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The return on assets of the investment universe tends to form a cluster structure. This study quantifies this strength of the clustering tendency as a single econometric measure, referred to as modularity. Through an empirical study of the US equity market, we demonstrate that the strength of the clustering tendency changes over time with market fluctuations. That is, normal markets tend to have a clear cluster structure (high modularity), while stressed markets tend to have a blurry cluster structure (low modularity). Modularity assesses the quality of an investment opportunity set in terms of potential diversification benefits. Modularity is an important pricing variable in the cross-sectional returns of US stocks. From 1992 to 2015, the average return of the stocks with the lowest sensitivity to modularity (low modularity beta) exceeds that of the stocks with the highest sensitivity (high modularity beta) by approximately 10.49% annually, adjusted for the Fama-French five-factor exposures. The inclusion of modularity as an asset pricing factor, therefore, expands the investment opportunity set for factor-based investors.
Min Kyu Sim; Shijie Deng; XiaoMing Huo. What can cluster analysis offer in investing? - Measuring structural changes in the investment universe. International Review of Economics & Finance 2020, 71, 299 -315.
AMA StyleMin Kyu Sim, Shijie Deng, XiaoMing Huo. What can cluster analysis offer in investing? - Measuring structural changes in the investment universe. International Review of Economics & Finance. 2020; 71 ():299-315.
Chicago/Turabian StyleMin Kyu Sim; Shijie Deng; XiaoMing Huo. 2020. "What can cluster analysis offer in investing? - Measuring structural changes in the investment universe." International Review of Economics & Finance 71, no. : 299-315.
Photovoltaics has gained popularity as a renewable energy source in recent decades. The main challenge for this energy source is the instability in the amount of generated energy owing to its strong dependency on the weather. Therefore, prediction of solar power generation is important for reliable and efficient operation. Popular data sources for predictors are largely divided into recent weather records and numerical weather predictions. This study proposes adequate deep neural networks that can utilise each data source or both. Focusing on a 24-hour-ahead prediction problem, the authors first design two deep neural networks for prediction: a deep feedforward network that uses the weather forecast data and a recurrent neural network that uses recent weather observations. Finally, a hybrid network, named PVHybNet, combines the both networks to enhance their prediction performance. In predicting the solar power generation by Yeongam power plant in South Korea, the final model yields an R-squared value of 92.7%. The results support the effectiveness of the combined network that utilises both weather forecasts and recent weather observations. The authors also demonstrate that the hybrid model outperforms several machine learning models.
Berny Carrera; Min‐Kyu Sim; Jae‐Yoon Jung. PVHybNet: a hybrid framework for predicting photovoltaic power generation using both weather forecast and observation data. IET Renewable Power Generation 2020, 14, 2192 -2201.
AMA StyleBerny Carrera, Min‐Kyu Sim, Jae‐Yoon Jung. PVHybNet: a hybrid framework for predicting photovoltaic power generation using both weather forecast and observation data. IET Renewable Power Generation. 2020; 14 (12):2192-2201.
Chicago/Turabian StyleBerny Carrera; Min‐Kyu Sim; Jae‐Yoon Jung. 2020. "PVHybNet: a hybrid framework for predicting photovoltaic power generation using both weather forecast and observation data." IET Renewable Power Generation 14, no. 12: 2192-2201.
Portfolio traders strive to identify dynamic portfolio allocation schemes that can allocate their total budgets efficiently through the investment horizon. This study proposes a novel portfolio trading strategy in which an intelligent agent is trained to identify an optimal trading action using deep Q-learning. We formulate a Markov decision process model for the portfolio trading process that adopts a discrete combinatorial action space and determines the trading direction at a prespecified trading size for each asset, thus ensuring practical applicability. Our novel portfolio trading strategy takes advantage of three features to outperform other strategies in real-world trading. First, a mapping function is devised to handle and transform any action that is initially proposed but found to be infeasible into a similar and valuable feasible action. Second, by overcoming the dimensionality problem, this study establishes agent and Q-network models to derive a multi-asset trading strategy in the predefined action space. Last, this study introduces a technique that can derive a well-fitted multi-asset trading strategy by designing an agent to simulate all feasible actions in each state. To validate our approach, we conduct backtesting for two representative portfolios and demonstrate superior results over the benchmark strategies.
Hyungjun Park; Min Kyu Sim; Dong Gu Choi. An intelligent financial portfolio trading strategy using deep Q-learning. Expert Systems with Applications 2020, 158, 113573 .
AMA StyleHyungjun Park, Min Kyu Sim, Dong Gu Choi. An intelligent financial portfolio trading strategy using deep Q-learning. Expert Systems with Applications. 2020; 158 ():113573.
Chicago/Turabian StyleHyungjun Park; Min Kyu Sim; Dong Gu Choi. 2020. "An intelligent financial portfolio trading strategy using deep Q-learning." Expert Systems with Applications 158, no. : 113573.
