Wavelet multiresolution analysis of financial time series

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Board gender diversity and workplace diversity: a machine learning approach

Purpose This study aims to examine the association between board gender diversity (BGD) and workplace diversity and the relative importance of various board and firm characteristics in predicting diversity. Design/methodology/approach With a novel machine learning (ML) approach, this study models the association between three workplace diversity variables and BGD using a social media data set of approximately 250,000 employee reviews. Using the tools of explainable artificial intelligence, the authors interpret the results of the ML model. Findings The results show that BGD has a strong positive association with the gender equality and inclusiveness dimensions of corporate diversity culture. However, BGD is found to have a weak negative association with age diversity in a company. Furthermore, the authors find that workplace diversity is an important predictor of firm value, indicating a possible channel on how BGD affects firm performance. Originality/value The effects of BGD on workplace diversity below management levels are mainly omitted in the current corporate governance literature. Furthermore, existing research has not considered different dimensions of this diversity and has mainly focused on its gender aspects. In this study, the authors address this research problem and examine how BGD affects different dimensions of diversity at the overall company level. This study reveals important associations and identifies key variables that should be included as a part of theoretical causal models in future research.© Mikko Ranta and Mika Ylinen. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcodefi=vertaisarvioitu|en=peerReviewed

Employer ratings in social media and firm performance : Evidence from an explainable machine learning approach

This study examines the ability of crowdsourced employee opinions about their workplace to reveal value-relevant information about corporate culture. We investigate the employee-friendly (EF) corporate culture values that are strongly associated with firm value and operating performance using a unique social media dataset of approximately 250,000 crowdsourced employee reviews to evaluate 18 distinct characteristics of a firm's corporate culture. The explainable machine learning model is used to examine the nonlinear associations and relative importance of employee-friendly cultural values. We find that several employee-friendly corporate culture features are associated with firms' value (Tobin's Q) and operating performance (ROA). Our findings reveal two features whose association is clearly superior to other EF culture variables in our explainable machine learning model: pride in the company for Tobin's Q and job security for ROA. Based on the SHAP values, their effects are positive, significant, and relatively linear.© 2023 The Authors. Accounting & Finance published by John Wiley & Sons Australia, Ltd on behalf of Accounting and Finance Association of Australia and New Zealand. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.fi=vertaisarvioitu|en=peerReviewed

Machine Learning in Management Accounting Research : Literature Review and Pathways for the Future

This paper explores the possibilities of employing machine learning (ML) methods and new data sources in management accounting (MA) research. A review of current accounting and related research reveals that ML methods in MA are still in their infancy. However, a review of recently published ML research from related fields reveals several new opportunities to utilize ML in MA research. We suggest that the most promising areas to employ ML methods in MA research lie in (1) the exploitation of the rich potential of various textual data sources; (2) the quantification of qualitative and unstructured data to create new measures; (3) the creation of better estimates and predictions; and (4) the use of explainable AI to interpret ML models in detail. ML methods can play a crucial role in MA research by creating, developing, and refining theories through induction and abduction, as well as by providing tools for interventionist studies.© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.fi=vertaisarvioitu|en=peerReviewed

Do Markets Value Advanced Service Development?

Purpose: Markets have a proven propensity for valuing research and development (R&D) intensity of manufacturing firms. This paper investigates whether coupling R&D intensity with advanced services (ADS) yields even higher market performance effect. Design/Methodology/Approach: The longitudinal financial and annual report data covered a period from 1994 to 2020 (n = 164, N = 2 844). Panel regression (fixed effects estimator) was used to investigate the relationships between market performance (regressand), R&D intensity (regressor) and annual report-level discourse related to ADS (moderator). Findings: The findings confirm that markets do in fact value R&D intensity of manufacturers more if the manufacturer publicizes ADS. However, in alignment with extant research the direct relationship between market performance and ADS discourse proved to be negative and significant. Originality/Value: The current study shows that ADS publicizing adds to the R&D-driven market value of manufacturing firms. Thus, the study contributes to the literature on financial consequences of servitization. However, it also highlights the challenging nature of ADS strategies.©2022 The Advanced Services Group.fi=vertaisarvioitu|en=peerReviewed

Blockchain in accounting research : current trends and emerging topics

Purpose This paper provides a structured literature review of blockchain in accounting. The authors identify current trends, analyse and critique the key topics of research and discuss the future of this nascent field of inquiry. Design/methodology/approach This study’s analysis combined a structured literature review with citation analysis, topic modelling using a machine learning approach and a manual review of selected articles. The corpus comprised 153 academic papers from two ranked journal lists, the Association of Business Schools (ABS) and the Australian Business Deans Council (ABDC), and from the Social Science Research Network (SSRN). From this, the authors analysed and critiqued the current and future research trends in the four most predominant topics of research in blockchain for accounting. Findings Blockchain is not yet a mainstream accounting topic, and most of the current literature is normative. The four most commonly discussed areas of blockchain include the changing role of accountants; new challenges for auditors; opportunities and challenges of blockchain technology application; and the regulation of cryptoassets. While blockchain will likely be disruptive to accounting and auditing, there will still be a need for these roles. With the sheer volume of information that blockchain records, both professions may shift out of the back-office toward higher-profile advisory roles where accountants try to align competitive intelligence with business strategy, and auditors are called on ex ante to verify transactions and even whole ecosystems. Research limitations/implications The authors identify several challenges that will need to be examined in future research. Challenges include skilling up for a new paradigm, the logistical issues associated with managing and monitoring multiple parties all contributing to various public and private blockchains, and the pressing need for legal frameworks to regulate cryptoassets. Practical implications The possibilities that blockchain brings to information disclosure, fraud detection and overcoming the threat of shadow dealings in developing countries all contribute to the importance of further investigation into blockchain in accounting. Originality/value The authors’ structured literature review uniquely identifies critical research topics for developing future research directions related to blockchain in accounting.© Tatiana Garanina, Mikko Ranta and John Dumay. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcodefi=vertaisarvioitu|en=peerReviewed

