Volumen 6 (2020): Heft 2 (December 2020)

The main objective of this analysis is to evaluate and compare the various classification algorithms for the automatic identification of favourable days for intraday trading using the Croatian stock index CROBEX data. Intra-day trading refers to the acquisition and sale of financial instruments on the same trading day. If the increase between the opening price and the closing price of the same day is substantial enough to earn a profit by purchasing at the opening price and selling at the closing price, the day is considered to be favourable for intra-day trading. The goal is to discover relation between selected financial indicators on a given day and the market situation on the following day i.e. to determine whether a day is favourable for day trading or not. The problem is modelled as a binary classification problem. The idea is to test different algorithms and to give greater attention to those that are more rarely used than traditional statistical methods. Thus, the following algorithms are used: neural network, support vector machine, random forest, as well as k-nearest neighbours and naïve Bayes classifier as classifiers that are more common. The work is an extension of authors’ previous work in which the algorithms are compared on resamples resulting from tuning the algorithms, while here, each derived model is used to make predictions on new data. The results should add to the increasing corpus of stock market prediction research efforts and try to fill some gaps in this field of research for the Croatian market, in particular by using machine learning algorithms.

The COVID-19 pandemic was triggered on December 2019 in the city of Wuhan, China, spreading across the world causing global economic crisis and public health emergency. One could ask: what are the socio-economic factors that catalyse the spread of the disease and why are some countries more affected by the COVID-19 pandemic. Therefore, the goal of this paper is to investigate these socio-economic catalysers of the COVID-19 spread. For that purpose, a cross-country regression analysis was conducted at three time points (April 1st, 2020, April 15th 2020 and April 29th, 2020) using OLS, Tobit and PPML estimators. The results of the analysis have shown that countries with higher gross domestic product per capita, population, HDI and HFI indices have been hardely hit with the global COVID-19 pandemic. When some variables were transformed with by dividing it with the population variable, POPDEN and TOUR variables appeared to be significant. The AGE variable was important in the model taking into account total deaths due to the COVID-19 infection. The limitations of the paper are related to data unavailability for some variables in the most recent year. The results obtained from this analysis should be repeated, taking into account other time points and additional COVID-19 socioeconomic catalysers.

Presentation skills are one of the most important tools that are required on numerous occasions in education and business. In most of the business and economics colleagues, presentation skills are taught as part of the curriculum, of at least several courses. Therefore, it could be expected that presentation skills would be highly developed among business and economics students. However, in practice, people develop numerous fears and barriers to the presentation in public. On the other hand, students do not behave in the same manner taking into account their fear towards the presentation in public. The goal of the paper is to investigate if there are homogenous groups of students according to their attitude towards the presentation, both as sources of success and as a source of fear. Cluster analysis has been employed to fulfil the paper goal. Non-hierarchical k-means analysis has been conducted on data collected by the research instrument about the benefits and fears from the presentations on the sample of students enrolled in business and economics colleagues. Results indicate that there are homogenous groups of students according to presentation perceived benefits and fears, but the composition of these groups indicates that perceived benefits and fears of presentations are not always related in the same direction.

In the autumn 2017, The Statistical Office of the Republic of Slovenia (SURS) has conducted for the first time a survey on daily passenger mobility of Slovenian residents. The key statistics are on persons’ daily traveling habits, such as number of trips, travelled distance, time spent on traveling, and so on. Two independent samples were selected for the simultaneous collection of data by two modes, face-to-face interview (CAPI) and online questionnaire (WEB). The goal of this study is to identify the possible sources of mode measurement errors, with the objective to better design and thus improve the whole data collection process. The detailed mode effect analysis is performed by the comparison of the key statistic estimates and the use of regression models. Usually the measurement mode effect is an issue in surveys on the more sensitive topics or persons’ opinions. This work points out that, first, the mode measurement effect can be an issue also in a more factual survey content, and second, the corresponding statistical data processes can have an important contribution to minimising measurement errors. The results show that WEB respondents are inclined to join two or more trips into one reported, which gives lower estimate of average number of daily trips. The main reason is the demanding questionnaire content. Additionally, the complex data editing process was still insufficient to correct completely for such measurement error. The possible improvements of the data collection process are also discussed.

The problem of selecting an optimal set of investment stocks is of a huge interest for both individual and institutional investors. This paper compares the hybrid multiple criteria decision making (MCDM) approach to selecting the best stock to invest in, with the stock selection using modern portfolio theory (MPT). When selecting stocks, it is very important to thoroughly analyse stocks, according to multiple criteria, including their equity market indicators, as well as financial indicators. The objective of the research is to compare the stock selection using a hybrid MCDM approach and MPT, which includes only the equity market indicators. The analysed sample includes 18 stocks, which are CROBEX components on the Croatian capital market from January 2017 to January 2019. The rankings of stocks were calculated using five MCDM methods. These were then used to obtain the final hybrid stock ranking, which was compared to the MPT stock selection. The results show that there is a significant difference in the stock rankings. However, the stocks which have not entered any portfolio in MPT selection were ranked as lowest according to the hybrid MCDM approach, which confirms that those stocks are the worst to invest in. The research can serve as a guidance for investors to use all available stock information in their decision making process of investment.

National statistical authorities are under pressure to provide reliable and readily accessible data with minimum costs and respondent burden. To this end, a use of administrative data, defined as those not primarily collected for statistical purposes, is increasingly encouraged to supplement sample and census surveys. In this paper, a possibility to produce new statistics by combining several sources where at least one is the administrative data is tested. This exercise was applied to measure a volume of tourists (residents and non-residents) staying in non-commercial accommodation facilities in Croatia. Four data sources were analysed: (i) administrative data from eVisitor system, a unique online central information system for tourists registration in Croatia that provides insights into tourist traffic and accommodation capacities (commercial and non-commercial), (ii) Croatian Bureau of Statistics Survey on Tourist Activity of the Croatian population, that provides information on number of domestic trips and nights in commercial and noncommercial accommodation establishments, (iii) Croatian National Bank Survey on foreign visitors in Croatia, that estimates number of foreign tourist nights in Croatia in, both, commercial and non-commercial type of accommodation, and (iv) administrative data from Tax Register on paid taxes on second homes – holiday houses and apartments. The results clearly demonstrated that two survey-based data sources and the administrative one regarding the second homes taxes had not provided a quality foundation to improve the current estimations of tourist traffic in non-commercial accommodation provided by the eVisitor system.