{"title":"A Prospect Theory Model for Predicting Cryptocurrency Returns","authors":"Alexander Thoma","doi":"10.2139/ssrn.3753530","DOIUrl":null,"url":null,"abstract":"This paper investigates the risk and return properties of a trading strategy for the cryptocurrency market. The main predictive power for portfolio formation comes from a simple prospect theory model that only uses price information readily available. The dataset consists of a large body of cryptocurrencies from 2014 to 2020. I find a strong outperformance over the market, even after controlling for known predictors. Factor regressions with a cryptocurrency three-factor model further reveal significant alphas. Robustness test emphasize the legitimacy of the strategy. On average, cryptocurrencies with a high (low) prospect theory value earn low (high) subsequent returns. Interestingly, traders in the cryptocurrency market seem to assess the attractiveness of cryptocurrency in a way described by prospect theory. Mechanical tests of the model show that probability weighting is a main driver behind this assessment. Cryptocurrencies with a high prospect theory value tend to be highly positively skewed. This skewness could be the reason why the cryptocurrency seems attractive to traders, similar to lottery-like gambles.","PeriodicalId":365642,"journal":{"name":"ERN: Behavioral Finance (Microeconomics) (Topic)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Behavioral Finance (Microeconomics) (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3753530","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
This paper investigates the risk and return properties of a trading strategy for the cryptocurrency market. The main predictive power for portfolio formation comes from a simple prospect theory model that only uses price information readily available. The dataset consists of a large body of cryptocurrencies from 2014 to 2020. I find a strong outperformance over the market, even after controlling for known predictors. Factor regressions with a cryptocurrency three-factor model further reveal significant alphas. Robustness test emphasize the legitimacy of the strategy. On average, cryptocurrencies with a high (low) prospect theory value earn low (high) subsequent returns. Interestingly, traders in the cryptocurrency market seem to assess the attractiveness of cryptocurrency in a way described by prospect theory. Mechanical tests of the model show that probability weighting is a main driver behind this assessment. Cryptocurrencies with a high prospect theory value tend to be highly positively skewed. This skewness could be the reason why the cryptocurrency seems attractive to traders, similar to lottery-like gambles.