Ibrahim Ozkan

Sustainable economic development requires transparent and consistent institutional communication from monetary authorities to maintain long-term financial stability and public trust. This study investigates the latent authorial structure and stylistic heterogeneity of central bank communications by applying stylometric analysis and unsupervised machine learning to official announcements of the Central Bank of the Republic of Turkey (CBRT). Using a dataset of 557 press releases from 2006 to 2017, we extract a range of linguistic features at both sentence and document levels—including sentence length, punctuation density, word length, and type–token ratios. These features are reduced using Principal Component Analysis (PCA) and clustered via Hierarchical Clustering on Principal Components (HCPC), revealing three distinct authorial groups within the CBRT’s communications. The robustness of these clusters is validated using multidimensional scaling (MDS) on character-level and word-level n-gram distances. The analysis finds consistent stylistic differences between clusters, with implications for authorship attribution, tone variation, and communication strategy. Notably, sentiment analysis indicates that one authorial cluster tends to exhibit more negative tonal features, suggesting potential bias or divergence in internal communication style. These findings challenge the conventional assumption of institutional homogeneity and highlight the presence of distinct communicative voices within the central bank. Furthermore, the results suggest that stylistic variation—though often subtle—may convey unintended policy signals to markets, especially in contexts where linguistic shifts are closely scrutinized. This research contributes to the emerging intersection of natural language processing, monetary economics, and institutional transparency. It demonstrates the efficacy of stylometric techniques in revealing the hidden structure of policy discourse and suggests that linguistic analytics can offer valuable insights into the internal dynamics, credibility, and effectiveness of monetary authorities. These findings contribute to sustainable financial governance by demonstrating how AI-driven analysis can enhance institutional transparency, promote consistent policy communication, and support long-term economic stability—key pillars of sustainable development.

This study investigates excessive movements in capital flows called surges or bonanzas. Contrary to the previous work that extensively uses ad‐hoc measures and discretionary thresholds; we adopt a distinctive methodology to detect capital flow surges based on right‐tailed unit root tests. Generalized supremum augmented Dickey‐Fuller (GSADF) proposed by Phillips et al. (2015) is successfully applied to identify asset price bubbles. Exploiting the technical and conceptual similarities in the formations of asset price bubbles and capital flow surges, we perform the GSADF procedure using quarterly net capital flows data from 43 developing countries. The advantages of this procedure are twofold: it can distinguish the behaviour of volatility and explosiveness and diagnose multiple surges in a series. We identified 727 individual surges, 130 different surge episodes, and 4 global capital flow waves over the periods of 1995–2017. Compared with the existing measures, the application of this surge‐detection technique provides a useful tool as a data‐driven method with no need for discretionary thresholds. We also investigate the factors triggering capital flow surges, employing the Fernandez‐Val and Weidner (2016) bias‐adjusted fixed effects probit model and find that domestic factors play a dominant role on the surge occurrences in developing countries.

Capturing the interdependencies between real valued time series can be achieved by finding common similar patterns. The abstraction of time series makes the process of finding similarities closer to the way as humans do. Therefore, the abstraction by means of a symbolic levels and finding the common patterns attracts researchers. One particular algorithm, Longest Common Subsequence, has been used successfully as a similarity measure between two sequences including real valued time series. In this paper, we propose Fuzzy Longest Common Subsequence matching for time series.

Decision under uncertainty is an active interdisciplinary research field. A decision process is generally identified as the action of choosing an alternative that best suites our needs. This process generally includes several areas of research including but not limited to Economics, Psychology, Philosophy, Mathematics, Statistics, etc. In this chapter the authors attempt to create a framework for uncertainties which surrounds the environment where human decision making takes place. For this purpose, the authors discuss how one ought to handle uncertainties within Fuzzy Logic. Furthermore, they present recent advances in Type 2 fuzzy system studies.

Recent Advancement in Fuzzy System: Full Type 2 Fuzziness: 10.4018/978-1-4666-6070-0.ch010: Decision under uncertainty is an active interdisciplinary research field. A decision process is generally identified as the action of choosing an alternative