Why dutch auction

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Your Practice. Popular Courses. What Is a Dutch Auction? Key Takeaways In a Dutch auction, the price with the highest number of bidders is selected as the offering price so that the entire amount offered is sold at a single price. This price may not necessarily be the highest or lowest price. A Dutch auction may also refer to a market where prices generally start high and incrementally drop until a bidder accepts the going price.

This is in contrast to competitive auctions where the price starts low and is bid higher. Compare Accounts. The offers that appear in this table are from partnerships from which Investopedia receives compensation.

These results indicate that the average winning bid did not change across blocks in either of the step-rate conditions. We assessed the relationship between the starting price and the price of the winning bid for both the continuous and discrete step conditions Fig.

We repeated the analysis for different bin sizes and found a similar trend see Appendix A. Experiment 1: relationship between auction starting price and the price of the winning bid for continuous left and discrete middle step conditions and the mean price of the winning bid for auctions separated into high and low starting price right.

Experiment 2 was identical to Experiment 1 except that purchasable stock on each trial varied randomly between 50 and units rather than being fixed at units. Stimuli and design were identical to Experiment 1 with the exception of the varying value of available stock. The available stock was presented to participants as a red bar in the game interface, where size of the bar varied to reflect the available stock i. A summary of Experiment 2 results is presented in Table 2. We calculated the correlation between the price and step of the winning bid in both the discrete and continuous conditions Fig.

As in Experiment 1, we anticipated negative correlation, which is imperfect due to variability in starting price, possibly compounded by the additional variability in the total items for sale for each auction. We continue the examination of the data using both of these measures.

We analysed the effect of participant group on the price of the winning bid Fig. A post hoc analysis using the Bonferroni correction for multiple comparisons was conducted which did not identify any significantly different relationships between the participant groups.

As there was no substantial variation between the price and step of the winning bid between participant groups, we continued analysis with data collapsed across participant groups. We then compared the price of the winning bid across the discrete and continuous step conditions using paired samples t tests as, unlike Experiment 1, all assumptions were met.

These findings indicate that there is no significant effect of the step-rates used on the price or timing of the winning bid in a competitive group computerised Dutch auction format.

We next examined the price and step of the winning bid across blocks Fig. Price of the winning bid data was analysed with a two-way ANOVA with within subjects factors condition discrete and continuous and block 1st-5th.

Finally, as in Experiment 1, we assessed the relationship between the starting price and the price of the winning bid for both the continuous and discrete step conditions see Fig. Experiment 2. Relationship between auction starting price and the price of the winning bid in continuous left and discrete middle step conditions.

The mean price of the winning bid for auctions was separated into low and high starting price right for both step conditions. We developed a novel platform for testing competitive decision making in a simulated Dutch auction.

The platform allows manipulation of three fundamental design features—start price, rate of price change and size of price change [see Cox et al. In both Experiments 1 and 2, there was no significant difference in the price or step i. These outcomes were supported by Bayes factor analysis which allowed the assessment of null effects, thereby overcoming limitations of frequentist tests.

We also found no significant difference in either the Price or Step of the winning bid across testing blocks in either experiment. The findings of the current study suggest there was no significant difference in the Price or Step of the winning bid between discrete and continuous price-change conditions. This supports the findings of Katok and Kwasnica where the overall duration of the auction, rather than differences in patterns of price changes, influenced bidding behaviour.

Katok and Kwasnica found that Dutch auctions with slow price changes i. However, their manipulations to the speed of price change resulted in changes in the overall duration of the auctions. The slow price change auction ran for a maximum of 10 min, while the fast price change auction ran for a maximum of 20 s.

It is also worth noting that Katok and Kwasnica used financial incentives for both winning auctions and for finishing the task. They acknowledged that participants may have bid earlier, thus raising the price of bids, to end the auction and receive their payout earlier.

In the current study, we maintained overall duration across the discrete and continuous price change conditions, so were able to focus on the effect of different patterns of price changes while controlling for the overall duration of the auction.

With no difference observed in bidding behaviours between the discrete and continuous condition, it is possible that the overall duration of the auction affects bidding behaviours rather than the pattern of price changes.

Our findings support a different aspect of Katok and Kwasnica theory—that difference in bidding behaviours is caused by bidders considering time as a valuable resource, resulting in a trade-off between time saved and price. In the current study, the overall duration of the auctions was held constant across price conditions fast, slow , so there was no need for bidders to trade-off between time saved and price, and without financial incentive for task completion there was no direct gain for ending auctions prematurely.

This resulted in similar Price and Step of winning bids across the discrete and continuous price-change conditions. This outcome may have been affected by the short duration of individual auction trials used in the current study.

Each individual auction-trial in both the discrete and continuous price-change conditions ran for a maximum of 5 s. This short duration of individual auction [relative to Katok and Kwasnica ] may have not allowed for the perceptual differences in the different patterns of price changes to visibly affect bidding behaviour. Future research may benefit from utilisation of our platform to examine the effect of different patterns of price changes over longer-duration auctions, where the perceptual difference is more apparent to the bidders.

