Metody Ilościowe w Badaniach Ekonomicznych

Unsupervised Classification of Japanese Candlesticks: Technical Analysis vs Machine Learning

Maciej Janowicz — 2025-12-30

The unsupervised clusterization of a set of Japanese Candlesticks generated by the currency pair prices on the Forex market has been performed. Several algorithms that do not require the number of clusters in advance have been used. It turns out that different algorithms give glaringly different numbers and description of clusters. Comparison with well-established candlestick types known from the technical analysis has been made.

Investments in Language Capital: A Network Based Analysis from the Human Capital Perspective

Robert Woźniak — 2025-12-30

This article analyzes investments in linguistic capital from the perspectives of human capital theory and network effects. Based on Duolingo data, a graph of linguistic relations was constructed and interpreted as a network. The results reveal a strong hierarchy, asymmetric flows, and a small-world structure, indicating a concentration of investments around languages with the highest economic value.

Skewness-Corrected Copula-Based Outlier Detection for High-Frequency Financial Data from the Warsaw Stock Exchange

Marcin Dudziński — 2025-12-30

The objective of outlier detection is to identify rare events, i.e. the observations in empirical data that have significantly different feature values or different characteristics than the rest of observations. Therefore, it is of high importance to conduct the outlier detection in high-frequency financial data for achieving a good quality of empirical analyses, as well as the risk management and model fitting tasks. In our paper, we investigate the use of COPOD (Copula-Based Outlier Detection) – a parameter-free and interpretable anomaly detection algorithm based on application of empirical copulas and computation of the corresponding tail probabilities. This performance is assessed on real-world data from the Warsaw Stock Exchange (GPW). In our research, we follow the theoretical framework of Li et al. [10] and restate its core formulas in a concise notation adapted later to the chosen financial setting. We provide the Python implementation of a skewness-corrected version of COPOD, closely connected to the original algorithm, and employ it to the empirical dataset, which is a pre-processed feature set derived from the GPW tick dataset, collected in the GPW20230403000tind_for_COPOD.csv file. The empirical results show that the skewness-corrected COPOD algorithm successfully identifies extreme price and return observations in the selected data, while simultaneously remaining computationally efficient and easy to interpret method. Additionally, we also discuss practical implications of the conducted research and present possible directions and extensions of the study towards anomaly identification for the future work.