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Nikolaos Vlastakis

Personal Details

First Name:Nikolaos
Middle Name:
Last Name:Vlastakis
Suffix:
RePEc Short-ID:pvl13

Affiliation

Essex Business School
University of Essex

Colchester, United Kingdom
http://www.essex.ac.uk/ebs/

:
020 76316416
Wivenhoe Park, Colchester C04 3SQ
RePEc:edi:daessuk (more details at EDIRC)

Research output

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Jump to: Articles

Articles

  1. Chronopoulos, Dimitris K. & Papadimitriou, Fotios I. & Vlastakis, Nikolaos, 2018. "Information demand and stock return predictability," Journal of International Money and Finance, Elsevier, vol. 80(C), pages 59-74.
  2. George Dotsis & Nikolaos Vlastakis, 2016. "Corridor Volatility Risk and Expected Returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(5), pages 488-505, May.
  3. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
  4. Nikolaos Vlastakis & George Dotsis & Raphael N. Markellos, 2009. "How efficient is the European football betting market? Evidence from arbitrage and trading strategies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 426-444.
  5. Nikolaos Vlastakis & George Dotsis & Raphael Markellos, 2008. "Nonlinear modelling of European football scores using support vector machines," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 111-118.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. George Dotsis & Nikolaos Vlastakis, 2016. "Corridor Volatility Risk and Expected Returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(5), pages 488-505, May.

    Cited by:

    1. Chen, Yu-Lun & Tsai, Wei-Che, 2017. "Determinants of price discovery in the VIX futures market," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 59-73.

  2. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.

    Cited by:

