On Predicting ESG Ratings Using Dynamic Company Networks

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Ang, Gary; Guo, Zhiling; Lim, Ee-Peng

Abstract: Environmental, social and governance (ESG) considerations play an increasingly i ... Expand

Abstract: Environmental, social and governance (ESG) considerations play an increasingly important role due to the growing focus on sustainability globally. Entities, such as banks and investors, utilize ESG ratings of companies issued by specialized rating agencies to evaluate ESG risks of companies. The process of assigning ESG ratings by human analysts is however laborious and time intensive. Developing methods to predict ESG ratings could alleviate such challenges, allow ESG ratings to be generated in a more timely manner, cover more companies, and be more accessible. Most works study the effects of ESG ratings on target variables such as stock prices or financial fundamentals of companies, but few works study how different types of company information can be utilized to predict ESG ratings. Previous works also largely focus on using only the financial information of individual companies to predict ESG ratings, leaving out the different types of inter-company relationship networks. Such inter-company relationship networks are typically dynamic, i.e., they evolve across time. In this paper, we focus on utilizing dynamic inter-company relationships for ESG ratings prediction, and examine the relative importance of different financial and dynamic network information in this prediction task. Our analysis shows that utilizing dynamic inter-company network information, based on common director, common investor and news event-based knowledge graph relationships, can significantly improve ESG rating prediction performance. Robustness checks over different time-periods and different number of time-steps in the future further validate these insights. Collapse

Topics: shapley additive explanation knowledge graph database system missing data network effect

Methods: machine learning econometric modeling longitudinal research Granger causality robustness check

Prior Experience of Managers and Maladaptive Responses to Performance Feedback: Evidence from Mutual Funds

2023 | Organization Science | Citations: 0

Authors: Gaba, Vibha; Lee, Sunkee; Meyer-Doyle, Philipp; Zhao-Ding, Amy

Abstract: In this study, we examine how the prior experiences of decision makers systemati ... Expand

Abstract: In this study, we examine how the prior experiences of decision makers systematically influence their assessment of and responses to negative performance feedback. We posit that, although greater and more specialized experiences enable managers to build relevant knowledge and expertise in specific domains, they also make them overconfident in their abilities and strategies. Such experience-induced overconfidence further leads to distortions in the performance assessment process, hindering a firm’s ability to recognize and respond to poor performance. We empirically test these arguments in the context of U.S. mutual fund managers making investment decisions in response to fund performance below aspirations. As hypothesized, we find that more experienced and more specialized fund managers change their investment decisions less when faced with negative performance feedback than managers who are less experienced and less specialized. In additional analyses, we further show that the lower responsiveness of more experienced (specialized) managers is associated with the fund’s lower future performance, supporting our proposed theoretical mechanism (overconfidence). This study augments existing performance feedback research by showing how decision makers’ prior experience can impede problem-solving behavior in organizations. It also contributes to the literature on human capital and organizational learning by documenting an unintended consequence of accumulated human capital on firm adaptive behavior. Funding: This work was supported by INSEAD research & development funds. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1605. Collapse

Topics: IT career organizational productivity human resource management unintended consequence problem solving

Methods: robustness check descriptive statistic theory development qualitative content analysis SEC EDGAR

Theories: theory of bounded rationality behavioral theory of the firm

Between Attention and Portfolio Adjustment: Insights from Machine Learning-based Risk Preference Assessment

2023 | International Conference on Information Systems | Citations: 0

Authors: Li, Xin; Rai, Arun; Song, Qingping; Xu, Sean Xin

Abstract: Financial firms recommend products to customers, intending to gain their attent ... Expand

Abstract: Financial firms recommend products to customers, intending to gain their attention and change their portfolios. Based on behavioral decision-making theory, we argue attention's effect on portfolio adjustment is through the risk deviation between portfolio risk and their risk preference. Thus, to fully understand the adjustment process, it is necessary to assess customers' risk preferences. In this study, we use machine learning methods to measure customers' risk preferences. Then, we build a dynamic adjustment model and find that attention's impact on portfolio adjustment speed is stronger when customers' risk preference is higher than portfolio risk (which needs an upward adjustment) and when customers' risk preference is within historical portfolio risk experience. We conducted a field experiment and found that directing customers' attention to products addressing the risk deviation would lead to more portfolio adjustment activities. Our study illustrates the role of machine learning in enhancing our understanding of financial decision-making. Collapse

Topics: decision making analytical information system electronic finance web page online banking

Methods: field experiment experimental group machine learning XGBoost digital trace data

Specialists, Generalists, or Both? Founders’ Multidimensional Breadth of Experience and Entrepreneurial Ventures’ Fundraising at IPO

2023 | Organization Science | Citations: 0

Authors: Souitaris, Vangelis; Peng, Bo; Zerbinati, Stefania; Shepherd, Dean A.

