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

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 pl...

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Bibliographic Details
Published in:	Physica A Vol. 654; p. 130162
Main Authors:	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.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 15-11-2024
Subjects:	Artificial Neural Network Investment Intention LightGBM LSTM Random forest classifier Social exchange theory Investment Intention LSTM Artificial Neural Network Random forest classifier LightGBM Social exchange theory
Online Access:	Get full text
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Summary:	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.
ISSN:	0378-4371
DOI:	10.1016/j.physa.2024.130162