    1. Ladislav Kristoufek, 2015. "Power-law correlations in finance-related Google searches, and their cross-correlations with volatility and traded volume: Evidence from the Dow Jones Industrial components," Papers 1502.00225, arXiv.org.
    2. Artem Meshcheryakov & Stoyu I Ivanov, 2017. "Investor's sentiment in predicting the Effective Federal Funds Rate," Economics Bulletin, AccessEcon, vol. 37(4), pages 2767-2796.
    3. Parag A. Pathak & Alvin E. Roth, 2013. "Matching with Couples: Stability and Incentives in Large Markets," The Quarterly Journal of Economics, Oxford University Press, vol. 128(4), pages 1585-1632.
    4. Jaroslav Bukovina, 2016. "Social Media and Capital Markets – an Overview," MENDELU Working Papers in Business and Economics 2016-57, Mendel University in Brno, Faculty of Business and Economics.
    5. Theologos Dergiades & Costas Milas & Theodore Panagiotidis, 2013. "Tweets, Google Trends and Sovereign Spreads in the GIIPS," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 78, Hellenic Observatory, LSE.
    6. Melissa S. Kearney & Phillip B. Levine, 2015. "Media Influences on Social Outcomes: The Impact of MTV's 16 and Pregnant on Teen Childbearing," American Economic Review, American Economic Association, vol. 105(12), pages 3597-3632, December.
    7. Schmidt, Torsten & Vosen, Simeon, 2012. "Using Internet Data to Account for Special Events in Economic Forecasting," Ruhr Economic Papers 382, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    8. Moussa, Faten & BenOuda, Olfa & Delhoumi, Ezzeddine, 2017. "The use of open source internet to analysis and predict stock market trading volume," Research in International Business and Finance, Elsevier, vol. 41(C), pages 399-411.
    9. D Aromi & A Clements, 2018. "Media attention and crude oil volatility: Is there any 'new' news in the newspaper?," NCER Working Paper Series 118, National Centre for Econometric Research.
    10. Prashant Das & Alan Ziobrowski & N. Coulson, 2015. "Online Information Search, Market Fundamentals and Apartment Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 51(4), pages 480-502, November.
    11. Semen Son-Turan, 2016. "The Impact of Investor Sentiment on the "Leverage Effect"," International Econometric Review (IER), Econometric Research Association, vol. 8(1), pages 4-18, April.
    12. Baur, Dirk G. & Dimpfl, Thomas, 2016. "Googling gold and mining bad news," Resources Policy, Elsevier, vol. 50(C), pages 306-311.
    13. Peltomäki, Jarkko & Vähämaa, Emilia, 2015. "Investor attention to the Eurozone crisis and herding effects in national bank stock indexes," Finance Research Letters, Elsevier, vol. 14(C), pages 111-116.
    14. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    15. Chouliaras, Andreas & Grammatikos, Theoharry, 2013. "News Flow, Web Attention and Extreme Returns in the European Financial Crisis," MPRA Paper 51335, University Library of Munich, Germany.
    16. Massimo PERI & Daniela VANDONE & Lucia BALDI, 2012. "Internet, noise trading and commodity prices," Departmental Working Papers 2012-07, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    17. Ding, Rong & Hou, Wenxuan, 2015. "Retail investor attention and stock liquidity," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 12-26.
    18. Bucher, Melk C., 2017. "Investor Attention and Sentiment: Risk or Anomaly?," Working Papers on Finance 1712, University of St. Gallen, School of Finance.
    19. Amal Aouadi & Mohamed Arouri & Frédéric Teulon, 2014. "Investor Following and Volatility: A GARCH Approach," Working Papers 2014-286, Department of Research, Ipag Business School.
    20. Moussa, Faten & Delhoumi, Ezzeddine & Ouda, Olfa Ben, 2017. "Stock return and volatility reactions to information demand and supply," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 54-67.
    21. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2014. "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics," Papers 1412.3948, arXiv.org, revised Dec 2015.
    22. Ana Brochado, 2016. "Investor attention and Portuguese stock market volatility: We’ll google it for you!," EcoMod2016 9345, EcoMod.
    23. Afkhami, Mohamad & Cormack, Lindsey & Ghoddusi, Hamed, 2017. "Google search keywords that best predict energy price volatility," Energy Economics, Elsevier, vol. 67(C), pages 17-27.
    24. Masaki Mori, 2015. "Information Diffusion in the U.S. Real Estate Investment Trust Market," The Journal of Real Estate Finance and Economics, Springer, vol. 51(2), pages 190-214, August.
    25. ap Gwilym, O. & Kita, A. & Wang, Q., 2014. "Speculate against speculative demand," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 212-221.
    26. Li, Shouwei & Zhuang, Yangyang & He, Jianmin, 2016. "Stock market stability: Diffusion entropy analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 462-465.
    27. Anenberg, Elliot & Kung, Edward, 2015. "Information technology and product variety in the city: The case of food trucks," Journal of Urban Economics, Elsevier, vol. 90(C), pages 60-78.
    28. Georgios Bampinas & Theodore Panagiotidis & Christina Rouska, 2018. "Volatility persistence and asymmetry under the microscope: The role of information demand for gold and oil," Working Paper series 18-13, Rimini Centre for Economic Analysis.
    29. Mohamed Arouri & Amal Aouadi & Philippe Foulquier & Frédéric Teulon, 2013. "Can Information Demand Help to Predict Stock Market Liquidity ? Google it !," Working Papers 2013-24, Department of Research, Ipag Business School.
    30. Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
    31. Goddard, John & Kita, Arben & Wang, Qingwei, 2015. "Investor attention and FX market volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 79-96.
    32. Ronald MacDonald & Xuxin Mao, 2015. "An Alternative way of Predicting the Outcome of the Scottish Independence Referendum: The Information in the Ether," SIRE Discussion Papers 2015-69, Scottish Institute for Research in Economics (SIRE).
