Feedback Pitching Documents 2024
Rafferty
I think I would like to see more of a theoretical comparison with other market micro structure innovations that are supposed to or have shown to increase liquidity: e.g. ETFs, fractional shares, decreases of tick sizes, REITS (specific to real estate) in financial assets. How tokenisation contrasts with some of these innovations would help to place your research in the broader finance literature and it might give some indication why these have not been tried for the real estate that you are going to study. This might also give you more arguments why it is whorthwhile studying the liquidity of a tokenised market. Does the increased liquidity lead to real world effects?
In the proposal, try to think through why it is useful to have data that spans the crypto winter or what the effect would be from the move away from Ethereum to Gnosis.
You also mention that the data collection could be challenging. Try to get some actual data before you submit your proposal. It would be good to have a decent idea about the practical feasability of your study by then.
P.S.: No need to mention IV or event studies in 2.1 if you do not mention the event or the instrumental variable.
Daniel
I know you have a lot more details but a lot of those details were lacking in the current description. A couple of things in no particular order:
- You emphasise the role of self-regulation but it is not clear how that effects the data? Does it influence which firms will be in the control group and the treatment group? Is the effect of self-regulation, that there is some sort of selection effect on who gets treated or is it more that the regulatory body thinks that some limited temporary ban is necessary for the market as a whole.
- You do not really describe the control group and the treatment group in your diff-in-diff design.
- What is the general theoretical contribution? What is the tension? Short-selling is generally seen as good for market health and market liquidity. Is your argument that because this is in a self-regulated market, the short-selling ban might actually be good for the market. Or is the argument that insiders are trying to protect their wealth? Or are you setting up a horse-race between those two?
Ben Tan
I like the focus on the mining operations because you seem to aim to look at the effect of a market micro structure on the actual operations in the industry.
Be carefull on how you set up the before-after comparison because the halvening events are anticipated. That does not mean that you cannot study the event but you need to be carefull. Very crudely: Miners might be able to earn the same amount in expectation from their mining equipment until the halvening. If the mining servers only last 6 months, the anticipation effect will only be noticeable from 6 months before the halvening. This is more a metafor on how to think about the anticipitation effect than what anything I expect to be real.
I do not really follow the “paradoxical” argument for why the halvening could lead to more technological investment and more decentralisation. That does not mean it is not plausible but more that it needs more explanation, for instance in the proposal.
It would also be good to see more details on what you are going to measure as outcomes in the proposal and whether that data is available.
Sami
There is a good theoretical foundation and motivation for your study from the literature. However, the connection between the theory and the variables of interest. For instance, how will delegation be operationalised in your model.
Towards, the end you get more into the details of adding randomness to the simulations to incorporate some variables’ influence. These variables are less important than for instance delegation.
Luke
Writing nitpick: “at what point” in the research question is probably not going to be feasible. From the remainder of your pitch I gather that you are studying larger mining firms compared to smaller ones and that makes more sense.
There are a number of theoretical arguments running through your pitching document for why larger mining companies might report more ESG related information. They might come under more scrutiny, they might need more equity funding, they might have more capabilities. In your literature review for the proposal, it would be good to think more careful about these arguments and how they work together. The first reason is that these explanations might be confounding factors for what you are interested in. For instance, if you want to say that the main driver is equity funding, you need to control in some way for capabilities. The second reason is that the mechanism will influence the take-away message from your study. If SME are less likely to report because they just do not have the same equity funding requirements, then maybe it is rational for them not to report.
Tom
Good use of the literature. Just be aware that some of it is not the most recent and might have been challenged by more recent papers.
You have a lot of hypotheses make sure that you focus initially on the more feasible and/or interesting one. Testing one relation well can be more difficult than you think.
Just a suggestion: Instead of working with a dummy variable for the risk of delisting, you can try to estimate the probability of delisting. This would give you more power to detect an effect (dummy variables are fairly noisy proxies of the probability).
One risk is that delisting risk leads to less attention from both institutions and retail but proportionally more from retail. Have a think about how that will affect your potential tests.
For the proposal and especially the literature review in the proposal, try to spend some time on the mechanisms for why you think certain investors will spend more or less attention to potentially delisted firms and how that plays with earnings management. I suspect (but might be wrong) that you have a model in mind where institutional investors are restricting what firms can do in terms of earnings management, delisting (risk) decreases institutional engagement and increases earnings managment, which in turn pushes away institutions even more than in favour of retail investors.