07 26_2 Dobrota:tipska.qxd 77 Marina Dobrota*, Nikola Zornić, Aleksandar Marković University of Belgrade, Faculty of Organizational Sciences, Serbia Management: Journal of Sustainable Business and Management Solutions in Emerging Economies 2021/26(2) FDI Time Series Forecasts: Evidence from Emerging Markets DOI: 10.7595/management.fon.2021.0010 Abstract: 1. Introduction Over the past few decades, opportunities and challenges of globalization for all economies have increased rapidly. Information and Communication Technologies (ICT) have connected the world to such an extent that, for example, an economic problem in one country can now have a harmful effect on financial sectors in faraway countries as well. Foreign direct investment (FDI) is one of globalization’s key aspects (Baldwin, 2016; Lacal-Arantegui, 2019; Maiga, Hu, Mekongcho, & Coulibaly, 2018). There are many reasons for a company to be really careful when looking for a FDI destination. Garsous, Ko- zluk, and Dlugosch (2020) investigated the influence of production energy prices on outward FDI and found that only firms that were faced with increased energy prices did increase their FDI activity and that this ef- fect was relatively small. Lee, Hong, and Makino (2020) mention some other reasons for outbound FDI, such as means to counter trade barriers, to achieve a financial hedge, or to obtain tax breaks. Other reasons in- * Corresponding author: Marina Dobrota, e-mail: marina.dobrota@fon.bg.ac.rs Research Question: This paper investigates the trend and flow of foreign direct investments (FDI) in emerging markets, with the focus on FDI in Serbia in comparison with akin countries from the region. Motivation: FDI is an important factor of growth and prosperity in developing countries. It largely influences trade, productivity, and economic development of a receiving country. Based on UNCTAD’s World Investment Report of 2019, the share of global FDI in developing countries was 54 per cent, which was a record. Recently, Serbia has been recognized as one of the most popular destinations for FDI in Southeastern Europe. This motivated us to analyze the chances and possibilities of enlargement of FDI in Serbia, as well in other Balkan countries. Idea: The main idea of the paper is to analyze and estimate time series of FDI net inflows for Serbia. We strive to investigate whether FDI demonstrates the durable growth in the future period of time. Furthermore, we compare the state of Serbian FDI with the former Yugoslav countries, in search for disparities or similarities. Data: We observed the FDI net inflows that are measured in current US dollars, while the data were retrieved from the World Bank database. The earliest available time point is 1992, while the latest available year of observation is 2018. Tools: We estimated the FDI net flow time series using a list of suitable ARIMA models, and we have chosen the best model fit among them using AIC and BIC criteria. Findings: We have found that Serbia and North Macedonia show a mild growth in future investments. A significant percentage of the cumulative FDI inflows from EU companies have been invested precisely in Serbia, while in North Macedonia, fostering FDI has been promoted as one of the main instruments for employment and economic development. Oher Yugoslav countries tend to stagnate in the future period, which is in literature called a negative ‘Western Balkans’ effect on FDI. Contribution: Findings of the mild growth in FDI inflows in Serbia and North Macedonia contribute to the policy of attracting the FDI inflows in the countries of Southeastern Europe. Keywords: FDI, Emerging markets, Time Series, ARIMA, AIC, BIC, Balkan countries JEL Classification F21, F30, F47 clude market, resource and efficiency seeking, low-cost workforce, or higher skills workforce on the other side (Jones, Serwicka, & Wren, 2020). Piperopoulos, Wu, and Wang (2018) focused on emerging market en- terprises (EMEs) and their outward FDI location choices. It turns out that those EMEs that were focused on innovation used to primarily choose developed countries, because of opportunities they provide. These op- portunities include access to cluster-specific environments for innovation, a possibility to foster network ties with knowledge-intensive firms, competitors, and universities, as well as access to ‘early warning indicators’ of global market trends and the pressure to meet high standards. By contrast, EMEs that are focused on adapting their existing technologies and products to local market needs will choose developing countries. The aim of this research is to examine the trends of