류두진 교수

Blockchain technology has been recommended for the sustainability in the manufacturing industry, owing to its benefits in terms of real-time transparency and cost savings. To verify this, we first examine how firms can employ distributed ledger technology by adopting blockchain technology to achieve real-time transparency and cost savings. We also review the current blockchain technology applications in the financial industry and supply chains to explain this technology’s mechanisms for enabling

Abstract This study examines if informed trading is present in the index option market by analyzing the KOSPI 200 options, the most actively traded derivative product in the world. The spread decomposition model developed by Madhavan, Richardson, and Roomans (1997) is utilized and the adverse‐selection cost component of the spread estimated by the model is then used as a proxy for the degree of informed trading. We find that adverse‐selection costs constitute a nontrivial portion of the transact

This study examines the market for green bonds, which have been in the spotlight as an eco-friendly investment product. We analyze the volatility dynamics and spillovers between the equity and green bond markets. As the return dynamics of financial products typically exhibit asymmetric volatility, we check whether green bonds also share this property. Our analyses confirm that although green bonds do exhibit the asymmetric volatility phenomenon, their volatility, unlike that of equity, is also s

This article examines how investor sentiment and trading behaviour affect asset returns. By analysing the unique stock trading dataset of the Korean market, we find that high investor sentiment induces higher stock market returns. We also find that institutional (individual) trades are positively (negatively) associated with stock returns, suggesting the information superiority (inferiority) of institutional (individual) investors. Investor sentiment generally plays a more important role in expl

Abstract This study examines and compares the information content of futures and options trades by analyzing the transaction dataset of derivatives underlying the KOSPI 200 index. This dataset contains detailed information about investor types and trade directions. Previous market microstructure studies of Korea's index derivatives market (i.e., KOSPI 200 futures and options market) may contain model biases and microstructure errors because they depend on structural models and/or they focus on i

While the recent COVID-19 pandemic has accelerated environmental, social, and governance (ESG) investing, there remains a growing sense of uncertainty in this sector. This study investigates the impacts of ESG-related information disclosures on firm value and tests the relationship between ESG scores and firm value. Using a Chinese dataset, we run a fixed-effects panel regression model to assess the impact of ESG performance on firm value in terms of enterprise multiples while controlling for co

This study examines the intraday formation process of transaction prices and bid–ask spreads in the KOSPI 200 futures market. By extending the structural model of Madhavan, A., Richardson, M., and Roomans, M. (1997), we develop a unique cross-market model that can decompose spread components and explain intraday price formation for the futures market by using the order flow information from the KOSPI 200 options market, which is a market that is closely related to the futures market as well as c

Abstract In the present study, we examine two important issues related to the information content of a trade in option markets: (i) whether trade size is related to information content; and (ii) whether buy and sell transactions carry different information content. Our analysis is based on comprehensive market microstructure data on the KOSPI 200 options, the single most actively traded derivative securities in the world. We use two structural models modified from the Madhavan et al. [ Review of

This study investigates the effects of investor sentiment on asset returns with respect to firm characteristics. By analysing a unique stock trading dataset of the Korean Stock Market that contains rich information on investor types and sentiment, we confirm that high investor sentiment induces higher stock market returns. The positive association between investor sentiment and stock returns is highly significant after controlling for trading behaviours, other risk factors and firm characteristi

Corporate default predictions play an essential role in each sector of the economy, as highlighted by the global financial crisis and the increase in credit risk. This study reviews the corporate default prediction literature from the perspectives of financial engineering and machine learning. We define three generations of statistical models: discriminant analyses, binary response models, and hazard models. In addition, we introduce three representative machine learning methodologies: support v