Optimizing Portfolio Construction Using Artificial Intelligence

Thim, Chan Kok; Choong, Yap Voon; Han, Eric

doi:10.4156/ijact.vol3.issue3.16

Cited by 3 publications

(1 citation statement)

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“…The applications of ML to portfolio choices is a wider field with a broader disclosure, see for example Chan et al [12], however there are some issues specific to sustainable investment that we had to overcome and for which it was more convenient to develop our own model. The first issue is that we need a model that can be easily understood, i.e., if our model is a black-box it is difficult to check that is going in the proper "ESG" direction.…”

Section: Machine Learning In Financementioning

confidence: 99%

Mind the gap! Machine Learning, ESG Metrics and Sustainable Investment

2020

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This work proposes a novel approach for overcoming the current inconsistencies in ESG scores by using Machine Learning (ML) techniques to identify those indicators that better contribute to the construction of efficient portfolios. ML can achieve this result without needing a model-based methodology, typical of the modern portfolio theory approaches. The ESG indicators identified by our approach show a discriminatory power that also holds after accounting for the contribution of the style factors identified by the Fama-French five-factor model and the macroeconomic factors of the BIRR model. The novelty of the paper is threefold: a) the large array of ESG metrics analysed, b) the model-free methodology ensured by ML and c) the disentangling of the contribution of ESG-specific metrics to the portfolio performance from both the traditional style and macroeconomic factors. According to our results, more information content may be extracted from the available raw ESG data for portfolio construction purposes and half of the ESG indicators identified using our approach are environmental. Among the environmental indicators, some refer to companies' exposure and ability to manage climate change risk, namely the transition risk.

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Section: Machine Learning In Financementioning

confidence: 99%

Mind the gap! Machine Learning, ESG Metrics and Sustainable Investment

2020

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Research on Data Security of Finance Software

Wang

2013

AMM

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Financial software technology development, promoted an enterprise effectively "integrated management", but also led to new safety problems. Financial software data security is a people pay much attention to the problem, in order to ensure the financial software of data security, many countries and enterprises not hesitate investment is huge endowment, to computer hardware and software research and development, in order to ensure the safety of computer data information. In computerized accounting work, accounting data security is a very important issue, it directly influences the success or failure of computerized accounting work, by the vast number of financial software users close concern.

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Artificial Bee Colony Algorithm for Portfolio Optimization Problems

Wang¹,

Liu²,

Kong³

2012

IJACT

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In this paper, a cardinality constrained mean-variance model is introduced for the portfolio optimization problems. This model is a mixed quadratic and integer programming problem for which efficient algorithms do not exist. The use of heuristic algorithms in this case is necessary. Some studies have investigated the cardinality constrained mean-variance model using heuristic algorithm. But almost none of these studies deal with artificial bee colony algorithm. The purpose of this paper is to use artificial bee colony algorithm to solve this model. The experimental results show that the proposed algorithm performs well for the portfolio optimization problem.

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Optimizing Portfolio Construction Using Artificial Intelligence

Cited by 3 publications

References 0 publications

Mind the gap! Machine Learning, ESG Metrics and Sustainable Investment

Mind the gap! Machine Learning, ESG Metrics and Sustainable Investment

Research on Data Security of Finance Software

Artificial Bee Colony Algorithm for Portfolio Optimization Problems

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