FDI in the emerging market of Serbia, as one of the at- tractive FDI destination countries. We additionally strive to compare the allurement of Serbia with countries from the region, more precisely, the countries from the former SFR Yugoslavia, as they are most similar to Serbia in the cultural and social sense (Dobrota, Zornić, & Marković, 2019). The research methodology used to examine FDI trends involve time series models. We expect that the findings of the research will have sig- nificant practical implications for policy makers in emerging countries, when it comes to attracting and re- locating FDI. Section 2 of this paper offers a detailed literature review on the topic of modelling the FDI, while Section 3 presents the empirical data used in the time series analysis. Introduction to methodology follows in Section 4. Results are presented and analyzed in the fifth section. Finally, discussion, conclusion, and future re- search directions are given. 2. Literature Review FDI largely influence trade, productivity, and economic development of a receiving country. Effects that FDI will have on productivity depend on human capital and countries’ institutions (Li & Tanna, 2019). Develop- ing countries usually introduce policies that should attract FDI, but that might not be enough. Among most important ones for attracting FDI is institutional quality, especially political stability, control of corruption, and rule of law (Bailey, 2018; Peres, Ameer, & Xu, 2018; Sabir, Rafique, & Abbas, 2019; Uddin, Chowdhury, Zafar, Shafique, & Liu, 2019). Based on the UNCTAD’s (United Nations Conference on Trade and Development) World Investment Report (United Nations, 2019), the share of global FDI in developing countries is 54 per cent, which is a record. Unfortunately, it is not enough only to have a high level of FDI to achieve economic growth. Sadni-Jallab, Gbakou, and Sandretto (2008) have found that macroeconomic stability is essential to translate the impact of FDI on economic growth. Another interesting information presented in the UNC- TAD’s report regards countries’ investment policy. Namely, in 2018, 55 economies introduced at least 112 measures affecting foreign investment. A high portion of these (more than one third) are restrictions re- flecting national security concerns about foreign ownership of critical infrastructure, core technologies, and other sensitive business assets. Ufimtseva (2020) explores this phenomenon and points out that investment recipient countries are more likely to protect a domestic business where foreign ownership threatens do- mestic industry. She proposes a ‘FDI acceptability threshold’ for countries to clarify conditions for accept- able FDI and prevent negative effects of rejecting specific FDI on attracting the future ones. Analyzing international data reveals that a significant part of FDI is directed through financial centers. Two main types of these centers are tax havens and investment hubs. The largest investment hubs include Ire- land, Luxemburg, Mauritius, the Netherlands, and Singapore. They accommodate around one-third of global FDI (Hers, Witteman, Rougoor, & van Buiren, 2018). Some authors analyzed the influence of FDI on the economy, general wellbeing, income inequality, and poverty. Bruno, Campos, and Estrin (2018) combined the country and enterprise-level evidence together in a meta-regression analysis to evaluate the evidence about FDI spillovers in emerging economies. Teixeira and Loureiro (2019) examine to what extent inward FDI contribute to income inequality and poverty in the long run. They show that an increase in FDI leads to a decrease in both income inequality and poverty rates. There is an ample body of research investigating the influence of FDI on the environment (Hanif, Faraz Raza, Gago-de-Santos, & Abbas, 2019; Liu, Wang, Zhang, Zhan, & Li, 2018; Salahuddin, Alam, Ozturk, & Sohag, 2018; Shahbaz, Nasir, & Roubaud, 2018). Wang and Luo (2020) used panel data from 30 provinces in China from 2006 to 2016, using FDI quantity and FDI quality as threshold variables. They found a com- plex nonlinear relationship between technological innovation capability and environmental pollution when taking FDI quantity and quality as dual thresholds. Namely, with the low level of FDI the capacity for scien- tific and technological innovation worsens environmental pollution levels; when FDI crosses a higher thresh- old, the capacity for scientific and technological innovation improves environmental quality, but with a further 78 Marina Dobrota, Nikola Zornić, Aleksandar Marković 2021/26(2) increase of the level of FDI, the positive