Online algorithms for conversion problems : an approach to conjoin worst-case analysis and empirical-case analysis

A conversion problem deals with the scenario of converting an asset into another asset and possibly back. This work considers financial assets and investigates online algorithms to perform the conversion. When analyzing the performance of online conversion algorithms, as yet the common approach is to analyze heuristic conversion algorithms from an experimental perspective, and to analyze guaranteeing conversion algorithms from an analytical perspective. This work conjoins these two approaches in order to verify an algorithms\u27 applicability to practical problems. We focus on the analysis of preemptive and non-preemptive online conversion problems from the literature. We derive both, empirical-case as well as worst-case results. Competitive analysis is done by considering worst-case scenarios. First, the question whether the applicability of heuristic conversion algorithms can be verified through competitive analysis is to be answered. The competitive ratio of selected heuristic algorithms is derived using competitive analysis. Second, the question whether the applicability of guaranteeing conversion algorithms can be verified through experiments is to be answered. Empirical-case results of selected guaranteeing algorithms are derived using exploratory data analysis. Backtesting is done assuming uncertainty about asset prices, and the results are analyzed statistically. Empirical-case analysis quantifies the return to be expected based on historical data. In contrast, the worst-case competitive analysis approach minimizes the maximum regret based on worst-case scenarios. Hence the results, presented in the form of research papers, show that combining this optimistic view with this pessimistic view provides an insight into the applicability of online conversion algorithms to practical problems. The work concludes giving directions for future work.Ein Conversion Problem befasst sich mit dem Eintausch eines Vermögenswertes in einen anderen Vermögenswert unter Berücksichtigung eines möglichen Rücktausches. Diese Arbeit untersucht Online-Algorithmen, die diesen Eintausch vornehmen. Der klassische Ansatz zur Performanceanalyse von Online Conversion Algorithmen ist, heuristische Algorithmen aus einer experimentellen Perspektive zu untersuchen; garantierende Algorithmen jedoch aus einer analytischen. Die vorliegende Arbeit verbindet diese beiden Ansätze mit dem Ziel, die praktische Anwendbarkeit der Algorithmen zu überprüfen. Wir konzentrieren uns auf die Analyse des präemtiven und des nicht-präemtiven Online Conversion Problems aus der Literatur und ermitteln empirische sowie analytische Ergebnisse. Kompetitive Analyse wird unter Berücksichtigung von worst-case Szenarien durchgeführt. Erstens soll die Frage beantwortet werden, ob die Anwendbarkeit heuristischer Algorithmen durch Kompetitive Analyse verifiziert werden kann. Dazu wird der kompetitive Faktor von ausgewählten heuristischen Algorithmen mittels worst-case Analyse abgeleitet. Zweitens soll die Frage beantwortet werden, ob die Anwendbarkeit garantierender Algorithmen durch Experimente überprüft werden kann. Empirische Ergebnisse ausgewählter Algorithmen werden mit Hilfe der Explorativen Datenanalyse ermittelt. Backtesting wird unter der Annahme der Unsicherheit über zukünftige Preise der Vermögenswerte durchgeführt und die Ergebnisse statistisch ausgewertet. Die empirische Analyse quantifiziert die zu erwartende Rendite auf Basis historischer Daten. Im Gegensatz dazu, minimiert die Kompetitive Analyse das maximale Bedauern auf Basis von worst-case Szenarien. Die Ergebnisse, welche in Form von Publikationen präsentiert werden, zeigen, dass die Kombination der optimistischen mit der pessimistischen Sichtweise einen Rückschluss auf die praktische Anwendbarkeit der untersuchten Online-Algorithmen zulässt. Abschließend werden offene Forschungsfragen genannt

FIDE Congress 2020 - EU Competition Law and the Digital Economy: United Kingdom Report

This report was prepared for the 29th biennial Congress of the International Federation of European Law (FIDE) to be held in The Hague in May 2020. It is the national report for the United Kingdom in response to Topic 3 of the 2020 FIDE Congress, titled ‘EU Competition Law and the Digital Economy’. This report offers an overview of UK competition enforcement in digital economy markets by answering twelve questions organised into four sections. Part A summarises key UK antitrust and merger decisions, agency publications, priorities and goals of enforcement in digital economy markets. Part B focuses upon the definition of markets and conceptualisation of market power by UK authorities in digital economy cases in light of their challenges and particularities. Part C offers a detailed overview of the issues underpinning UK antitrust and merger scrutiny in this field: the types of conduct investigated, relevant factors and concepts, theories of harm, efficiency justifications and remedies in digital economy cases. Finally, Part D identifies the potential for incoherent enforcement in this field from two different sources: the overlap between UK competition law and ex ante regulatory regimes (e.g. consumer protection, data protection); and the overlap between the powers of various UK competition decision-makers (e.g. sectoral regulators, the Competition Appeal Tribunal, and the courts)

