Metody Ilościowe w Badaniach Ekonomicznych

Beating the Machine: Why Simple Random Walks Outshine Google's Gemini in Forecasting Earnings on Poland's Exchange.

2026-05-14T09:31:29+00:00

This study investigates the application of Large Language Models (LLMs) for forecasting Earnings Per Share (EPS) on the Warsaw Stock Exchange, comparing Google's Gemini model against the seasonal random walk (SRW) approach. Analyzing quarterly EPS data from 267 Polish companies (2010-2019), the mean arctangent absolute percentage error (MAAPE) is employed to address limitations of traditional error metrics. Results demonstrate that despite Gemini's sophisticated capabilities, the simpler SRW model consistently produces lower error rates when measured by MAAPE, although Gemini outperforms on RMSE and MAE metrics. The divergence highlights how model selection should be guided by specific error tolerance requirements. Additionally, we explore Gemini's chain-of-thought reasoning and certainty scores to assess model confidence. These findings contribute to the understanding of EPS prediction in emerging markets with limited analyst coverage and suggest that complex models may not provide significant advantages in markets characterized by relatively unsophisticated EPS dynamics.

Proximal Policy Optimisation Versus Ant Colony Optimisation For The Three-Dimensional Bin Packing Problem: A Comparative Study

2026-05-14T09:31:24+00:00

This paper compares a Proximal Policy Optimisation (PPO) deep reinforcement-learning agent with an Ant Colony Optimisation (ACO) solver on the offline, heterogeneous-bin three-dimensional bin packing problem (3D-BPP). Both algorithms were evaluated on fifty synthetic instances using a unified composite scoring function covering placement ratio, volume utilisation, bin-count penalty and mean per-bin waste. PPO achieves a higher mean composite score (0.346 vs. 0.283), wins on 38 of 50 instances with an average winning margin of 0.101, and resolves each instance in under 60 seconds on a commodity CPU. ACO exhibits greater score variance and resolves instances in up to 1,706 seconds, but its training-free character makes it relevant when the instance distribution changes too rapidly for policy retraining. The PPO training cost of approximately 5.5 hours is recovered after 58 instances compared with ACO at mean inference times. A paired Wilcoxon signed-rank test is identified as the appropriate significance test once per-instance data are made available.

Analiza Wpływu Lokalizacji I Cech Nieruchomości Na Ich Ceny W Polsce W Latach 2023-2024

2026-05-14T09:31:26+00:00

W artykule przeprowadzono badanie wpływu metrażu, lokalizacji i udogodnień na ceny ofertowe mieszkań w Polsce w latach 2023-2024, ze szczególnym uwzględnieniem Warszawy, Krakowa, Łodzi i Trójmiasta. Połączono metody ekonometryczne i uczenie maszynowe, aby wskazać kluczowe czynniki cenotwórcze. W analizie zastosowano modele OLS, Lasso, GWR, k-means oraz Random Forest. Badanie oparte na próbie 49 531 ofert wykazuje wyraźną przewagę modeli nieliniowych w predykcji. Potwierdzono również dominującą rolę lokalizacji i cech fizycznych w objaśnianiu przestrzennego zróżnicowania cen na rynku.

A Multidimensional Quantitative Assessment Of Labor Market Dynamics In Türkiye: Methodological Divergences, Human Capital Bottlenecks, And Spatial Inequalities

2026-05-14T09:31:16+00:00

This study provides a multidimensional quantitative examination of the Turkish labor market. The primary objective is to evaluate the structural efficiency of employment by analyzing the statistical gap between official headline unemployment figures and broadly defined labor underutilization. Utilizing a descriptive statistical framework, the research analyzes 2024 cross-sectional data alongside historical time-series data sourced from the Turkish Statistical Institute (TÜİK) and the International Labour Organization (ILO). Empirical data reveals significant statistical divergences. While historical headline rates showed declines to 9.4% in 2023, the broad unemployment rate (labor slack) reached 26.7% in 2024, creating an 18.0 percentage point gap. Youth unemployment remains structurally high at 16.3%, with a persistent gender gap (22.3% for female youth vs. 13.1% for male youth). Spatial analysis identifies severe polarization, with unemployment peaking in the TRB2 region at 19.2%. Standard indicators mask structural fragilities such as informal employment and human capital mismatch. Effective mitigation necessitates data-driven Active Labor Market Policies (ALMPs) and targeted regional investments.