Fair Value Accounting And Financial Stability – Based On The Adoption

Fair value accounting refers to the accounting method which an asset or liability is estimated based on the current market price, so called fair value. Under the fair value accounting, it is more difficult for managers to hide bad information, because the value of an asset or liabilities is re-estimated periodically to reflect the changes in fair value in the market. In this case, firms’ financial stability will be increased. On the other hand, fair value accounting can intensity the volatility of the numbers in the financial statement, which leads to decreases the financial stability. This papers empirically examines the effect of the fair value accounting on the financial stability based on the IFRS adoption in Korea. Using the non-financial firms listed in KOSPI and KOSDAQ from 2000 to 2013, we find that the expansion of fair value accounting increases financial stability. The results support the argument that fair value accounting prohibits managers from hiding bad information, rather it enforces the disclosure of value-relevant information to the investors. The results are consistent with a battery of robustness checks. Thus, the overall results show that the expansion of fair value accounting increase financial stability.

How attractive is it to use the internet while commuting? A work-attitude-based segmentation of Northern California commuters

We explore how the hypothetical provision of Wi-Fi on transit affects the willingness of non-transit commuters (solo drivers, ridesharers, and cyclists) in Northern California (N = 1402) to switch from their current mode to public transit with internet access (PTWIA). Beyond the prima facie interpretation of our survey results, they shed light on the heterogeneously-perceived utility of hands-free travel more generally, and, as such, speak to an automated-vehicle future. We develop latent class binary choice models of the likelihood of switching to PTWIA, stratified by current commute mode. Each model identifies two latent classes, based largely on work-related attitudes: solo drivers divide into the work-oriented (22%) and pleasure-oriented (78%); ridesharers into the over-traveled monotaskers (77%) and multitasking commuters (23%); and cyclists into work-oriented (28%) and non-work-oriented (72%). Thus, non-work-oriented commuters are a sizable majority of non-transit users, and also have a much lower weighted probability of switching to PTWIA (0.17, on average) compared to the work-oriented commuters (0.48). In sum, work-friendly hands-free travel can be an appealing alternative to those who are oriented toward working (and especially on the commute), but (1) not for all of them, and (2) such people only constitute about a quarter of the non-transit commuters (in Northern California). These results provide empirical insight into the extent to which the productive use of travel time made possible by automated vehicles will be exploited by future commuters

Investigating commuters’ satisfaction with public transit: A latent class modeling approach

This study explores the factors associated with commute satisfaction of public transit users in the Seoul Metropolitan Area, identifying the critical role of commuters’ attitudes in influencing their commute satisfaction. Using a latent class ordered probit model, the study identifies two latent segments—optimistic multitasker (Class 1) and dissatisfied commuter (Class 2)—assuming that the association between determinants and satisfaction with commute could depend highly on attitudes. A majority of transit commuters (71.8%) belong to Class 1, meaning that most Seoul commuters are satisfied with their commute and use their travel time productively. Moreover, in comparing the two-class profiles, Class 1 commuters tend to be more satisfied with their lifestyle, perceive themselves as healthier, have a positive personality, and are more satisfied with their commute. Such empirical evidence demonstrates the existence of taste heterogeneity in determining commute satisfaction and the role of attitudes in that mechanism

Is the Relationship between Transportation and Communications Industries Complementary or Substitutional? An Asian Countries-Based Empirical Analysis Using Input-Output Accounts

The relationship between transportation and communications has been discussed throughout the past decades. This study also investigates that relationship to determine whether they are complementary or substitutive in terms of the industrial perspective, focusing mainly on six Asian countries (China, Japan, India, Korea, Indonesia, and Taiwan). National input-output (I-O) tables from the World Input-Output Database (WIOD) were used to construct research dataset. Each activity in the table was examined and fell into either transportation or communications category when they are related to those categories, thereby establishing six categories: Transportation manufacturing (TM), transportation utilities (TU), communications manufacturing (CM), communications utilities (CU), all transportation (AT), and all communications (AC). To examine the interrelationship between two sectors, direct and total coefficients were calculated for four benchmark years (2000, 2005, 2010, and 2014), then Spearman correlation analysis was conducted using those two coefficient matrices after weighting each coefficient using the economic contribution-based weight (ECBW). As a result, we confirm the predominant complementary relationship between two industries. Most Asian countries present consistent, dominant complementarity in both direct and total analysis. Although there are mixed total effects in Japan and Taiwan, the overall pattern demonstrates remarkable positive relationships. In analyzing the same effects in western countries, we also find the same straightforward positive association between two sectors, mostly in France, the US, and the UK. We believe that our findings can contribute to the literature by providing compelling evidence of the overall trend of a complementary relationship between two industries

Identifying the Causal Relationship between Travel and Activity Times: A Structural Equation Modeling Approach

