PREreview estructurada del Psychological Biases in Investment Decisions: A Behavioral Finance Approach

Does the introduction explain the objective of the research presented in the preprint?

Yes

The introduction explains the objective by first setting the theoretical context and then explicitly detailing the research goals.

Are the methods well-suited for this research?

Highly appropriate

The study uses a mixed-method approach that integrates qualitative and quantitative research. This is essential for studying behavioral finance, as it allows for both: 1. Quantitative Measurement: A survey of 398 retail investors provides statistical data on the prevalence and impact of specific cognitive and emotional biases. 2. Qualitative Insight: Interviews with financial experts and retail investors yield an in-depth understanding of how these biases manifest and allow for the proposal of practical mitigation strategies

Are the conclusions supported by the data?

Highly supported

1. Loss Aversion and Emotional Attachment: The conclusion that retail investors have a strong emotional attachment and loss aversion is supported by high mean scores for statements like "My sense of accomplishment and emotional well-being are closely tied to the profitability of my trades" and "Experiencing losses in trading significantly impacts my mood and confidence". 2. Overconfidence and Subpar Returns: The conclusion that overconfidence leads to frequent trading and potentially subpar returns is evidenced by investors having a relatively high confidence score (e.g., "My confidence in trading stems from my perception that my overall gains surpass my losses,"), which aligns with the literature stating that overconfidence leads to excessive trading and diminished returns due to higher costs 3. Herd Mentality and Reliance on External Information: The conclusion that herd mentality drives behavior is supported by the data showing that investors' decisions are heavily influenced by external information, with a high mean score for the statement "My trading decisions are predominantly shaped by external information, such as market news and stock-related updates" 4. Emotional Investing and Volatility: The conclusion that emotional investing contributes to market volatility is supported by high scores confirming that emotional reactions significantly impact behavior, such as "Experiencing losses in trading significantly impacts my mood and confidence" and that they "resist closing losing positions, expecting the market to eventually reverse in my favor"

Are the data presentations, including visualizations, well-suited to represent the data?

Highly appropriate and clear

How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?

Very clearly

The clarity stems from structuring the discussion around specific biases (e.g., loss aversion, overconfidence) and consistently supporting interpretations with precise quantitative survey data (mean scores) while proposing actionable mitigation strategies.

Is the preprint likely to advance academic knowledge?

Highly likely

It challenges the traditional assumption of investor rationality by systematically integrating psychological insights with economic principles through a mixed-method approach (survey of 398 retail investors and expert interviews). This empirically quantifies how biases like loss aversion, overconfidence, and herd behavior distort rational financial choices, contributing to a deeper understanding of investor behavior and offering actionable insights for improved decision-making

Would it benefit from language editing?

Yes

Yes, the preprint would benefit from language editing to enhance academic conciseness and flow, especially given its status as the "Not peer-reviewed version"

Would you recommend this preprint to others?

Yes, it’s of high quality

Is it ready for attention from an editor, publisher or broader audience?

Yes, after minor changes

1. Peer Review and Validation: It must successfully complete the peer review process, as it is explicitly the "Not peer-reviewed version". 2. Language Editing: The paper would benefit from professional language editing to enhance academic conciseness and flow before formal submission [Conversation History].

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.