My Experience Presenting at an Academic Conference
The ICF supports PhD students by hosting PhD student conferences, purchasing data, & covering travel to present their work at conferences. Current PhD candidate, Chen Wang, recently traveled to Sweden to present a paper with the assistance of the ICF.
This fall, my paper "Rediscover Predictability," which obtains strong stock market predictability using information from prices of dividend at different horizons, was accepted by the Workshop on Predicting Asset Returns organized by Örebro University in Sweden. Thanks to the generous support from ICF, I was able to make my first trip to Sweden and, more importantly, present my work to an audience of researchers who share very similar research interests.
The workshop is a two-day formal academic conference packed with eight interesting papers and discussions in the field of asset return predictability. The accepted papers survey a broad spectrum of the topic, covering various asset classes and featuring new approaches and datasets to the fundamental questions in asset pricing. I gave my presentation on the second day of the conference. Though not the first time giving a talk in such a formal set-up, I felt great excitement presenting the work and engaging the audience during and after the talk. Most of the comments and feedbacks are constructive, along which we could further improve the paper. Many of them are also inspirational, compelling me to think deeper about the underlying economics behind our strong and robust evidence on stock market return predictability. It's an understatement to say that I have learned a great deal from these exchanges of ideas from the presentation and numerous stimulating conversations with fellow participants. Overall the trip is well worth the long travel and I enjoyed it. I'm grateful to the support from our faculty at SOM and the ICF, without whom the trip wouldn't be possible.
University in Sweden on November 13-14, 2018.
About the research: The paper, "Rediscover Predictability: Information from the Relative Prices of Long-term and Short-term Dividends" is joint work with Ye Li from the Ohio State University. In this paper, we have found that the prices of dividends at alternative horizons contain critical information on the behavior of the aggregate stock market. We extract the critical information using the ratio between prices of long- and short-term dividends, which predicts annual market return strongly and robustly. Our predictor outperforms almost all popular return predictors used in previous studies and works internationally. We demonstrate the economics of the predictability using a simple model. When people expect the future dividend to be close to a random walk, which we verify in the data, our predictor reveals purely information about expected return. Lastly, our estimated expected return declines during monetary expansions and varies strongly with the conditions of the macroeconomy, financial intermediaries, and sentiment. There are many more interesting results which you could find in the full paper.
About Chen Wang: Chen Wang is a fifth-year Ph.D. candidate in finance at Yale School of Management. His research lies in the intersection of behavioral finance and asset pricing. Before his study at Yale SOM, Chen obtained his BA in finance from Peking University and MS in financial economics from Columbia Business School. Check out his website for more information.