Forecasting Beta Using Machine Learning and Equity Sentiment Variables

Jourovski, Alexei; Dubikovskyy, Vladyslav; Adell, Pere; Ramakrishnan, Ravi; Kosowski, Robert

doi:10.1002/9781119751182.ch7

Cited by 8 publications

(2 citation statements)

References 10 publications

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“…In general, NLP-based sentiment analysis methods could be divided into two categories. First, NLP combined with traditional machine learning like SVM (Long et al, 2019 ), LightGBM (Wu et al, 2020 ), XGBoost and RF (Jourovski et al, 2020 ; Petropoulos and Siakoulis, 2021 ). Evidence supports that financial news or social media information can provide an additional advantage in predicting price or market turbulence trends.…”

Section: Artificial Intelligence Approaches For Signal Generationmentioning

confidence: 99%

Enhancing portfolio management using artificial intelligence: literature review

Sutiene,

Schwendner,

Sipos

et al. 2024

Front. Artif. Intell.

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Building an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process.

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Section: Artificial Intelligence Approaches For Signal Generationmentioning

confidence: 99%

Enhancing portfolio management using artificial intelligence: literature review

Sutiene,

Schwendner,

Sipos

et al. 2024

Front. Artif. Intell.

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“…Instead of predicting stock returns, ML can also be used to predict other relevant metrics, such as market betas (Jourovski et al, 2020) or stock volatilities (Filipovic and Khalilzadeh, 2021). However, the same caveat that applies to enhancing factors applies here as well, namely that simply incorporating some information from other variables such as value and momentum can already lead to better risk forecasts.…”

Section: Predicting Metrics Other Than Returnmentioning

confidence: 99%

How Can Machine Learning Advance Quantitative Asset Management?

Blitz¹,

Hoogteijling²,

Lohre³

et al. 2023

JPM

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The emerging literature suggests that machine learning (ML) is beneficial in many asset pricing applications because of its ability to detect and exploit nonlinearities and interaction effects that tend to go unnoticed with simpler modelling approaches. In this paper, we discuss the promises and pitfalls of applying machine learning to asset management, by reviewing the existing ML literature from the perspective of a prudent practitioner. The focus is on the methodological design choices that can critically affect predictive outcomes and on an evaluation of the frequent claim that ML gives spectacular performance improvements. In light of the practical considerations, the apparent advantage of ML is reduced, but still likely to make a difference for investors who adhere to a sound research protocol to navigate the intrinsic pitfalls of ML.

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