• citati u SCIndeksu: 0
  • citati u CrossRef-u:0
  • citati u Google Scholaru:[]
  • posete u poslednjih 30 dana:5
  • preuzimanja u poslednjih 30 dana:1


članak: 3 od 36  
Back povratak na rezultate
2021, vol. 18, br. 2, str. 15-35
Strukturni prekidi, Twiter i likvidnost akcija internet Dot-com kompanije - dokazi američkih kompanija
Ural Federal University, School of Economics and Management, Department of International Economics, Yekaterinburg, Russia
Ključne reči: strukturni prekidi; Twitter; akcionarsko društvo; Andrews-Ploberger; likvidnost; režim
Cilj ovog rada je istražiti odnos između Twitter-a i likvidnosti akcija nekih velikih američkih internet Dot-com kompanija u prisustvu nepoznatih strukturnih prekida za period od septembra 2019. do aprila 2020. Koristeći Andrews-Ploberger i Andrews-Quandt strukture modela prekida, identifikujemo glavne strukturne tačke prekida u likvidnosti akcija i otkrivamo da se većina ovih strukturnih promena značajno percipira. Kada smo pregledali podperiode, kao i ceo uzorak, otkriveno je da tvitovi i lajkovi većine kompanija nemaju veze sa likvidnošću akcija. Ovi rezultati pružaju ključan uvid u portfeljsku strategiju kako međunarodnim tako i domaćim investitorima.
Affuso, E., Lahtinen, K.D. (2019) Social media sentiment and market behavior. Empirical Economics, 57(1): 105-127
Ajjoub, C., Walker, T., Zhao, Y. (2020) Social media posts and stock returns: The Trump factor. International Journal of Managerial Finance, 17(2): 185-213
Albarrak, M.S., Elnahass, M., Papagiannidis, S., Salama, A. (2020) The effect of twitter dissemination on cost of equity: A big data approach. International Journal of Information Management, 50: 1-16
Amihud, Y., Mendelson, H., Pedersen, L.H. (2006) Liquidity and asset prices. Boston: Publishers Inc
Amihud, Y. (2002) Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5(1): 31-56
Amiraslany, A., Luitel, H.S., Mahar, G.J. (2019) Structural breaks, biased estimations, and forecast errors in a GDP series of Canada versus the United States. International Advances in Economic Research, 25(2): 235-244
Andrews, D.W.K. (1993) Tests for parameter instability and structural change with unknown change point. Econometrica, 59: 817-858
Andrews, D.W.K., Ploberger, W.K. (1994) Optimal tests when a nuisance parameter is present only under the alternative. Econometrica, 62(6): 1383-1414
Anguyo, F.L., Gupta, R., Kotzé, K. (2020) Inflation dynamics in Uganda: A quantile regression approach. Macroeconomics and Finance in Emerging Market Economies, 13(2): 161-187
Behrendt, S., Schmidt, A. (2018) The Twitter myth revisited: Intraday investor sentiment, Twitter activity and individual-level stock return volatility. Journal of Banking & Finance, 96: 355-367
Benson, K., Faff, R., Smith, T. (2015) Injecting liquidity into liquidity research. Pacific-Basin Finance Journal, 35: 533-540
Brans, H., Scholtens, B. (2020) Under his thumb the effect of president Donald Trump's Twitter messages on the US stock market. PLoS One, 15(3): e0229931
Broadstock, D.C., Zhang, D. (2019) Social-media and intraday stock returns: The pricing power of sentiment. Finance Research Letters, 30: 116-123
Cabellon, E.T., Ahlquist, J. (2016) Engaging the digital generation: New directions for student services. New York: Jossey-Bass, 155
Chahine, S., Malhotra, N.K. (2018) Impact of social media strategies on stock price: The case of Twitter. European Journal of Marketing, 52(7/8): 1526-1549
Chuen, D.L.K., Gregoriou, G.N. (2014) Handbook of Asian finance: Financial markets and sovereign wealth funds. New York: Academic Press
Clemente, J., Gadea, M.D., Montañés, A., Reyes, M. (2017) Structural breaks, inflation and interest rates: Evidence from the G7 countries. Econometrics, 5(1): 11-11
Datar, V.T., Y.Naik, N., Radcliffe, R. (1998) Liquidity and stock returns: An alternative test. Journal of Financial Markets, 1(2): 203-219
