17,055 research outputs found

    A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling

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    The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to address this challenge. The techno-economic model in our hybrid approach provides a deep understanding of the market. It captures the underlying factors and their impacts on electricity prices, which is impossible with statistical models alone. The statistical models incorporate non-techno-economic aspects, such as the expectations and speculative behaviour of market participants, through the interpretation of prices. The hybrid model generates both conventional point predictions and probabilistic forecasts, providing a comprehensive understanding of the market landscape. Probabilistic forecasts are particularly valuable because they account for market uncertainty, facilitating informed decision-making and risk management. Our model delivers state-of-the-art results, helping market participants to make informed decisions and operate their systems more efficiently

    Fair Assortment Planning

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    Many online platforms, ranging from online retail stores to social media platforms, employ algorithms to optimize their offered assortment of items (e.g., products and contents). These algorithms tend to prioritize the platforms' short-term goals by solely featuring items with the highest popularity or revenue. However, this practice can then lead to undesirable outcomes for the rest of the items, making them leave the platform, and in turn hurting the platform's long-term goals. Motivated by that, we introduce and study a fair assortment planning problem, which requires any two items with similar quality/merits to be offered similar outcomes. We show that the problem can be formulated as a linear program (LP), called (FAIR), that optimizes over the distribution of all feasible assortments. To find a near-optimal solution to (FAIR), we propose a framework based on the Ellipsoid method, which requires a polynomial-time separation oracle to the dual of the LP. We show that finding an optimal separation oracle to the dual problem is an NP-complete problem, and hence we propose a series of approximate separation oracles, which then result in a 1/21/2-approx. algorithm and a PTAS for the original Problem (FAIR). The approximate separation oracles are designed by (i) showing the separation oracle to the dual of the LP is equivalent to solving an infinite series of parameterized knapsack problems, and (ii) taking advantage of the structure of the parameterized knapsack problems. Finally, we conduct a case study using the MovieLens dataset, which demonstrates the efficacy of our algorithms and further sheds light on the price of fairness.Comment: 86 pages, 7 figure

    2022-1 A Partial Identification Approach to Identifying the Determinants of Human Capital Accumulation: An Application to Teachers

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    This paper views teacher quality through the human capital perspective. Teacher quality exhibits substantial growth over teachers’ careers, but why it improves is not well understood. I use a human capital production function nesting On-the-Job-Training (OJT) and Learning-by-Doing (LBD) and experimental variation from Glewwe et al. (2010), a teacher incentive pay experiment in Kenya, to discern the presence and relative importance of these forces. The identified set for the OJT and LBD components has a closed-form solution, which depends on experimentally estimated average treatment effects. The results provide evidence of an LBD component, as well as an informative upper bound on the OJT component

    Discovering the hidden structure of financial markets through bayesian modelling

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    Understanding what is driving the price of a financial asset is a question that is currently mostly unanswered. In this work we go beyond the classic one step ahead prediction and instead construct models that create new information on the behaviour of these time series. Our aim is to get a better understanding of the hidden structures that drive the moves of each financial time series and thus the market as a whole. We propose a tool to decompose multiple time series into economically-meaningful variables to explain the endogenous and exogenous factors driving their underlying variability. The methodology we introduce goes beyond the direct model forecast. Indeed, since our model continuously adapts its variables and coefficients, we can study the time series of coefficients and selected variables. We also present a model to construct the causal graph of relations between these time series and include them in the exogenous factors. Hence, we obtain a model able to explain what is driving the move of both each specific time series and the market as a whole. In addition, the obtained graph of the time series provides new information on the underlying risk structure of this environment. With this deeper understanding of the hidden structure we propose novel ways to detect and forecast risks in the market. We investigate our results with inferences up to one month into the future using stocks, FX futures and ETF futures, demonstrating its superior performance according to accuracy of large moves, longer-term prediction and consistency over time. We also go in more details on the economic interpretation of the new variables and discuss the created graph structure of the market.Open Acces

    The determinants of value addition: a crtitical analysis of global software engineering industry in Sri Lanka