This study aims to identify the causal relationship between travel and activity times using the dataset collected from the 2019 Time Use Survey in Korea. As a statistical solution, a structural equation model (SEM) was developed. A total number of 31,177 and 20,817 cases were used in estimating the weekday and weekend models, respectively. Three types of activities (subsistence, maintenance, and leisure), 13 socio-demographic variables, and a newly proposed latent variable (vitality) were incorporated in the final model. Results showed that (1) the magnitude of indirect effects were mostly greater than that of direct effects, (2) all types of activities affected travel time regardless of what the travel purpose was, (3) travel can be treated as both a utility and disutility, and (4) personal status could affect the travel time ratio. It indicates the significance of indirect effects on travel time, thereby suggesting a broad perspective of activities when establishing a transportation policy in practical areas. It also implies that unobserved latent elements could play a meaningful role in identifying travel time-related characteristics. Lastly, we believe that this study contributes to literature by clarifying a new perspective on the lively debated issue discussing whether travel time is wasted or productive

Does the Inclusion of Spatio-Temporal Features Improve Bus Travel Time Predictions? A Deep Learning-Based Modelling Approach

With the abundance of public transportation in highly urbanized areas, it is common for passengers to make inefficient or flawed transport decisions due to a lack of information. The exact arrival time of a bus is an example of such information that can aid passengers in making better decisions. The purpose of this study is to provide a method for predicting path-based bus travel time, thereby assisting accurate bus arrival and departure time predictions at each bus stop. Specifically, we develop a Geo-conv Long Short-term Memory (LSTM) model that (1) extracts subsequent spatial features through a 1D Convolution Neural Network (CNN) for the entire bus travel sequence and (2) captures the temporal dependencies between subsequences through the LSTM network. Additionally, this study utilizes additional variables that affect two components of bus travel time (dwelling time and transit time) to precisely predict travel time. The constructed model is then evaluated by the practical application to two bus lines operating in Seoul, Korea. The results show that our model outperforms three other baseline models. Two bus lines with different types of operation show different model performance patterns that are dependent on travel distance. Interestingly, we find that the variable related to the link of the stop location appears to play an important role in predicting bus travel time. We believe that these novel findings will contribute to the literature on transportation and, in particular, on deep learning-based travel time prediction

Identifying the Causal Relationship between Travel and Activity Times: A Structural Equation Modeling Approach

This study aims to identify the causal relationship between travel and activity times using the dataset collected from the 2019 Time Use Survey in Korea. As a statistical solution, a structural equation model (SEM) was developed. A total number of 31,177 and 20,817 cases were used in estimating the weekday and weekend models, respectively. Three types of activities (subsistence, maintenance, and leisure), 13 socio-demographic variables, and a newly proposed latent variable (vitality) were incorporated in the final model. Results showed that (1) the magnitude of indirect effects were mostly greater than that of direct effects, (2) all types of activities affected travel time regardless of what the travel purpose was, (3) travel can be treated as both a utility and disutility, and (4) personal status could affect the travel time ratio. It indicates the significance of indirect effects on travel time, thereby suggesting a broad perspective of activities when establishing a transportation policy in practical areas. It also implies that unobserved latent elements could play a meaningful role in identifying travel time-related characteristics. Lastly, we believe that this study contributes to literature by clarifying a new perspective on the lively debated issue discussing whether travel time is wasted or productive

Does the Inclusion of Spatio-Temporal Features Improve Bus Travel Time Predictions? A Deep Learning-Based Modelling Approach

With the abundance of public transportation in highly urbanized areas, it is common for passengers to make inefficient or flawed transport decisions due to a lack of information. The exact arrival time of a bus is an example of such information that can aid passengers in making better decisions. The purpose of this study is to provide a method for predicting path-based bus travel time, thereby assisting accurate bus arrival and departure time predictions at each bus stop. Specifically, we develop a Geo-conv Long Short-term Memory (LSTM) model that (1) extracts subsequent spatial features through a 1D Convolution Neural Network (CNN) for the entire bus travel sequence and (2) captures the temporal dependencies between subsequences through the LSTM network. Additionally, this study utilizes additional variables that affect two components of bus travel time (dwelling time and transit time) to precisely predict travel time. The constructed model is then evaluated by the practical application to two bus lines operating in Seoul, Korea. The results show that our model outperforms three other baseline models. Two bus lines with different types of operation show different model performance patterns that are dependent on travel distance. Interestingly, we find that the variable related to the link of the stop location appears to play an important role in predicting bus travel time. We believe that these novel findings will contribute to the literature on transportation and, in particular, on deep learning-based travel time prediction

Heat conduction in graphite-nanoplatelet-reinforced polymer nanocomposites

Heat transport in polymer nanocomposites reinforced with graphite nanoplatelets (GNPs) is studied using high-precision thermal conductivity measurements. The resistance to heat conduction across interfaces between GNPs and the polymer matrix has a strong effect on energy transport in the nanocomposites. The thermal conductivity is observed to increase when GNPs are pretreated with nitric acid to improve interfacial bonding. The improvement in the thermal conductivity, however, is much smaller than the corresponding improvement in mechanical properties. The thermal interface resistance extracted from the present thermal conductivity data is comparable to that obtained from the previously reported data on carbon nanotube suspensions. (c)2006 American Institute of Physics