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A study of the peculiarities of signals affecting the behavior of the stock market in a global environment

Abstract

The country’s economy is strongly influenced by investment, so it is important to identify the factors that determine investors’ choices to invest in certain areas, which means it is important to anticipate how to create favorable economic, social, legal and other investment conditions to attract investment. The situation of stock markets during COVID-19 has only once again shown the important role that stock markets play for national economies. Numerous scientific sources describe how stock markets work in relation to the global economy, but do not make enough suggestions or conduct sufficient research to decide how to successfully forecast stock markets in the face of increasing globalization. After the analysis of the scientific literature and the correlation analysis, the aim will be to identify the peculiarities of the signals affecting the behavior of the stock market, and what importance they may have in proper investment management. The study will use global annual growth rates for the healthcare and technology sectors and the annual return funds: SEB Medical Fund and SEB Technology Fund. The correlation analysis will use 5-year data to determine whether growth in different sectors can be signals in stock market forecasting and will be used in planned further research using artificial intelligence techniques.


Article in Lithuanian.


Akcijų rinkos elgseną veikiančių signalų ypatumų globalioje aplinkoje tyrimas


Santrauka


Šalies ekonomikai didelę įtaką daro investicijos, todėl labai svarbu identifikuoti, kokie veiksniai lemia investuotojų pasirinkimus investuoti į tam tikras sritis, o tai reiškia, kad svarbu numatyti, kaip sukurti palankias investavimo sąlygas, susijusias su ekonominiais, socialiniais, teisiniais ir kitais aspektais, siekiant pritraukti investicijų. Akcijų rinkų situacija COVID-19 metu tik dar kartą parodė, kokį svarbų vaidmenį valstybių ekonomikoms atlieka akcijų rinkos. Daugybėje mokslinių šaltinių aprašoma, kaip veikia akcijų rinkos, kaip susijusios su globalia ekonomika, tačiau nėra pateikiama pakankamai pasiūlymų, ar atlikta tiek tyrimų, kad būtų galima nuspręsti, kaip sėkmingai prognozuoti akcijų rinkas susiduriant su vis plačiau pasireiškiančiais globalizacijos procesais. Po atliktos mokslinės literatūros analizės ir koreliacinės analizės bus siekiama identifikuoti akcijų rinkos elgseną veikiančių signalų ypatumus, kokią svarbą jie gali turėti tinkamai valdant investicijas. Atliekant tyrimą bus remiamasi globaliais metiniais sveikatos apsaugos sektorių ir technologijų sektoriaus bei metinės grąžos fondų: ,,SEB Medical Fund“ ir ,,SEB Technology Fund“ – augimo tempais. Atliekant koreliacinę analizę, naudojami 5 metų duomenys siekiant nustatyti, ar skirtingų sektorių augimas gali būti signalas atliekant akcijų rinkų prognozes. Duomenys naudojami planuojamuose tolesniuose tyrimuose taikant dirbtinio intelekto metodus.


Reikšminiai žodžiai: akcijų rinkos, investiciniai sprendimai, akcijų rinkų svyravimai, ekonomika, signalai.

Keyword : stock markets, investment decisions, stock market fluctuations, economics, signals

How to Cite
Kiškienė, K., & Vasiliauskaitė, A. (2022). A study of the peculiarities of signals affecting the behavior of the stock market in a global environment. Mokslas – Lietuvos Ateitis / Science – Future of Lithuania, 14. https://doi.org/10.3846/mla.2022.15872
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May 4, 2022
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References

AB SEB bankas. (2020). SEB akcijų fondai. https://www.seb.lt/taupymas-ir-investavimas/investiciniai-fondai/akciju-fondai

AB SEB bankas. (2021). Pagrindinė informacija investuotojams. https://seb.se/pow/fmk/KIID/LU/LT/LU2249630331_lt.pdf

Alzazah, F. S., & Cheng, X. (2020). Recent advances in stock market prediction using text mining: A survey. In R. M. Wu & M. Mircea (Eds.), E-business – higher education and intelligence applications. IntechOpen. https://doi.org/10.5772/intechopen.92253

Cao, B. (2019). Hot events detection of stock market based on time series data of stock and text data of network public opinion. Journal of Data Analysis and Information Processing, 7(4), 174–189. https://doi.org/10.4236/jdaip.2019.74011

City Wire. (2020a). SEB medical fund. https://citywireamericas.com/fund/seb-medical-d/c12576?periodMonths=36

City Wire. (2020b). SEB technology fund. https://citywireamericas.com/fund/seb-technology-fund/c12578

CSI Market. (2020). Growth rates by sector. https://csimarket.com/Industry/Industry_Growth.php??s=800