effect decreases to some extent. Other authors showed on the ex- ample of China that FDI has insignificant influence on environmental quality in the long run, but in the short term, pollution variables cause significant variances on the amount of FDI inflows (Ayamba, Haibo, Abdul- Rahaman, Serwaa, & Osei-Agyemang, 2020). Baek (2016) examines the FDI-income-energy-environment nexus and uses panel data of 5 ASEAN (Association of Southeast Asian Nations) countries. He shows that FDI deteriorates the environment, so not everything about FDI is positive. There is a number of papers dealing with the prediction of FDI using a time series analysis (Shi, Zhang, Su, & Chen, 2012; Vega-Nieva et al., 2019). Jere and his co-authors explored Simple exponential smoothing (SES), Holt-Winters exponential smoothing (HWES), and Autoregressive Integrated Moving Average (ARIMA) and used the best fit model to forecast Zambia’s annual net FDI (Jere, Kasense, & Chilyabanyama, 2017), while the similar has been done by Fadhil and Almsafir (2015) for Malaysia’s FDI inflows. In Central and Eastern Europe, the choice of FDI location is highly affected by market-seeking (GDP, labor cost, and open economy index), while not so much by the country risk for example (Gunther & Kristalova, 2016; Jirasavetakul & Rahman, 2018). When it comes to the Balkan countries, it is peculiar to note that the relationship between FDI and poverty reduction has a positive effect in the Western Balkan region, whereas this is not the case in the Central European region (Ganic, 2019; Shimbov, Alguacil, & Suárez, 2019). After World War II, the Balkans expansion was the result of the profound industrialization, and the fact that the in- dustry dominated the trade and services (Estrin & Uvalic, 2016). At that time FDI were attracted to the Balkan countries by the development of the industry. Later on, the Balkans started to develop the service sector, causing the change in the distribution of FDI, where, for example, the Croatian economic growth depends on tourism (Kurtovic, Maxhuni, Halili, & Talovic, 2020). In later days, the leaders in attracting FDI among the Western Balkan countries are Serbia and Croatia, making up 60% of the total amount of FDI inflows (Kur- tovic et al., 2020). In Serbia, when it comes to manufacturing, FDI were attracted by the production of food, rubber and plastic products, motor vehicles, and metals, and when it comes to services, by wholesaling and retailing, the financial sector, and telecommunications. Croatia had a much higher average of FDI inflows share in the service sector, attracted by the wholesaling and retailing, real estate, and tourism. 3. Methodology As highlighted in the literature review, given the importance of international investments for markets and economies, in this research we observe the status and trends of the main variable FDI. FDI refers to direct investment equity flows (sum of equity capital, reinvestment of earnings, and other capital) in the reporting economy (IMF, 2020; World Bank, 2020). It represents an investment made by a stakeholder resident in one economy into business activities that is resident in another economy (Brooks & Hill, 2004; Brouthers, Werner, & Wilkinson, 1996; Neuhaus, 2006; Patterson, Montanjees, Motala, & Cardillo, 2004; Rivoli & Salorio, 1996). We observed the FDI net inflows that are measured in current US dollars (IMF, 2020), while the data were retrieved from the World Bank database (World Bank, 2020). Our main focus was to examine and inspect the trends of FDI in the Balkan emerging economies, with the focus on Serbia and the countries from the former SFR Yugoslavia, as they are most similar in the cultural and social sense, to make the appropriate comparisons. In order to do so, we spotlighted Serbia, Croatia, Montenegro, Bosnia and Herzegovina, North Macedonia, and Slovenia. Table 1 shows the descriptive statistics for the abovementioned economies. Table 1: Observed countries’ FDI descriptive statistics (values in billions of dollars) Source: World Bank, Foreign direct investments The data presented in Table 1 are collected for all the publicly available time points. While for most of the countries in the World Bank database, data are given from 1970, the earliest available time point for the ob- served economies is 1992. The latest available year of observation is 2018. Yet, not all the economies from the list in Table 1 have the same number of time data points. For some countries, the records of FDI started 79 Management: Journal of Sustainable Business and Management Solutions in Emerging Economies 2021/26(2) Country Mean SD