Factor models

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  • Chris Adcock is Professor of Financial Econometrics in the University of Sheffield. His career includes several years working in quantitative investment management in the City and, prior to that, a decade in management science consultancy. His research interests are in the development of robust and non-standard methods for modelling expected returns, portfolio selection methods and the properties of optimized portfolios. He has acted as an advisor to a number of asset management firms. He is the founding editor of the European Journal of Finance. George A.

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  • Bài giảng Chapter 5: Risk and return - Portfolio theory and asset pricing models presents of portfolio theory, capital asset pricing model (CAPM) (efficient frontier, capital market line (CML), security market line (SML), beta calculation, beta calculation), arbitrage pricing theory, fama french 3 factor model.

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  • Tham khảo tài liệu 'báo cáo sinh học: " research article uncovering transcriptional regulatory networks by sparse bayesian factor model"', khoa học tự nhiên phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

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  • Chapter 11, Arbitrage pricing theory and multifactor models of risk and return. In this chapter, we show how such no-arbitrage conditions together with the factor model introduced in Chapter 10 allow us to generalize the security market line of the CAPM to gain richer insight into the risk-return relationship.

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  • Mathematical finance and financial engineering have been rapidly expanding fields of science over the past three decades. The main reason behind this phenomenon has been the success of sophisticated quantitative methodologies in helping professionals to manage financial risks. The newly developed credit derivatives industry has grown around the need to handle credit risk, which is one of the fundamental factors of financial risk. In recent years, we have witnessed a tremendous acceleration in research efforts aimed at better apprehending, modeling and hedging of this kind of risk ...

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  • In this activity, you will participate in a class discussion to identify the factors that will impact the selection of a programming model. Microsoft Official Curriculum (MOC), available to IT Academies at a discounted price, is professional courseware intended for IT professionals and developers who build, support, and implement solutions by using Microsoft products and technologies. MOC is designed to cover the topics that employers know are mission-critical in the real world.

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  • Social media marketing is a recent addition to organizations’ integrated marketing communications plans. Integrated marketing communications is a principle organizations follow to connect with their targeted markets. Integrated marketing communications coordinates the elements of the promotional mix;advertising, personal selling, public relations, publicity, direct marketing, and sales promotion. [1] In the traditional marketing communications model, the content, frequency, timing, and medium of communications by the organization is in collaboration with an external agent, i.e.

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  • This book has been written to present elementary practical and efficient applications in saving energy concept, as well as propose a solitary action for this category of topics. The book aims to illustrate various methods in treatment the concept of thermal insulation such as processes and the attempt to build an efficient passive building model.

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  • Business models have become the primary tools for the financial analysis of nearly all major business decisions. However, the structure and design of most models have evolved without reference to an effective business-modelling methodology. In writing this book we hope to provide the terms of reference for best-practice business modelling.

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  • For instance, based on the pure management fee model described above, a fund with a 1.5% management fee and fixed expenses of $600,000 would break even at $40 mil- lion in AUM. By decreasing fixed expenses by $60,000, or 10%, the fund’s breakeven AUM drops by $4 million to $36 million. Stated differently, $15,000 in fixed expenses equates to $1 million in AUM.

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  • Factored Statistical Machine Translation extends the Phrase Based SMT model by allowing each word to be a vector of factors. Experiments have shown effectiveness of many factors, including the Part of Speech tags in improving the grammaticality of the output. However, high quality part of speech taggers are not available in open domain for many languages.

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  • This paper describes an unsupervised dynamic graphical model for morphological segmentation and bilingual morpheme alignment for statistical machine translation. The model extends Hidden Semi-Markov chain models by using factored output nodes and special structures for its conditional probability distributions. It relies on morpho-syntactic and lexical source-side information (part-of-speech, morphological segmentation) while learning a morpheme segmentation over the target language. Our model outperforms a competitive word alignment system in alignment quality. ...

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  • Sentiment classification refers to the task of automatically identifying whether a given piece of text expresses positive or negative opinion towards a subject at hand. The proliferation of user-generated web content such as blogs, discussion forums and online review sites has made it possible to perform large-scale mining of public opinion. Sentiment modeling is thus becoming a critical component of market intelligence and social media technologies that aim to tap into the collective wisdom of crowds.

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  • In predicate-argument structure analysis, it is important to capture non-local dependencies among arguments and interdependencies between the sense of a predicate and the semantic roles of its arguments. However, no existing approach explicitly handles both non-local dependencies and semantic dependencies between predicates and arguments.

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  • Morphological processes in Semitic languages deliver space-delimited words which introduce multiple, distinct, syntactic units into the structure of the input sentence. These words are in turn highly ambiguous, breaking the assumption underlying most parsers that the yield of a tree for a given sentence is known in advance. Here we propose a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity.

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  • Automated service quality has been recognised as the factor which determines the suc cess or failure of electronic commerce. Those models current ly available to measure automated service quality are limited in their focus, encompassing only one electronic channel – the internet – thereby ignoring attributes of the other automated service channels.

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  • Probabilistic models of sentence comprehension are increasingly relevant to questions concerning human language processing. However, such models are often limited to syntactic factors. This paper introduces a novel sentence processing model that consists of a parser augmented with a probabilistic logic-based model of coreference resolution, which allows us to simulate how context interacts with syntax in a reading task. Our simulations show that a Weakly Interactive cognitive architecture can explain data which had been provided as evidence for the Strongly Interactive hypothesis. ...

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  • Building on earlier work that integrates different factors in language modeling, we view (i) backing off to a shorter history and (ii) class-based generalization as two complementary mechanisms of using a larger equivalence class for prediction when the default equivalence class is too small for reliable estimation. This view entails that the classes in a language model should be learned from rare events only and should be preferably applied to rare events.

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  • Language models based on word surface forms only are unable to benefit from available linguistic knowledge, and tend to suffer from poor estimates for rare features. We propose an approach to overcome these two limitations. We use factored features that can flexibly capture linguistic regularities, and we adopt confidence-weighted learning, a form of discriminative online learning that can better take advantage of a heavy tail of rare features.

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