Nataliya Strochenko

This paper examines momentum and contrarian effects in the Ukrainian stock market after one-day abnormal returns. To do this, UX futures data over the period 2010–2018 are used. The following hypotheses are tested: H1) hourly returns on overreaction days differ from hourly returns on normal days, H2) there are price patterns on overreaction days, and H3) to test these hypotheses, visual inspection and average analysis are used, as well as t-tests, cumulative abnormal returns, and trading simulation approaches. The results suggest that there are statistically significant differences between intraday dynamics during the usual days and the overreactions day. There is a strong momentum effect present on the day of overreaction: prices tend to change only in the direction of the overreaction during the whole day. The fact of the overreaction becomes clear after 13:00-14:00. This gives a lot of time to explore the momentum effect in the day of overreaction. On the day after the overreaction, prices tend to go in the opposite direction: contrarian pattern is detected, which is in line with the overreaction hypothesis. Based on detected price patterns, rules of trading and trading strategies for the Ukrainian stock market are developed. Momentum Strategy (based on price patterns on the day of overreaction) generates several successful trades; close to with 90%, and their number being is profitable (trading results differ from the random ones – confirmed by t-tests). Contrarian Strategy (based on price patterns on the day after the overreaction) demonstrates low efficiency, and results do not differ from random trading.

This paper explores price effects in the “passion investments” market after days with abnormal returns. To do this, daily prices for stamps and diamonds over the periods 1999–2021 and 1989–2021 are analyzed. The following hypothesis is tested: One-day abnormal returns create stable patterns in price behavior on the next day. Statistic tests (t-test, ANOVA, Mann–Whitney U test, modified cumulative abnormal returns approach, regression analysis with dummy variables) confirm the presence of price patterns related to extreme returns: price fluctuations on the day after extreme returns are higher than returns on “normal” days. On the days after positive abnormal returns, the momentum effect is detected. Contrarian effect is typical for the days after negative abnormal returns. A trading strategy based on detected price effects showed the presence of exploitable profit opportunities. Results of this paper provide additional pieces of evidence in favor of inconsistencies between the efficient market hypothesis and practice and can be used by traders to generate extra profits in the “passion investments” market.

Acknowledgment
The authors gratefully acknowledge financial support from the Ministry of Education and Science of Ukraine (0121U100473).

Type of the article: Research Article

 Abstract
The growing pressure on tourism systems caused by overtourism, environmental degradation, and uneven spatial distribu-tion of tourist flows highlights the need for adaptive and sustainability-oriented management approaches. This study develops and tests an AI-driven eco-routing and tourist behavior modelling framework (ER-TBM) as a choice-architecture tool for sustainable destination marketing. The framework combines a fixed graph representation of a synthetic destination, Markov decision states, tabular Q-learning, and congestion-sensitive recommendations. To address reproducibility, the benchmark specifies ten points of interest, 22 undirected edges, all edge attributes, tourist preference and fatigue distributions, Poisson arrivals, recommendation compliance, congestion and emission functions, training settings, stopping rules, and random seeds. Five baselines and ER-TBM were evaluated under normal, peak, and event-demand conditions using 30 paired independent runs per scenario with 120, 180, and 240 agents, respectively. Relative to Dijkstra routing, ER-TBM reduced mean congestion exposure by 5.1-5.3% and increased tourist utility by 0.6-0.7%, while mean CO2 remained statistically indistinguishable and monetary route cost was 9.4-9.8% higher. The simulation-based Tourism Sustainability Index ranged from 0.712 to 0.732 for ER-TBM and did not show practically meaningful superiority over optimized baselines. The findings indicate that behavior-aware eco-routing can redistribute simulated visitor flows without a statistically significant increase in emissions, although this benefit is accompanied by higher monetary route costs. From a sustainable destination marketing perspective, the proposed framework represents a computational choice-architecture proposition. Its potential to influence actual tourist behavior, recommendation acceptance, destination image, satisfaction, and revisit intention requires further field-based validation.

Acknowledgment
The research was conducted within the framework of the prospective development plan for the scientific field “Social Sci-ences” No. BF/25-2026 on the topic “Formation of a Competitive Development Model for the Ukrainian Economy: Human Capital, Financial Stability, and the Development of Innovative Industries amid War-Related Challenges and the Emergence of the Sixth Technological Paradigm”.