Identifying the Factors that influence the Process Optimization of New Investment Appraisal
Keywords:
investment appraisal, artificial intelligence, machine learning
Abstract
The FDI approval process is one of the decisive factors of successful implementation of investments in a country. This paper attempts to identify the relationship and association between several factors that involves in new FDI appraisal process by the host country. It identifies factors such as global presence of the intended investor; the type of Industry; expected contribution from the investment; potential for gaining competence regarding human resource; expected developments in the country’s infrastructure. The study suggests that above factors are critical to the relevant authorities who involved in FDI promotion. This research also ranks these factors to make the conclusions more useful in the real-life application. It also highlights the number of employments generated for local workers under the new investment. The new investments bring additional knowledge, skills, and competence that usually not quantified at the appraisal level. The success rate of similar investment in other countries also to be critically evaluated. The findings of this research could be extremely useful for countries who wish to host FDIs. A clear understanding about the key influencing factors and their association with the investment appraisal process would be the key to optimize the process.
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Campisi-Pinto, S., Adamowski, J., & Oron, G. (2012). Forecasting UrbanWater Demand ViaWavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy. Water Resour Manage, 26(1), 3539–3558. https://doi.org/10.1007/s11269-012-0089-y
Charles Schwab & Co. (2021). Knowledge Center. Retrieved November 17, 2021, from https://www.schwab.com/knowledge-center
E*TRADE Financial Holdings. (2021). Knowledge. Retrieved November 17, 2021, from https://us.etrade.com/knowledge/investing-basics
Edirisinghe, L. (2017). The Influence of Logistics Competence in International Trade in Sri Lanka: A Contemporary Study. Mihintale, Rajarata University of Sri Lanka, pp. 53-59. https://doi.org/10.2139/ssrn.3305996
Edwards, R. E., New, J., & Parker, L. E. (2012). Predicting future hourly residential electrical consumption: A machine learning case study. Energy and Buildings, 49(1), 591–603. https://doi.org/10.1016/j.enbuild.2012.03.010
Fidelity Investments Inc (2021). We are Fidelity. Retrieved November 17, 2021, from https://www.fidelity.com/about-fidelity/our-company
Guo, P. et al. (2017). Developing a dengue forecast model using machine learning: A case study in China. PLOS Neglected Tropical Diseases, 11(10), 1 - 22. https://doi.org/10.1371/journal.pntd.0005973
Habib, M., & Zurawick, L. (2002). Corruption and Foreign Direct Investment. Journal Of Internationablu Siness Studies, 2q, 291-307. https://doi.org/10.1057/palgrave.jibs.8491017
Hafezi, R., Shahrabi, J., & Hadavandi, E. (2015). A Bat-Neural Network Multi-Agent System (BNNMAS) For Stock Price Prediction: Case Study of DAX Stock Price. Applied Soft Computing, 29(1), 196-210. https://doi.org/10.1016/j.asoc.2014.12.028
Hanson, U. E., Efanga, U. O., Ekanem, B. C., & Umoh, E. A. (2021). Foreign direct investment inflows and its impact on the performance of the Nigerian economy (1981-2017). Journal on foreign direct investment, 8(1), 4191-4215.
