I am not sure whether or not performing relevant test on your question mean how to set the assumption for hypothesis test and how to proof your research hypothesis. But there is some statistic analysis tool for you to download online. Let try SPSS, stata, MS excel for start and familiar with some crazy statistic terminologies

2. Data for answer you question, i recommend to download bond price. Make sure that it is enough to see your event effect eg. Five or ten, or whole if you want to study and see how is the investor behaviour change. It is follow your assumptions or not? If testing by yourself is not necessary, you can use some investment analysis programme like boomerang , infnitive, Thomson Reuter.

3. Understand your research model is important before testing data by youself. I can tell you about some general concepts related to hypothesis testing and p values.

Hypothesis testing is a method of statistical inference that allows you to evaluate a claim or a prediction based on data. You start by stating a null hypothesis and an alternative hypothesis that are mutually exclusive and exhaustive. The null hypothesis is usually a default or baseline assumption, such as “there is no difference between two groups” or “there is no relationship between two variables”. The alternative hypothesis is what you want to test or prove, such as “there is a difference between two groups” or “there is a relationship between two variables”.

To test your hypotheses, you collect data and calculate a test statistic that summarizes how well your data fit the null hypothesis. The test statistic depends on the type of data and the statistical test you choose. For example, if you want to compare the means of two groups, you can use a t test and calculate a t statistic. If you want to test the association between two categorical variables, you can use a chi-square test and calculate a chi-square statistic.

The test statistic is then used to calculate the p value, which is the probability of obtaining a test statistic as extreme or more extreme than the one you observed, assuming that the null hypothesis is true. The p value tells you how likely it is that your data could have occurred under the null hypothesis. The smaller the p value, the more evidence you have against the null hypothesis and in favor of the alternative hypothesis.

To decide whether to reject or fail to reject the null hypothesis, you compare the p value to a predetermined level of significance, usually 0.05 or 0.01. This level of significance is also called alpha and represents the maximum probability of making a type I error, which is rejecting the null hypothesis when it is actually true. If the p value is less than or equal to alpha, you reject the null hypothesis and conclude that there is a statistically significant difference or relationship. If the p value is greater than alpha, you fail to reject the null hypothesis and conclude that there is not enough evidence to support the alternative hypothesis.

For more information on these concepts, you can check out these sources:

• The audit of assertions | ACCA Global
• Understanding P-values | Definition and Examples - Scribbr
• Test statistics | Definition, Interpretation, and Examples - Scribbr

The Halloween Effect, also known as the "Sell in May and Go Away" effect, is a phenomenon observed in financial markets where stock returns tend to be significantly higher during the period from November to April compared to the period from May to October. While the Halloween Effect is primarily associated with stock markets, its impact on corporate bonds has received less attention in the literature.

The empirical evidence on the Halloween Effect's impact on corporate bond returns is limited and mixed. However, here are a few possible explanations and perspectives: