Assessment of street-level greenness and its association with housing prices in a metropolitan area.

Rapid global urbanization has made environmental amenities scarce despite their considerable advantages, ranging from aesthetics to health benefits. Street greenness is a key urban environmental amenity. This study developed a green index as an objective measure of greenness using street view images and assessed its predictive power along with that of other environmental amenities for metropolitan housing prices. Spatial interpolation was used to transform point data into areal data, enabling effective analysis of a dataset covering an entire metropolis. A series of hedonic models revealed that (1) street greenness is significantly and negatively associated with housing prices, (2) a traditional greenness indicator and the green index provide complementary information, indicating that they could be used for different purposes, and (3) environmental amenities, in general, demonstrated significant relationships with housing prices. Our analysis strategy including spatial interpolation can be widely employed for studies using different types of data. The findings demonstrating a complementary relationship between our two greenness indicators provide valuable insights for policymakers and urban planners to improve street-level greenness and green accessibility. Considering the significance of environmental amenities, this study provides practical approaches for executing sustainable and healthy city development.

Scaling behavior and text cohesion in Korean texts.

This study examines whether different types of texts, particularly in Korean, can be distinguished by the scaling exponent and degree of text cohesion. We use the controlled growth process model to incorporate the interaction effect into a power-law distribution and estimate the implied parameter explaining the degree of text cohesiveness in a word distribution. We find that the word distributions of Korean languages differ from English regarding the range of scaling exponents. Additionally, different types of Korean texts display similar scaling exponents regardless of their genre. However, the interaction effect is higher for expert reports than for the benchmark novels. The findings suggest a valid framework for explaining the scaling phenomena of word distribution based on microscale interactions. It also suggests that a viable method exists for inferring text genres based on text cohesion.

Market efficiency of cryptocurrency: evidence from the Bitcoin market.

This study examines whether the Bitcoin market satisfies the (weak-form) efficient market hypothesis using a quantum harmonic oscillator, which provides the state-specific probability density functions that capture the superimposed Gaussian and non-Gaussian states of the log return distribution. Contrasting the mixed evidence from a variance ratio test, the high probability allocated to the ground state suggests a near-efficient Bitcoin market. Findings imply that as Bitcoin evolves into an efficient market, speculators might encounter difficulty in exploiting profitable trading strategies. Furthermore, when policymakers initiate tight regulations to control the market, they should closely monitor market efficiency as an index of price distortion.

Information flow among stocks, bonds, and convertible bonds.

This study examines the information flow between convertible bonds (CBs) and other investment assets, such as stocks and bonds. In particular, we employ transfer entropy (TE) as a proxy for the causal effect between the two assets considering that one of the most widely used methods, Granger causality, requires strict assumptions. When adopting TE, we find that asymmetric information flow arising between assets depends on macroeconomic phases. The stock and bond markets affected the CB market prior to and during the global financial crisis, respectively. In the post-crisis period, we find no meaningful information exchange between CBs and other investment assets concerning their return series. However, we observe a significant cause-effect relationship between CBs and stocks in the rise-fall patterns of their price series. The findings suggest that the appearance of one-directional information flow depends on macroeconomic conditions and the level of data, for example, return series or price fluctuations. Accordingly, investors could exploit this pattern predictability in their portfolio management. In addition, policymakers must closely monitor the information flow among the three markets. When any two markets exchange information in a state of strong market integration, unbalanced regulation between them could lead to market distortions and regulatory arbitrage.

Hedonic dataset of the metropolitan housing market - Cases in South Korea.

This article presents a database cleaned and generated for analyzing the economic impact of subway network on housing prices in metropolitan areas. The provision of transit networks and accompanying improvement in accessibility induce various impact and we focused on the economic impact reflected in housing prices. Although our emphasis is on transit accessibility and housing prices, the dataset presented is applicable to other analyses. It includes a wide range of variables closely related to housing prices such as housing properties, local demographic characteristics, local amenities, and seasonal control variables. Various distance variables constructed in a geographic information system environment using public data are useful for exploring the environmental impact on housing prices. These data cover four metropolitan areas-Busan, Daegu, Daejeon, and Gwangju-and provide accurate information on their metropolitan structures distinct from the capital city, Greater Seoul. An empirical analysis performed by Ahn et al. [1] is based on this dataset.

Dynamics of analyst forecasts and emergence of complexity: Role of information disparity.

We report complex phenomena arising among financial analysts, who gather information and generate investment advice, and elucidate them with the help of a theoretical model. Understanding how analysts form their forecasts is important in better understanding the financial market. Carrying out big-data analysis of the analyst forecast data from I/B/E/S for nearly thirty years, we find skew distributions as evidence for emergence of complexity, and show how information asymmetry or disparity affects financial analysts' forming their forecasts. Here regulations, information dissemination throughout a fiscal year, and interactions among financial analysts are regarded as the proxy for a lower level of information disparity. It is found that financial analysts with better access to information display contrasting behaviors: a few analysts become bolder and issue forecasts independent of other forecasts while the majority of analysts issue more accurate forecasts and flock to each other. Main body of our sample of optimistic forecasts fits a log-normal distribution, with the tail displaying a power law. Based on the Yule process, we propose a model for the dynamics of issuing forecasts, incorporating interactions between analysts. Explaining nicely empirical data on analyst forecasts, this provides an appealing instance of understanding social phenomena in the perspective of complex systems.