Čekanavičius, V. ir Murauskas, G. (2014). Taikomoji regresinė analizė socialiniuose tyrimuose. Vilniaus universiteto leidykla. http://www.statistika.mif.vu.lt/wp-content/uploads/2014/04/regresine-analize.pdf

Činčikaitė, R. ir Pabedinskaitė, A. (2016). Kiekybiniai modeliavimo metodai. Technika. https://doi.org/10.20334/1563-S

Ding, X. S., & Zhong, L. (2020). Challenges and opportunities in China’s financial markets. The Chinese Economy, 53(3), 217–220. https://doi.org/10.1080/10971475.2020.1720956

Elsayed, A. M. M. (2021). Studying changes on stock market transactions using different techniques for multivariate time series. American Journal of Theoretical and Applied Statistics, 10(1), 72–88. https://doi.org/10.11648/j.ajtas.20211001.18

Eurostat. (2020). Methodology. https://ec.europa.eu/eurostat/documents/276524/7736915/EU+SDG+methodology/3d6d2aad-769c-4f06-9144-6a80933a8f5f

Gasparėnienė, L. ir Kartašova, J. (2015). Finansinių investicijų ir investicinių projektų vertinimas: monografija. VĮ Registrų centras.

Griffith, J., Najand, M., & Shen, J. (2020). Emotions in the stock market. Journal of Behavioral Finance, 21(1), 42–56. https://doi.org/10.1080/15427560.2019.1588275

Ho, S. Y. (2018). Determinants of economic growth in Hong Kong: The role of stock market development. Cogent Economics & Finance, 6(1), 1510718. https://doi.org/10.1080/23322039.2018.1510718

Hoque, M. E., & Yakob, N. A. (2017). Revisiting stock market development and economic growth nexus: The moderating role of foreign capital inflows and exchange rates. Cogent Economics & Finance, 5, 1329975. https://doi.org/10.1080/23322039.2017.1329975

Imran, Z. A., Ejaz, A., Spulbar, C., Birau, R., & Nethravathi, P. S. R. (2020). Measuring the impact of governance quality on stock market performance in developed countries. Economic Research-Ekonomska Istraživanija, 33(1), 3406–3426. https://doi.org/10.1080/1331677X.2020.1774789

Jurevičienė, D. (2016). Finansų rinkos ir institucijos: vadovėlis. Technika. https://doi.org/10.3846/1550-S

Klose, J., & Tillmann, P. (2021). COVID-19 and financial markets: A panel analysis for European countries. Journal of Economics and Statistics, 241(3), 297–347. https://doi.org/10.1515/jbnst-2020-0063

Lietuvos bankas. (2020). Finansinio stabilumo apžvalga. https://www.lb.lt/uploads/publications/docs/25927_98d0769dba6472b550304486342b2d1f.pdf

Naji, H. I., Ali, H. R., & Al-Zubaidi, E. A. (2019). Risk management techniques. In Strategic management – a dynamic view. IntechOpen. https://doi.org/10.5772/intechopen.85801

Nazario, R. T. F., Silva, J. L., Sobreiro, V. A., & Kimura, H. (2017). A literature review of technical analysis on stock markets. The Quarterly Review of Economics and Finance, 66, 115–126. https://doi.org/10.1016/j.qref.2017.01.014

Nikolić, S., & Nikolić, G. (2019). Analysis of financial time series in frequency domain using neural networks. In G. S. Nikolić & D. Z. Marković-Nikolić (Eds.), Fourier transforms – century of digitalization and increasing expectations. IntechOpen. https://doi.org/10.5772/intechopen.85885

Omane-Adjepong, M., Alagidede, I. P., & Dramani, J. B. (2020). COVID-19 outbreak and co-movement of global markets: insight from dynamic wavelet correlation analysis. In Wavelet theory. IntechOpen. https://doi.org/10.5772/intechopen.95098

Pahwa, K., & Agarwal, N. (2019). Stock market analysis using supervised machine learning. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (pp. 197–200), Faridabad, India. https://doi.org/10.1109/COMITCon.2019.8862225

Patel, S. A., & Sarkar, A. (2019). Crises in developed and emerging stock markets. Financial Analysts Journal, 54(6), 50–61. https://doi.org/10.2469/faj.v54.n6.2225

Rutkauskas, V. A. (2014). Įžvalgi investavimo strategija puoselėjant universalųjį plėtros tvarumą: mokslo monografija. BMK leidykla.

Sabri, N. R. (2021). The reliability of prediction factors, for the world stock markets. Theoretical Economics Letters, 11(3), 462–476. https://doi.org/10.4236/tel.2021.113030

Wang, Y., Wang., Z., & Dang, Y. (2020). Illiquidity and the risk of stock market crash. American Journal of Industrial and Business Management, 10(2), 421–431. https://doi.org/10.4236/ajibm.2020.102028