Dugast, J., Foucault, T. (2016) Data abundance and asset price informativeness. Luxembourg: Luxembourg School of Finance, Working paper
Fan, R., Talavera, O., Tran, V. (2020) Social media, political uncertainty, and stock markets. Review of Quantitative Finance and Accounting, 55(3): 1137-1153
Florackis, C., Gregoriou, A., Kostakis, A. (2011) Trading frequency and asset pricing on the London Stock Exchange: Evidence from a new price impact ratio. Journal of Banking & Finance, 35(12): 3335-3350
Gabrielsen, A., Marzo, M., Zagaglia, P. (2011) Measuring market liquidity: An introductory survey. Munich Personal RePEc Archive
Ge, Q., Kurov, A., Wolfe, M.H. (2019) Do investors care about presidential company-specific Tweets?. Journal of Financial Research, 42(2): 213-242
Gil-Alana, L.A., Dadgar, Y., Nazari, R. (2019) Iranian inflation: Peristence and structural breaks. Journal of Economics and Finance, 43(2): 398-408
Gil-Alana, L.A., Mudida, R. (2017) CPI and inflation in Kenya: Structural breaks, non-linearities and dependence. International Economics, 150: 72-79
Gil-Alana, L.A., Dos, S.F.O.H., Wanke, P. (2019) Structural breaks in Brazilian tourism revenues: Unveiling the impact of exchange rates and sports mega-events. Tourism Management, 74: 207-211
Groß-Klußmann, A., König, S., Ebner, M. (2019) Buzzwords build momentum: Global financial Twitter sentiment and the aggregate stock market. Expert Systems with Application, 136: 171-186
Guijarro, F., Moya-Clemente, I., Saleemi, J. (2019) Liquidity risk and investors' mood: Linking the financial market liquidity to sentiment analysis through Twitter in the S&P500 index. Sustainability, 11(24): 7048
Hansen, B.E. (1997) Approximate asymptotic P-values for structural-change tests. Journal of Business & Economic Statistics, 15(1): 60-67
Hegerty, S.W. (2020) Structural breaks and regional inflation convergence for five new Euro members. Economic Change and Restructuring, 53(2): 219-239
Holden, C.W., Jacobsen, S.E., Subrahmanyam, A. (2014) The empirical analysis of liquidity. Kelley School of Business Research Paper, 2014-09
Hur, S., Chung, C.Y. (2018) A novel measure of liquidity premium: Application to the Korean stock market. Applied Economics Letters, 25(3): 211-215
Kisling, W., Lam, E., Mehta, N. (2013) Human beats machine this time as fake report roils stocks. Accessed 24.05.2020
Klaus, J., Koser, C. (2021) Measuring Trump: The Volfefe index and its impact on European financial markets. Finance Research Letters, 38: 101447
Kumar, N., Kumar, R.R., Patel, A., Stauvermann, P.J. (2019) Exploring the effect of tourism and economic growth in Fiji: Accounting for capital, labor, and structural breaks. Tourism Analysis, 24(2): 115-130
Li, L., Li, R., Wu, Z., Liu, J., Chen, D. (2020) Therapeutic strategies for critically ill patients with Covid-19. Annals of Intensive Care, 10(1): 45
Lone, S.A., Ahmad, A. (2020) Covid-19: An African perspective. Emerging Microbes & Infections, 9(1): 1300-1308
Makrehchi, M., Shah, S., Liao, W. (2013) Stock prediction using event-based sentiment analysis. u: Web Intelligence, New York: IEEE, 1, 6690034, (pp. 337-342)
Min, J.C.H., Kung, H.-H., Chang, T. (2019) Testing the structural break of Taiwan inbound tourism markets. Romanian Journal of Economic Forecasting, 22(2): 117-130
Nasir, M.A., Vo, X.V. (2020) A quarter century of inflation targeting & structural change in exchange rate pass-through: Evidence from the first three movers. Structural Change and Economic Dynamics, 54: 42-61
Nath, H.K., Sarkar, J. (2019) Inflation and relative price variability: New evidence from survey-based measures of inflation expectations in Australia. Empirical Economics, 56(6): 2001-2024
Nguyen, A.D.M., Dridi, J., Unsal, F.D., Williams, O.H. (2017) On the drivers of inflation in Sub-Saharan Africa. International Economics, 151: 71-84
Nisar, T.M., Yeung, M. (2018) Twitter as a tool for forecasting stock market movements: A short-window event study. Journal of Finance and Data Science, 4(2): 101-119
