درخت حوزه‌های تخصصی

Tourism is regarded as a complementary activity that contributes to the sustainability of nomads' livelihoods and raises welfare and living standards, as well as the economic growth of nomadic areas, through the capabilities and attractions of the Shahsavan nomads. The present study tries to identify the scenarios of nomadic tourism in Shahsavan, Ardabil province. The research strategy is a case study through a qualitative study using a multi-faceted method and a cross-sectional time horizon. In data collection, in the first step, key experts were used to collect data to extract the factors affecting nomadic tourism development (NTD) and to examine the impact of these variables on each other. The influential factors were divided into 12, seven promoting and five inhibiting factors. The mutual influence matrix was used to determine the effect of variables on one another, and scenarios were created using propulsions extracted from the influencing factors. After collecting the questionnaires, the scenario was conducted using Scenario Wizard software. The scenarios in the current research were divided into three groups: favourable, intermediate, and unfavourable. According to experts, access to favourable scenarios is accompanied by many limitations and obstacles. Its occurrence requires coherent planning, commitment, and participation from all stakeholders, and the occurrence of unfavourable and catastrophic scenarios also looks pretty pessimistic. According to current trends, the vast majority of experts believe that a slight improvement in the current situation is possible in the future of this type of tourism.

The excess volatility puzzle refers to the observation of returns that cannot be explained only by fundamentals, and this research attempts to explain such volatilities using the concept of endogenous jumps and modelling them based on the generalized Langevin equation. Based on stylized facts, price behaviour in financial markets is not simply a continuous process, but rather jumps are observed in asset prices that may be exogenous or endogenous. It is claimed that the source of exogenous jumps is news, and the source of endogenous jumps is internal interactions between the agents. The goal is to extract these endogenous jumps as a function of the state variable and time. For this purpose, the generalized Langevin equation is introduced and it is shown that the parameters of this model can be extracted based on the Kramers-Moyal coefficients. The results of self-consistency tests to evaluate the accuracy of the Kramers-Moyal method in extracting the generalized Langevin equation show that this method has good accuracy. In a practical application of the aforementioned method, Ethereum cryptocurrency price data was used between October 2017 and February 2024 with a sampling rate of one minute. By simulating the extracted dynamics, the probability distribution of the first time passage of this cryptocurrency from a specific level was calculated, and an examination of the price behavior of this asset shows that the aforementioned distribution was extracted with good accuracy. The potential function, which is calculated from the first KM coefficient, will be a quadratic parabola for the studied process, and as a result, we have a stable equilibrium at the zero point. Also using the extracted dynamics we show that this model has good out-of-sample prediction ability.

This study explores the impact of emotions on financial markets. Recent research highlights the role of psychological factors in financial crises. Investors, not always rational, base asset risk decisions on emotions and beliefs. Optimism, pessimism, and self-confidence influence decision making processes over time. Irrational investors can cause market prices to deviate from fundamental values. This emotional price anomaly can be seen as a persistent sentiment risk factor significantly affecting stock returns. The research investigates the effect of emotional shocks (positive and negative) on order flow imbalances and their impact on price movements. A three-stage clustering approach is used. The sample includes 172 companies listed on the Tehran Stock Exchange from 2021, with daily data extracted from Rahvard Navin software. Information on independent and dependent variables is analysed using a three-step clustering method to identify different time scales. Finally, regression analysis in MATLAB software is used to examine the relationship between variables within specified time intervals. The results reveal a positive relationship between positive shocks caused by order imbalances and company returns. This relationship is reversed for negative shocks. Three-stage clustering separates companies based on co-movement, revealing distinct behaviour and relationships between variables within each cluster. These findings demonstrate the effectiveness of the three-step clustering method for analysing company data.