A Better Alternative to Piecewise Linear Time Series Segmentation

Time series are difficult to monitor, summarize and predict. Segmentation organizes time series into few intervals having uniform characteristics (flatness, linearity, modality, monotonicity and so on). For scalability, we require fast linear time algorithms. The popular piecewise linear model can determine where the data goes up or down and at what rate. Unfortunately, when the data does not follow a linear model, the computation of the local slope creates overfitting. We propose an adaptive time series model where the polynomial degree of each interval vary (constant, linear and so on). Given a number of regressors, the cost of each interval is its polynomial degree: constant intervals cost 1 regressor, linear intervals cost 2 regressors, and so on. Our goal is to minimize the Euclidean (l_2) error for a given model complexity. Experimentally, we investigate the model where intervals can be either constant or linear. Over synthetic random walks, historical stock market prices, and electrocardiograms, the adaptive model provides a more accurate segmentation than the piecewise linear model without increasing the cross-validation error or the running time, while providing a richer vocabulary to applications. Implementation issues, such as numerical stability and real-world performance, are discussed.Comment: to appear in SIAM Data Mining 200

Online Computation with Untrusted Advice

The advice model of online computation captures a setting in which the algorithm is given some partial information concerning the request sequence. This paradigm allows to establish tradeoffs between the amount of this additional information and the performance of the online algorithm. However, if the advice is corrupt or, worse, if it comes from a malicious source, the algorithm may perform poorly. In this work, we study online computation in a setting in which the advice is provided by an untrusted source. Our objective is to quantify the impact of untrusted advice so as to design and analyze online algorithms that are robust and perform well even when the advice is generated in a malicious, adversarial manner. To this end, we focus on well-studied online problems such as ski rental, online bidding, bin packing, and list update. For ski-rental and online bidding, we show how to obtain algorithms that are Pareto-optimal with respect to the competitive ratios achieved; this improves upon the framework of Purohit et al. [NeurIPS 2018] in which Pareto-optimality is not necessarily guaranteed. For bin packing and list update, we give online algorithms with worst-case tradeoffs in their competitiveness, depending on whether the advice is trusted or not; this is motivated by work of Lykouris and Vassilvitskii [ICML 2018] on the paging problem, but in which the competitiveness depends on the reliability of the advice. Furthermore, we demonstrate how to prove lower bounds, within this model, on the tradeoff between the number of advice bits and the competitiveness of any online algorithm. Last, we study the effect of randomization: here we show that for ski-rental there is a randomized algorithm that Pareto-dominates any deterministic algorithm with advice of any size. We also show that a single random bit is not always inferior to a single advice bit, as it happens in the standard model

Big Data, Small Credit: The Digital Revolution and Its Impact on Emerging Market Consumers

This research report sheds light on a new cadre of technology companies who are disrupting the credit scoring business in emerging markets. Using non-financial data -- such as social media activity and mobile phone usage patterns -- complex algorithms and big data analytics are forever changing the economics of how we identify, score, and underwrite credit to consumers who have been invisible to lenders until now

Marketing relations and communication infrastructure development in the banking sector based on big data mining

Purpose: The article aims to study the methodological tools for applying the technologies of intellectual analysis of big data in the modern digital space, the further implementation of which can become the basis for the marketing relations concept implementation in the banking sector of the Russian Federation‘ economy. Structure/Methodology/Approach: For the marketing relations development in the banking sector in the digital economy, it seems necessary: firstly, to identify the opportunities and advantages of the big data mining in banking marketing; secondly, to identify the sources and methods of processing big data; thirdly, to study the examples of the big data mining successful use by Russian banks and to formulate the recommendations on the big data technologies implementation in the digital marketing banking strategy. Findings: The authors‘ analysis showed that big data technologies processing of open online and offline sources of information significantly increases the data amount available for intelligent analysis, as a result of which the interaction between the bank and the target client reaches a new level of partnership. Practical Implications: Conclusions and generalizations of the study can be applied in the practice of managing financial institutions. The results of the study can be used by bank management to form a digital marketing strategy for long-term communication. Originality/Value: The main contribution of this study is that the authors have identified the main directions of using big data in relationship marketing to generate additional profit, as well as the possibility of intellectual analysis of the client base, aimed at expanding the market share and retaining customers in the banking sector of the economy.peer-reviewe

Uber Effort: The Production of Worker Consent in Online Ride Sharing Platforms

The rise of the online gig economy alters ways of working. Mediated by algorithmically programmed mobile apps, platforms such as Uber and Lyft allow workers to work by driving and completing rides at any time or in any place that the drivers choose. This hybrid form of labor in an online gig economy which combines independent contract work with computer-mediated work differs from traditional manufacturing jobs in both its production activity and production relations. Through nine interviews with Lyft/Uber drivers, I found that workers’ consent, which was first articulated by Michael Burawoy in the context of the manufacturing economy, is still present in the work of the online gig economy in post-industrial capitalism. Workers willingly engage in the on-demand work not only to earn money but also to play a learning game motivated by the ambiguity of the management system, in which process they earn a sense of self-satisfaction and an illusion of autonomous control. This research points to the important role of technology in shaping contemporary labor process and suggests the potential mechanism which produces workers’ consent in technology-driven workplaces