O'Hara, M. (2004) Liquidity and financial market stability. National Bank of Belgium, Working paper 55
Orlowski, L.T. (2017) Sensitivity of interest rates to inflation and exchange rate in Poland: Implications for direct inflation targeting. Comparative Economic Studies, 59(4): 545-560
Pöppe, T., Kolaric, S., Kiesel, F., Schiereck, D. (2020) Information or noise: How twitter facilitates stock market information aggregation. u: Chairs T. [ur.] Crowds, Social Media and Digital Collaborations, Munich: ICIS
Quandt, R.E. (1960) Tests of the hypothesis that a linear regression system obeys two separate regimes. Journal of the American Statistical Association, 55(290): 324-330
Reboredo, J.C., Ugolini, A. (2018) The impact of Twitter sentiment on renewable energy stocks. Energy Economics, 76: 153-169
Reed, M. (2016) A study of social network effects on the stock market. Journal of Behavioral Finance, 17(4): 342-351
Ruch, F., Balcilar, M., Gupta, R., Modise, M.P. (2020) Forecasting core inflation: The case of South Africa. Applied Economics, 52(28): 3004-3022
Sakhare, N.N., Imambi, S.S., Kagad, S., Malekar, H., Dalal, M. (2020) Stock market prediction using sentiment analysis. International Journal of Advanced Science and Technology, 29(4): 1126-1133
Sani, Z., Salisu, A.A., Onyia, E., Anih, O., Kanu, L. (2020) Modeling exchange rate-interest rate differential nexus in BRICS: The role asymmetry and structural breaks. Economics and Business Letters, 9(2): 73-83
Saurabh, S., Dey, K. (2020) Unraveling the relationship between social moods and the stock market: Evidence from the United Kingdom. Journal of Behavioral and Experimental Finance, 26: 100300
Shiva, A., Singh, M. (2019) Stock hunting or blue-chip investments: Investors' preferences for stocks in virtual geographies of social networks. Qualitative Research in Financial Markets, 12(1): 1-23
Stauvermann, P.J., Kumar, R.R., Shahzad, S.J.H., Kumar, N.N. (2018) Effect of tourism on economic growth of Sri Lanka: Accounting for capital per worker, exchange rate and structural breaks. Economic Change and Restructuring, 51(1): 49-68
Sul, H.K., Dennis, A.R., Yuan, L. (2014) Trading on twitter: The financial information content of emotion in social media. u: System Sciences, Hawaii, 806-815; 6758703
Twitter (2020) Company: About. Accessed May 9, 2020
Urlam, S.P.S., Mandal, S., Poornima, S. (2020) Stock prediction using twitter sentiment analysis. International Journal of Psychosocial Rehabilitation, 24(8): 1031-1035
Vanstone, B.J., Gepp, A., Harris, G. (2019) Do news and sentiment play a role in stock price prediction?. Applied Intelligence, 49(11): 3815-3820
Vayanos, D., Wang, J. (2012) Market liquidity: Theory and empirical evidence. National Bureau of Economic Research, w18251
Wu, C., Wang, C., Chang, T., Yuan, C. (2019) The nexus of electricity and economic growth in major economies: The United States-India-China triangle. Energy, 188: 116006
Wu, D. (2019) Does social media get your attention?. Journal of Behavioral Finance, 20(2): 213-226
Yongchen, Z., Shen, H., Wang, X., Chen, Y., Gu, B. (2020) Different longitudinal patterns of nucleic acid and serology testing results based on disease severity of Covid-19 patients. Emerging microbes & infections, 9(1): 833-836
Zhang, X., Shi, J., Wang, D., Fang, B. (2018) Exploiting investors social network for stock prediction in China's market. Journal of Computational Science, 28(2): 294-303
Zhang, X., Fuehres, H., Gloor, P.A. (2012) Predicting asset value through Twitter buzz. Advances in Intelligent and Soft Computing, 113: 23-34

O članku

jezik rada: engleski
vrsta rada: izvorni naučni članak
DOI: 10.5937/EJAE18-27857
primljen: 07.02.2020.
revidiran: 30.10.2020.
prihvaćen: 20.09.2021.
objavljen u SCIndeksu: 22.10.2021.
metod recenzije: dvostruko anoniman
Creative Commons License 4.0

Povezani članci

Nema povezanih članaka