Lim, B. G., Dayta, D., Tiu, B. R., Tan, R. R., Garces, L. P. D., & Ikeda, K. (2026). Dynamic Factor Analysis of Price Movements in the Philippine Stock Exchange. Financial Innovation. (In Press). The intricate dynamics of the stock markets have led to extensive research on models that can effectively explain their inherent complexities. Toward this goal, the paper leverages econometrics literature and explores the dynamic factor model as an interpretable model possessing sufficient predictive capabilities for capturing essential market phenomena. In particular, while the model has been largely used for predictive applications, the paper focuses on analyzing the extracted loadings and common factors as an alternative framework for understanding the dynamics of stock price movements. When applied to the Philippine Stock Exchange using Kalman methods and maximum likelihood estimation and subsequently validated against the capital asset pricing model, the results reveal novel insights into traditional market theories. Notably, a one-factor model extracts a common factor representing systematic or market dynamics similar to the composite index while a two-factor model extracts common factors representing market trend and volatility. Furthermore, an application on nowcasting the Philippine gross domestic product growth rates highlights the potential of the extracted common factors as viable real-time market indicators, providing over 34% decrease in out-of-sample prediction error. Overall, the results underscore the value of dynamic factor analysis in gaining a deeper understanding of price movement dynamics in the market.
Lim, B. G., Tan, R. R., de Jesus, R., Garciano, L. E., Garciano, A., & Ikeda, K. (2025). Path survival reliabilities as measures of reliability for lifeline utility networks. Journal of Combinatorial Optimization, 49(57). Lifeline utility networks have been studied extensively within the domain of network reliability due to the prevalence of natural hazards. The reliability of these networks is typically investigated through graphs that retain their structural characteristics. This paper introduces novel connectivity-based reliability measures tailored for stochastic graphs with designated source vertices and failure-probability-weighted edges. In particular, the per-vertex path survival reliability quantifies the average survival likelihood of single-source paths from a vertex to any source. A consolidated per-graph reliability measure is also presented, incorporating graph density and the shortest distance to a source as regulating elements for network comparison. To highlight the advantages of the proposed reliability measures, a theoretical discussion of their key properties is presented, along with a comparison against standard reliability measurements. The proposal is further accompanied by an efficient calculation procedure utilizing the zero-suppressed binary decision diagram, constructed through the frontier-based search, to compactly represent all single-source paths. Finally, the path survival reliabilities are calculated for a set of real-world networks and demonstrated to provide practical insights.
Lim, B. G., Lim, G. B. S., Tan, R. R., & Ikeda, K. (2025). Contextualized Messages Boost Graph Representations. Transactions on Machine Learning Research. (Reproducibility Certification). Graph neural networks (GNNs) have gained significant attention in recent years for their ability to process data that may be represented as graphs. This has prompted several studies to explore their representational capability based on the graph isomorphism task. Notably, these works inherently assume a countable node feature representation, potentially limiting their applicability. Interestingly, only a few study GNNs with uncountable node feature representation. In the paper, a new perspective on the representational capability of GNNs is investigated across all levels—node-level, neighborhood-level, and graph-level—when the space of node feature representation is uncountable. Specifically, the injective and metric requirements of previous works are softly relaxed by employing a pseudometric distance on the space of input to create a soft-injective function such that distinct inputs may produce similar outputs if and only if the pseudometric deems the inputs to be sufficiently similar on some representation. As a consequence, a simple and computationally efficient soft-isomorphic relational graph convolution network (SIR-GCN) that emphasizes the contextualized transformation of neighborhood feature representations via anisotropic and dynamic message functions is proposed. Furthermore, a mathematical discussion on the relationship between SIR-GCN and key GNNs in literature is laid out to put the contribution into context, establishing SIR-GCN as a generalization of classical GNN methodologies. To close, experiments on synthetic and benchmark datasets demonstrate the relative superiority of SIR-GCN, outperforming comparable models in node and graph property prediction tasks.
Lim, B., Saavedra, M., Tan, R., Ikeda, K., & Yu, W. (2025). Geo-distributed multi-cloud data centre storage tiering and selection with zero-suppressed binary decision diagrams. International Journal of Cloud Computing. (In Press). The exponential growth of data in recent years prompted cloud providers to introduce diverse geo-distributed storage solutions for various needs. The vast amount of storage options, however, presents organisations with a challenge in determining the ideal data placement configuration. The study introduces a novel optimisation algorithm utilising the zero-suppressed binary decision diagram to select the optimal data centre, storage tiers, and cloud provider. The algorithm takes on a holistic approach that considers cost, latency, and high availability, applicable to both geo-distributed on-premise environments and public cloud providers. Furthermore, the proposed methodology leverages the recursive structure of the zero-suppressed binary decision diagram, allowing for the enumeration and ranking of all valid configurations based on total cost. Overall, the study offers flexibility for organisations in addressing specific priorities for cloud storage solutions by providing alternative near-optimal configurations.
