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Mr. Bumho Son
Seoul National University

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0 Asset Pricing
0 Deep Learning
0 Machine Learning
0 Scalability
0 Blockchain

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Journal article
Published: 05 April 2021 in Finance Research Letters
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We propose a latent multi-factor asset pricing model that estimates risk exposure based on firm characteristics and connectivity between assets. To handle connected high-dimensional characteristics, we adopted a graph convolutional network while estimating the connectivity between assets from the correlation of asset returns. Unlike recent literature involving the deep-learning-based latent factor model, we propose a forward stagewise additive factor modeling architecture that constructs latent factors sequentially to maintain the previous stage’s factors. Our empirical results on individual U.S. equities show that the proposed graph factor model outperforms other benchmark models in terms of explanatory power and the Sharpe ratio of the factor tangency portfolio.

ACS Style

BumHo Son; Jaewook Lee. Graph-based multi-factor asset pricing model. Finance Research Letters 2021, 102032 .

AMA Style

BumHo Son, Jaewook Lee. Graph-based multi-factor asset pricing model. Finance Research Letters. 2021; ():102032.

Chicago/Turabian Style

BumHo Son; Jaewook Lee. 2021. "Graph-based multi-factor asset pricing model." Finance Research Letters , no. : 102032.

Journal article
Published: 18 February 2020 in Sustainability
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The Internet of Things (IoT) suffers from various security vulnerabilities. The use of blockchain technology can help resolve these vulnerabilities, but some practical problems in terms of scalability continue to hinder the adaption of blockchain for application in the IoT. The directed acyclic graph (DAG)-based Tangle model proposed by the IOTA Foundation aims to avoid transaction fees by employing a different protocol from that used in the blockchain. This model uses the Markov chain Monte Carlo (MCMC) algorithm to update a distributed ledger. However, concerns about centralization by the coordinator nodes remain. Additionally, the economic incentive to choose the algorithm is insufficient. The present study proposes a light and efficient distributed ledger update algorithm that regards only the subtangle of each step by considering the Bayesian inference. Experimental results have confirmed that the performance of the proposed methodology is similar to that of the existing methodology, and the proposed methodology enables a faster computation time. It also provides the same resistance to possible attacks, and for the same reasons, as does the MCMC algorithm.

ACS Style

BumHo Son; Jaewook Lee; Huisu Jang. A Scalable IoT Protocol via an Efficient DAG-Based Distributed Ledger Consensus. Sustainability 2020, 12, 1529 .

AMA Style

BumHo Son, Jaewook Lee, Huisu Jang. A Scalable IoT Protocol via an Efficient DAG-Based Distributed Ledger Consensus. Sustainability. 2020; 12 (4):1529.

Chicago/Turabian Style

BumHo Son; Jaewook Lee; Huisu Jang. 2020. "A Scalable IoT Protocol via an Efficient DAG-Based Distributed Ledger Consensus." Sustainability 12, no. 4: 1529.

Journal article
Published: 25 June 2019 in Sustainability
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Developing a robust and sustainable system is an important problem in which deep learning models are used in real-world applications. Ensemble methods combine diverse models to improve performance and achieve robustness. The analysis of time series data requires dealing with continuously incoming instances; however, most ensemble models suffer when adapting to a change in data distribution. Therefore, we propose an on-line ensemble deep learning algorithm that aggregates deep learning models and adjusts the ensemble weight based on loss value in this study. We theoretically demonstrate that the ensemble weight converges to the limiting distribution, and, thus, minimizes the average total loss from a new regret measure based on adversarial assumption. We also present an overall framework that can be applied to analyze time series. In the experiments, we focused on the on-line phase, in which the ensemble models predict the binary class for the simulated data and the financial and non-financial real data. The proposed method outperformed other ensemble approaches. Moreover, our method was not only robust to the intentional attacks but also sustainable in data distribution changes. In the future, our algorithm can be extended to regression and multiclass classification problems.

ACS Style

Hyungjin Ko; Jaewook Lee; Junyoung Byun; BumHo Son; Saerom Park. Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis. Sustainability 2019, 11, 3489 .

AMA Style

Hyungjin Ko, Jaewook Lee, Junyoung Byun, BumHo Son, Saerom Park. Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis. Sustainability. 2019; 11 (12):3489.

Chicago/Turabian Style

Hyungjin Ko; Jaewook Lee; Junyoung Byun; BumHo Son; Saerom Park. 2019. "Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis." Sustainability 11, no. 12: 3489.