decision tree and several tree-based ensemble models. We showed that constructed models allow to effectively predict consumer acceptance of robo-advisory services.

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Section: Introductionmentioning

confidence: 99%

Prediction of robo-advisory acceptance in banking services using tree-based algorithms

Orzeszko,

Piotrowski

2024

PLoS ONE

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Unveiling Portfolio Resilience: Harnessing Asymmetric Copulas for Dynamic Risk Assessment in the Knowledge Economy

2023

J Knowl Econ

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A Machine Learning Model for Algorithmic Optimization of Superannuation Schemes

MUKUNZI,

MUGANDA,

KASAMANI

2024

Preprint

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This paper sought to address the challenge of designing a superannuation pension scheme by developing a machine learning-based recommendation model for optimal asset portfolio selection and allocation. Pension schemes face challenges in dealing with the uncertainties associated with financial markets, especially in selecting an appropriate assets portfolio that can optimize the Return-on-Investment. This study used various machine learning algorithms to build optimal portfolios, which were evaluated based on the portfolio’s return and Sharpe ratio. Data used was obtained from the annual financial reports on pension assets’ cost and market value as well as asset income for the period of July 2013 to June 2023. The Genetic Algorithm, Particle Swarm Optimization, K-means clustering, and Mean-Variance Optimization techniques were employed to construct optimal portfolios. Evaluation based on portfolio return and Sharpe ratio revealed the K-means cluster focused on government securities as a high-performing, low-risk option with 11.16% return and 7.11 Sharpe ratio. Conversely, the genetic algorithm and particle swarm optimized portfolio demonstrated a more diversified conservative asset allocation, leading to a mean return of 7.63% with a Sharpe ratio of 3.91, and a mean return of 6.61% with a 3.16 Sharpe ratio respectively. Comparing these constructed portfolios with OECD (2022) which reported that Kenya achieved a real investment return of 2.9% in 2021, signifies that 7/10 of the constructed optimal superannuation portfolios would have resulted in better performance.

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Parallel Programming for Portfolio Optimization: A Robo-Advisor Prototype using Genetic Algorithms with Recurrent Neural Networks

Cited by 3 publications

References 21 publications

Prediction of robo-advisory acceptance in banking services using tree-based algorithms

Prediction of robo-advisory acceptance in banking services using tree-based algorithms

Unveiling Portfolio Resilience: Harnessing Asymmetric Copulas for Dynamic Risk Assessment in the Knowledge Economy

A Machine Learning Model for Algorithmic Optimization of Superannuation Schemes

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