|
on Financial Markets |
| By: | Situngkir, Hokky; Muhammad Aldy, Hasan |
| Abstract: | Thecollectivemovementofstockpricesharborscomplexinterdependenciesthatareconventionally simplified only through a linear lens. This paper explores structural network representations in the Indonesian capital market by testing the limits of Pearson correlation and Mutual Information (MI) in unveiling the spectral dynamics of the market. Across 2, 328 rolling observation windows from 2015 to 2025, we examine 24 methodological configurations that combine three dependency estimators (Pearson, MI adaptive binning, and MI-kNN), two graph filtering schemes (Minimum Spanning Tree/MST and Planar Maximally Filtered Graph/PMFG), and four community decoders. The empirical results unveil a fundamental reality: topological richness does not always resonate with sectoral classification precision. The Pearson, MST, and Infomap configuration is shown to remain the most robust foundation for recovering conventional sectoral taxonomy. Nevertheless, when deeper observation demands the exposition of local structures and the weave of heterogeneous communities, the architectural relaxation through PMFG demonstrates its superiority. In the realm of residual information detection, MI adaptive binning appears far more proportional than kNN; histogram-based regularization successfully tames empirical noise without sweeping away traces of non-linear dependency. Ultimately, the synergy of MI and PMFG is not positioned to dethrone the dominance of linear correlation, but ratherto provide an essenti alanalytical lens for excavating hidden economic sub-structures—such as the cohesion of commodity regimes—that have long transcended the rigid boundaries of the market’s formal sectors. |
| Keywords: | Econophysics; stock prices; Minimum Spanning Tree (MST); Planar Maximally Filtered Graph (PMFG); Mutual Information; community detection; financial networks |
| JEL: | C1 C6 G11 G28 M0 |
| Date: | 2026–04–25 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128875 |
| By: | Andrea Bellucci (University of Insubria); Alberto Citterio (University of Insubria); Kambar Farooq (University of Insubria); Rossella Locatelli (University of Insubria); Andrea Uselli (University of Insubria) |
| Abstract: | This paper investigates the causal effect of ESG rating initiation on corporate borrowing costs. Using a staggered difference-in-differences design, we analyze a panel of Italian publicly listed non-financial firms from 2013 to 2023. We find that becoming ESG-rated leads to a statistically and economically significant reduction in the firm's cost of debt. On average, the cost of debt declines by approximately 90 basis points following ESG rating initiation. This effect strengthens over time indicating that the benefits of ESG certification in debt markets accumulate as lenders incorporate the ESG information. These findings hold up under a range of robustness tests including various matching strategies, alternative difference-indifferences estimators, placebo tests, and the use of different control groups. Moreover, this relation is stronger for firms that are financially constrained, highly levered, and capital-intensive, as well as firms operating in low carbon industries. Overall, our results offer causal evidence that getting ESG-rated lead to lower cost of debt. |
| Keywords: | Environmental, Social, and Governance (ESG) ratings; Cost of debt; Corporate borrowing costs; Sustainable finance |
| JEL: | G32 G14 M14 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:anc:wmofir:197 |
| By: | LIU JIENI (Graduate School of Economics, The University of Osaka) |
| Abstract: | This paper proposes a search-then-forecast framework for mid-term (20-trading-day) stock price forecasting and evaluates it on Chinese A-share data. The framework combines a multi-distance voting-based similar-stock search for sample augmentation, a sample-level PCA–ICA feature reconstruction, and a Transformer encoder–decoder whose decoder is initialised at inference with the single-step return at the end of the observation window rather than the conventional zero-padding. Using Luoyang Molybdenum (stock code 603993) from the SSE 180 pool as a single-stock case study, we compare six configurations—TransE, TransED (nhead = 3 and 6), BiLSTM, ARMAGARCH, and a TransED-Embed ablation—across ten observation window lengths, and introduce two baseline-referenced metrics, R2 hist and MASEnaive, to address the limited interpretability of standard R2 and MASE on non-stationary financial series. ARMAGARCH attains the lowest root mean squared error (RMSE) across all tested windows, outperforming the best deep learning model (TransE) by 1.4% to 17.0%; MASEnaive further reveals that most deep learning models fail to surpass a random-walk naive baseline. Observation window length and model architecture exhibit a clear interaction, and a smaller internal Transformer dimension does not hurt performance. Within this single-stock case study, the findings suggest that parsimonious statistical models can match or outperform highly parameterised deep learning architectures for mid-term Chinese A-share forecasting. |
| Keywords: | stock price forecasting; Transformer; ARMAGARCH; similar-stock search; Chinese A-share market. |
| JEL: | C22 C45 C53 G12 G17 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:osk:wpaper:2606 |