    33. Vakrman, Tomas & Kristoufek, Ladislav, 2015. "Underpricing, underperformance and overreaction in initial pubic offerings: Evidence from investor attention using online searches," FinMaP-Working Papers 35, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    34. Sun, Hang, 2016. "Crisis-Contingent Dynamics of Connectedness: An SVAR-Spatial-Network “Tripod” Model with Thresholds," Research Memorandum 032, Maastricht University, Graduate School of Business and Economics (GSBE).
    35. Matthias Bank & Martin Larch & Georg Peter, 2011. "Google search volume and its influence on liquidity and returns of German stocks," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 25(3), pages 239-264, September.
    36. Matija Piv{s}korec & Nino Antulov-Fantulin & Petra Kralj Novak & Igor Mozetiv{c} & Miha Grv{c}ar & Irena Vodenska & Tomislav v{S}muc, 2014. "News Cohesiveness: an Indicator of Systemic Risk in Financial Markets," Papers 1402.3483, arXiv.org.
    37. Alexander F. McQuoid & Charles Moore & Stephen Sawyer & David C. Vitt, 2017. "Trigger Warning: The Causal Impact of Gun Ownership on Suicide," Departmental Working Papers 55, United States Naval Academy Department of Economics.
    38. Ana Brochado, 2016. "Retail Investor Sentiment: Can We Google It?," EcoMod2016 9341, EcoMod.
    39. Chen, Linda H. & Dyl, Edward A. & Jiang, George J. & Juneja, Januj A., 2015. "Risk, illiquidity or marketability: What matters for the discounts on private equity placements?," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 41-50.
    40. Tang, Wenbin & Zhu, Lili, 2017. "How security prices respond to a surge in investor attention: Evidence from Google Search of ADRs," Global Finance Journal, Elsevier, vol. 33(C), pages 38-50.
    41. Massimiliano Caporin & Francesco Poli, 2017. "Building News Measures from Textual Data and an Application to Volatility Forecasting," Econometrics, MDPI, Open Access Journal, vol. 5(3), pages 1-46, August.
    42. Li, Xin & Ma, Jian & Wang, Shouyang & Zhang, Xun, 2015. "How does Google search affect trader positions and crude oil prices?," Economic Modelling, Elsevier, vol. 49(C), pages 162-171.
    43. Latoeiro, Pedro & Ramos, Sofía B. & Veiga, Helena, 2013. "Predictability of stock market activity using Google search queries," DES - Working Papers. Statistics and Econometrics. WS ws130605, Universidad Carlos III de Madrid. Departamento de Estadística.
    44. Chronopoulos, Dimitris K. & Papadimitriou, Fotios I. & Vlastakis, Nikolaos, 2018. "Information demand and stock return predictability," Journal of International Money and Finance, Elsevier, vol. 80(C), pages 59-74.
    45. Takayuki Morimoto & Yoshinori Kawasaki, 2017. "Forecasting Financial Market Volatility Using a Dynamic Topic Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 24(3), pages 149-167, September.
    46. Smith, Geoffrey Peter, 2012. "Google Internet search activity and volatility prediction in the market for foreign currency," Finance Research Letters, Elsevier, vol. 9(2), pages 103-110.
    47. Peri, Massimo & Vandone, Daniela & Baldi, Lucia, 2014. "Internet, noise trading and commodity futures prices," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 82-89.
    48. Peri, Massimo & Vandone, Daniela & Baldi, Lucia, 2012. "Information Demand and Agriculture Commodity Prices," 2012 International European Forum, February 13-17, 2012, Innsbruck-Igls, Austria 144973, International European Forum on Innovation and System Dynamics in Food Networks.
    49. Minjian Ye & Guangzhong Li, 2017. "Internet big data and capital markets: a literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-18, December.
    50. Qadan, Mahmoud & Nama, Hazar, 2018. "Investor sentiment and the price of oil," Energy Economics, Elsevier, vol. 69(C), pages 42-58.
    51. Feng, Jiabao & Wang, Yudong & Yin, Libo, 2017. "Oil volatility risk and stock market volatility predictability: Evidence from G7 countries," Energy Economics, Elsevier, vol. 68(C), pages 240-254.
    52. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.
    53. Peltomäki, Jarkko & Graham, Michael & Hasselgren, Anton, 2018. "Investor attention to market categories and market volatility: The case of emerging markets," Research in International Business and Finance, Elsevier, vol. 44(C), pages 532-546.
    54. Aouadi, Amal & Arouri, Mohamed & Roubaud, David, 2018. "Information demand and stock market liquidity: International evidence," Economic Modelling, Elsevier, vol. 70(C), pages 194-202.
    55. Zhang, Yongjie & Song, Weixin & Shen, Dehua & Zhang, Wei, 2016. "Market reaction to internet news: Information diffusion and price pressure," Economic Modelling, Elsevier, vol. 56(C), pages 43-49.
    56. Takeda, Fumiko & Wakao, Takumi, 2014. "Google search intensity and its relationship with returns and trading volume of Japanese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 1-18.
    57. Aouadi, Amal & Arouri, Mohamed & Teulon, Frédéric, 2013. "Investor attention and stock market activity: Evidence from France," Economic Modelling, Elsevier, vol. 35(C), pages 674-681.
    58. Bouoiyour, Jamal & Selmi, Refk & Tiwari, Aviral, 2014. "Is Bitcoin business income or speculative bubble? Unconditional vs. conditional frequency domain analysis," MPRA Paper 59595, University Library of Munich, Germany.
    59. Adachi, Yuta & Masuda, Motoki & Takeda, Fumiko, 2017. "Google search intensity and its relationship to the returns and liquidity of Japanese startup stocks," Pacific-Basin Finance Journal, Elsevier, vol. 46(PB), pages 243-257.
    60. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
    61. Tantaopas, Parkpoom & Padungsaksawasdi, Chaiyuth & Treepongkaruna, Sirimon, 2016. "Attention effect via internet search intensity in Asia-Pacific stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 38(C), pages 107-124.

