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Abstract:
The purpose of this study is to analyze the subjective value of lotteries, which represent stock and options on the stock. Subjects were asked to bid the prices for buying, selling and short selling of different lotteries in a second price auction. In addition, we classify subjects by risk attitude based on auctions. In our experiment, we found the existence of a group of subjects with win-loving behavior. We argue that win-loving subjects seem to have a strong win bias value in their utility function, therefore, the top-dog effect influence their financial decision making for risky assets. As a result, they are bidding higher prices for buying assets in auctions, while asking lower prices for selling the same assets (opposite to the Endowment effect). One of our results shows that hedging opportunity influences differently risk-averse, risk seekers and win-lovers subjects. The new classification of win-lovers, a sub-group of risk-seekers, as well as the classification of subjects according to their risk attitude, have important implications for the relevance of the results of many previous works, that have used second-price auction to determine the value of an asset. It also explains why participants in auctions often tend to pay prices that are above the market value of those assets. We also found that the influence of the Framing effect is different for risk-averse, risk seekers and win-lovers subjects. This result has important implications on offering financial contracts to investors in future and options exchanges.
Behavioral economics, lotteries, experiment, auction, risk aversion
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Abstract:
The experimental approach is applied to explore the value of unidentified historical information in stock-return prediction. Return sequences were randomly drawn cross section and time from historical S&P500 data. Subjects were requested to predict returns or select stocks from 12 preceding realizations. The hypothesis that predictions are randomly assigned to historical sequences is rejected in permutation tests and prediction-errors decrease with expertise. The best-stock portfolios by experimental predictions significantly outperform worst-stock portfolios in joint examination of mean-return and volatility. Actual predictions are significantly more effective than various statistical rules in separating the “best” stock from the “worst” in random 6-stock menus.
return forecasting, predictability, expertise, prediction regime
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