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    It was evident through the literature that the perceived value delivery of the global software engineering industry is low due to various facts. Therefore, this research concerns global software product companies in Sri Lanka to explore the software engineering methods and practices in increasing the value addition. The overall aim of the study is to identify the key determinants for value addition in the global software engineering industry and critically evaluate the impact of them for the software product companies to help maximise the value addition to ultimately assure the sustainability of the industry. An exploratory research approach was used initially since findings would emerge while the study unfolds. Mixed method was employed as the literature itself was inadequate to investigate the problem effectively to formulate the research framework. Twenty-three face-to-face online interviews were conducted with the subject matter experts covering all the disciplines from the targeted organisations which was combined with the literature findings as well as the outcomes of the market research outcomes conducted by both government and nongovernment institutes. Data from the interviews were analysed using NVivo 12. The findings of the existing literature were verified through the exploratory study and the outcomes were used to formulate the questionnaire for the public survey. 371 responses were considered after cleansing the total responses received for the data analysis through SPSS 21 with alpha level 0.05. Internal consistency test was done before the descriptive analysis. After assuring the reliability of the dataset, the correlation test, multiple regression test and analysis of variance (ANOVA) test were carried out to fulfil the requirements of meeting the research objectives. Five determinants for value addition were identified along with the key themes for each area. They are staffing, delivery process, use of tools, governance, and technology infrastructure. The cross-functional and self-organised teams built around the value streams, employing a properly interconnected software delivery process with the right governance in the delivery pipelines, selection of tools and providing the right infrastructure increases the value delivery. Moreover, the constraints for value addition are poor interconnection in the internal processes, rigid functional hierarchies, inaccurate selections and uses of tools, inflexible team arrangements and inadequate focus for the technology infrastructure. The findings add to the existing body of knowledge on increasing the value addition by employing effective processes, practices and tools and the impacts of inaccurate applications the same in the global software engineering industry

    Life-Cycle Portfolio Choice with Stock Market Loss Framing: Explaining the Empirical Evidence

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    We develop a life-cycle model with optimal consumption, portfolio choice, and flexible work hours for households with loss-framing preferences giving them disutility if they experience losses from stock investments. Structural estimation using U.S. data shows that the model tracks the empirical age-pattern of stock market participants’ financial wealth, stock shares, and work hours remarkably well. Including stock market participation costs in the model allows us to also predict low stock market participations rates observed in the overall population. Allowing for heterogeneous agents further improves explanatory power and accounts for the observed discrepancy in wealth accumulation between stockholders and non-stockholders

    Healthcare Innovation Absenteeism: The Rise of Physician Entrepreneurs & Medical Startups

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    For years, warning signs have illuminated imminent days of reckoning for stalled healthcare innovation across the dynamic American healthcare landscape. An evolving epic battle for healthcare innovation delivery has silently raged and set arena stages throughout the healthcare industry. Urgent innovative healthcare delivery is needed to ameliorate longstanding points of failures in providing healthcare delivery to society. Historically, the science of medicine has fostered cultural practices of innovation absenteeism and resistance to change. Mired by archaic processes, legacy systems, and fractionally useful equipment, our current healthcare ecosystems are unsustainable. Recently, some unhindered frontline physicians opted to take on a portion of critical healthcare challenges and followed their ideas to leverage clinical expertise and drive the agenda for changing healthcare innovation delivery. Our qualitative multi-case study design centered around empirical evidence that answered the research question: How do physician entrepreneurs navigate decision-making strategies for medical startups from ideation, innovation, to commercialization of new medical products and services? We examined 21 cases of physician founded medical startups to understand particularizations around physician entrepreneurship. Findings suggest three contributions towards knowledge accumulation about physician entrepreneurs and medical startups: exclusive decision-making processes, industry-specific insights, and illuminations of physician voices that might not otherwise be heard

    Introduction to the Minitrack on Crowd-based Platforms

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    Tracking and Nowcasting Directional Changes in the Forex Market

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    Price changes in financial markets are typically summarized as time series (TS). Directional Change (DC) is an alternative, data-driven way to sample data points. The main objective of this thesis is to find new ways to extract new, useful information from the market. This is broken down into three directions: (1) to summarize price changes with DC, one must first determine the threshold to be used. We ask: could a threshold be too big or too small? If so, how could we determine the range of usable thresholds? (2) Could DC indicators extract volatility information from the market that is not observable under TS? (3) In DC, the start of a new trend is only confirmed in hindsight – to be precise, at the DC Confirmation (DCC) point when the price has reversed by the threshold specified. Could we detect that a new trend has begun before the DCC point? This is known as a nowcasting problem. This thesis has made three contributions. Firstly, we have created a guideline to determine the range of useable thresholds under DC. This supports the research that follows. Secondly, we have demonstrated how DC indicators could complement TS in tracking the market for volatility information. Thirdly, we have introduced new DC indicators; by using these indicators, we have proposed an algorithm and demonstrated how it could help us nowcast whether a new trend has begun in DC
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