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A Decision Support System for Stock Investment Recommendations Using Collective Wisdom

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  • Gottschlich, Jörg
  • Hinz, Oliver

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  • Gottschlich, Jörg & Hinz, Oliver, 2014. "A Decision Support System for Stock Investment Recommendations Using Collective Wisdom," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 69939, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    • Handle: RePEc:dar:wpaper:69939
    Note: for complete metadata visit http://tubiblio.ulb.tu-darmstadt.de/69939/
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    Cited by:

    1. Theofanis Petropoulos & Konstantinos Liapis & Eleftherios Thalassinos, 2023. "Optimal Structure of Real Estate Portfolio Using EVA: A Stochastic Markowitz Model Using Data from Greek Real Estate Market," Risks, MDPI, vol. 11(2), pages 1-19, February.
    2. Mohamed Masry, 2017. "The Impact of Technical Analysis on Stock Returns in an Emerging Capital Markets (ECM¡¯s) Country: Theoretical and Empirical Study," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(3), pages 91-107, March.
    3. Peter Gomber & Jascha-Alexander Koch & Michael Siering, 2017. "Digital Finance and FinTech: current research and future research directions," Journal of Business Economics, Springer, vol. 87(5), pages 537-580, July.
    4. MohammadAmin Fazli & Mahdi Lashkari & Hamed Taherkhani & Jafar Habibi, 2022. "A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management," Papers 2212.14477, arXiv.org.
    5. Dag, Ali & Dag, Asli Z. & Asilkalkan, Abdullah & Simsek, Serhat & Delen, Dursun, 2023. "A Tree Augmented Naïve Bayes-based methodology for classifying cryptocurrency trends," Journal of Business Research, Elsevier, vol. 156(C).
    6. Abdel-Karim, Benjamin M. & Benlian, Alexander & Hinz, Oliver, 2021. "The Predictive Value of Data from Virtual Investment Communities," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124589, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    7. Frantisek Darena & Jonas Petrovsky & Jan Zizka & Jan Prichystal, 2016. "Analyzing the correlation between online texts and stock price movements at micro-level using machine learning," MENDELU Working Papers in Business and Economics 2016-67, Mendel University in Brno, Faculty of Business and Economics.
    8. Aleksandra Kuzior & Aleksy Kwilinski & Ihor Hroznyi, 2021. "The Factorial-Reflexive Approach to Diagnosing the Executors’ and Contractors’ Attitude to Achieving the Objectives by Energy Supplying Companies," Energies, MDPI, vol. 14(9), pages 1-16, April.
    9. Steininger, Dennis M. & Gatzemeier, Simon, 2019. "Digitally forecasting new music product success via active crowdsourcing," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 167-180.
    10. Anita Mirchandani & Namrata Gupta & Esinath Ndiweni, 2020. "Understanding the Fintech Wave: A Search for a Theoretical Explanation," International Journal of Economics and Financial Issues, Econjournals, vol. 10(5), pages 331-343.
    11. Riccardo Reith & Maximilian Fischer & Bettina Lis, 2020. "Explaining the intention to use social trading platforms: an empirical investigation," Journal of Business Economics, Springer, vol. 90(3), pages 427-460, April.
    12. Rohit Aggarwal & Michael J Lee & Braxton Osting & Harpreet Singh, 2021. "Improving Funding Operations of Equity‐based Crowdfunding Platforms," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4121-4139, November.
    13. Valeria Croce & Karl Wöber & John Kester, 2016. "Expert identification and calibration for collective forecasting tasks," Tourism Economics, , vol. 22(5), pages 979-994, October.

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