The power of priors: How confirmation bias impacts market prices

One form of confirmation bias is the tendency for people to ignore information that is inconsistent with their current beliefs. While confirmation bias is the subject of both analytical models and experiments in accounting and finance, its effect on market prices has not been studied due to limitations associated with traditional financial markets. In eleven real-money movie box office prediction markets, confirmation bias was induced in all traders via the explanation effect, i.e. a requirement to submit a box office forecast and an explanation prior to trading. When all traders are subject to confirmation bias, market prices do not accurately reflect new, value-relevant information. However, in comparable a set of seven real-money movie prediction markets that included both traders who have not been subject to explanation requirement and those who have, we find efficient incorporation of new information into market prices. This study extends our knowledge of the conditions under which individual trader biases affect market prices and provides potential insights into open questions about forecasting errors among financial analysts.

PUBLIC INFORMATION BIAS AND PREDICTION MARKET ACCURACY

How do prediction markets achieve high levels of accuracy? We propose that, in some situations, traders in prediction markets improve upon publicly available information. Specifically, when there is a known bias in publicly available information, markets provide an incentive for traders to “de-bias” this information. In such a situation, a prediction market will provide a more accurate forecast than the public information available to traders. We test our conjecture using real-money prediction markets for seven local elections in the United States. We find that the prediction market forecasts are significantly more accurate than those generated using the pre-election polls.Previous versions of this paper were presented at the Workshop on The Growth of Gambling and Prediction Markets, the ISBM Conference on Prediction Markets in Marketing: Issues, Challenges and Research Opportunities, the DIMACS Workshop on Markets as Predictive Devices, and the 17th Annual AMA Advanced Research Techniques Forum. The authors thank the participants for their insights which have helped improve this researc

Private Information, Overconfidence and Trader Returns in Prediction Markets

In lab experiments on the value of information in financial markets, groups of “insiders” are randomly chosen to receive perfect information. However, in typical (non-experimental) financial markets, investors often engage in extensive fundamental analysis, a process which may result in over-confidence in one’s private information. In this study, we examine trading volume, prices and trader returns in a set of four real money prediction markets where the values of securities are tied to a movie’s box office performance. Before the markets opened, every trader submitted a detailed forecast of the movie’s future performance. Therefore, all traders have self-generated private information, the accuracy of which can only be known ex-post.            As expected, the volume and timing of trading were consistent with over-confidence. In three of the four markets, contract prices were consistent with the prior information equilibrium. In those three markets, traders whose forecast was associated with the winning contract had significantly higher returns than traders whose forecasts suggested that another contract would pay off. In the other market, there were no significant differences in returns across trader groups. This research suggests that when traders are overconfident and trade accordingly, there can value to being better informed if the information is accurate.

Bringing the doctor to the patients: cardiology outreach to rural areas

Clinical outreach is a crucial but understudied healthcare service delivery model. Physicians staffing rural outreach clinics must allocate a limited resource (i.e., their time) between caring for patients at their main sites and outreach locations. Using a unique 30-year dataset of decisions made by cardiologists, we estimate a constrained utility maximization model of time allocations across home and outreach locations. The results show that travel distance, potential competition, and patient demand for cardiology services significantly influence allocation decisions. This structural model is used to simulate the impact of a predicted reduction in cardiologist supply. The expected impacts are unevenly distributed, with some rural locations experiencing large decreases in access. We evaluate two policies to restore rural access: targeted immigration and a subsidy program. A subsidy program with an estimated cost of $406,000 can restore outreach after a 10% reduction in cardiologist supply. This option should be preferred to recruiting and supporting five additional cardiologists under a targeted immigration strategy. This research demonstrates the value of marketing modeling in addressing limited access to healthcare services and evaluating alternative policies for maintaining access in the face of coming physician shortages

INCENTIVE AND ACCURACY ISSUES IN MOVIE PREDICTION MARKETS

We compare the forecasts of nineteen movie box office results from real money (Iowa Electronic Market) and play money (Hollywood Stock Exchange) prediction markets. The forecasts were not significantly different, contrary to recent research on incentives and prediction market accuracy. Proponents of play money incentives suggest that (play) wealth concentrates in the hands of knowledgeable traders over time. This should lead to improved accuracy over time. A longitudinal analysis of results (1999-2002) from the play money Hollywood Stock Exchange fails to find significant improvement over time. This may be due to an increased number of less knowledgeable traders who, nevertheless, provide liquidity in the market

Can Investing Diaries be Hazardous to Your Financial Health?

Business writers and academics have suggested keeping an investing diary to avoid hindsight bias. In the diary, investors justify their predictions of future events, e.g., “This stock will go up because…” Eliminating hindsight bias should improve future returns. However, psychological research on the “explanation effect” suggests that justifying one’s predictions in writing induces overconfidence and, by consequence, reduces current returns. We test these propositions in a set of prediction markets populated by two types of traders: forecasters who completed a required investing diary task and non-forecasters who did not. The portfolios of forecasters were significantly over-invested in securities associated with the forecaster’s prediction. This is consistent with prior psychological research and a clear sign of investor over-confidence. We further find that forecasters with accurate predictions have higher returns than those with inaccurate predictions. However, the returns for forecasters with inaccurate predictions were generally no worse than the returns of the non-forecasters. Our results suggest that while keeping an investing diary may lead to biased portfolios, it does not have an overall negative effect on current returns. Therefore, contrary to expectations, there is not a trade-off between the long-term and short-term effects of an investing diary

The Effects of Sensitization and Habituation in Durable Goods Markets

We develop a model to study the impact of changes in price sensitivity on the firm as it introduces multiple generations of a durable product where unit costs are a convex function of quality. We incorporate the psychological processes of sensitization and habituation into a model of discretionary purchasing of replacement products motivated by past experience. When price sensitivity decreases with each purchase (sensitization), the myopic firm offers a higher quality product at a much higher price with each generation. When price sensitivity increases with each purchase (habituation), the myopic firm engages in price skimming. When sensitization is followed by habituation, the myopic firm eventually provides higher quality than the market is willing to pay for, leading to a steep drop-off in sales and profits. The actions of the forward-looking firm depend on the discount rate. A firm with a low discount rate builds its customer base before offering a higher quality and higher priced product. In contrast, a firm with a high discount rate quickly increases price and quality following the same path to falling profits of the myopic firm. These results provide insight into the firm and consumer behaviors underlying the phenomenon of 'performance oversupply' identified in the innovation literature

Advancing microbiome research with machine learning : key findings from the ML4Microbiome COST action

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices