Asset pricing theory has recently been criticized for its inability to explain historical data from field markets. Yet its poor record does not mean that it is scientifically invalid. Our experience (as experimentalists) is that theorists are not aware of the scientific record of asset pricing theory (how well it works in a controlled setting). The aim of the workshop is to sensitize theorists to this record. It should make them aware not only of the successes (of the theory), but also of where the theory fails (and how the failures can be addressed). It is hoped that the workshop heralds a new era where theorists work in closer collaboration with experimentalists. The PIs have a long track record not only in financial markets experimentation, but also in the experimental method in general (having contributed successfully to neuroscience), and they have published in asset pricing theory. As such, they are uniquely positioned to moderate the proposed dialogue between theorists and experimentalists.
Financial economics is rather abstract and mathematical, and its value is difficult to ascertain from merely observing real-world financial markets, which operate in a complex environment where many key variables either remain unobserved or cannot be measured reliably. In the last decade, however, tools have been developed to study financial markets in the laboratory, where real people trade for real money. The controlled setting is designed to emulate the theoretical context and as such has proven to be an ideal testing ground. The workshop aims at bringing together theorists and experimentalists in order to start a dialogue. Experimentalists are to be taught what aspects of the theory are defining, and hence, need to be tested, and theorists are to be encouraged to explain their models in terms of experiments with which to gauge scientific validity, which requires theorists to understand the experimental approach. We thereby import into finance a longstanding tradition from the physical sciences. The goal is to move finance to an evidence-based discipline. This will ultimately benefit financial markets policy formulation and rule making, which until now have primarily been model-based, or in the rare occasions where the data were available, informed only by empirical analysis of historical data.
