Study 2 Survey Study Design

This study addresses some of the knowledge gaps as identified by the systematic literature review in Study 1. With a quantitative research design and a deductive approach of a survey study, I provide a detailed step-by-step approach to answer the second research question on what are the emotional impacts of the individual trading order transactions.

Data

A self-rating web-based questionnaire is designed to check the current emotional states when executing a transaction. Hence, to avoid a time gap between the trading decision and filling out the survey questions, I propose this to be done right at the point of execution. A collaborating trading application could send out the questions to its users upon usage. Furthermore, the net return per type of transaction can be found in the company annual reports as a secondary data source.

Hypotheses Overview

The highest coefficient, which attributes to the highest likelihood for buying, is expected to be Self-Assurance, again attributed to the overconfidence bias. Attentiveness should also be a good predictor, because of following the news and media trends. Even though significant, Joviality might have less explanatory power, because it may not be attributed solely to their trading decisions but to something else.

Fear shall have the most explanatory power, as already seen by prior research. Sadness and Hostility would also play a significant role, though with lower coefficients. The results for Guilt are yet unclear. This study would reveal whether it is worth having more investigations.

By including uncertainty, some of the emotional states will change. Joviality and Attentiveness would potentially have a drop in their coefficients, and Self-Assurance might even turn to be insignificant predictor. On the other hand, Fear and Hostility would be even stronger predictors for selling. I expect high correlation between these negative affective states and Uncertainty. Furthermore, the undiscovered other affective states of Shyness, Fatigue, Surprise and Serenity may alter as well. All of them becoming stronger predictors for selling, with Serenity having the highest impact. This could be attributed to lower trust in the crypto market.

As instant transactions are granting immediate rewards, they would feel more important to traders and therefore I suppose they would be more emotionally involved. On the contrary, the future return of automated transactions would feel less important due to the hyperbolic discounting. H3a is not likely to be rejected, because the present bias preference supports the feeling of urgency for instant transactions and the rationality for automated transactions.

Automatic transactions have no emotional involvement at the execution phase, but only when setting it up initially. Thus, the net return is independent of the emotional states of the trader and should be rational to a certain extent. On the opposite, instant transactions are often provoked by emotions. And because emotions are the reason for investment mistakes due to biases that speculative predictions would work against a profitability strategy and lead to lower returns.

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