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Market Trend Analysis

In: The Butterfly Effect in Competitive Markets

Author

Listed:
  • Rajagopal

    (ITESM Mexico City
    Boston University)

Abstract

Most firms making efforts to gain competitive advantage build their marketing strategies on product improvements as impulsive changes to attract consumers and the market. Changes in existing products, services, or marketing strategies often face consumer resistance. This chapter defines market resistance as an opposition to traditions in the marketplace to create new behavior among consumers. Market shifts explore purposive behavior of consumers and attempt to recreate the modern social conventions that manifest positive impact on competitive differentiations in the marketplace. Many companies recognize that their dispersed global operations drive innovative ideas and build capabilities for introducing competitive market changes. This chapter explains the role of managers in successfully bringing innovations to an enterprise and expanding at the global scale. Arguments are illustrated with examples in reference to phases of innovations, behavioral metrics, the change-resistance cycle, critical success factors, opportunities, threats and disruptions, and chain reactions of innovation. The chapter also addresses the concept and practice of conceiving ideas and nurturing strategic business shifts in a competitive marketplace and suggests ways to increase effectiveness in managing innovations and corporate goals.

Suggested Citation

  • Rajagopal, 2015. "Market Trend Analysis," Palgrave Macmillan Books, in: The Butterfly Effect in Competitive Markets, chapter 4, pages 95-118, Palgrave Macmillan.
    • Handle: RePEc:pal:palchp:978-1-137-43497-5_4
    DOI: 10.1057/9781137434975_4
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    Citations

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    Cited by:

    1. Junran Wu & Ke Xu & Jichang Zhao, 2019. "Online reviews can predict long-term returns of individual stocks," Papers 1905.03189, arXiv.org.
    2. De Castro, Angelo, 2022. "The Ebb of Fiat and the Flow of Cryptocurrency," OSF Preprints trpwc, Center for Open Science.
    3. Paloviita, Maritta & Haavio, Markus & Jalasjoki, Pirkka & Kilponen, Juha & Vänni, Ilona, 2020. "Reading between the lines : Using text analysis to estimate the loss function of the ECB," Research Discussion Papers 12/2020, Bank of Finland.
    4. Hyejung Chung & Kyung-shik Shin, 2018. "Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
    5. Lin, Yu & Yan, Yan & Xu, Jiali & Liao, Ying & Ma, Feng, 2021. "Forecasting stock index price using the CEEMDAN-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    6. Jihong Xiao & Xuehong Zhu & Chuangxia Huang & Xiaoguang Yang & Fenghua Wen & Meirui Zhong, 2019. "A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 287-310, January.
    7. 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.
    8. U, JuHyok & Lu, PengYu & Kim, ChungSong & Ryu, UnSok & Pak, KyongSok, 2020. "A new LSTM based reversal point prediction method using upward/downward reversal point feature sets," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    9. repec:zbw:bofrdp:2020_012 is not listed on IDEAS
    10. Packey, Daniel J. & Kingsnorth, Dudley, 2016. "The impact of unregulated ionic clay rare earth mining in China," Resources Policy, Elsevier, vol. 48(C), pages 112-116.
    11. Jani KINNUNEN & Armenia ANDRONICEANU & Irina GEORGESCU, 2019. "Digitalization Of Eu Countries: A Clusterwise Analysis," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 13(1), pages 1-12, November.
    12. Das, Smruti Rekha & Kuhoo, & Mishra, Debahuti & Rout, Minakhi, 2019. "An optimized feature reduction based currency forecasting model exploring the online sequential extreme learning machine and krill herd strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 339-370.
    13. Ru Zhang & Chenyu Huang & Weijian Zhang & Shaozhen Chen, 2018. "Multi Factor Stock Selection Model Based on LSTM," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(8), pages 1-36, August.
    14. Aqila Rafiuddin & Jennifer Daffodils & Jesus Cuauhtemoc Tellez Gaytan & Gyanendra Singh Sisodia, 2021. "Trend of Oil Prices, Gold, GCC Stocks Market during Covid-19 Pandemic: A Wavelet Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 560-572.
    15. Elona Marku & Manuel Castriotta & Michela Loi & Maria Chiara Di Guardo, 2021. "General Purpose Technology: The Blockchain Domain," International Journal of Business and Management, Canadian Center of Science and Education, vol. 15(11), pages 192-192, July.
    16. Wilhelm Berghorn & Sascha Otto, 2017. "Momentum: An Economic View," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 8(3), pages 142-153, July.
    17. Asit Kumar Das & Debahuti Mishra & Kaberi Das & Pradeep Kumar Mallick & Sachin Kumar & Mikhail Zymbler & Hesham El-Sayed, 2022. "Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine," Mathematics, MDPI, vol. 10(7), pages 1-33, March.
    18. Ziniu Hu & Weiqing Liu & Jiang Bian & Xuanzhe Liu & Tie-Yan Liu, 2017. "Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction," Papers 1712.02136, arXiv.org, revised Feb 2019.
    19. Diego Lopez-Bernal & David Balderas & Pedro Ponce & Arturo Molina, 2021. "Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems," Future Internet, MDPI, vol. 13(8), pages 1-14, July.
    20. Suppawong Tuarob & Poom Wettayakorn & Ponpat Phetchai & Siripong Traivijitkhun & Sunghoon Lim & Thanapon Noraset & Tipajin Thaipisutikul, 2021. "DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-32, December.
    21. Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
    22. Umair Khan & Farhan Aadil & Mustansar Ali Ghazanfar & Salabat Khan & Noura Metawa & Khan Muhammad & Irfan Mehmood & Yunyoung Nam, 2018. "A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets," Sustainability, MDPI, vol. 10(10), pages 1-20, October.
    23. Faizal Hafiz & Jan Broekaert & Davide La Torre & Akshya Swain, 2021. "A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting," Papers 2111.08060, arXiv.org.
    24. Liu, Keyan & Zhou, Jianan & Dong, Dayong, 2021. "Improving stock price prediction using the long short-term memory model combined with online social networks," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).

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