Predicting the Past

Drawing from the social theories of Niklas Luhmann and Mary Douglas, Predicting the Past advocates a reflexive understanding of the paradoxical institutional dynamic of American literary history as a professional discipline and field of study. Contrary to most disciplinary accounts, Michael Boyden resists the utopian impulse to offer supposedly definitive solutions for the legitimation crises besetting American literature studies by “going beyond” its inherited racist, classist, and sexist underpinnings. Approaching the existence of the American literary tradition as a typically modern problem generating diverse but functionally equivalent solutions, Boyden argues how its peculiarity does not, as is often supposed, reside in its restrictive exclusivity but rather in its massive inclusivity which drives it to constantly revert to a self-negating “beyond” perspective. Predicting the Past covers a broad range of both well-known and lesser known literary histories and reference works, from Rufus Griswold’s 1847 Prose Writers of America to Sacvan Bercovitch’s monumental Cambridge History of American Literature. Throughout, Boyden focuses on particular themes and topics illustrating the selfinduced complexity of American literary history such as the early “Anglocentric” roots theories of American literature; the debate on contemporary authors in the age of naturalism; the plurilingual ethnocentrism of the pioneer Americanists of the mid-twentieth century; and the genealogical misrepresentation of founding figures such as Jonathan Edwards, Emily Dickinson, and Robert Lowell

Predicting the Past: Digital Art History, Modeling, and Machine Learning

Case study from the Getty’s digital art history team shows how modeling and machine learning are shedding light on the history of the art market

Comparing the EPA Indoor Air Quality Personal Computer Model and Field Data

The authors recommend caution in using an EPA model for reconstructing past exposure events as well as for predicting future exposures

Predicting the Past and Forecasting the Future

A commentary on Santos\u27 article, Explaining Scholarship Addressing Hispanic Children’s Issues

Forecasting stock market volatility and the informational efficiency of the DAX-index options market

Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors, such as GARCH models, are investigated, to determine if they are more appropriate for predicting future return volatility. Employing German DAX-index return data it is found that past returns do not contain useful information beyond the volatility expectations already reflected in option prices. This supports the efficient market hypothesis for the DAX-index options market

Predicting the past to avoid financial crises

__Abstract__ It is often said that people who fail to learn the lessons of history are fated to repeat it, both at the micro and the macro level. Many will argue that the assertion is as true of the banking industry as any other field of human activity

The prediction of future from the past: an old problem from a modern perspective

The idea of predicting the future from the knowledge of the past is quite natural when dealing with systems whose equations of motion are not known. Such a long-standing issue is revisited in the light of modern ergodic theory of dynamical systems and becomes particularly interesting from a pedagogical perspective due to its close link with Poincar\'e's recurrence. Using such a connection, a very general result of ergodic theory - Kac's lemma - can be used to establish the intrinsic limitations to the possibility of predicting the future from the past. In spite of a naive expectation, predictability results to be hindered rather by the effective number of degrees of freedom of a system than by the presence of chaos. If the effective number of degrees of freedom becomes large enough, regardless the regular or chaotic nature of the system, predictions turn out to be practically impossible. The discussion of these issues is illustrated with the help of the numerical study of simple models.Comment: 9 pages, 4 figure

Predicting Corporate Bankruptcy: Lessons from the Past

The need for corporate bankruptcy prediction models arises in 1960 after the increase in incidence of some major bankruptcies. Over the years, the episodes of financial turmoil increase in number and so does these bankruptcy prediction models. Existing reviews of bankruptcy models are either narrowly focused or outdated. Current study aims to provide an overview of the existing models for predicting bankruptcy and review the significance of these models. Furthermore, it highlights the problems and issues in the existing models which hinders the accuracy in predicting bankruptcy