Abstract: Different streams of research have led to contradictory conclusions about the ve ... Expand

Abstract: Different streams of research have led to contradictory conclusions about the venture performance implications of founders’ breadth of experience. Although extant empirical studies have explored the performance implications of founders’ breadth of experience at the start-up stage, we focus on the later stage of the initial public offering (IPO). We theorize that investors categorize venture founders based on two salient dimensions—their industry and functional background—and we relate this categorization to resource acquisition at IPO. To test our model, we use a hand-collected data set of 175 entrepreneurial IPOs in the Alternative Investment Market in London (2002–2013) and two randomized experiments. We theorize and find that compared with entrepreneurial ventures with a lead founder specializing in one industry or one function, investors generally devalue those with a category-spanning lead founder (a generalist). However, devaluation is less severe when a lead founder is a generalist in one dimension (e.g., industry) but a specialist in the other dimension (e.g., function). We also theorize and empirically test trust as a mechanism for the generalist penalty. Specifically, audience members (investors) have low trust in a generalist producer (founder) in contexts where the two parties consider entering into a partnership (equity investment at IPO), and so that generalist producer is devalued. Finally, we show that an external expert endorsement—in our case, from intensive venture capital affiliations—offsets the generalist penalty, especially when category spanning occurs in multiple category dimensions. Funding: This work was supported by the University of St. Gallen and the Natural Science of China [Grant 71832012]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2022.1581. Collapse

Topics: marketing management IT career entrepreneurship database system decision making

Methods: experiment theory development robustness check qualitative interview survey

Theories: social identity theory

Growing pains and blessings: Manifestations and implications of equity crowdfunding industry maturation

2022 | Decision Support Systems | Citations: 0

Authors: Lukkarinen, Anna; Shneor, Rotem; Wallenius, Jyrki

Abstract: The equity crowdfunding industry has grown significantly in the past decade. Ind ... Expand

Abstract: The equity crowdfunding industry has grown significantly in the past decade. Industry life cycle theory suggests that growth dynamics and relations between stakeholders change as industries mature. The present study ex­ amines the characteristics and implications of maturation in the equity crowdfunding industry via the lens of industry life cycle theory. Specifically, we explore whether the industry is reverting to traditional entrepreneurial finance practice, or whether it retains its original distinguishing characteristics. Accordingly, we first assess changes with respect to users (investors) and products (campaigns and investment objects). Second, we assess the implications of these changes by comparing the determinants of fundraising campaign success in earlier and later industry stages. We use a longitudinal database and survey data sourced from a long-standing European equity crowdfunding platform. We show that equity crowdfunding seems to be converging towards traditional entre­ preneurial finance practice, yet maintaining certain unique features stemming from digitality, platform nature, and investor diversity. Specifically, we show that (1) fundraising ventures and their campaigns have become larger and more professional, and (2) engaged investors became more knowledgeable and return-oriented. Accordingly, traditional investment criteria, such as team and commercial terms ratings, have become more important predictors of campaign success, and easily observable campaign characteristics, such as B2C business models, and minimum investment thresholds, less so. The findings support platform managers and entrepreneurs as they plan for campaigns seeking to attract investors in the industry's later stages. Collapse

Topics: crowdsourcing crowdfunding data quality database system business model

Methods: survey longitudinal research regression analysis method Levene's test forums posting

Authority and consensus in group decision making with fallible individuals

2022 | Decision Support Systems | Citations: 1

Authors: Sáenz-Royo, Carlos; Salas-Fumás, Vicente; Lozano-Rojo, Álvaro

Abstract: This paper compares the performance, in terms of expected opportunity loss of mi ... Expand

Abstract: This paper compares the performance, in terms of expected opportunity loss of mistakes, of group decision making with fallible members, under different organization structures. These structures result from combining the decision mechanisms of authority and consensus with the communication networks of star, tree, and full network. Simulation results from Agent Based Modeling show that the authority (consensus) is preferred when the time to reach a group decision matters (not matters), and in organizational environments of high (low) risk and high (low) prospects of growth. Additionally, the results of the paper recommend that organizations that want to migrate from authority to consensus decision making, as defined here, should switch from the lowdensity tree like connecting network, common in hierarchical structures, to a high-density full network. Collapse

Topics: decision making organization structure innovation management big data digital innovation

Methods: simulation agent based simulation literature study

Theories: theory of bounded rationality

Automated Coding and Scoring of Text: Artifact Design, Application, and Evaluation

2021 | International Conference on Information Systems | Citations: 0

Authors: Bhattacherjee, Anol; Silveira, Alysson De Oliveira

Abstract: This paper presents an artifact to automate and quantify qualitative coding of ... Expand

