Combining Regression and Clustering for Financial Analysis

Artificial Intelligence is used in business through machine learning algorithms. Machine learning is a part of computer science focused on computer systems learning to perform a specific task without using explicit instructions, relying on patterns and inference instead. Though it might seem like we’ve come a long way in the last ten years, which is true from a research perspective, the adoption of AI among corporations is still relatively low. Over time it became possible to automate more tasks and business processes than ever before. The benefit of using artificial intelligence is that does not require to program every step of the process, predicting at each step what could happen and how to resolve it. The algorithms decide for themselves in each case how the problems should be solved, based on the data that is used. I apply Python language to create a synthetic feature vector that allows visualizations in two dimensions for EDIBTA financial ratio. I use Mean-Square Error in order to evaluate the success, having the optimal parameters. In this section, I also mentioned about the purpose, goals, and applications of cluster analysis. I indicated about the basics of cluster analysis and how to do it and also did a demonstration on how to use K-Means.

Spatial changes in entrepreneurship in relation to economic crisis and recovery. Insights from Romanian counties

Trying to explain the sources of persisting high inequalities in the regional distribution of entrepreneurship in Romania, this paper puts a spotlight on the spatial interactions among neighbour regions in a spatial modelling framework. We explored the interplay of factors that inform the territorial distribution of SMEs by employing various spatial panel data models that not only provided better estimations of the parameters, but also removed the cross – sectional dependence detected in our previous research using classic panel models. We found that the existing regional inequalities in entrepreneurial activity are strongly associated with differences in economic development, gross investments, research and development, age of the population, and and differences in regional resilience to economic crises. Additional useful information was brought about by the computation of direct and indirect effects of these factors of influence.

The Evolution of Romania’s Internal Migration. Findings from the 1977, 1992, 2002 and 2011 Censuses

Although internal migration has been rather overlooked, both in terms of its magnitude and importance, its ability to reflect socio-economic changes is providing useful insights on the evolution of the Romanian society over the last decades. Based on the analysis of census microdata over the past 4 censuses, some major shifts in the magnitude and patterns in internal migration reveal the fact that characteristics of internal migrants have not only mirrored, but also preceded the changes observed for the total population. Among the most important developments revealed by our analysis have been a slight decrease in migration incidence since 1992, an increase in migrants residing in rural areas, especially in the South region, and a higher incidence among women, perhaps as a counterweight for higher international migration rates among men. Internal migrants’ age profile shows that they are 11 years older than the total population, up from a gap of only 6 years in 1977. Although they tend to be relatively more educated, their advantage has been on a declining trend and, contrary to common perceptions, are less likely to be single. At the county level, data reveals diverging patterns triggered by post-communist development, among which deindustrialization of some countries and strong international migration. These findings help portray the socio-economic changes as revealed by the analysis of census data, and provide any additional feedback to the annual internal migration flow estimates, by assessing the stock of those who moved from their birthplace, and showing how net internal migration patterns have morphed over time, both reflecting and effecting demographic and socioeconomic evolutions of the Romanian society1.

Romania’s tourism industry in free fall

Tourism is one of the consistent branches of the national economy, which can ensure some concrete results and a tailor-made contribution to the formation of the Gross Domestic Product. The tourism industry is also called invisible trade in the sense that, although it does not export goods and services, by practicing it, by developing it, it ensures consistent revenues to the state budget, but also ensures the possibility of increasing Gross Domestic Product. Analysing the current situation of the health and economic-financial crisis, it is found that in 2020 HoReCa, the tourism industry, complementary services have decreased alarmingly. Against this background, tourism has reduced its contribution to the formation of the Gross Domestic Product, which can lead to an even greater decrease. Investments in tourism are eroding. There is no possibility of refinancing despite support measures provided by the authorities. We say in spite of some measures granted because the postponement of some payments, the postponement of some credits, the transition to technical unemployment and others will be coupled later with other measures with almost devastating effect for the Romanian economy. Thus, many jobs will be lost. On the other hand, tourism companies will not be able to move from technical unemployment to normal activity and give a minimum of six months to those in this situation. The tourist market practically does not exist because there are only sequential possibilities in which it takes place, but especially under the rule of business activities, which are also considered tourist activities. The tourist activity materialized through arrivals, overnight stays, arrivals and departures has decreased steadily and this result mainly from the data subject to the study we mentioned. It is necessary to interpret these data and possibly find ways to recover.

Testing the Feldstein Horioka puzzle in Algeria: Maki co-integratioan and hidden causality analysis

The aim of this paper is to test the existence of Feldstein Horioka puzzle in the case of Algerian economy for the period 1970-2019 by examining the link between domestic savings and investments, we use in this paper both the co-integration tests under Gregory-Hansen (1996), Hatemi-J (2008) and Maki (2012) tests in the context of structural breaks, and the symmetric and asymmetric causality (hidden causality) proposed by Hacker-Hatemi (2010) and Hatemi (2012) respectively, the results suggest that there is a co-integration relationship between saving and investment with five endogenous structural breaks, and the saving retention coefficient is equal to 0.324 which means the existence of Feldstein-Horioka puzzle in a weaker form and high capital mobility, on the other hand, the results indicate asymmetric causal relationship between savings and investments.

