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Analysis of investment behavior among Filipinos: Integration of Social exchange theory (SET) and the Theory of planned behavior (TPB)

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  • Ong, Ardvin Kester S.
  • Mendoza, Mary Christy O.
  • Ponce, Jean Rondel R.
  • Bernardo, Kent Timothy A.
  • Tolentino, Seth Angelo M.
  • Diaz, John Francis T.
  • Young, Michael N.

Abstract

Despite the emergence of more accessible and modern forms of investment, the ever competitive and volatile market remains subject to anomalous irrationalities caused by investors. To this day, predicting their behavior remains difficult with lacking information, and poses a problem for investment platforms to effectively adjust to their predispositions. Therefore, this study aimed to comprehensively analyze the factors that have influenced investors’ behaviors using the integrated construct of the Social Exchange Theory and the Theory of Planned Behavior. With consideration of convenience sampling, a total number of 10,725 data points were collected and analyzed through machine learning algorithms of decision tree and neural network. Specifically, the comparison between long short-term memory (LSTM) and neural network, and random forest classifier and LightGBM were considered. It was found that the investor’s attitude, accessibility to financial services, and perceived economic benefits were the most influential predictors to their behavior, while six other factors also showed varying levels of significance. This study aimed to provide a unique framework which could be utilized by investment platforms to cater to the different behavioral factors expressed by investors. In line with these findings, it is recommended that platforms create flexible solutions that are based on their intentions and preferences, and more user-friendly through the implementation of new technologies. In addition, they are suggested to appeal to novice investors by reducing the burden of costs, promising future benefits, and promoting financial education. The results of this study proved the reliability of the integrated model as a social and behavioral framework, and consequently, LSTM overpowering other tools on accurate forecast made, followed by neural network, and random forest.

Suggested Citation

  • Ong, Ardvin Kester S. & Mendoza, Mary Christy O. & Ponce, Jean Rondel R. & Bernardo, Kent Timothy A. & Tolentino, Seth Angelo M. & Diaz, John Francis T. & Young, Michael N., 2024. "Analysis of investment behavior among Filipinos: Integration of Social exchange theory (SET) and the Theory of planned behavior (TPB)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).
    • Handle: RePEc:eee:phsmap:v:654:y:2024:i:c:s037843712400671x
    DOI: 10.1016/j.physa.2024.130162
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    1. Saleh M. Shehata & Alaa M. Abdeljawad & Loqman A. Mazouz & Lamia Yousif Khalaf Aldossary & Maryam Y. Alsaeed & Mohamed Noureldin Sayed, 2021. "The Moderating Role of Perceived Risks in the Relationship between Financial Knowledge and the Intention to Invest in the Saudi Arabian Stock Market," IJFS, MDPI, vol. 9(1), pages 1-16, January.
    2. Wongkitrungrueng, Apiradee & Assarut, Nuttapol, 2020. "The role of live streaming in building consumer trust and engagement with social commerce sellers," Journal of Business Research, Elsevier, vol. 117(C), pages 543-556.
    3. Mohd Adil & Yogita Singh & Mohammad Subhan & Mamdouh Abdulaziz Saleh Al-Faryan & Mohd Shamim Ansari, 2023. "Do trust in financial institution and financial literacy enhances intention to participate in stock market among Indian investors during COVID-19 pandemic?," Cogent Economics & Finance, Taylor & Francis Journals, vol. 11(1), pages 2169998-216, December.
    4. Sembada, Agung Y. & Koay, Kian Yeik, 2021. "How perceived behavioral control affects trust to purchase in social media stores," Journal of Business Research, Elsevier, vol. 130(C), pages 574-582.
    5. Johe, Miles H. & Bhullar, Navjot, 2016. "To buy or not to buy: The roles of self-identity, attitudes, perceived behavioral control and norms in organic consumerism," Ecological Economics, Elsevier, vol. 128(C), pages 99-105.
    6. Cristina Bicchieri & Erte Xiao & Ryan Muldoon, 2011. "Trustworthiness is a social norm, but trusting is not," Politics, Philosophy & Economics, , vol. 10(2), pages 170-187, May.
    7. Cronqvist, Henrik & Siegel, Stephan, 2014. "The genetics of investment biases," Journal of Financial Economics, Elsevier, vol. 113(2), pages 215-234.
    8. Minh-Hoang Nguyen & Quy Van Khuc & Viet-Phuong La & Tam-Tri Le & Quang-Loc Nguyen & Ruining Jin & Phuong-Tri Nguyen & Quan-Hoang Vuong, 2022. "Mindsponge-Based Reasoning of Households’ Financial Resilience during the COVID-19 Crisis," JRFM, MDPI, vol. 15(11), pages 1-18, November.
    9. Vasiliki Vamvaka & Chrysostomos Stoforos & Theodosios Palaskas & Charalampos Botsaris, 2020. "Attitude toward entrepreneurship, perceived behavioral control, and entrepreneurial intention: dimensionality, structural relationships, and gender differences," Journal of Innovation and Entrepreneurship, Springer, vol. 9(1), pages 1-26, December.
    10. Annamaria Lusardi, 2019. "Financial literacy and the need for financial education: evidence and implications," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 155(1), pages 1-8, December.
    11. Cheng-Po Lai, 2019. "Personality Traits and Stock Investment of Individuals," Sustainability, MDPI, vol. 11(19), pages 1-20, October.
    12. Riccardo Fini & Rosa Grimaldi & Gian Luca Marzocchi & Maurizio Sobrero, 2012. "The Determinants of Corporate Entrepreneurial Intention within Small and Newly Established Firms," Entrepreneurship Theory and Practice, , vol. 36(2), pages 387-414, March.
    13. Last Mazambani & Emmanuel Mutambara, 2019. "Predicting FinTech innovation adoption in South Africa: the case of cryptocurrency," African Journal of Economic and Management Studies, Emerald Group Publishing Limited, vol. 11(1), pages 30-50, October.
    14. Tabea Bucher-Koenen & Michael Ziegelmeyer, 2014. "Once Burned, Twice Shy? Financial Literacy and Wealth Losses during the Financial Crisis," Review of Finance, European Finance Association, vol. 18(6), pages 2215-2246.
    15. Sarstedt, Marko & Hair, Joseph F. & Cheah, Jun-Hwa & Becker, Jan-Michael & Ringle, Christian M., 2019. "How to specify, estimate, and validate higher-order constructs in PLS-SEM," Australasian marketing journal, Elsevier, vol. 27(3), pages 197-211.
    16. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    17. Marija Ham & Marina Jeger & Anita Frajman Ivković, 2015. "The role of subjective norms in forming the intention to purchase green food," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 28(1), pages 738-748, January.
    18. Dongao Li & Songdong Shen, 2022. "Social Environment and Healthy Investment Behavior: Joint Influence of Culture and Institution on China," IJERPH, MDPI, vol. 19(1), pages 1-17, January.
    19. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
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