Abstract: This paper presents an artifact to automate and quantify qualitative coding of text documents on predefined constructs of interest. The quantified codes, similar to Likert-scaled measures in survey research, can be analyzed using statistical models. Our approach employs sentence transformers, a deep learning model to estimate semantic text similarity between text documents and predefined construct operationalizations. We demonstrate an application of our artifact by coding and scoring a sample of corporate 10-K reports for two types of organizational innovation processes: exploration and exploitation. Our artifact is a significant methodological contribution beyond manual coding of text documents, which cannot scale up to thousands or millions of documents, or word-based automated coding, which cannot capture the semantic meaning of text and cannot score text documents. Our approach offers new possibilities in mixed-mode research by integrating qualitative and quantitative methods using design science research. Collapse

Topics: knowledge representation organizational innovation innovation management Google+ extensible markup language

Methods: ontological modelling qualitative coding computational algorithm transformer model machine learning

Social, commercial, or both? An exploratory study of the identity orientation of digital social innovations

2021 | Information Systems Journal | Citations: 43

Authors: Bonina, Carla; López-Berzosa, David; Scarlata, Mariarosa

Abstract: The information systems literature has studied digital innovation extensively an ... Expand

Abstract: The information systems literature has studied digital innovation extensively and focused primarily on its commercial related objectives. Yet, digital social innovations (DSIs) seek to solve social problems, while implementing commercially viable approaches. Drawing from the social entrepreneurship literature and using computational social science methods, we identify the peculiar utilitarian (commercial) and collectivistic (social) identity orientations that shape DSI. We use a sample of 292 DSI initiatives from Latin America and find that DSIs present both utilitarian and collectivist identity orientation. We also find that the collectivistic identity orientation tends to be more salient in less developed contexts. This allows us to discuss implications for the research on DSI and to open up a discussion on the role of context and possible tensions that may emerge in combining both identity orientations. Collapse

Topics: innovation management information technology capability entrepreneurship digital innovation business model

Methods: topic model multivariate analysis of variance case study design artifact theory development

Tokenized index funds: A blockchain-based concept and a multidisciplinary research framework

2021 | International Journal of Information Management | Citations: 0

Authors: Ciriello, Raffaele Fabio

Abstract: In response to the bleak prospects of today’s financial markets, a wave of finan ... Expand

Abstract: In response to the bleak prospects of today’s financial markets, a wave of financial and technological innovations emerges, bringing about potential benefits but also new challenges. For instance, tokenized securities are a new kind of blockchain-based asset enabling price stability, programmability, pseudonymity, and transaction effi­ ciency, while also introducing new regulatory challenges and uncertainties. Conversely, index funds are an established investment device enabling broad diversification in a cost-effective, tax-efficient, and transparent way, while potentially also contributing to concentration of market power, intermediation cost, access barriers for underbanked or impoverished investors, increased market volatility, and human behavioral challenges. This paper conceptually develops Tokenized Index Funds as a hybrid approach that combines the benefits of toke­ nized securities and index funds while alleviating some of their drawbacks. Based thereupon, a corresponding multidisciplinary research framework is presented, with sample research questions along the activities of design and features, business and economics, management and organization, and law and regulation. Collapse

Topics: blockchain cryptocurrency peer-to-peer model

A novel architecture to identify locations for Real Estate Investment

2021 | International Journal of Information Management | Citations: 0

Authors: E, Sandeep Kumar; Talasila, Viswanath; Pasumarthy, Ramkrishna

Abstract: The identification of a favorable location for investment is a key aspect influe ... Expand

Abstract: The identification of a favorable location for investment is a key aspect influencing the real estate market of a smart city. The number of factors that influence the identification easily runs into a few hundreds (including floor space area, crime in the locality and so on). Existing literature predominantly focuses on the analysis of price trends in a given location. This paper aims to develop a set of tools to compute an optimal location for investment, a problem which has received little attention in the literature (analysis of house price trends has received more attention). In previous work the authors proposed a machine learning approach for computing optimal locations. There are two main issues with the previous work. All real estate factors were assumed to be independent and identically distributed random variables. To address this, in the current paper we propose a network structure to derive the relational inferences between the factors. However, solving the location iden­ tification problem using only a network incurs computational burden. Hence, the machine learning layers from the previous work is combined with a network layer for computing an optimal location with proven lower computational cost. A second issue is that the computations are performed on an online database which has inherent privacy risks. The online data, user information and the algorithms can be tampered through privacy breaches. We present a privacy preservation technique to protect the algorithms, and use blockchains to secure the identity of the user. This paper presents solutions to two interesting problems in the analysis of real estate networks: a) to design tools that can identify an optimal location for investment and b) to preserve the privacy of the entire process using privacy preserving techniques and block chains. Collapse

Topics: privacy differential privacy smart contract blockchain database system

Methods: computational algorithm machine learning principal component analysis data transformation simulation