Lim, B. G., Tan, R. R., Kawahara, J., Minato, S.-I., & Ikeda, K. (2024). A Recursive Framework for Evaluating Moments Using Zero-Suppressed Binary Decision Diagrams. IEEE Access, 12, 91886–91895. The zero-suppressed binary decision diagram (ZDD) is a compact data structure widely used for the efficient representation of families of sparse subsets. Its inherent recursive structure also facilitates easy diagram manipulation and family operations. Practical applications generally fall under discrete optimization, such as combinatorial problems and graph theory. Given its utility, summarizing the subsets represented in the diagram using key metrics is of great value as this provides valuable insights into the characteristics of the family. The paper proposes a recursive algorithm to extract information on moments from families represented as a ZDD. Given a value for every element in the universe, the value of a subset is first formulated as the sum of the values of its elements. The moments of a family are then calculated as the mean of the exponentiated subset values, akin to the method of moments. Leveraging the structure of the ZDD, the proposed algorithm recursively traverses a given diagram for efficient moments evaluation via multinomial expansion. Its utility is then demonstrated with three classical problems—power sets, the knapsack problem, and paths in graphs—offering orders of magnitude increase in computational efficiency relative to conventional method. Overall, the proposed algorithm enhances the functionality of the ZDD by introducing an efficient family operation to uncover the distribution of subset values in a represented family.
Yu, Z., Guinto, M. C. S. G., Lim, B. G. S., Tan, R. R. P., Yoshimoto, J., Ikeda, K., Ohta, Y., & Ohta, J. (2023). Engineering a data processing pipeline for an ultra-lightweight lensless fluorescence imaging device with neuronal-cluster resolution. Artificial Life and Robotics, 28, 483–495. In working toward the goal of uncovering the inner workings of the brain, various imaging techniques have been the subject of research. Among the prominent technologies are devices that are based on the ability of transgenic animals to signal neuronal activity through fluorescent indicators. This paper investigates the utility of an original ultra-lightweight needle-type device in fluorescence neuroimaging. A generalizable data processing pipeline is proposed to compensate for the reduced image resolution of the lensless device. In particular, a modular solution centered on baseline-induced noise reduction and principal component analysis is designed as a stand-in for physical lenses in the aggregation and quasi-reconstruction of neuronal activity. Data-driven evidence backing the identification of regions of interest is then demonstrated, establishing the relative superiority of the method over neuroscience conventions within comparable contexts.
Lim, B. G., Liu, J., Ong, H. J., Chan, J. A., Tan, R. R., King, I., & Ikeda, K. (2025). FinSIR: Financial SIR-GCN for Market-Aware Stock Recommendation. 2025 International Joint Conference on Neural Networks (IJCNN). (In Press). Existing works on stock price prediction have largely treated stocks in a market independently of one another. Nevertheless, recent advances in graph neural networks (GNNs) have enabled the efficient processing of diverse stock relations. This paper introduces the Financial SIR-GCN (FinSIR) for market-aware stock price prediction and recommendation. By modeling stock markets as spatio-temporal graphs, FinSIR addresses the key architectural limitation of existing graph-based models. Notably, the proposed model integrates the soft-isomorphic relational graph convolution network (SIR-GCN) with the “sandwich” structure employed in GNN for time series analysis (GNN4TS) to jointly process the two key dimensions of stock market graphs and to contextualize hidden states with both spatial and temporal stock relations. Backtesting results on the New York Stock Exchange (NYSE) and the National Association of Securities Dealers Automatic Quotation System (NASDAQ) reveal FinSIR consistently achieving up to 65% and 36% larger cumulative investment returns, respectively, compared to baseline models. Additionally, an ablation study further highlights the contribution of each FinSIR module in providing better investment recommendations. Overall, the paper incorporates recent advances in GNN and GNN4TS to provide a new perspective on graph-based solutions for improved stock price prediction and recommendation.
Lim, B. G., Ong, H. J., Tan, R. R., & Ikeda, K. (2024). Dynamic Principal Component Analysis for the Construction of High-Frequency Economic Indicators. Proceedings of the 4th International Conference on Advances in Computational Science and Engineering, 645–663. Recent progress in data analysis and machine learning has enabled the efficient processing of large data; however, the public sector has yet to fully adopt these advancements. The study investigates the application of dynamic principal component analysis in offering real-time insights into various facets of an economy, potentially aiding in the informed decision-making of policymakers. In brief, dynamic principal component analysis generates dynamic principal components representing latent factors that account for the autocovariance in time series data. In examining daily data from the Philippine stock exchange, Philippine peso exchange rates, and Philippine peso to United States dollar forward rates, results demonstrate the effectiveness of the first three dynamic principal components as high-frequency indicators for business and investment conditions, economic performance, and economic outlook, respectively. Moreover, an application of the isolation forest anomaly detection algorithm validates the sensitivity of the constructed indicators to systematic economic shocks, which identified events such as the taper tantrum of 2013 and the 2020 lockdown due to the novel coronavirus pandemic, among others. Overall, the practical applicability of the proposed methodology suggests potential extensions incorporating nontraditional data sources for more comprehensive economic indicators.
Tan, R. R. P., Asuncion, A. E. C., Lim, B. G. S., Soos, M., & Ikeda, K. (2023). The Pancake Graph of Order 10 Is 4-Colorable. Proceedings of the 2023 6th International Conference on Mathematics and Statistics, 1–6. The pancake graph has served as a model for real-world networks due to its unique recursive and symmetric properties. Defined as the Cayley graph on the symmetric group of order n generated by prefix reversals, the n-pancake graph exhibits a rapid increase in the number of vertices and edges with respect to order n. While there are considerable graph-theoretic results on the graph, findings on chromatic properties for larger n are limited. In this paper, the 10-pancake graph is established to be 4-colorable through an efficient Boolean-satisfiability-based stochastic local search framework for vertex coloring. Building on the aforementioned, a new linear bound for the chromatic number of the pancake graph is put forward. In addition, the range of possible bounds that may be obtained from the same technique is determined.
