Reduced form modeling of limit order markets

This paper proposes a parametric approach for stochastic modeling of limit order markets. The models are obtained by augmenting classical perfectly liquid market models by few additional risk factors that describe liquidity properties of the order book. The resulting models are easy to calibrate and to analyze using standard techniques for multivariate stochastic processes. Despite their simplicity, the models are able to capture several properties that have been found in microstructural analysis of limit order markets. Calibration of a continuous-time three-factor model to Copenhagen Stock Exchange data exhibits e.g.\ mean reversion in liquidity as well as the so called crowding out effect which influences subsequent mid-price moves. Our dynamic models are well suited also for analyzing market resiliency after liquidity shocks

Finding Optimal Strategies in a Multi-Period Multi-Leader-Follower Stackelberg Game Using an Evolutionary Algorithm

Stackelberg games are a classic example of bilevel optimization problems, which are often encountered in game theory and economics. These are complex problems with a hierarchical structure, where one optimization task is nested within the other. Despite a number of studies on handling bilevel optimization problems, these problems still remain a challenging territory, and existing methodologies are able to handle only simple problems with few variables under assumptions of continuity and differentiability. In this paper, we consider a special case of a multi-period multi-leader-follower Stackelberg competition model with non-linear cost and demand functions and discrete production variables. The model has potential applications, for instance in aircraft manufacturing industry, which is an oligopoly where a few giant firms enjoy a tremendous commitment power over the other smaller players. We solve cases with different number of leaders and followers, and show how the entrance or exit of a player affects the profits of the other players. In the presence of various model complexities, we use a computationally intensive nested evolutionary strategy to find an optimal solution for the model. The strategy is evaluated on a test-suite of bilevel problems, and it has been shown that the method is successful in handling difficult bilevel problems.Comment: To be published in Computers and Operations Researc

To Reach the Clouds: Application of Topic Models to the Meta-review on Cloud Computing Literature

Cloud computing remains an increasingly popular topic among practitioners as well as researchers. The literature spans across multiple disciplines, and the knowledge is fragmented and not systematized. To address this issue we apply topic models to conduct a meta-review on cloud computing. We identify twenty research topics across multiple disciplines, and demonstrate the use of the approach to conduct reviews in the field of information systems (IS). In additionally, we discuss multidisciplinary nature of cloud research, as well as research topics attracting contributions from various scientific fields.Peer reviewe

Optimizing high-dimensional stochastic forestry via reinforcement learning

In proceeding beyond the generic optimal rotation model, forest economic research has applied various specifications that aim to circumvent the problems of high dimensional-ity. We specify an age-and size-structured mixed-species optimal harvesting model with binary variables for harvest timing, stochastic stand growth, and stochastic prices. Rein-forcement learning allows solving this high-dimensional model without simplifications. In addition to presenting new features in reservation price schedules and effects of stochas-ticity, our setup allows evaluating the simplifications in the existing research. We find that one-or two-dimensional models lose a high fraction of attainable economic output while the commonly applied size-structured matrix model overestimates economic profitability, yields deviations in harvest timing, including optimal rotation, and dilutes the effects of stochasticity. Reinforcement learning is found to be an efficient and promising method for detailed age-and size-structured optimization models in resource economics. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )Peer reviewe

Towards Sustainable Forest Management Strategies with MOEAs

Sustainable forest management is a crucial element in combating climate change, plastic pollution, and other unsolved challenges of the 21st century. Forests not only produce wood - a renewable resource that is increasingly replacing fossil-based materials - but also preserve biodiversity and store massive amounts of carbon. Thus, a truly optimal forest policy has to balance profit-oriented logging with ecological and societal interests, and should thus be solved as a multi-objective optimization problem. Economic forest research, however, has largely focused on profit maximization. Recent publications still scalarize the problem a priori by assigning weights to objectives. In this paper, we formulate a multi-objective forest management problem where profit, carbon storage, and biodiversity are maximized. We obtain Pareto-efficient forest management strategies by utilizing three state-of-the-art Multi-Objective Evolutionary Algorithms (MOEAs), and by incorporating domain-specific knowledge through customized evolutionary operators. An analysis of Pareto-efficient strategies and their harvesting schedules in the design space clearly shows the benefits of the proposed approach. Unlike many EMO application studies, we demonstrate how a systematic post-optimality trade-off analysis can be applied to choose a single preferred solution. Our pioneering work on sustainable forest management explores an entirely new application area for MOEAs with great societal impact.Peer reviewe