Effects of Personality on Trading Performance in Social Trading Platforms

Social trading platforms offer opportunities for amateur investors to copy professional traders’ behavior. However, past studies on behavioral finance have largely neglected the role of personality in shaping traders’ behavior. To this end, we aim to scrutinize the effects of leader traders’ personality on their trading behaviors and subsequent performance on social trading platforms. Particularly, we employ the Myers–Briggs Type Indicator (MBTI) personality classification scheme to delineate leader traders’ personality into the four dimensions of Extraversion-Introversion (E-I), Sensing-Intuition (S-N), Thinking-Feeling (T-F), and Judging-Perceiving (J-P). Next, we draw on machine learning techniques to advance a novel text-based approach for extracting the personality dimensions of leader traders automatically. Analytical results attest to the impact of personality dimensions on trading behavior and that of trading behavior on performance. Findings from this study yield insights for both social trading platforms and followers by identifying profitable leader traders based on their personality

Unraveling the Relationship between Content Design and Kinesthetic Learning on Communities of Practice Platforms

As a variant of the sharing economy, Communities of Practice (CoP) platforms have allowed kinesthetic learners to acquire skillsets corresponding to their interests for immediate or future use in practice. However, the impact of digital learning content design on kinesthetic learning remains underexplored in the field of information systems. We hence extend prior research by advancing content richness and structure clarity as antecedents affecting kinesthetic learners’ digestibility of contents, culminating in differential kinesthetic learning effects. To substantiate our arguments, we collected data from a leading Chinese recipe sharing platform. Whereas content richness was measured in terms of readability, verb richness, and prototypicality, structure clarity was operationalized as block structure, block quantity, and block regularity. Employing a machine learning model, we simulated and tested learners’ digestibility of image content embodied within recipes. Plans for future research beyond the current study are also discussed

Effects of Personality on Social Performance in Social Trading

On social trading platforms, the income of leader traders is largely dictated by the number of copy trades conducted by their followers. Consequently, it is imperative for leader traders to exhibit appealing personalities to entice their followers to conduct copy trades. Drawing on social capital theory, we endeavor to scrutinize the effects of traders’ personalities on the accumulation of social capital, which in turn bolsters social performance as measured by the number of copy trades. Data was extracted from a leading social trading platform. The Myers–Briggs Type Indicator personality classification system was then employed to depict leader traders’ personalities based on a novel text-based, machine learning approach. Preliminary analytical results reveal significant relationships among personality traits, social capital dimensions, and social performance. Findings from this study generate insights for social trading platforms and leader traders on exhibiting desirable personalities conducive for accumulating social capital that entice followers to conduct copy trades

Association study of dopamine transporter gene (DAT1) variable tandem repeat sequence (VNTR) with obsessive-compulsive disorder in Chinese Han Population

Abstract: Objective: Multiple evidence suggests an involvement of the dopamine neurotransmitter system in Obsessive-compulsive disorder (OCD). Therefore, we explore the association of 3'UTR region of 40 bp variable tandem repeat (VNTR) polymorphism in Dopamine Transporter Gene (DAT1) in Chinese Han population. Methods: A total of 305 OCD patients and 435 healthy individuals were recruited for the study. OCD was diagnosed with the Forth Edition (DSM-IV) diagnostic criteria. After polymerase chain reaction of VNTR was used to evaluate the 40 bp VNTR polymorphism in DAT1, a case-control association analysis was performed by the χ 2 test. Results: The results showed that no association was found between OCD patients and controls for the genotype distribution (X 2 =0.743, P=0.690, df=2) as well as allelic (X 2 =0.172, P=0.678, OR=0.928, 95% Cl=0.885-1.224) distribution. Conclusions: Our data suggest that the 40 bp VNTR polymorphism in DAT1 may not be associated with susceptibility to OCD in the Chinese Han population studied. However, this result needed to be replicated from different populations