Study on the effect of crises on economic correlations and macrostability

Economic growth is a goal of every country and equally of the European Community. In this sense, all national strategies related and not subordinated to the European Union’s strategy aim at economic growth, which will ensure the improvement of the quality of life. Economic growth is always achieved by the level registered by the Gross Domestic Product (Gross Domestic Product per capita) these being the most important indicators of results calculated at macroeconomic level. The proper functioning of a country’s economy must be based, first of all, on certain correlations that are established between socio-economic variables, a context in which there must be certain proportions. The evolution of the economy in free market conditions reaches imbalances at certain times, a context in which macroeconomic stability is affected. Most often, crises, regardless of their health, economic, economic or financial nature, have the first effect of affecting macroeconomic stability. In the current conditions, when we face the health crisis, combined with the economic and financial crisis, the macroeconomic imbalance is obvious by not respecting some proportions and correlations, which must be established at the macroeconomic level. The analysis of this aspect of crises and their effect on economic correlations and macrostability is the subject of the study in this article.

Consumer recommendation dynamics in online retail business under logistic regression and naïve Bayes analyses

Competitive businesses need to study the behavior of their current and potential customer base. Relevant data on the behavior can be obtained from online, where the purchase decisions are increasingly made and often based on product reviews, ratings and recommendations available in social media networks. The original data consists of 23486 customer reviews with ten variables/features of the reviewing customers, the products under review and the feedback to their reviews from online retail clothing business, and about half of the dataset is analyzed after cleaning the data. To find out, which features are the most important factors leading to a recommendation, the naïve Bayes and logistic regression methods are applied. Earlier research has shown that the sentiment of textual reviews and the given numerical ratings are key factors for the decision to recommend or not recommend products. The focus of this paper is to identify and rank-order the most relevant (numerical) factors affecting the review process leading to a recommendation. After applying the logistic regression classifier, we have found that rating, positive feedback count and age are statistically significant factors, in that order. The results support online retailers and manufacturers, as well, in adjusting their product portfolios and marketing efforts optimally to obtain recommendations for their products, reach potential customers and expose them to the given recommendations leading to positive purchase decisions. Further, the results indicate some future research opportunities.

Use of Social Networks in Determining stockmarket Evolution

This article aims to use text mining methods and sentiment analysis to determine the stock market evolution of companies as well as virtual currencies such as Bitcoin. The source of the text is the social media channel Twitter and the text is composed of individual messages sent by users. Although previous papers proved with a degree of certainty that this paper hypothesis is true, as we will see bellow, the area of research was focused only on the professional environment or known opinion makers and not taking into account a high population mass. To ensure that a high level of information is maintained after the sentiment analysis process, we will use multiple algorithms based on different calculation methods and different word dictionaries. In addition, indicators such as the number of assessments, the number of replays etc. will be added to the methodology. By the end of the paper we will be able to both identify a working methodology of analyzing text for the purposes of stock market prediction and also we will touch on the limitations faced when creating it and the ways through which we can expand and improve it’s reliability. The implementation of all these methods and of the multiple dictionaries helped us in simulating human behavior and the differences of opinion, when a group wants to analyze a text. The algorithm becoming a way to balance the different “opinions” that resulted out of the sentiment analysis.

Looking ahead - from fossil fuel to a circular bioeconomy in Europe. Evolution and behaviour

Giving due consideration to sustainability, the new concept of bioeconomy has faced strong support from international policymakers, changing the way the economy is currently working. And it does not involve only the replacement of fossil feedstock with bio-based fuels, but also acquiring value from waste and bringing to light the resource efficiency that can lead to a smooth transition from a linear to a circular economy. This paper aims to track the EU states movement in implementing the bioeconomy best practices by building clusters based on their progress in adapting their industries to the new requirements. To make this possible, we created two scenarios for recording the evolution of the energy industry in 27 countries: the first one that groups them according to the fossil-fuel sources, and the second one based on renewable resources. Our results revealed that the biomass tends to be used as a complementary source and not a substitute, in comparison to the traditional fuels. Same actors keep the leading positions in both scenarios, making us believe that they may face strong challenges in reaching the European Commission goals. Thus, the present study emphasizes the need to develop highly efficient policies for all EU members to keep the same path.

Labor force shortage analysis in Romania - size, impact and measures

Under the impact of unfavourable demographic developments, some existing imbalances on the Romanian labor market have worsened. Thus, in 2019 the labor force shortage was estimated to 300000 persons, while in the last ten years the number of vacancies has exceeded 60,000 places, more than double the level at the beginning of the period (2010). This phenomenon may have negative social and economic effects. In this context, the present paper aims at analysing the labor shortages in Romania, at identifying its main determinants and the most important social and economic consequences and recommends a series of measures to mitigate the negative effects of this phenomenon.