Purpose: This study builds a stochastic model of a discrete-time Markov chain (DTMC) that fits well with a dataset of professional playing records. Methods: The point-by-point dataset of Men’s single matches played in the Association of Tennis Professionals (ATP) tour from 2011 to 2015 is analyzed. A long-debated assumption on the iid-ness in the point winning probability of the server is statistically tested. A DTMC model is then developed to analyze the dataset further. Results: The statistical test results indicate that the identicality of point winning probabilities is not a valid assumption. For example, the server’s point winning probability from scores 40:0, 30:15, 15:30, and 0:40 are significantly different. On the other hand, the independence is a generally valid assumption except for 40:15 where who won the previous point influences the point winning probability. Game winning probabilities and the importance of each point in winning a game are analyzed using the DTMC model by court surfaces and player groups of the different levels of serve effectiveness. Conclusion: Extensive empirical validation concludes unsealed debates over the stochastic models for tennis. The presented results reveal interesting properties in professional tennis matches.
Min Kyu Sim; Dong Gu Choi. The Winning Probability of a Game and the Importance of Points in Tennis Matches. Research Quarterly for Exercise and Sport 2019, 91, 361 -372.
AMA StyleMin Kyu Sim, Dong Gu Choi. The Winning Probability of a Game and the Importance of Points in Tennis Matches. Research Quarterly for Exercise and Sport. 2019; 91 (3):361-372.
Chicago/Turabian StyleMin Kyu Sim; Dong Gu Choi. 2019. "The Winning Probability of a Game and the Importance of Points in Tennis Matches." Research Quarterly for Exercise and Sport 91, no. 3: 361-372.
Among the many components of material delivery operations, packaging is one of the foundations of secure and cost-efficient on-time delivery. Current environmental concerns have increased the popularity of returnable packaging over disposable packaging. This study considers a supply chain in the automotive industry where a single supplier adopts returnable packages for delivery operations to a single recipient. If a returnable package is not available, then an expendable package will be used as a more expensive alternative. Thus, an investment decision on the number of returnable packages must be made prior to launching a returnable packaging system. Using the actual data from an automotive supply chain, this study conducts simulated experiments, under the uncertainty of future demand and required lead time of reverse logistics, to identify the optimal quantity of returnable packages. Sensitivity analysis is then performed by varying the assumptions on operation duration, demand variability, and lead time variability. In general, the results indicate that a greater initial purchase of returnable packages is desirable for longer operation duration, higher demand variability, and higher lead time variability. However, if operation duration is short and the uncertainty is high, then there may be little benefit in using reusable packages. These results generalize the findings from previous studies. This approach and solution can enhance reliable and efficient supply chain operations in the automotive industry and may be applied to other industries where packaging is important and expensive.
Byungsoo Na; Min Kyu Sim; Won Ju Lee. An Optimal Purchase Decision of Reusable Packaging in the Automotive Industry. Sustainability 2019, 11, 6579 .
AMA StyleByungsoo Na, Min Kyu Sim, Won Ju Lee. An Optimal Purchase Decision of Reusable Packaging in the Automotive Industry. Sustainability. 2019; 11 (23):6579.
Chicago/Turabian StyleByungsoo Na; Min Kyu Sim; Won Ju Lee. 2019. "An Optimal Purchase Decision of Reusable Packaging in the Automotive Industry." Sustainability 11, no. 23: 6579.
Many invisible forms of market liquidity exist since many market participants often prefer to hide their trade intentions. Among others, hidden limit order placements are allowed in many public exchanges and generate hidden liquidity. As a result, only part of market liquidity is visible, leading to markets with incomplete information. This study investigates how hidden liquidity alters the econophysical dynamics of limit order books and price impact functions. Accordingly, this study proposes an estimation method of level-I hidden liquidity (hidden waiting orders at best prices) using only publicly available data. Though direct validation with actual hidden liquidity was not possible, this study demonstrates that estimated hidden liquidity provides two empirical benefits. First, estimated hidden liquidity enhances an existing price-impact function and achieves an R-squared value of 70.8% on average. Second, estimated hidden liquidity improves order-book pressure models that forecast the future direction of price change. Using the central notion of market liquidity, this study investigates the different subjects of high-frequency data in an integrated manner, such as the dynamics of execution, price-impact function, and order-book pressure.
Min Kyu Sim; Shijie Deng. Estimation of level-I hidden liquidity using the dynamics of limit order-book. Physica A: Statistical Mechanics and its Applications 2019, 540, 122703 .
AMA StyleMin Kyu Sim, Shijie Deng. Estimation of level-I hidden liquidity using the dynamics of limit order-book. Physica A: Statistical Mechanics and its Applications. 2019; 540 ():122703.
Chicago/Turabian StyleMin Kyu Sim; Shijie Deng. 2019. "Estimation of level-I hidden liquidity using the dynamics of limit order-book." Physica A: Statistical Mechanics and its Applications 540, no. : 122703.