IBM (2020). Machine Learning. Retrieved January 10, 2021, from https://www.ibm.com/cloud/learn/machine-learning
Interactive Brokers LLC. (2021). Why IBKR. Retrieved November 18, 2021, from https://www.interactivebrokers.com/en/whyib/overview.php
Iwasaki, I. & Suganuma, K. (2015). Foreign direct investment and regional economic development in Russia: an econometric assessment. Econ Change Restruct Springer Science. https://doi.org/10.1007/s10644-015-9161-y
Lingitz, L. et al. (2018). Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer. Stockholm, 1051-1056. https://doi.org/10.1016/j.procir.2018.03.148
Lu, J., Liu, X., Wright, M., & Filatotchev, I. (2014). International experience and FDI location choices of Chinese firms: The moderating effects of home country government support and host country institutions. International experience and FDI location choices. https://doi.org/10.1057/jibs.2013.68
McCoy, J. T., & Auret, L. (2019). Machine learning applications in minerals processing: A review. Minerals Engineering, 132(1), 95-109. https://doi.org/10.1016/j.mineng.2018.12.004
McQueen, R. J., Gamer, S. R., Nevill-Manning, C. G., & Witten, I. H. (1995). Applying machine learning to agricultural data. Computers and Electronics in Agriculture, 1(12), 275-293. https://doi.org/10.1016/0168-1699(95)98601-9
Moran, T., Görg, H., Seric, A. & Krieger-Boden, C. (2017). Attracting quality foreign direct investment in developing countries. Retrieved May 10, 2021, from https://www.theigc.org/blog/attracting-quality-foreign-direct-investment-developing-countries/
Neven, D., & Siotis, G. (1996). Technology sourcing and FDI in the EC: An empirical evaluation. International Journal of Industrial Organization, 14, 543-560. https://doi.org/10.1016/0167-7187(95)00510-2
Nordal, K. B. (2001). Country risk, country risk indices and valuation of FDI: a real options approach. Emerging Markets Review, 2, 197-217. https://doi.org/10.1016/S1566-0141(01)00017-6
OECD (2002). Foreign Direct Investment for Development, France: Organisation for Economic Co-operation and Development.
Ram, R., & Zhang, K. H. (2002). Foreign Direct Investment and Economic Growth: Evidence from Cross-Country Data for the 1990s. Economic Development and Cultural Change, 51(1), 205-215. https://doi.org/10.1086/345453
Research FDI (2021). www.researchfdi.com/. Retrieved March 5, 2022, from https://researchfdi.com/foreign-direct-investment-advantages-disadvantages/
Ridwan, W. M. et al. (2021). Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia. Ain Shams Engineering Journal, 12(2), 1651-1663. https://doi.org/10.1016/j.asej.2020.09.011
Saeed, K., & Lamech, R. (2003). What International Investors Look for When Investing in Developing Countries, Washington, DC: World Bank.
Singh, K. P., Basant, A., Malik, A., & Jain, G. (2009). Artificial neural network modeling of the river water quality—A case study. Ecological Modelling, 220(1), 888–895. https://doi.org/10.1016/j.ecolmodel.2009.01.004
Sinn, H. W., & Weichenrieder, A. J. (1997). Foreign direct investment, political resentment and the privatization process in eastern Europe. Economic Policy, 179-210. https://doi.org/10.1111/1468-0327.00019
Yao, J., & Tan, C. L. (2000). A case study on using neural networks to perform technical forecasting of forex. Neurocomputing, 34(1), 2000. https://doi.org/10.1016/S0925-2312(00)00300-3
Zhang, K. H. (2001). Does Foreign Direct Investment Promote Economic Growth? Evidence From East Asia and Latin America. Contemporary Economic Policy, 19(2), 175–185. https://doi.org/10.1111/j.1465-7287.2001.tb00059.x
Zupan, B. et al. (2000). Machine learning for survival analysis: a case study on recurrence of prostate cancer. Artificial Intelligence in Medicine, 20(1), 59–75. https://doi.org/10.1016/S0933-3657(00)00053-1
Fabry, N., & Zeghni, S. (2002). Foreign direct investment in Russia: how the investment climate matters. Communist and Post-Communist Studies, 35, 289–303. https://doi.org/10.1016/S0967-067X(02)00012-0
Fabry, N., & Zeghni, S. (2003). FDI in CEECs: how do western investors survive? Thunderbird International. Business Review. https://doi.org/10.1002/tie.10066
Bevan, A., & Estrin, S. (2004). The determinants of foreign direct investment into European transition economies. Journal of Comparative Economics, 32(24), 775–787. https://doi.org/10.1016/j.jce.2004.08.006
Chui, M., Henke, N., & Miremad, M. (2018). Most of AI’s Business Uses Will Be in Two Areas. Retrieved December 11, 2021, from https://hbr.org/2018/07/most-of-ais-business-uses-will-be-in-two-areas
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2023-07-02
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