Minimum Maximum Count Period Serbia 1,678.72 1,596.86 0.001 4,929.90 27 1992-2018 Croatia 1,574.82 1,369.79 13.00 5,187.84 27 1992-2018 Montenegro 565.88 374.15 49.43 1,549.31 17 2002-2018 Bosnia and Herzegovina 489.78 401.05 66.74 1,841.97 21 1998-2018 North Macedonia 287.69 216.08 9.49 733.47 25 1994-2018 Slovenia 648.94 626.58 -346.27 1,884.93 27 1992-2018 to be kept later in comparison with the larger countries. For example, the data for Montenegro are available from 2002. It is interesting to note that this is the year when Montenegro adopts euro as its currency (Euro- pean Central Bank, 2019), and that the EU mediated accord was signed to set up the new state to be called Serbia and Montenegro (BBC News, 2019). North Macedonia has its data available from 1994, and it is in- teresting to note that in 1993 it gained the UN membership (Wood, 1997). The lack of data for a longer pe- riod of time is the main limitation of this study. Since the time period is not the same for all the countries, the minimal values vary in what seems to be an unexpected way. For example, Serbia has the smallest FDI value in 1996 (what can be noted from Figure 1), and Slovenia has an even negative value in 2009 (Figure 3c). However, this matter will not affect the time series modeling results since each time series is observed separately. In order to estimate and forecast the trends of FDI, we exploited various time series models. We run Palisade RISK plug-in software for Microsoft Office Excel, that is used as a risk analysis tool, based on a Monte Carlo simulation (Hastings, 1970). Palisade automatically estimates the best fit time series model, based on model selection criteria, the Akaike information criterion (AIC) (Akaike, 1974) and Bayesian information criterion (BIC) (Schwarz, 1978), used to resolve which of the numerous models is seemingly the best model fit for a given dataset. The criteria estimate the quality of each model, relative to other observed models. The software estimates the best model from the list of the following: AR(p), MA(q), ARMA(p,q), ARCH, GARCH, BMMR, BMMRJD, GBM, GBMJD (Bollerslev, 1986; Engle, 1982; Ross, 2014; Whittle, 1951). If the observed time series is non-stationary, or the seasonality is detected, it can be estimated with the specific order of integration or some deseasonalizing mechanism. So, for example, integrated ARMA(p,q) of the d- th integration order represents ARIMA(p,d,q) model (Box & Jenkins, 1970). 4. Results Each time series is estimated with the list of models as specified in section 3. They were compared and ranked according to AIC and BIC, that represents the amount of information the model loses; the quality of the model is higher if it loses less information (Akaike, 1974; Schwarz, 1978). The best five models for each time series, as presented in Tables 2, 3, and 4, are AR(1), AR(2), MA(1), MA(2), and ARMA(1,1). In most of the cases, rank orders are the same for both AIC and BIC information criteria. The models where these val- ues are not equally ordered are sorted by AIC criterion, nevertheless, the differences are minor. Yearly time points of the data were collected, thus there was no need for deseasonalizing the data. The model fit for each time series did not perform any data transformation, nor detrended the time series, unless specified other- wise. Table 2 shows the results of the model fit for the specified FDI time series for Serbia. Table 2: Time series model fit results and ranks for Serbi Non-stationarity was detected for Serbia and thus the data were detrended using the first order level of in- tegration to achieve stationarity. The data were also transformed using the square root transformation func- tion. The best fit model for Serbia was shown to be integrated MA(1) model that can also be presented as ARIMA(0,1,1) model. According to AIC and BIC (see table 2), it was followed by ARIMA(0,1,2), ARIMA(1,1,0), ARIMA(2,1,0), and ARIMA(1,1,1) respectively. Figure 1 presents the time series model ARIMA(0,1,1) for Ser- bia. The time series was detrended using first-order difference to achieve stationarity. 