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CPA广告活动与CPM广告库存之间的统计套利

文章 arXiv papers · 作者: Weinan Zhang et al.
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总结

本文将统计套利方法应用于实时展示广告领域。在分散的市场或定价机制中，预期效果相近的广告展示可能价格不同。该方法将按行动计费的广告活动与按千次展示计费的广告库存联系起来。元竞价器根据广告活动组合评估每个收到的广告展示竞价请求，并使用根据历史数据估算的转化率、竞价分布等统计数据估算成本和潜在利润。

系统将旨在最大化预期净套利利润的竞价优化，与在不同广告活动间重新分配竞价量和预算、以平衡风险与回报的投资组合风险管理结合起来。作者使用期望最大化方法联合优化这两个部分。他们报告称，离线实验使用了大型真实数据集，在线A/B测试则在商业平台的多种模型设置和市场环境下进行。所提供的描述没有数值结果或部署细节，因此无法据此判断收益有多大或能持续多久。该方法针对广告竞价，而非金融证券市场。

核心观点

  • 分散的广告市场可能使预期效果相近的广告展示出现价格差异。
  • 元竞价器根据历史估算数据，将CPM库存与CPA广告活动组合进行评估。
  • 该方法将预期利润竞价优化与广告活动层面的预算和竞价量分配结合起来。
  • 期望最大化过程联合优化竞价与风险管理。
  • 作者报告了离线数据集实验和在线A/B测试，但所提供文本没有数值结果。

标签

套利统计学风险管理投资组合构建交易执行
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# Statistical Arbitrage Mining for Display Advertising


# Statistical Arbitrage Mining for Display Advertising









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。