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This entry pertains to the 3rd Annual Valuation Workshop held at The Wharton School on June 13-14, 2024. I summarize my reactions to each of the papers followed by some outstanding questions at the end.

Introduction

The conference was primarily about valuations. Valuation ratios (of various kinds) were featured quite prominently, and the two main questions of interest were (i) how do we explain movements in valuations, and (ii) how are valuations today useful for forecasting future movements in the stock market? Each paper touched on either (i) or (ii) (or even both!).

Day 1

• Ben Knox talked about how to decompose each period's returns into components. This echoes some of the themes in Richard Roll's famous 1988 AFA address titled "R2" where he argues that even with hindsight, the ability to explain stock price changes is modest. In the authors' approach, there are some key assumptions being made. The first assumption disciplines how the expected return on the market is connected to the expected return on dividend strips, and the other is a Taylor approximation which generates the linear decomposition (a standard asset pricing trick).
• Ricardo De la O provided a cross-sectional decomposition using professional forecasts, which is a follow-up to their previous work. They rationalize their empirical pattern with a model, which basically says that constant gain learning can generate the patterns in survey data. It's a reduced-form model in the sense that constant gain is imposed (as opposed to being micro-founded through mechanisms like fading memory). It would be interesting to see what implications a more standard Bayesian learning model would have, which is prevalent in the literature.
• Paul Decaire presented his work in which he collected data from equity reports, enabling them to observe the entire discounted cash flow (DCF) model. The "price" measure is the 12-month target price that analysts derive, and they provide a granular decomposition of what drives the variation in the inputs into the final price estimate. In its current form, the paper provides very good insights into analyst decision-making. What seemed incomplete in the current version is the link to actual market prices, which the authors say is still in progress. One result they had related to this is that analysts' security market line is steeper than the econometrician's, which suggests that analysts may be overestimating the risk premium that investors demand for holding risky securities.
• Sebastian Hillenbrand talked about the construction of the "optimal" stock-valuation ratio. The main conclusion is that we should be using forward-looking price ratios rather than trailing price ratios, as trailing price ratios also contain expected cash flow growth. They show that the outperformance in forecasting ability is real, both in-sample and out-of-sample. This result is interesting because we typically think there is a trade-off between maximizing in-sample $R^2$ and out-of-sample $R^2$.
• Toomas Laarits presented his finding on how the risk-return tradeoff of timing strategies deteriorates substantially as the investment horizon increases. He then provides a model with seasonalities in the volatility of the state variables, which makes timing strategies profitable in the short-run but not so much in the long-run.

Day 2