80 Marina Dobrota, Nikola Zornić, Aleksandar Marković 2021/26(2) Model rank #1 #2 #3 #4 #5 Serbia MA1 MA2 AR1 AR2 ARMA Data Transform Function Square Root Square Root Square Root Square Root Square Root Detrend First Order First Order First Order First Order First Order AIC 562.52 565.86 570.03 572.14 572.75 BIC 562.52 568.99 572.71 575.27 575.87 Mu (Billions) 20,337,549 20,337,549 20,337,549 20,337,549 155,893,161 Sigma (Billions) 120,091,820 112,628,474 12,270,653 122,143,742 119,809,133 α1 (AR) / β1 (MA) -0.412 -0.776 -0.441 -0.483 0.111 (α1) α2 (AR) / β2 (MA) -0.489 -0.957 -0.556 (β1) Figure 1: Serbia FDI time series model forecast Table 3 shows the results of the model fit for the specified FDI time series for North Macedonia. Similarly to Serbia, this time series is non-stationary, but there was no need to transform the dataset. The data were de- trended using the first order level of integration. Table 3: Time series model fit results and ranks for North Macedonia The best fit model for North Macedonia was shown to be integrated MA(1) model, thus ARIMA(0,1,1) model. According to AIC and BIC (see table 3), it was followed by ARIMA(0,1,2), ARIMA(1,1,1), ARIMA(2,1,0), and ARIMA(1,1,0) respectively. The best fitting model ARIMA(0,1,1) is similar to the simple exponential smooth- ing model, but with some additional flexibility: the estimated MA(1) coefficient is negative (β1), a constant mu term can be included, the trajectory of the long-term forecasts is typically a sloping line (whose slope is equal to mu) rather than a horizontal line, etc. Figure 2 presents the time series model for North Macedonia. 81 Management: Journal of Sustainable Business and Management Solutions in Emerging Economies 2021/26(2) Model rank #1 #2 #3 #4 #5 North Macedonia MA1 MA2 ARMA AR2 AR1 Detrend First Order First Order First Order First Order First Order AIC 980.07 985.17 989.53 989.80 992.20 BIC 980.07 987.77 992.14 992.40 994.53 Mu (Billions) 260,305,177 260,305,177 231,353,595 260,305,177 260,305,177 Sigma (Billions) 182,842,920 163,944,684 177,271,721 179,664,662 2,013,179 α1 (AR) / β1 (MA) -0.834 -0.640 0.109 (α1) -0.382 -0.263 α2 (AR) / β2 (MA) -0.424 -0.760 (β1) -0.451 Figure 2: North Macedonia FDI time series model forecast Table 4 shows the results of the model fit for the specified FDI time series for other former SFR Yugoslav coun- tries. Table 4: Time series model fit results and ranks 82 Marina Dobrota, Nikola Zornić, Aleksandar Marković 2021/26(2) Model rank #1 #2 #3 #4 #5 Bosnia and Herzegovina MA1 MA2 ARMA AR1 AR2 AIC 879.36 884.68 889.80 892.14 893.72 BIC 879.36 886.36 891.47 893.86 895.39 Mu (Billions) 489,781,899 489,781,899 425,435,633 489,781,899 489,781,899 Sigma (Billions) 291,904,935 270,433,714 285,699,434 343,802,080 335,428,109 α1 (AR) / β1 (MA) 0.767 0.793 0.212 (α1) 0.515 0.628 α2 (AR) / β2 (MA) -0.294 0.735 (β1) -0.219 Montenegro MA1 AR1 ARMA MA2 AR2 AIC 712.55 719.31 719.92 721.73 721.88 BIC 712.55 719.96 719.92 721.73 721.88 Mu (Billions) 565,879,435 565,879,435 611,458,021 565,879,435 565,879,435 Sigma (Billions) 307,972,594 300,660,738 268,808,099 278,977,539 300,589,182 α1 (AR) / β1 (MA) 0.431 0.595 0.664 (α1) 0.246 0.582 α2 (AR) / β2 (MA) -0.207 (β1) 0.288 0.218 Slovenia MA1 MA2 ARMA AR1 AR2 AIC 1,161.20 1,164.42 1,170.93 1,171.45 1,173.52 BIC 1,161.20 1,167.79 1,174.30 1,174.29 1,176.88 Mu (Billions) 648,935,559 648,935,559 633,502,961 648,935,559 648,935,559 Sigma (Billions) 51,420,749 504,351,991 522,797,224 570,498,011 566,495,314 α1 (AR) / β1 (MA) 0.699 0.793 -0.574 (α1) 0.414 0.462 α2 (AR) / β2 (MA) -0.338 0.694 (β1) -0.118 Croatia MA1 AR1 ARMA AR2 MA2 AIC 1,204.38 1,208.46 1,208.51 1,209.52 1,209.99 BIC 1,204.38 1,211.30 1,211.88 1,212.88 1,213.36 Mu (Billions) 157,481,674 157,481,674 180,278,910 157,481,674 157,481,674 Sigma (Billions) 118,938,574 111,829,416 105,249,210 110,152,307 112,268,010 α1 (AR) / β1 (MA) 0.455 0.577 0.664 (α1) 0.478 0.318 α2 (AR) / β2 (MA) -0.219 (β1) 0.173 0.477 The non-stationarity was not detected with these time series and thus there was no need for detrending the data. The best fit model for all the listed FDI time series was MA(1) model (see Table 4). The model was not integrated, thus it can be presented as ARIMA(0,0,1) for all of the above-mentioned economies. These mod- els show both, the unpredictable random motion features of the Wiener process and also the mean rever- sion features, which suggests that the values tend to move to the historical average over time. The order of the models fit differ among time series of different countries (see Table 4). Figure 3 presents time series best fit models for (a) Bosnia and Herzegovina, (b) Montenegro, (c) Slovenia, and (d) Croatia. Figure 3: (a) Bosnia and Herzegovina, (b) Montenegro, (c) Slovenia, and (d) Croatia FDI time series model forecast Figures 1, 2, and 3 show the forecast period up to 2025, with the mean value of the forecast, 50% confidence interval and 90% confidence interval. The red lines in figures for all the time series represent the sample paths of the forecast. 