Unraveling E-Sports Team Tactical Recipes: A Configurational Perspective

Research on e-Sports teams is gaining momentum. Yet, despite a small but growing body of work that investigates how team formation affects performance in e-Sports, the bulk of extant literature has primarily accentuated the effects of individualized factors on team performance while neglecting the nature of e-sports competition as a causally complex phenomenon. Consistent with the configurational view, we advance a research model that seeks to unveil how different types of team composition in conjunction with teams’ tactical implementation together to impact eventual outcomes. Contextualizing extant literature on team composition to e-sports setting, we delineate team composition into four types (i.e., individual offensive, collective offensive, individual defensive, and collective defensive composition) and divide tactical implementation into three dimensions (i.e., specialization, isolation, and advancing speed). To empirically validate our hypothesized relationships, we employed fuzzy set Qualitative Comparative Analysis (fsQCA) to analyze data gathered for a popular Multiplayer Online Battle Arena (MOBA) game

Laboratory Study on the Effect of Fluid Pressurization Rate on Fracture Instability

Fluid injection-induced earthquakes have been a scientific and social issue of wide concern, and fluid pressurization rate may be an important inducement. Therefore, a series of stepwise and conventional injection-induced shear tests were carried out under different fluid pressurization rates and effective normal stresses. The results show that the magnitude of fluid pressure is the main factor controlling the initiation of fracture slipping. The contribution of fluid pressure heterogeneity and permeability evolution on the initiation of fracture slipping is different with the increase of fluid pressurization rate. When the fluid pressurization rate is small, permeability evolution plays a dominant role. On the contrary, the fluid pressure heterogeneity plays a dominant role. The increase of fluid pressurization rate may lead to the transition from creep slip mode to slow stick-slip mode. Under the laboratory scale, the fluid pressure heterogeneity causes the coulomb failure stress to increase by about one times than the predicted value at the initiation of fracture slipping, and the coulomb stress increment threshold of 1.65 MPa is disadvantageous to the fracture stability

How Do Information Content and Structure Influence Kinesthetic Learning? An Empirical Perspective from Recipe Analysis

The prevalence of Communities of Practice Platforms (CoPs) enables learners to acquire practical skillsets corresponding to their interests. However, the effects of online pedagogical content design on kinesthetic learning remains underexplored. To this end, we extend extant literature by advancing content richness and structure clarity as antecedents influencing kinesthetic learners’ digestibility of contents, culminating in differential kinesthetic learning outcomes. To validate our hypothesized relationships, we collected data from a leading Chinese recipe sharing platform. Whereas content richness is measured in terms of its readability, verb richness, and prototypicality, structure clarity is operationalized as block structure, block quantity and block regularity. Employing a machine learning model, we simulated and tested learners’ digestibility of a recipe containing both image and text. Plans for future research beyond this short paper are also discussed

Isolated Single-Atom Pd Sites in Intermetallic Nanostructures: High Catalytic Selectivity for Semihydrogenation of Alkynes

Improving the catalytic selectivity of Pd catalysts is of key importance for various industrial processes and remains a challenge so far. Given the unique properties of single-atom catalysts, isolating contiguous Pd atoms into a single-Pd site with another metal to form intermetallic structures is an effective way to endow Pd with high catalytic selectivity and to stabilize the single site with the intermetallic structures. Based on density functional theory modeling, we demonstrate that the (110) surface of <i>Pm</i>3̅<i>m</i> PdIn with single-atom Pd sites shows high selectivity for semihydrogenation of acetylene, whereas the (111) surface of <i>P</i>4/<i>mmm</i> Pd₃In with Pd trimer sites shows low selectivity. This idea has been further validated by experimental results that intermetallic PdIn nanocrystals mainly exposing the (110) surface exhibit much higher selectivity for acetylene hydrogenation than Pd₃In nanocrystals mainly exposing the (111) surface (92% vs 21% ethylene selectivity at 90 °C). This work provides insight for rational design of bimetallic metal catalysts with specific catalytic properties