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

A New Empirical Approach to Explain the Stock Market Yield: A Combination of Dynamic Panel Estimation and Factor Analysis

This paper presents an empirical approach that combines competing paradigms of modeling in empirical capital market research. The approach simultaneously estimates the explanatory power of fundamentals, expectations, and historic yield patterns, making it possible to test the extent to which the efficient market hypothesis, fundamental data analysis, and behavioral finance contribute to explaining stock market yield. The core of the approach is a dynamic panel model (Arellano-Bond estimator with an MA restriction of the residuals), complemented with an upstream factor analysis to reduce multicollinearity. Due to the complexity of the data set, a great many parameters that influence the yield can be determined. Highly significant parameter estimates are possible even though the information in the data set is interdependent. For the German stock market (the 160 companies listed in DAX, MDAX, SDAX, and TecDAX), the quarterly yield is analyzed for the period between 2004 and 2009. The model has high explanatory power for the entire observation period, even in light of the fact that the period includes the financial crisis of 2008

Cross-Sectional Analysis of Stock Returns in Athens Stock Exchange for the Period 2004-2011

This study is an investigation of the factors affecting the average returns of stocks that were traded on the Athens Stock Exchange for the period July 2004 - June 2011. The methodological approach is similar to that applied by Fama and French (1992), in the first stage, stocks are grouped into portfolios with predefined criteria, and subsequently monthly cross sectional regressions are carried out, according to the Fama-MacBeth approach (1973). The main result of this study is that average stock returns in the ASE are not associated with the market beta (market risk) and there is not a strong relationship with any other risk factor for the stocks market value or book to market ratio