Strategies to understand and mitigate it

Last month, at a leadership development program, I went back to my alma mater, IIM Ahmedabad. It is always fantastic to go back to the classroom and that too at IIMA! A topic that we detailed deeply there was Winner’s Curse. For an investor, entering into auctions can be fraught with the risk of overbidding such that while in the short-term you “win” the asset, in the long-term, you “lose” value.

Aside: The discussion on auctions reminded me of a report that I had co-written at Kotak (along with my fantastic colleague, Rohit Chordia) when the first set of 3G auctions were to take place in India. We built out a dynamic model of bidding strategies by various bidders and came to an estimate of >Rs 1 trillion as the cumulative bids that the government would receive – this was more than double what the government has penciled in its Budget that year. We called the report “Auction house takes all”. A few weeks later the bidding “surprised” everyone by going way beyond this amount. Whether the telecom bidders ended up with a Winner’s Curse is a separate topic – however, it became clear (once again!) that auctions are able to extract significant value from bidders.

Coming back to this topic, I converted the converted the abstract idea of Winner’s Curse into a mathematical model. The core idea is that there are two opposing forces at play on: (a) the probability of winning at the bid [p(win)], and (b) the probability that, if one wins, one makes a positive net present value (NPV) out of the bid [p(π)]. As is obvious, as the bid value increases, it pulls these two probabilities in opposite directions. A higher bid at the auction will increase the probability of winning but will also bring down the probability that the NPV will be positive.

(Now again, if you want an aside, read this para or move ahead: We have p(π:NPV>0) to denote that the NPV of the win is at least zero. Nothing stops us from making out a curve which shows that given the uncertainties in estimating the NPV, it will not be a point estimate but a distribution. If someone is interested, this is an easy build out – however, I will keep the discussion to the point estimate as of now to make it easier.)

Two important concepts:

  • The interesting point to note is how the “margin of safety” and “winner’s curse” emerge even in a single bidder scenario (slides 5 and 6): this can happen, for example, when there is a reserve price for the bidding (especially when the reserve price is not known upfront).
  • The analysis becomes richer when one takes into account what the emergence of a competing bidder does (slides 7-10): a new bidder can change the margin of safety into a winner’s curse!

This takes us to the more practical point about the learnings and strategies to be adopted when entering an auction. Two important takeaways:

  • To the extent possible, avoid auctions! If you can, try to enter in bilateral negotiations rather than get into a public bidding.
  • If you do have to end up in an auction, be very conscious of what the p(win) and p(π) are for you and your competing bidders.
    • At some point in the bidding frenzy, it might be wise, from a long-term perspective, to lose in the short-term. (Cue: Baazigar!)

Given that auctions can drain value from the bidders, the value gets transferred to the auctioneer. Since India has a policy of auctioning natural resources, it creates a natural situation of the auctioneer creating significant value. A fair distribution of value between the winning bidder and the auctioneer should be a matter of deep public policy debate.

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