IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v60y2022i2d10.1007_s10614-021-10165-y.html
   My bibliography  Save this article

Averages: There is Still Something to Learn

Author

Listed:
  • José Dias Curto

    (Instituto Universitário de Lisboa (ISCTE-IUL), BRU-UNIDE
    Av. Prof. Aníbal Bettencourt)

Abstract

The common way to deal with outliers in empirical Economics and Finance is to delete them, either by trimming or winsorizing, or by computing statistics robust to outliers. However, due to their importance, there are situations where the exclusion of these observations is not reasonable and may even be counterproductive. For example, should we exclude the very high stock prices of Amazon and Google from an empirical analysis? Even if the purpose is to compute an average of tech stock prices, does it make economic and financial sense? Maybe not. A solution that would keep the two companies in the data set and yet not penalize the higher observations as much as the median, harmonic and geometric averages, might—were such a solution to be available—constitute an attractive alternative. In this paper we propose and analyze a modified measure, the adjusted median, where the influence of the outlying observations, while not as high as in the arithmetic average would, however, give more weight to the outlying observations than the median, harmonic and geometric averages. Monte Carlo simulations and bootstrapping real financial data confirm how useful the adjusted median could be.

Suggested Citation

  • José Dias Curto, 2022. "Averages: There is Still Something to Learn," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 755-779, August.
    • Handle: RePEc:kap:compec:v:60:y:2022:i:2:d:10.1007_s10614-021-10165-y
    DOI: 10.1007/s10614-021-10165-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-021-10165-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-021-10165-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gan, Lirong & Wang, Huamao & Yang, Zhaojun, 2020. "Machine learning solutions to challenges in finance: An application to the pricing of financial products," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    2. Wellalage, Nirosha Hewa & Fernandez, Viviana, 2019. "Innovation and SME finance: Evidence from developing countries," International Review of Financial Analysis, Elsevier, vol. 66(C).
    3. Cheuk, Terry H F & Vorst, Ton C F, 1999. "Average Interest Rate Caps," Computational Economics, Springer;Society for Computational Economics, vol. 14(3), pages 183-196, December.
    4. Rodolphe Priam, 2020. "Visualization of generalized mean estimators using auxiliary information in survey sampling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(18), pages 4468-4489, September.
    5. Tomás Barrio & Mariam Camarero & Cecilio Tamarit, 2019. "Testing for Periodic Integration with a Changing Mean," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 45-75, June.
    6. Choi, Tsan-Ming & Wen, Xin & Sun, Xuting & Chung, Sai-Ho, 2019. "The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 178-191.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jie Fang & Shutao Xia & Jianwu Lin & Yong Jiang, 2019. "Automatic Financial Feature Construction," Papers 1912.06236, arXiv.org, revised Oct 2020.
    2. Muhammad Arif & Mudassar Hasan & Ahmed Shafique Joyo & Christopher Gan & Sazali Abidin, 2020. "Formal Finance Usage and Innovative SMEs: Evidence from ASEAN Countries," JRFM, MDPI, vol. 13(10), pages 1-19, September.
    3. Zhou, Yu & Gao, Xiang & Luo, Suyuan & Xiong, Yu & Ye, Niangyue, 2022. "Anti-Counterfeiting in a retail Platform: A Game-Theoretic approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    4. Xu, Xiaoping & He, Ping & Zhou, Li & Cheng, T.C.E., 2023. "Coordination of a platform-based supply chain in the marketplace or reselling mode considering cross-channel effect and blockchain technology," European Journal of Operational Research, Elsevier, vol. 309(1), pages 170-187.
    5. Ruzhi Xu & Tingting Guo & Huawei Zhao, 2022. "Research on the Path of Policy Financing Guarantee to Promote SMEs’ Green Technology Innovation," Mathematics, MDPI, vol. 10(4), pages 1-24, February.
    6. Govindan, Kannan & Mina, Hassan & Alavi, Behrouz, 2020. "A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    7. Meng, Jia & Zhang, ZhongXiang, 2022. "Corporate Environmental Information Disclosure and Investor Response: Empirical Evidence from China's Capital Market," FEEM Working Papers 317842, Fondazione Eni Enrico Mattei (FEEM).
    8. Curto, José Dias & Serrasqueiro, Pedro, 2022. "Averaging financial ratios," Finance Research Letters, Elsevier, vol. 48(C).
    9. Zhu, Qingyun & Bai, Chunguang & Sarkis, Joseph, 2022. "Blockchain technology and supply chains: The paradox of the atheoretical research discourse," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    10. Kijkasiwat, Ploypailin, 2021. "The influence of behavioral factors on SMES’ owners intention to adopt private finance," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    11. Hewa Wellalage, Nirosha & Hunjra, Ahmed Imran & Manita, Riadh & Locke, Stuart M., 2021. "Information communication technology and financial inclusion of innovative entrepreneurs," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    12. Edmund Mallinguh & Christopher Wasike & Zeman Zoltan, 2020. "Technology Acquisition and SMEs Performance, the Role of Innovation, Export and the Perception of Owner-Managers," JRFM, MDPI, vol. 13(11), pages 1-19, October.
    13. Sun, Xuting & Chung, Sai-Ho & Choi, Tsan-Ming & Sheu, Jiuh-Biing & Ma, Hoi Lam, 2020. "Combating lead-time uncertainty in global supply chain's shipment-assignment: Is it wise to be risk-averse?," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 406-434.
    14. Wellalage, Nirosha Hewa & Locke, Stuart, 2020. "Formal credit and innovation: Is there a uniform relationship across types of innovation?," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 1-15.
    15. Sun, Yige & Chung, Sai-Ho & Wen, Xin & Ma, Hoi-Lam, 2021. "Novel robotic job-shop scheduling models with deadlock and robot movement considerations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    16. Li, Zhiwen & Xu, Xianhao & Bai, Qingguo & Guan, Xu & Zeng, Kuan, 2021. "The interplay between blockchain adoption and channel selection in combating counterfeits," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    17. Dutta, Pankaj & Choi, Tsan-Ming & Somani, Surabhi & Butala, Richa, 2020. "Blockchain technology in supply chain operations: Applications, challenges and research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    18. Mangla, Sachin Kumar & Kazancoglu, Yigit & Ekinci, Esra & Liu, Mengqi & Özbiltekin, Melisa & Sezer, Muruvvet Deniz, 2021. "Using system dynamics to analyze the societal impacts of blockchain technology in milk supply chainsrefer," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    19. Khan, Waqar Ahmed & Ma, Hoi-Lam & Ouyang, Xu & Mo, Daniel Y., 2021. "Prediction of aircraft trajectory and the associated fuel consumption using covariance bidirectional extreme learning machines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    20. Ștefan Cristian Gherghina & Mihai Alexandru Botezatu & Alexandra Hosszu & Liliana Nicoleta Simionescu, 2020. "Small and Medium-Sized Enterprises (SMEs): The Engine of Economic Growth through Investments and Innovation," Sustainability, MDPI, vol. 12(1), pages 1-22, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:60:y:2022:i:2:d:10.1007_s10614-021-10165-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.