We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.
The authors in @cite_24 study auction mechanisms considering arbitrage between CPC and CPM pricing schemes. The study aims for designing an auction mechanism on behalf of the ad exchange and yielding truthful bidding from advertisers and truthful CTR reporting from arbitrageurs. By contrast, our work focuses on developing a statistical method for mining and exploiting arbitrage opportunities between CPA and CPM.
@cite_24
1973081445 2087873422 2114833467 2138043622
We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments. Abstract Most search engines use the weighted Generalized Second Price (wGSP) auction to sell keyword-based text ads, generating billions of dollars of advertising revenue every year. Designing and implementing near-optimal reserve prices for these wGSP auctions are naturally important problems for both academia and industry. In this paper, we show how to calculate and implement the near-optimal reserve price of the wGSP mechanism in realistic settings. Unlike reserve prices in standard single-item auctions, optimal reserve prices in wGSP auctions are discriminatory, different even for advertisers bidding on the same keyword. The optimal reserve price results can be extended to support CPA CPC CPM 1 hybrid auctions. Our simulations indicate that setting a proper reserve price will transfer some bidder utility (payoff) to auctioneer utility, resulting in higher revenue for the search engine. We describe a practical methodology to implement optimal reserve prices in production systems. Online display advertising exchanges connect Web publishers with advertisers seeking to place ads. In many cases, the advertiser obtains value from an ad impression (a viewing by a user) only if it is clicked, and frequently advertisers prefer to pay contingent on this occurring. But at the same time, many publishers demand payment independent of clicks. Arbitragers with good estimates of click-probabilities can resolve this conflict by absorbing the risk and acting as an intermediary, paying the publisher on allocation and being paid only if a click occurs. This article examines the incentives of advertisers and arbitragers and contributes an efficient mechanism with truthful bidding by the advertisers and truthful reporting of click predictions by arbitragers as dominant strategies while, given that a hazard rate condition is satisfied, yielding increased revenue to the publisher. We provide empirical evidence based on bid data from Yahoo's Right Media Exchange suggesting that the mechanism would increase revenue in practice. We analyze the problem of designing a truthful pay-per-click auction where the click-through-rates (CTR) of the bidders are unknown to the auction. Such an auction faces the classic explore exploit dilemma: while gathering information about the click through rates of advertisers, the mechanism may loose revenue; however, this gleaned information may prove valuable in the future for a more profitable allocation. In this sense, such mechanisms are prime candidates to be designed using multi-armed bandit techniques. However, a naive application of multi-armed bandit algorithms would not take into account the strategic considerations of the players -- players might manipulate their bids (which determine the auction's revenue) in a way as to maximize their own utility. Hence, we consider the natural restriction that the auction be truthful. The revenue that we could hope to achieve is the expected revenue of a Vickrey auction that knows the true CTRs, and we define the truthful regret to be the difference between the expected revenue of the auction and this Vickrey revenue. This work sharply characterizes what regret is achievable, under a truthful restriction. We show that this truthful restriction imposes statistical limits on the achievable regret -- the achievable regret is Θ(T2 3), while for traditional bandit algorithms (without the truthful restriction) the achievable regret is Θ(T1 2) (where T is the number of rounds). We term the extra T1 6 factor, the price of truthfulness'.