Effect of Behavioural Biases on Investors’ Decision Making: A Systematic Literature Review

S.C. Sharma & Vinod Negi

Metamorphosis- A Journal of Management Research2025https://doi.org/10.1177/09726225251319119article
AJG 1
Weight
0.44

What the paper says

A subset of behavioural economics known as “behavioural finance” asserts that individuals are significantly less rational in their financial decision-making, such as investing, than traditional financial theory suggests. Investors interested in the influence of emotions and biases on stock prices can discover intriguing insights and analyses within behavioural finance. Numerous biases that can influence investors’ rational decision-making have been identified in finance. In 2002, Professor of Psychology Daniel Kahneman received the Nobel Prize in Economics for his contributions to behavioural finance. Professor Kahneman examined the biases and heuristics that may arise while selecting investments under conditions of uncertainty. Following the introduction of prospect theory in 1979, numerous studies investigating various biases and their influence on the decision-making of individual investors yielded noteworthy discoveries. This systematic literature review will determine if several behavioural biases, such as loss aversion, overconfidence, mental accounting, representativeness, regret aversion, and herding, influence the decision-making of individual investors during investment decisions.

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https://doi.org/https://doi.org/10.1177/09726225251319119

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@article{s.c.2025,
  title        = {{Effect of Behavioural Biases on Investors’ Decision Making: A Systematic Literature Review}},
  author       = {S.C. Sharma & Vinod Negi},
  journal      = {Metamorphosis- A Journal of Management Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/09726225251319119},
}

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.