Selecting Computational Models for Asset Management: <i>Financial Econometrics versus Machine Learning—Is There a Conflict?</i>

Cerniglia, Joseph A.; Fabozzi, Frank J.

doi:10.3905/jpm.2020.1.184

Cited by 8 publications

(4 citation statements)

References 49 publications

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“…These methodologies hold a critical role in evaluating the validity of foundational theories (Cornelissen, 2017;Wolstenholme, 1999). The discourse commonly revolves around the appropriateness of regression methods, widely employed in quantitative analyses within this domain (Cerniglia and Fabozzi, 2020). It is crucial to acknowledge that there's no universal method or standardized rules dictating the use of regression methods across the majority of empirical studies in innovation.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning to determine factors affecting product and product–service innovation

Bustinza,

Molina Fernandez,

Mendoza Macías

2024

JEIM

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PurposeMachine learning (ML) analytical tools are increasingly being considered as an alternative quantitative methodology in management research. This paper proposes a new approach for uncovering the antecedents behind product and product–service innovation (PSI).Design/methodology/approachThe ML approach is novel in the field of innovation antecedents at the country level. A sample of the Equatorian National Survey on Technology and Innovation, consisting of more than 6,000 firms, is used to rank the antecedents of innovation.FindingsThe analysis reveals that the antecedents of product and PSI are distinct, yet rooted in the principles of open innovation and competitive priorities.Research limitations/implicationsThe analysis is based on a sample of Equatorian firms with the objective of showing how ML techniques are suitable for testing the antecedents of innovation in any other context.Originality/valueThe novel ML approach, in contrast to traditional quantitative analysis of the topic, can consider the full set of antecedent interactions to each of the innovations analyzed.

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Section: Introductionmentioning

confidence: 99%

Using machine learning to determine factors affecting product and product–service innovation

Bustinza,

Molina Fernandez,

Mendoza Macías

2024

JEIM

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“…As far as we know, the methodology of economics develops from qualitative to quantitative, and mathematic models play important roles (Lindenlaub & Prummer, 2021; Page & Clemen, 2013; Tsakas et al, 2021). But it is undeniable that the criticism and reflection on this methodology system has never stopped (Cerniglia and Fabozzi, 2020 ; Rattinger, 1976). Especially with the emergence of many “black swan events” such as the financial crisis and epidemic disease, the interpretation and prediction ability of Positivist Economics has been greatly challenged, and the effectiveness of the policy measures proposed by it has been seriously questioned.…”

Section: Introductionmentioning

confidence: 99%

Deep learning in economics: a systematic and critical review

2023

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From the perspective of historical review, the methodology of economics develops from qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of the superiority in learning inherent law and representative level, deep learning models assist in realizing intelligent decision-making in economics. After presenting some statistical results of relevant researches, this paper systematically investigates deep learning in economics, including a survey of frequently-used deep learning models in economics, several applications of deep learning models used in economics. Then, some critical reviews of deep learning in economics are provided, including models and applications, why and how to implement deep learning in economics, research gap and future challenges, respectively. It is obvious that several deep learning models and their variants have been widely applied in different subfields of economics, e.g., financial economics, macroeconomics and monetary economics, agricultural and natural resource economics, industrial organization, urban, rural, regional, real estate and transportation economics, health, education and welfare, business administration and microeconomics, etc. We are very confident that decision-making in economics will be more intelligent with the development of deep learning, because the research of deep learning in economics has become a hot and important topic recently.

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“…Machine learning algorithms have been compared sporadically to mainstream statistics in fields such as human capital [8], econometric models [9,10], asset management [11], monetary policy [12], the film industry [13], unemployment [14], transportation [15], experimental frameworks [16], infrastructure effects [17], health economics [18], audit decision making [19], food inflation [20], qualitative demography [21], the replicability of findings based on the European Social Survey [22], poverty predictions [23], and the forecasting of unemployment in the Euro area [24]. Their results do not point to any specific direction; there seem to be no generalizable findings about the worth of ML with respect to mainstream statistics, and no systematic comparisons have been carried out thus far.…”

Section: Introductionmentioning

confidence: 99%

Does Machine Learning Offer Added Value Vis-à-Vis Traditional Statistics? An Exploratory Study on Retirement Decisions Using Data from the Survey of Health, Ageing, and Retirement in Europe (SHARE)

et al. 2022

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Do machine learning algorithms perform better than statistical survival analysis when predicting retirement decisions? This exploratory article addresses the question by constructing a pseudo-panel with retirement data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). The analysis consists of two methodological steps prompted by the nature of the data. First, a discrete Cox survival model of transitions to retirement with time-dependent covariates is compared to a Cox model without time-dependent covariates and a survival random forest. Second, the best performing model (Cox with time-dependent covariates) is compared to random forests adapted to time-dependent covariates by means of simulations. The results from the analysis do not clearly favor a single method; whereas machine learning algorithms have a stronger predictive power, the variables they use in their predictions do not necessarily display causal relationships with the outcome variable. Therefore, the two methods should be seen as complements rather than substitutes. In addition, simulations shed a new light on the role of some variables—such as education and health—in retirement decisions. This amounts to both substantive and methodological contributions to the literature on the modeling of retirement.

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Selecting Computational Models for Asset Management: Financial Econometrics versus Machine Learning—Is There a Conflict?

Cited by 8 publications

References 49 publications

Using machine learning to determine factors affecting product and product–service innovation

Using machine learning to determine factors affecting product and product–service innovation

Deep learning in economics: a systematic and critical review

Does Machine Learning Offer Added Value Vis-à-Vis Traditional Statistics? An Exploratory Study on Retirement Decisions Using Data from the Survey of Health, Ageing, and Retirement in Europe (SHARE)

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