5. Discussion Based on the presented results, we drew some general conclusions regarding the observed countries. Ser- bia and North Macedonia are the countries that have shown the mild growth in FDI for the observed future period. This is in accordance with the literature because, for example, the analysis of FDI by municipalities and cities in Serbia indicated a growing movement in the period (Stojanovic, Ilic, & Mihajlovic, 2017). Serbia has been recognized as one of very popular destinations for FDI in Southeastern Europe. EU com- panies have invested almost three-quarters of the cumulative FDI inflows to Serbia over the past 8 years, amounting to over EUR 11 billion in total (Dudic, Dudic, Smolen, & Mirkovic, 2018). The European countries that have invested most in the Republic of Serbia are Austria, Norway, Netherlands, Russia, Switzerland; and among non-European there are United Arab Emirates, China, and the USA (Dudic et al., 2018). The growth of FDI in Serbia coincided in 2006, which happened throughout the entire region, primarily as a re- 83 Management: Journal of Sustainable Business and Management Solutions in Emerging Economies 2021/26(2) sult of the privatization of the mobile network operator Mobtel, which was bought by Telenor from Norway for 1,513 million euros (Radenkovic, 2016). According to Radenkovic (2016), this was an exceptional year since the share of FDI in Serbian GDP went up to 14.10%. However, it should be stressed that greenfield in- vestments such as Ball Packaging, Vip Mobile, and Microsoft’s Development Center were practically the only bigger greenfield investments made in Serbia over the period from 2000-2011 (Radenkovic, 2016). In North Macedonia, fostering FDI has been promoted by the government as one of the main instruments for generating employment and providing further economic development (Nikoloski, 2017). Furthermore, it was found in North Macedonia that foreign ownership helped restructure and enhance the productivity of domestic firms, that FDI had a positive influence in reinforcing the creation of new firms, and that FDI was likely to influence the job seeker to get employed rather than start their own business (Apostolov, 2017). According to the results, Bosnia and Herzegovina, Montenegro, Slovenia, and Croatia tend to stagnate in the future period. From Figure 3c, it can be noted that for Slovenia FDI even seem to decrease, but in a longer run they flatten around the constant level. A number of years ago, the question arose by Estrin and Uvalic (2014) whether the Balkans are different in view of FDI; they have come to the conclusion that they are found to be so because even when the size of their economies, distance from the source economies, institutional quality, and prospects of EU membership are taken into account, the Western Balkans countries receive less FDI than other transition countries (Estrin & Uvalic, 2014), calling it a negative ‘Western Balkans’ effect on FDI. This can to some extent be noted as the reason that the FDI gravitates around the mean value. To further discuss the findings, we have compared the results of the examined Southeastern European economies with FDI net inflows of Germany, one of the strongest economies in Europe. Figure 4: Germany FDI time series model forecast The forecast of FDI time series for Germany is given in Figure 4. FDI time series in Germany are best fitted with ARIMA(0,1,1), showing the trend of growth for the future period. More than 2,000 foreign companies have been reported to have opened up businesses in Germany in 2018. This seems to be a new record, and is much due to the fact that some British companies are investing in Germany in response to the threat of Brexit (GTAI, 2019b). Around 60% of all FDI in Germany originate from within the EU and 9% from European non-EU countries, while investments from outside the EU continue to grow (19% North America, 11% Asia). For example, Germany is the world’s largest recipient of new Chinese FDI projects (GTAI, 2019a). Regard- ing the investment sectors, in 2017, the major among them were manufacturing (27.7%), wholesale and re- tail (10.7%), financial and insurance activities (9.6%), ICT (6.6%), etc. (UNCTAD, 2019). In Serbia there were 160, while Germany boasted with 691 greenfield investments (UNCTAD, 2019). However, German FDI net inflows show the similar trend of flow such as Serbian and North Macedonian. The main 84 Marina Dobrota, Nikola Zornić, Aleksandar Marković 2021/26(2) strengths of Serbia, regarding attracting FDI, are: positive, liberal business environment (e.g., lowest corporate tax rate in Europe of 10%); public sector reforms (e.g., various agreements reached with the IMF and the EU); young, low-cost workforce, that is well trained and multilingual (nearly half of the population are fluent in Eng- lish); comfortable level of foreign currency reserves (UNCTAD, 2019). The main strengths of Germany are: strategic location in the centre of Europe; political stability; large population; developed infrastructure; strong manufacturing basis (almost a third of the GDP); strong exports (high range products and diversified clients); advanced technology and expertise; highly qualified work force (UNCTAD, 2019). 