Next, we consider the potential effect of hypothetical funds on bidding behaviour. Without financial incentives to motivate participants to engage in real-world behaviours, it is possible our non-significant results may be an outcome of this design feature.

However, in an extensive review of incentivised versus non-incentivised experiments in economics and psychology, Camerer and Hogarth concluded that incentives are less likely to affect mean performance in games, auctions, and risky choice tasks; however, incentives can reduce response variance. From a psychological perspective on participant effort, Erkal et al.

Nonetheless, there are also studies suggesting different behaviour in incentivised versus non-incentivised tasks, and this could be tested in future investigations of Dutch auction bidding behaviour. We found that the mean price and step of winning bids in Dutch auctions with either a fixed number of units for sale Experiment 1 or varying units for sale Experiment 2 was not significantly different.

These results may arise from participants beginning the experiment with a near optimal or good estimation of item value based on experimental design features. For example, in each block participants were asked to use a fixed allocation of funds to purchase stock to fill their fixed size virtual warehouse.

While the competitive environment may drive the final bids up from this optimal price, Turocy et al. Whether all group members identified an equivalent bidding strategy from the auction design or some individuals adapted their bidding strategies and others did not, our results indicate that the price of winning bids within a group competitive bidding environment are not affected by exposure to other competitors or the auction format.

A fundamental feature of the Dutch auction is the certainty of winning and losing —the bidder who is first to bid wins the available item with certainty Turocy et al. However, this is only true to the extent that no other player had yet placed a bid at that point in time. With passing time, there is an increasing likelihood that other stakeholders will place a bid, reducing the chances the item is still available.

Our experiments required participants to trade off between the certainty of winning the bid, which decreases over the time course of the auction trial, and the price they are willing to pay for the available items which also goes down. Balancing risk, certainty, and value alternatively, utility is a standard feature in theories of economic decision-making. Prospect theory has been widely considered in ascending price auction formats; however, it has not been applied to Dutch auctions in a quantitative manner.

For example, Kuruzovich discussed processes by which bidders increase their valuation of items through interaction with an online auction mechanism but not necessarily Dutch Auctions. They argued from a prospect theory perspective that Dutch auctions present the individual with a different decisional frame compared to other auction formats, as Dutch auctions begin at a high price point and decreases, rather than start at a small value that increases.

By commencing auctions at a higher value, the auctioneer changes the external framing of the choice, which should theoretically result in a higher bid and overall revenue from the auction. Fu et al. They hypothesised that higher starting prices in a Dutch auction would increase the perceived valuation of items, resulting in higher bids as individuals become more loss averse. Similarly, Dodonova and Khoroshilov argue that the endowment effect, another example of loss aversion where individuals place higher value on items they already own, should be seen in the reserve prices set in a Dutch auction by sellers.

While prospect theory has provided a sound theoretical framework to develop hypotheses and interpret results, we are not aware of any quantitative adaptation of prospect theory to Dutch auction decision making. Our approach was to extend prospect theory in the time domain, by assuming that each player makes a sequence of choices during the auction.

These repeated choices are all binary decisions: each time, the player must decide whether to bid immediately, or to wait just a few moments longer. Waiting is a prospect comparable to the risky option above: the player must estimate the risk associated with waiting longer, the probability that another player will bid in the next few moments. Prospect theory Tversky and Kahneman provides a well-established way to predict the choices of people faced with decisions between these options. We operationalise the prospect theory model as follows.

Suppose t represents the time in the auction, and C t represents the selling price, or cost, at time t in our experiments, C is a linear function. The details of those functions are standardised in prospect theory and are reproduced below as well.

We assume standard forms for the utility and probability weighting functions. Utility U is a power function of price x , with different behaviour on losses negative prices than gains:. When c is large, the player will almost certainly choose the option with the larger weighted utility, even if the differences between options are very small.

When c is small, the player sometimes chooses randomly, selecting the lower-utility option on some occasions. The model relies on the player having some estimate of the probability that one of the other players will bid in the next few moments, r. We operationalise this by assuming that the player maintains some representation of the bidding-time distributions of the other players.

The above functions describe the probability of a single player making a bid in the next few moments. This is a hazard function, which can be converted to a probability density function say, g and associated cumulative distribution function say, G by standard transformations.

However, the empirical distributions collected in our experiment depict the price and time of the winning bids, across all three players e.

To link our theoretical predictions with the data, we must infer from the model the empirical distribution of bidding prices in all auctions, by marginalising over all players—not just a single player. This reflects the summary distributions shown in figures such as Fig. Thus, the final step is to let the model define the behaviour of three concurrent players and to derive from this the distribution of the minimum bidding times.

We now provide a sufficiency proof for the model, by demonstrating that it is capable of generating bidding patterns similar to those observed in empirical data at least for some parameter combinations—a sufficiency proof.

