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This study aims to investigate the impact of facial-based perceived trustworthiness on stock valuation, particularly, in the initial public offering (IPO). IPO settings provide the opportunity to investigate whether information asymmetry resulting from company newness in the market would influence the incorporation of soft information in the form of executive facial trustworthiness in stock valuation.

We use a recent machine learning algorithm to detect facial landmarks and then calculate a composite facial trustworthiness measure using several facial features that have previously been observed in neuroscience and psychological studies to be the most determining factor of perceived trustworthiness. We then regress the facial trustworthiness of IPO firm executives to IPO underpricing.

Utilizing machine learning algorithms, we find that the facial trustworthiness of the company executive negatively impacts the extent of IPO underpricing. This result implies that investors incorporate the facial trustworthiness of company executives into stock valuation. The IPO underpricing also shows that the cost of equity is higher when perceived trustworthiness is low. With regard to the higher information asymmetry in IPO transactions, such a negative impact implies the role of facial trustworthiness in alleviating information asymmetry.

This study provides evidence of the impact of top management personal characteristics on firms’ financial transactions in the Indonesian context. From the perspective of investors and other fund providers, this study shows evidence that heuristics still play an important role in financial decision-making. This is also an indication of investor reliance on soft information. Our research method also provides a new opportunity for the use of machine-learning algorithms in processing non-conventional types of data in finance research, which is still relatively rare in emerging markets like Indonesia. To the best of our knowledge, our study is the first to use personalized measures of trust generated through machine-learning algorithms in IPO settings in Indonesia.

The purpose of the study is to examine the use of alternative information in bank lending to small and medium enterprises (SMEs). Understanding alternative information and its use in bank lending to SMEs is important because it has become a growing part of the future of SME finance. The results and findings of my study not only enrich the finance literature but, more importantly, also address the use of Fintech in the risk management of SME lending, a new and complex problem that is specific to both the information technology and finance field.

To answer the research question, the author used a case study approach that relies upon qualitative data and analysis. By iterating between the existing literature, theoretical pieces and empirical findings, the author explain and interpret in detail how the use of alternative information impacts loan outcomes and develop insights to guide future research.

The case is outlined in two time periods including the prepartnership period and the postpartnership period. It highlights the establishment of a partnership between LoanBank and FintechInc (pseudonym), aimed at SME-focused Fintech lending. The findings underscore how the partnership has enabled a mutually beneficial situation where LoanBank and FintechInc leverage each other’s strengths to provide efficient and effective lending services. The adoption of alternative information in the risk management Fintech (RMF) platform of FintechInc has transformed LoanBank’s lending processes, showcasing how technological innovations can enhance SME lending practices.

The study’s originality mainly lies in the three detailed insights regarding alternative information’s impact on SME lending: information, platform properties and financial inclusion. The information part demonstrates that RMF platforms expand the information used for lending decisions, shifting from traditional hard and soft data to incorporating various alternative information sources. The platform properties part suggests that location, openness and technology also play a pivotal role in shaping lending outcomes. Finally, the financial inclusion part proposes that the use of alternative information has the potential to improve financial inclusion and offer better credit terms to previously underserved borrowers.

The structural adaptive ability of the soft robot is fully demonstrated in the grasping task of the soft hand. A soft hand can easily realize the envelope operation of the object without planning. With the continuous development of robot applications, researchers are no longer satisfied with the ability of the soft hand to grasp. The purpose of this paper is to perceive the object’s shape while grasping to provide a decision-making basis for more intelligent robot applications.

This paper proposes a dual-signal comparison method to obtain the fingertip position. The dual signal includes the displacement calculated by the static model without considering the external load change and the displacement calculated by the bending sensor. The dual-signal comparison method can use the obvious change trend difference between the above two signals in the hover and contact states to identify the touch position. The authors make the soft hand scan around the object through touch operation to detect the object’s shape, and the tracks of every touch fingertip position can envelop the object’s shape.

The experimental results show that the dual-signal comparison method can accurately identify the contact moment of soft fingers. This detection method makes the soft hand develop the shape detection ability. The soft hand in the experiment can perceive squares, circles and a few other complex shapes.

The dual-signal comparison method proposed in this paper can detect a touch action by using the signal change trend when the working condition suddenly changes with the rough robotic model and sensing, thus improving the utilization value of the measured signal. The problems of large model errors and inaccurate sensors also negatively impact the use of other soft robots. It is generally difficult to achieve good results by directly using these models and sensors with the thinking of rigid robot analysis. The dual-signal comparison method in this paper can provide some reference for this aspect.

This study analyzes the role of regional cooperative banks in identifying and financing small and medium-sized enterprises (SMEs) from a proximity perspective. Access to finance is a major challenge for SMEs. Regional cooperative banks can remove this barrier based on cooperative bank's characteristics and geographic proximity to SMEs. Understanding the interplay between these financial actors and firms can contribute to a better support of SMEs development.

The results are based on a case study of eight SMEs located in southwestern France. Interviews were conducted with two regional cooperative funds and eight SMEs. The interview guide included questions related to the company, the projects financed and how financing was accessed.

Results reveal that a combination of three forms of proximity allows regional cooperative banks and SMEs to establish effective financing operations. They show that regional cooperative banks are key players in the existing financing mechanisms for SMEs. Such financing is often used to gain access to larger players at a later stage. The findings suggest the need for public policies that promote the integration of financing actors in regional ecosystems to advance SMEs' development.

