26,072 research outputs found
Author Peter FitzSimons speaking at the National Library of Australia, Canberra, 13 November 2012 /
No full textTitle from acquisitions documentation.; Part of the collection: Portraits of author Peter FitzSimons speaking at the National Library of Australia, Canberra, 13 November 2012.; Acquired in digital format; access copy available online.; Mode of access: Online.; Photographed by a staff member of the National Library of Australia
Penalized Least Squares for Optimal Sparse Portfolio Selection
No full textMarkowitz portfolios often result in an unsatisfying out-of-sample performance, due to the presence of estimation errors in inputs parameters, and in extreme and unstable asset weights, especially when the number of securities is large. Recently, it has been shown that imposing a penalty on the 1-norm of the asset weights vector not only regularizes the problem, thereby improving the out-of-sample performance, but also allows to automatically select a subset of assets to invest in. Here, we propose a new, simple type of penalty that explicitly considers financial information and consider several alternative non-convex penalties, that allow to improve on the 1-norm penalization approach. Empirical results on U.S.-stock market data support the validity of the proposed penalized least squares methods in selecting portfolios with superior out-of-sample performance with respect to several state-of-art benchmarks
Constructing optimal sparse portfolios using regularization methods
No full textThe ideas of Markowitz indisputably constitute a milestone in portfolio theory, even though the resulting mean-variance portfolios typically exhibit an unsatisfying out-of-sample performance, especially when the number of securities is large and that of observations is not. The bad performance is caused by estimation errors in the covariance matrix and in the expected return vector that can deposit unhindered in the portfolio weights. Recent studies show that imposing a penalty in form of a l1-norm of the asset weights regularizes the problem, thereby improving the out-of-sample performance of the optimized portfolios. Simultaneously, l1-regularization selects a subset of assets to invest in from a pool of candidates that is often very large. However, l1-regularization might lead to the construction of biased solutions. We propose to tackle this issue by considering several alternative penalties proposed in non-financial contexts. Moreover we propose a simple new type of penalty that explicitly considers financial information. We show empirically that these alternative penalties can lead to the construction of portfolios with superior out-of-sample performance in comparison to the state-of-the-art l1-regularized portfolios and several standard benchmarks, especially in high dimensional problems. The empirical analysis is conducted with various U.S.-stock market datasets
A Fuzzy clustering approach for textual data
No full textDocument clustering is a process of partitioning a corpus of documents into distinctive clusters based on the content similarity. Traditional (hard or fuzzy) document clustering algorithms are usually relying on the vector representation of documents based on the bag-of-words (BOW) approach, leading to very high dimensions in the vector representation of the corpus. In recent years, spectral clustering has been extensively applied in the field of text classification with support vector machines (SVMs) in combination with string kernels, but little has been done in the field of fuzzy document clustering with kernel-based methods. This work proposes a novel approach to text clustering, by grouping documents into clusters based on a new version of fuzzy spectral clustering with string kernels
Moral Good, the Beatific Vision, and God’s Kingdom Writings by Germain Grisez and Peter Ryan, S.J.. Edited by Peter J. Weigel
No full textFor close to half a century, the work of Germain Grisez has been highly influential, and his writings continue to receive considerable attention from philosophers and theologians of diverse viewpoints. His co-author for this work is the professor and noted moral theologian Fr. Peter Ryan, S.J., currently the executive director of the Secretariat of Doctrine and Canonical Affairs of the United States Conference of Catholic Bishops (USCCB). These two eminent scholars explore fundamental questions about Christian eschatology, moral theory, the purpose of human life, and the promise of human fulfilment. The authors examine Christian teaching on the final destiny of persons, investigating the meaning of God's kingdom, the hope of the beatific vision, and the centrality of moral goodness and divine grace in one's final end. This work is an ideal source for students, scholars, ministers and lay persons interested in basic questions of Christian theology, the philosophy of religion, ethical theory, and Catholic doctrin
Murder on the mountain: author talk with Peter J. Wosh
No full textAuthor talk by Peter J. Wosh on May 5th, 2022, on his book, "Murder on the Mountain: crime, passion, and punishment in gilded age New Jersey.
Lunchtime Talk with Author and Attorney Peter Godwin
No full textAuthor and attorney Peter Godwin gave a lunchtime talk about the topics discussed in his book, The Fear, which focuses on the human rights situation in Zimbabwe under the rule of Robert Mugabe
The Stochastics of Threshold Accepting: Analysis of an Application to the Uniform Design Problem
No full textThreshold Accepting (TA) is a powerful optimization heuristic from the class of stochastic local search algorithms. It has been applied successfully to different optimization problems in statistics and econometrics, including the uniform design problem. Using the latter application as example, the stochastic properties of a TA implementation are analyzed. We provide a formal framework for the analysis of optimization heuristics like TA, which can be used to estimate lower bounds and to derive convergence results. It is also helpful for tuning real applications. Based on this framework, empirical results are presented for the uniform design problem. In particular, for two problem instances, the rate of convergence of the algorithm is estimated to be of the order of a power of -0.3 to -0.7 of the number of iterations. --Heuristic optimization,Threshold Accepting,Stochastic analysis of heuristics
Forecasting Russian Foreign Trade Comparative Advantages in the Context of a Potential WTO Accession
No full textFor the private and public sector in any particular country it is crucial to know, which industries may exhibit comparative advantages, that for some reasons are not realized. This can efficiently help all current and potential actors to improve their economic strategy both at the micro- and macroeconomic level. In this paper we propose an approach of forecasting comparative advantages dynamics in foreign trade. The instrument is based on relative price differences and is efficient for countries in the process of economic liberalization. An empirical analysis based on the example of Central and East European countries confirms a good performance in the sense of predictive power of this instrument. On the example of Russia, experiencing a period of economic liberalization and with the prospect to join the WTO agreements, we demonstrate which sectors are most likely to contain comparative advantages in the near future.comparative advantage, economy in transition, Balassa index, Lafay index
Optimal Lag Structure Selection in VEC-Models
No full textFor modelling economic and financial time series, multivariate linear and nonlinear systems of equations have become a standard tool. These models can also be applied to non-stationary processes. However, the resulting finite-sample estimates may depend strongly on the specification of the model dynamics. We propose a method for automatic identification of the dynamic part of VEC-models. Model selection is based on a modified information criterion. The lag structure of the model is selected according to this objective function allowing for "holes". The resulting complex discrete optimization problem is tackled using a hybrid heuristic combining ideas from threshold accepting and memetic algorithms. We present the algorithm and the results of a simulation study showing the method's performance both with regard to the dynamic structure and the rank selection in the VEC-model. The results indicate that the selection of the cointregation rank might depend strongly on the specification of the dynamic part of the VEC-modelModel selection; cointegration rank; reduced rank regression
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