Numerous psychological studies show that weather conditions affect people's mood and that mood states are correlated with people's subjective evaluation of future probabilities. In this paper, a new approach is developed and asset market data are employed to test the mood-subjective probability relation. Cloud cover and precipitation volume serve as two mood proxies. Our statistical analysis suggests that bad mood states are characterized by investors placing higher probabilities on adverse events.

Using asset market data, as well as theoretical relations between investors’ preferences, option-implied, risk-neutral, probability distribution functions (PDFs,) and index-implied, actual, PDFs, this paper extracts a time-series of investors’ relative risk aversion (RRA) functions. Based on results recently derived by Benninga and Mayshar (2000), these functions are used to recover the evolution of risk preferences heterogeneity. Applying non-parametric estimation on European call options written on the S & P500 index, we find that: (i) the RRA functions are decreasing; and (ii) the constructed risk preferences heterogeneity series is positively correlated in a static, as well as a dynamic, setup with a prevalent proxy for investors heterogeneity, namely, the spread between auction- and market-yields of Treasury bills.

The presented research tests cumulative prospect theory (CPT, [Kahneman, D., Tversky, A., 1979. Prospect theory: An analysis of decision under risk. Econometrica 47, 263–291; Tversky, A., Kahneman, D., 1981. The framing of decisions and the psychology of choice. Science 211, 453–480]) in the financial market, using US stock option data. Option prices possess information about actual investors’ preferences in such a way that an exploitation of conventional option analysis, along with theoretical relationships, makes it possible to elicit investor preferences. The option data in this study serve for estimating the two essential elements of the CPT, namely, the value function and the probability weighting function. The main part of the work focuses on the functions’ simultaneous estimation under CPT original parametric specification. The shape of the estimated functions is found to be in line with theory. Comparing to results of laboratory experiments, the estimated functions are closer to linearity and loss aversion is less pronounced.