Managing Risk of Bidding in Display Advertising

In this paper, we deal with the uncertainty of bidding for display advertising. Similar to the financial market trading, real-time bidding (RTB) based display advertising employs an auction mechanism to automate the impression level media buying; and running a campaign is no different than an investment of acquiring new customers in return for obtaining additional converted sales. Thus, how to optimally bid on an ad impression to drive the profit and return-on-investment becomes essential. However, the large randomness of the user behaviors and the cost uncertainty caused by the auction competition may result in a significant risk from the campaign performance estimation. In this paper, we explicitly model the uncertainty of user click-through rate estimation and auction competition to capture the risk. We borrow an idea from finance and derive the value at risk for each ad display opportunity. Our formulation results in two risk-aware bidding strategies that penalize risky ad impressions and focus more on the ones with higher expected return and lower risk. The empirical study on real-world data demonstrates the effectiveness of our proposed risk-aware bidding strategies: yielding profit gains of 15.4% in offline experiments and up to 17.5% in an online A/B test on a commercial RTB platform over the widely applied bidding strategies

Scope for Credit Risk Diversification

This paper considers a simple model of credit risk and derives the limit distribution of losses under different assumptions regarding the structure of systematic risk and the nature of exposure or firm heterogeneity. We derive fat-tailed correlated loss distributions arising from Gaussian risk factors and explore the potential for risk diversification. Where possible the results are generalised to non-Gaussian distributions. The theoretical results indicate that if the firm parameters are heterogeneous but come from a common distribution, for sufficiently large portfolios there is no scope for further risk reduction through active portfolio management. However, if the firm parameters come from different distributions, then further risk reduction is possible by changing the portfolio weights. In either case, neglecting parameter heterogeneity can lead to underestimation of expected losses. But, once expected losses are controlled for, neglecting parameter heterogeneity can lead to overestimation of risk, whether measured by unexpected loss or value-at-risk

Changes in capital allocation practices – ERM and organisational change

This paper aims to study changes in capital allocation routines following the introduction of a new risk management system, enterprise risk management (ERM). Based on an institutional framework and empirical evidence from multiple sources in a large UK insurance company, we evaluated the extent and nature of organisational change. ERM was seen as an external driver to the change in the existing routines, which in turn led to internal changes in new capital allocation routines. The change was extreme, which signifies that existing capital allocation routines were not strong enough to deal with ERM as a key driver of change

Do audit fees and audit hours influence credit ratings?: A comparative analysis of Big4 vs Non-Big4

We examine the relationship between credit ratings / changes and audit fees (hours) for Big4 and Non-Big4 firms. Audit fee (hours) may be considered as a default risk metric for credit ratings agencies. However, firms audited by Big4 are larger, better performing and operate with lower leverage compared to firms followed by Non-Big4. Therefore, the association between audit fee (hours) may be different for firms followed by Big4 and Non-Big4 audit firms. We find that there is a negative association between audit fees and credit ratings for firms followed by Big4 audit firms. However, we find an insignificant relation for firms followed by Non-Big4. We conjecture the different association due to the Big4 firms having more robust accounting procedures; Big4 firms must offer competitive audit fees because they are engaged in fierce competition with other Big4 firms. Moreover, Big4 and Non-Big4 firms have different relationships with their clients because Non-Big4 firms are more income dependent on their clients. Using a sample of 1,717 firm–year observations between 2002 and 2013, we establish a relation between audit fees in period t and credit ratings in period t+1, for firms followed by Big4 auditors. We do not find a significant relation for firms followed by Non-Nig4 firms, suggesting that credit ratings agencies perceive audit fee differently for Big4 and Non-Big4 firms. Client firms followed by Big4 auditors that experience a credit rating change in period t+1 pay lower audit fees in period t compared to firms that do not experience a credit rating change. Our additional analysis suggests a different association between firms audit fees and firm performance for firms that experience a credit rating increase and decrease. Firms that experience a credit ratings increase in period t+1 have strong performance and lower audit fees in period t. On the other hand, firms that experience a credit rating decrease have weak financial performance and negative audit fees compared to firms that do not experience a credit ratings change. Our results suggest that audit fees combined with financial performance influence a credit ratings agency' perception of default risk