We simulated three-player group bidding data over fixed unit auctions. Figure 14 illustrates empirical distributions of Experiment 1 data top, similar to the distributions plotted in Fig. For the other parameters, we investigated numerous combinations and plot here one example set. These parameter combinations, and others, can generate distributions of winning bid price similar to the empirical data, as can be seen in Fig.

This represents an interesting theoretical observation that participants were less sensitive to changes in the stated cost i. This may suggest a reference point effect. We constrained our model to use a very standard account, in which all prospects were treated as changes from a zero-dollar reference point. However, our players may have treated the prospects differently, as gains and losses relative to their current circumstances.

With nonzero reference points, the nonlinear effects of the weighting and utility functions are changed, leading to effects such range compression and expansion e. This represents an interesting insight that may be investigated in future research. The model produces predicted distributions of bids that are similar to the empirical data, for some parameter values. We wanted to test more specific predictions of the model by examining predictions of auction starting price, for which prospect theory can be used to make predictions in Dutch auctions see Kuruzovich There is a lack of consensus in the general auction literature not necessarily Dutch auctions concerning the effect of starting price.

Some evidence shows lower starting prices lead to higher bids e. Based on eBay field data and survey experiments, the authors found low starting prices attracted more bidders and thereby result in higher final prices. Walley and Fortin confirmed in their controlled field experiment that lower starting prices increase the number of bidders and eventually final prices.

Ariely and Simonson also confirm the results of Ku et al. However, and in contrast to Ku et al. The authors argue that this can be explained by an anchoring effect: starting prices may serve as a value signal for bidders, with higher starting prices indicating a higher product value.

Although this effect has not been examined in Dutch auctions, Kuruzovich argued that because Dutch auctions start at a high price and decrease they should theoretically produce higher revenue compared to ascending price auctions; however, this was not empirically tested. In both Experiments 1 and 2, there was a positive correlation between starting price and price of winning bids, in both fixed- and multi-unit Dutch auctions.

We found that the price of winning bids was significantly different when separated into high and low starting price bins. Here, we implement the same analysis on the simulated data from our prospect theory based model and show the model predicts this qualitative pattern.

Figure 15 depicts the positive relationship between the auction starting price and the price of the winning bid for model-simulated data. Positive relationship between auction starting price and winning bid for continuous left and discrete middle conditions in model simulated Dutch auction group bidding. Mean bid price across low and high auction start prices right in the model-simulated data displays the same increase pattern in bid prices as the empirical data cf.

We explored this relationship across different bin sizes in Appendix A and found it to be a robust effect in both data sets. These results suggest starting price is related to the price of the winning bid in fixed unit Dutch auctions under continuous and discrete step-rates. This finding is important for both theoretical and practical reasons.

From a theoretical perspective, it allows to compare the empirical pattern with model predictions. Practically, starting price of an auction is a design choice of the auctioneer or market designer. If they start the auction with too high a price, they might waste valuable time, which is especially critical when selling perishable items as is the case in most Dutch auctions.

If they start too low, they might miss out on additional revenue as predicted by both our data and model. The current study aimed to develop a computerised platform for Dutch auctions and test how different design parameters affect the decision-making processes involved in this competitive group context.

Results from Experiments 1 fixed item quantity and 2 variable item quantity showed no significant effect for different patterns of price changes on the price or time-step of the winning bid. There was no difference in the price or step of the winning bid between testing blocks. This suggests that participants either 1 began with a good estimate of item value or 2 did not change their bidding behaviour through experience.

Empirical data were collected in-lab. This had limited the sample size, as the scheduling of multiple participants to concurrent testing is non-trivial. Future studies could employ online testing to obtain larger sample. However, in-lab testing rewarded the study with very real and vivid group context. In post testing interviews, some participants reported they were excited by the competitive nature of the task.

Future research may look into physiological measures, to assess arousal and how it affects bidding behaviour a-la Malhotra et al. In conclusion, this paper offers theoretical and practical contributions. The objective of this research is to show the various factors responsible for an increase in price of stocks' buyback values. Dutch auction repurchases: An analysis of shareholder heterogeneity , Bagwell, L. The Journal of Finance , 47 1 , This research explores the impact of firm's involvement in a Dutch auction.

Results show that firms with larger trading volumes benefits more from supply elasticity as a result of repurchased shares from Dutch auction. Auction institutional design: Theory and behavior of simultaneous multiple-unit generalizations of the Dutch and English auctions , McCabe, K. The American Economic Review , 80 5 , This paper analyses the effect of call market on Dutch and English auctions. Earnings signals in fixed-price and Dutch auction self-tender offers1 , Lie, E.

Journal of Financial Economics , 49 2 , This article aims to show how self-tender offers can affect the market signal or debt ratios of firms. Emphasis is placed on self-tender offers between , as case study. Institutional investors may be well-versed in IPOs, but many individuals have never participated in one. Before participating in IPO via a Dutch auction, be sure to inform yourself about the auction process and the company.

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