  3. Nikolaos Vlastakis & George Dotsis & Raphael N. Markellos, 2009. "How efficient is the European football betting market? Evidence from arbitrage and trading strategies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 426-444.

    Cited by:

    1. Babatunde Buraimo & David Peel & Rob Simmons, 2013. "Systematic Positive Expected Returns in the UK Fixed Odds Betting Market: An Analysis of the Fink Tank Predictions," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 1(4), pages 1-15, December.
    2. Lahvicka, Jiri, 2013. "The Fibonacci Strategy Revisited: Can You Really Make Money by Betting on Soccer Draws?," MPRA Paper 47649, University Library of Munich, Germany.
    3. Egon Franck & Erwin Verbeek & Stephan Nüesch, 2013. "Inter-market Arbitrage in Betting," Economica, London School of Economics and Political Science, vol. 80(318), pages 300-325, April.
    4. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.
    5. Adrian Bell & Chris Brooks & David Matthews & Charles Sutcliffe, 2009. "Over the Moon or Sick as a Parrot? The Effect's of Football Results on a Club's Share Price," ICMA Centre Discussion Papers in Finance icma-dp2009-08, Henley Business School, Reading University.
    6. Hofer, Vera & Leitner, Johannes, 2017. "Relative pricing of binary options in live soccer betting markets," Journal of Economic Dynamics and Control, Elsevier, vol. 76(C), pages 66-85.
    7. Jinook Jeong & Jee Young Kim & Yoon Jae Ro, 2017. "On the Efficiency of Racetrack Betting Market: A New Test for the Favorite-Longshot Bias," Working papers 2017rwp-106, Yonsei University, Yonsei Economics Research Institute.
    8. Masahiro Ashiya, 2013. "Lock! Risk-Free Arbitrage in the Japanese Racetrack Betting Market," Discussion Papers 1301, Graduate School of Economics, Kobe University.
    9. Auld, T. & Linton, O., 2017. "The Behaviour of Betting and Currency Markets on the Night of the EU Referendum," Cambridge Working Papers in Economics 1750, Faculty of Economics, University of Cambridge.
    10. Colantonio Emiliano, 2013. "Betting Markets: Opportunities For Many?," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(2), pages 200-208, December.
    11. Leitner, Christoph & Zeileis, Achim & Hornik, Kurt, 2010. "Forecasting sports tournaments by ratings of (prob)abilities: A comparison for the EUROÂ 2008," International Journal of Forecasting, Elsevier, vol. 26(3), pages 471-481, July.
    12. Dominic Cortis & Steve Hales & Frank Bezzina, 2013. "Profiting On Inefficiencies In Betting Derivative Markets: The Case Of Uefa Euro 2012," Journal of Gambling Business and Economics, University of Buckingham Press, vol. 7(1), pages 39-51.

  4. Nikolaos Vlastakis & George Dotsis & Raphael Markellos, 2008. "Nonlinear modelling of European football scores using support vector machines," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 111-118.

    Cited by:

    1. Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," AQR Working Papers 201410, University of Barcelona, Regional Quantitative Analysis Group, revised May 2014.

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