This article examines how SMEs access financing, with a focus on regional cooperative banks, which have received little attention in the literature. Moreover, the relationships between these actors are studied through the lens of proximity. Regional cooperative banks are able to finance projects that may have been overlooked by traditional banks due to trust-building local dynamics.

This paper aims to examine the impact of traditional banks’ financial technology (FinTech) adoption on corporate loan spreads and lending practices.

This study examines the impact of FinTech adoption by banks on corporate loan spreads and lending practices. By analyzing data from bank 10-K filings, we develop a novel metric to assess FinTech adoption at the individual bank level. Our analysis reveals a significant positive correlation between increased FinTech adoption and higher corporate loan spreads, particularly for loans that are relatively informationally opaque. This causality is further validated through a quasi-natural experiment. Additionally, we identify trends toward loans with smaller sizes and longer maturities in banks with advanced FinTech integration.

Using a sample of corporate loans issued from 1993 to 2020, this paper documents a significant positive relationship between a bank’s increased FinTech adoption and higher loan spreads. This correlation is especially noticeable for loans that are informationally opaque. Moreover, the paper reveals trends toward smaller loan sizes and longer maturities with advanced FinTech integration in banks. Overall, these findings indicate FinTech enhances efficiency in processing hard information and holds the potential to enhance financial inclusion.

This paper contributes to two significant strands of finance literature. First, it highlights how banks with advanced FinTech integration gain advantages through enhanced processing of hard information. Furthermore, it underscores the role of FinTech in promoting financial inclusion, particularly for those borrowers facing informational opacity.

The purpose of this paper is to compare nine different models to evaluate consumer credit risk, which are the following: Logistic Regression (LR), Naive Bayes (NB), Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) in Peer-to-Peer (P2P) Lending.

The author uses data from P2P Lending Club (LC) to assess the efficiency of a variety of classification models across different economic scenarios and to compare the ranking results of credit risk models in P2P lending through three families of evaluation metrics.

The results from this research indicate that the risk classification models in the 2013–2019 economic period show greater measurement efficiency than for the difficult 2007–2012 period. Besides, the results of ranking models for predicting default risk show that GBDT is the best model for most of the metrics or metric families included in the study. The findings of this study also support the results of Tsai et al. (2014) and Teplý and Polena (2019) that LR, ANN and LDA models classify loan applications quite stably and accurately, while CART, k-NN and NB show the worst performance when predicting borrower default risk on P2P loan data.

The main contributions of the research to the empirical literature review include: comparing nine prediction models of consumer loan application risk through statistical and machine learning algorithms evaluated by the performance measures according to three separate families of metrics (threshold, ranking and probabilistic metrics) that are consistent with the existing data characteristics of the LC lending platform through two periods of reviewing the current economic situation and platform development.

Signals, e.g. information released by firms about new products attract the attention and scrutiny of customers, competitors and other stakeholders. In product management, an important area of research focuses on the economic value of such signals. However, extant studies consider valuation effects of product signals independently, and largely ignore how the value of a product signal at launch depends upon prior preannouncements. This study aims to investigate how the dependence of new product development (NPD) signals on past preannouncements affects firms’ security prices.

The study develops a conceptual model that draws upon information asymmetry theories, i.e. signaling and agency theory to hypothesize the effect of firms’ product introduction announcements on security prices given two antecedent preannouncement types (costless and costly signals). Hypotheses are tested by conducting an event study analysis on a sample of 149 matched observations (product introduction announcement preceded by a certain type of preannouncement).

Empirical results confirm the hypothesis that positive valuation effects are observed during product launch that is preceded by initial costless product signaling. In contrast, for ex ante costly product signaling, launch events are not diagnostic enough to affect value. Since organizations’ NPD communications can revise investors’ prior beliefs, they need to be understood in more detail and managed strategically.

Valuation metrics can be noisy with a potential to influence information events. In addition, product introduction signals may be deployed more frequently in certain fast-paced industries, e.g. hi-tech.

Managers can incorporate signal dependence in product communications. For example, in costless ex ante product signaling situations, initial economic loss may be recovered through launch announcements. Furthermore, when costly signals have been used earlier, firms may economize on promotion costs during launch.

Past research has focused on assessing the economic value of new product signals independently, i.e. as discrete events. Absent is an examination of valuation effects due to the dependence of launch signals on prior preannouncements. This paper addresses the dependence gap, and empirical results show that even if firms do not deploy product signals ex ante, value can be created through ex post launch announcements.

The objective of this study is to examine how the heterogeneity of the institutional environments within a single country influences International Financial Reporting Standards (IFRS) convergence and earnings quality based on a meso- and multi-level approach.

Using hierarchical linear modeling (HLM) to capture the between-group heteroskedasticity and within-cluster interdependence, this study investigates the simultaneous effect by incorporating institutional factors residing at different hierarchical levels and the interaction effects of factors within the same level on IFRS convergence and earnings quality in the largest IFRS adopter, China.

The results show that after IFRS convergence (i.e. 2007–2015), earnings quality decreases in terms of conservatism. However, the further analysis indicates that the strong institutional environment could mitigate the negative impact of IFRS on conservatism.

Consistent with the emphasis of heterogeneity within a country by Terracciano et al. (Science, 2005, 310 (5745)), this study indicates that the heterogeneity in the institutional environments and the simultaneous effect of the multilevel institutional environments within a single country cannot be ignored. This study also indicates that, equally important, research methodology plays a substantial role in investigating the outcomes of IFRS convergence. Finally, this study, based on an integrated theory, adopts a meso-paradigm linking macro- and micro-level institutions to provide comprehensive insights into IFRS convergence and conservatism.