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.Comment: In the proceedings of the 21st ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2015

Optimal Media Allocation of Generic Fluid Milk Advertising Expenditures: The Case of New York State

A fixed-effects panel data demand model for five New York State markets is estimated to determine the differential impacts of generic fluid milk advertising by media type. Empirical results indicate that among the four media outlets, television advertising has the largest impact on per capita demand, followed by radio, outdoor, and print. Based on the estimated media-specific elasticities, media reallocation of advertising expenditures suggests that milk sales could increase significantly. The results indicate that cooperative media plan strategies developed between the New York regional advertising program and the national advertising programs would achieve the greatest benefits.generic advertising, milk, optimal media allocation, panel data, Marketing,

MARKET ALLOCATION RULES FOR NONPRICE PROMOTION WITH FARM PROGRAMS: U.S. COTTON

Rules are derived to indicate the optimal allocation of a fixed promotion budget between domestic and export markets when the commodity in question represents a significant portion of world trade and is protected in the domestic market by a deficiency-payment program. Optimal allocation decisions are governed by advertising elasticities in the domestic and export markets and the export market share. PromotionÂ’'s ability to lower deficiency payments is inversely related to the absolute value of demand elasticities in the domestic and export markets and directly related to advertising elasticities and certain policy parameters. The empirical application suggests subsidies for nonprice export promotion may be efficiency increasing in a second-best sense. That is, the heightened subsidies associated with the Targeted Export Assistance program and the Market Promotion Program appear to have corrected allocative errors that favored domestic market promotion.Agricultural and Food Policy,

Assessing the effect of advertising expenditures upon sales: a Bayesian structural time series model

We propose a robust implementation of the Nerlove--Arrow model using a Bayesian structural time series model to explain the relationship between advertising expenditures of a country-wide fast-food franchise network with its weekly sales. Thanks to the flexibility and modularity of the model, it is well suited to generalization to other markets or situations. Its Bayesian nature facilitates incorporating \emph{a priori} information (the manager's views), which can be updated with relevant data. This aspect of the model will be used to present a strategy of budget scheduling across time and channels.Comment: Published at Applied Stochastic Models in Business and Industry, https://onlinelibrary.wiley.com/doi/full/10.1002/asmb.246

Inefficiencies in Digital Advertising Markets

Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research