Feedback Pitching Documents 2026

Author

Stijn Masschelein

Travis Carter

The Basic Research Question is a specific hypothesis. This is fine but it does not show why it matters.
The motivation seems obviously true. The effect should be there for China if they are net importer?
I agree that this is mainly a predictive exercise. I do wish the idea has a better explanation of why the yields are more informative and more informative than what exactly. For instance, are some of the futures that you are using more influenced by China specific factors. I had to learn a lot about the oil markt works over the last couple of weeks and there are multiple crude oil futures which are influenced by different regional factors. Is that the type of information that you are exploiting? Or is it more aggregate worldwide inventory levels? Why would expected future prices (yields) predict contemporaneous inflation? Is the monthly level, the right level?
I like that you are going to use out-of-sample tests.
Data: You have to explain what convience yields are at least in the data. Going forward, try to motivate why you are using the methods and time frames that you are using.

Motivation: Explain at the start what is different by your definition of risk-taking if you are going to change what it means compared to the literature. It’s not really clear now what the setting is an experiment for.
Idea: Why do these proxies measure risk? What are you trying to capture with the independent variables? At face value, it looks like what you are doing is connecting incentives for risk taking with measures of non-conservative or aggressive reporting. There is literature out there that you could relate your study to.
What’s new: It’s not really clear what the behavioural angle is if you are looking at the effect of incentives.

Clear Motivation
What are the theoretical motivations, the story behind the hypotheses? There are too many directions I think. Either focus on the macro story (Q3), firm specific decisions (Q1,Q2), or market reactions (Q4). Start from a specific story of how AI adoption effects hiring and firing.
Data: There have been studies looking at the adoption of machine learning by looking at the internal control section of 10K filings.
Tools: I don’t necessarily disagree with the idea that there will be lagged effects but the story/theory should give you a better idea of the length of the lags and whether that is different for different firms.

It’s not clear from the motivation and RQ what you mean by tax management ability. The way you plan on measuring it seems to be that you are interested in a firm level ability with a DEA model. In that case, I think you need to think really careful about what potential input factors are in reaching the tax outcomes that you will be using in the DEA model and which variables are other determinants of those outcomes that might also be determinants of the ability that you want to measure. You need a stronger theoretical argument for those choice because they are untestable assumptions.
This comment is partly stemming from your propsal presentation: I am worried that your outcome variable in the DEA model and your main independent variable income shifting will be mechanically correlated with each other because they have overlapping components.
The What’s new part is hard to follow because it is not clear what the difference is in the Demerijian et al. approach and the Scholes-Wolfson framework.
What you write in the contribution needs to come much earlier in the proposal (for this unit).

The research question is about operational and corporate synergies. In the key paper section, you emphasise geographic locations as an important measure. The link is not necessarily clear. This is clarified in the motivation section.