The resource-based view, stakeholder capitalism, ESG, and sustainable competitive advantage : The firm's embeddedness into ecology, society, and governance

The main research question of the study is this: Is the firm embedded into ecology, society, and governance (ESG), or vice versa? Using the resource-based view as a theoretical lens, and stakeholder capitalism as a paradigm anchored in the Dashgupat Review, we demonstrate in a panel data over 26 years that at the firm level, the relationship between sustained competitive advantage and the ESG footprint is concave shaped, and the impact inequality multiple gaps of the ESG footprint are 4.75 times the providing capacity of the natural and business environment. To solve the common method variance, endogeneity, and unobserved heterogeneity, system GMM is used as a method in a dataset of US manufacturing firms from 1992 to 2019. At the end, we argue that extant attributes of a resource base for sustained competitive advantage have an inherent flaw anchored in the resource-based view, as they ignore the "environmental, social, and governance (ESG) friendliness" attribute of a resource. Managers need to rethink the objective of their firms if they want to survive in the new ESG-friendly economy with stakeholder supremacy.Peer reviewe

Zero-Emission Truck Powertrains for Regional and Long-Haul Missions

Zero-emission trucks for regional and long-haul missions are an option for fossil-free freight. The viability of such powertrains and system solutions was studied conceptually in project ESCALATE for trucks with GVW of 40 tonnes and beyond through various battery electric and fuel cell prime mover combinations. The study covers battery and fuel cell power sources with different degrees of battery electric as well as H2 and fuel cell operation. As a design basis, two different missions with a single-charge/H2 refill were analysed. The first mission was the VECTO long-haul profile repeated up to 750 km, whereas the second was a real 520 km on-road mission in Finland. Based on the simulated energy consumption on the driving cycle, on-board energy demand was estimated, and the initial single-charge and H2 refill operational scenarios were produced with five different power source topologies and on-board storage capacities. The traction motors of the tractor were dimensioned so that a secondary mission of GVW up to 76 tonnes on a shorter route or a longer route with more frequent battery recharge and/or H2 refill can be operated. Based on the powertrain and vehicle model, various infrastructure options for charging and H2 refuelling strategies as well as various operative scenarios with indicative total cost of ownership (TCO) were analysed

Community-oriented family-based intervention superior to standard treatment in improving depression, hopelessness and functioning among adolescents with any psychosis-risk symptoms

The aim of the present study was to compare change in functioning, affective symptoms and level of psychosis-risk symptoms in symptomatic adolescents who were treated either in an early intervention programme based on a need-adapted Family- and Community-orientated integrative Treatment Model (FCTM) or in standard adolescent psychiatric treatment (Treatment As Usual, TAU). 28 pairs were matched by length of follow-up, gender, age, and baseline functioning. At one year after the start of treatment, the matched groups were.compared on change in functioning (GAF-M), five psychosis-risk dimensions of the Structured Interview for Psychosis-Risk Syndromes (SIPS), and self-reported anxiety, depression, and hopelessness symptoms (BAI, BDI-II, BHS). FCTM was more effective in improving functioning (20% vs. 6% improvement on GAF-M), as well as self-reported depression (53% vs. 14% improvement on BDI-II) and hopelessness (41% vs. 3% improvement on BHS). However, for psychosis-risk symptoms and anxiety symptoms, effectiveness differences between treatment models did not reach statistical significance. To conclude, in the present study, we found greater improvement in functioning and self-reported depression and hopelessness among adolescents who received a need-adapted Family and Community-orientated integrative Treatment than among those who were treated in standard adolescent psychiatry. (C) 2016 Elsevier Ireland Ltd. All rights reserved.Peer reviewe

37 GHz observations of a large sample of BL Lacertae objects

We present 37 GHz data obtained at Metsahovi Radio Observatory in 2001 December - 2005 April for a large sample of BL Lacertae objects. We also report the mean variability indices and radio spectral indices in frequency intervals 5 - 37 GHz and 37 - 90 GHz. Approximately 34 % of the sample was detected at 37 GHz, 136 BL Lacertae objects in all. A large majority of the detected sources were low-energy BL Lacs (LBLs). The variability index values of the sample were diverse, the mean fractional variability of the sample being \Delta S_2 = 0.31. The spectral indices also varied widely, but the average radio spectrum of the sample sources is flat. Our observations show that many of the high-energy BL Lacs (HBL), which are usually considered radio-quiet, can at times be detected at 37 GHz.Comment: 12 pages, 5 figures + 5 tables. Published in Astronomical Journa