PhD Defense F. Bekjarovski
The easiest way to understand active investing and the contribution of this Ph.D. thesis is via a simple apple trading analogy.
Warren and I like apples. And we both like them fresh. But they are stowed far away, and we don’t know which apple will be fresh and which apple will be stale.
I suddenly get a brilliant idea. I will predict apple freshens. The more recently they were harvested the fresher they will be. I make the necessary calculations and I find that apples harvested more recently have ended up being fresher historically. Additionally, Warren states that he is willing to part with an apple that I predict to be fresh for an apple that I predict to be stale!?
But what should the price of apples be in the first place? The classical apple pricing model (or CAPM) states that vendors that deliver stale apples when there is a famine increase the risk of your consumption. In other words, these apples are “risky” as they move together with the overall quality of the fruit basket. You want to pay less for these apples.
I double check the results and find that the co-movement of the “fresh” apple and the “stale” apple with the fruit basket are the same. So, they are equally risky according to the CAPM, even though one is predicted to be fresher. I test a bunch of alternative models, but they all give me the same result. Some apples give you more bang for the buck than others.
I have discovered my first apple pricing “anomaly”. In other words, apple predictability seems to lead to a profitable investment opportunity. To exploit it, I need to engage in active investing; that is, I don’t just passively hold a basket of apples. Rather, I trade to hold more of the predicted good apples and less of the predicted bad apples.
I tell a colleague about my trading strategy. He tells me that I have missed a risk. True, the apples I predict to be good are indeed good on average; however, occasionally, an apple that is predicted to be good ends up being terrible. He tells me the strategy has crash risk.
I check and he is right; but not completely. I also predict apple freshness with another signal, the quality of the delivery agent. When apples predicted to be fresh with the ‘day of harvest’ signal are stale, the apples predicted to be fresh with the ‘quality of delivery agent’ signal are splendid. In other words, the crash risk is diversifiable as the two signals lead to apple trading strategies that underperform at different times. I tell my friend that risk stories are a valid concern; however, they should not be specific to an apple trading strategy.
But what do I do when I predict that an apple that I don’t have will end up being stale? How do I profit from the fall in the price when the apple quality becomes observed? I will need to borrow an apple to sell to the market and then later buy it back to return it to the original owner. This process known as short selling delivers positive gross profits but there is a borrowing fee that I also need to consider. How high is the borrowing fee? While it is indeed substantial, I find that it does not overweight the benefit I can get from undertaking the short selling process; that is, short-selling apples is still a profitable business even after the borrowing fee. Moreover, I find that if I don’t want to short sell, I can also just buy good apples at a reasonable profit.
If you understand the apple trading example, you will understand how the research manuscripts contribute to the asset pricing literature. In the “pricing implications of shareholder voting”, I show that two new signals, shareholder meetings and voting outcomes, can be used to predict stock prices.
In the chapter “testing rational asset pricing models” I show that new rational explanations should also work on alternative anomalies if they are to be considered rational.
Finally, in the paper “how do short selling costs and restrictions affect the profitability of stock anomalies” I show that the costs associated with executing short selling trades are small relative to the contribution of short anomaly positions and that long-only anomaly investing in stocks works as well.
- Location: Cobbenhagen building, Aula (access via Koopmans building)
- Supervisors: Prof. L.D.R. Renneboog, Prof. S. Pouget, Dr. M. Brière