SOIL CONSERVATION OR COMMODITY PROGRAMS: TRADE OFFS DURING THE TRANSITION TO DRYLAND CROP PRODUCTION

Predicted crop yields and wind erosion rates from a multi-year/multi-crop growth simulation model provided input into a multi-period recursive QP model to evaluate erosion implications during the transition to dryland crop production on the Texas Southern High Plains. Three farm-program participation options were considered in this study. Participation in an extension of the current farm program resulted in an increase in net returns and wind erosion rates above nonparticipation. Imposition of a soil loss limit without consideration of a flexible base option can significantly reduce discounted present values. Increasing risk aversion across producers affects crop mix selection which can result in lower per acre wind erosion rates for this particular region.Crop Production/Industries,

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

Reliability-based economic model predictive control for generalized flow-based networks including actuators' health-aware capabilities

This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalized flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamically allocate safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators’ availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the considered case study.Peer ReviewedPostprint (author's final draft

Financial Market Runs

Our paper offers a minimalist model of a run on a financial market. The prime ingredient is that each risk-neutral investor fears having to liquidate after a run, but before prices can recover back to fundamental values. During the urn, only the risk-averse market-making sector is willing to absorb shares. To avoid having to possibly liquidate shares at the marginal post-run price in which case the market-making sector will already hold a lot of share inventory and thus be more reluctant to absorb additional shares all investors may prefer selling their shares into the market today at the average run price, thereby causing the run itself. Consequently, stock prices are low and risk is allocated inefficiently. Liquidity runs and crises are not caused by liquidity shocks per se, but by the fear of future liquidity shocks.