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However, this largely depends on politics, economic stability, law, etc. The mild growth in FDI inflows in Serbia and North Macedonia is undoubtedly a decisive and significant result. Recently, many headlines could be seen, such as “Serbia tops global greenfield investment index” (Ralev, 2019) or “Serbia’s manufacturing indus- try drives record foreign investment” (Financial Times, https://www.ft.com/content/c87ee832-cd94-11e9-b018- ca4456540ea6) (Shehadi & Irwin-Hunt, 2019). Anyhow, it is highly encouraging that trends of future Serbian FDI seem to exhibit the similar behaviour as FDI of a stronger economy such as Germany. Future directions of the study might cover analysis of factors influencing FDI inflows, both in a positive and a negative di- rections. Acknowledgments Parts of this paper have been presented at the XLVI International Symposium on Operational Research - SYM-OP-IS 2019. 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G., Calleros-Flores, E., López-Serrano, P. M., Briseño-Reyes, J., López-Sánchez, C., … Ruiz-González, A. D. (2019). Temporal patterns of active fire density and its relationship with a satellite fuel greenness index by vegetation type and region in Mexico during 2003– 2014. Fire Ecology, 15(1), 28. DOI:10.1186/s42408-019-0042-z [63] Wang, X., & Luo, Y. (2020). Has technological innovation capability addressed environmental pollution from the dual perspective of FDI quantity and quality? Evidence from China. Journal of Cleaner Production, 258. DOI:10.1016/j.jclepro.2020.120941 [64] Whittle, P. (1951). Hypothesis testing in times series analysis. Uppsala: Almqvist & Wiksells Boktryckeri AB. 87 Management: Journal of Sustainable Business and Management Solutions in Emerging Economies 2021/26(2) 88 Marina Dobrota, Nikola Zornić, Aleksandar Marković 2021/26(2) [65] Wood, M. c. (1997). Participation of Former Yugoslav States in the United Nations and in Multilateral Treaties. Max Planck Yearbook of United Nations Law, 1, 231–257. Retrieved from https://www.mpil.de/files/pdf1/mpunyb_wood_1.pdf [66] World Bank. (2020). Foreign direct investment, net inflows. Retrieved April 28, 2020, from https://data.worldbank.org/indicator/BX.KLT.DINV.CD.WD Received: 2020-05-15 Revisions requested: 2020-08-20 Revised: 2020-09-15 Accepted: 2021-02-09 Marina Dobrota University of Belgrade, Faculty of Organizational Sciences, Serbia marina.dobrota@fon.bg.ac.rs Marina Dobrota is an Associate Professor at the Faculty of Organizational Sciences, University of Belgrade, where she completed her PhD thesis in the field of Operational Research and Computational Statistics. She has published more than 70 research papers in scientific journals, conference proceedings, or monographs, both international and national. Her research focus is in Composite Indicators, Econometric Modelling, and Data Science. Her research interests include Statistical inference, Data Analysis, Data Mining, and Time Series Analysis. Nikola Zornić University of Belgrade, Faculty of Organizational Sciences, Serbia nikola.zornic@fon.bg.ac.rs Nikola Zornić is a Teaching Assistant and PhD student at the Faculty of Organizational Sciences, University of Belgrade, Serbia. Nikola’s research interests include business decision making, simulation methods, including, but not limited to agent-based modelling and simulation, Monte Carlo simulation, and business analytics. He is actively using simulation and data analysis software such as NetLogo, Vensim, R. Aleksandar Marković University of Belgrade, Faculty of Organizational Sciences, Serbia aleksandar.markovic@fon.bg.ac.rs Aleksandar Marković is a Full Professor at the Faculty of Organizational Sciences, University of Belgrade, Serbia, where he got his MSc and PhD degrees in the field of Computer Simulation. The areas of his research include: e-business management, computer simulation, business simulation, business dynamics. Since October 2016, he has been the Vice-Dean for Organization and Finance at the Faculty of Organizational Sciences. 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