scholarly journals Choosing the Lesser Evil: Forecasting Presidential Elections in Peru

2021 ◽  
pp. 1-26
Author(s):  
Moisés Arce ◽  
Sofía Vera
Keyword(s):  
Political Support ◽  
Electoral Volatility ◽  
Party Organizations ◽  
Political Landscape ◽  
Continuous Rotation ◽  
Political Center ◽  
Out Of Sample ◽  
Vote Intention ◽  
Out Of Sample Prediction ◽  
Lesser Evil

The Peruvian political landscape is dominated by the weakness of party organizations, the continuous rotation of political personalities, and, in turn, high electoral volatility and uncertainty. Nevertheless, we observe patterns of electoral competition that suggest candidates learn to capture the political center and compete over the continuation of an economic model that has sustained growth. We use this information to record the vote intention for the candidate viewed as the lesser evil. Our forecasting results predict a good share of the variation in political support for this candidate. The out-of-sample prediction also comes fairly close to the real electoral results. These findings provide some degree of electoral certainty in an area that, to date, remains understudied.

Trial by Jury as “Mockery of Justice”: Party Contention, Courtroom Corruption, and the Ironic Judicial Legacy of Antimasonry

2016 ◽  
Vol 34 (1) ◽  
pp. 155-198
Author(s):  
Elizabeth Bussiere
Keyword(s):  
Political Culture ◽  
Early Republic ◽  
Democratic Politics ◽  
Third Party ◽  
Party Organization ◽  
Party Organizations ◽  
Political Landscape ◽  
The Social ◽  
Modern Mass ◽  
True Mass

Sweeping across the social and political landscape of the northeastern United States during the late 1820s and early 1830s, the Antimasonic Party has earned a modest immortality as the first “third” party in American history. In pamphlets, speeches, sermons, protests, and other venues, Antimasons lambasted the fraternal order of Freemasonry as undemocratic, inegalitarian, and un-Christian, reviling it as a threat to the moral order and civic health of the Early Republic. Because they believed that the fraternal organization largely controlled all levels of government, antebellum Antimasons first created a social movement and then an independent political party. Even before the full emergence of modern mass democratic politics, Antimasons demonstrated the benefits of party organization, open national nominating conventions, and party platforms. Scholars with otherwise different perspectives on the “party period” tend to agree that Antimasonry had an important impact on what became the first true mass party organizations—the Jacksonian Democrats and the Whigs—and helped push the political culture in a more egalitarian and populist direction.

Predicting the trend of stock index based on feature engineering and CatBoost model

2021 ◽  
pp. 2150027
Author(s):  
Renzhe Xu ◽  
Yudong Chen ◽  
Tenglong Xiao ◽  
Jingli Wang ◽  
Xiong Wang
Keyword(s):  
Time Scale ◽  
Stock Index ◽  
Feature Engineering ◽  
Multiple Time ◽  
Multiple Time Scale ◽  
Back Testing ◽  
Price Series ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Sharp Ratio

As an important tool to measure the current situation of the whole stock market, the stock index has always been the focus of researchers, especially for its prediction. This paper uses trend types, which are received by clustering price series under multiple time scale, combined with the day-of-the-week effect to construct a categorical feature combination. Based on the historical data of six kinds of Chinese stock indexes, the CatBoost model is used for training and predicting. Experimental results show that the out-of-sample prediction accuracy is 0.55, and the long–short trading strategy can obtain average annualized return of 34.43%, which is a great improvement compared with other classical classification algorithms. Under the rolling back-testing, the model can always obtain stable returns in each period of time from 2012 to 2020. Among them, the SSESC’s long–short strategy has the best performance with an annualized return of 40.85% and a sharp ratio of 1.53. Therefore, the trend information on multiple time-scale features based on feature engineering can be learned by the CatBoost model well, which has a guiding effect on predicting stock index trends.

Polarization and Electoral Incentives: The End of the Chilean Consensus Democracy, 1990–2014

2018 ◽  
Vol 60 (4) ◽  
pp. 49-68 ◽  
Author(s):  
Jorge Fábrega ◽  
Jorge González ◽  
Jaime Lindh
Keyword(s):  
Bargaining Power ◽  
Electoral System ◽  
Political Support ◽  
The Political ◽  
Parliamentary Elections ◽  
Consensus Democracy ◽  
Electoral Incentives ◽  
Spatial Voting ◽  
Political Forces ◽  
Political Center

AbstractConsensus democracy among the main Chilean political forces ended abruptly after the 2013 presidential and parliamentary elections, the most polarized elections since the return to democracy in 1990. Relying on spatial voting theory to uncover latent ideological dimensions from survey data between 1990 and 2014, this study finds patterns of gradual polarization starting at least ten years before the collapse of consensus, based on an increasing demobilization of the political center that misaligned politicians from their political platforms (particularly in the center-left parties). That phenomenon changed the political support for the two main political coalitions and the intracoalition bargaining power of their various factions. The pattern also helps to explain the process behind the 2015 reform of the electoral system.

Customizing persuasive messages; the value of operative measures

2018 ◽  
Vol 35 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Maurits Kaptein
Keyword(s):  
Interactive Media ◽  
Database Systems ◽  
Customer Behavior ◽  
Customer Relationship ◽  
Persuasive Messages ◽  
Psychological Traits ◽  
Content Type ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Operative Measures

Purpose This paper aims to examine whether estimates of psychological traits obtained using meta-judgmental measures (as commonly present in customer relationship management database systems) or operative measures are most useful in predicting customer behavior. Design/methodology/approach Using an online experiment (N = 283), the study collects meta-judgmental and operative measures of customers. Subsequently, it compares the out-of-sample prediction error of responses to persuasive messages. Findings The study shows that operative measures – derived directly from measures of customer behavior – are more informative than meta-judgmental measures. Practical implications Using interactive media, it is possible to actively elicit operative measures. This study shows that practitioners seeking to customize their marketing communication should focus on obtaining such psychographic observations. Originality/value While currently both meta-judgmental measures and operative measures are used for customization in interactive marketing, this study directly compares their utility for the prediction of future responses to persuasive messages.

Microstructure in the Machine Age

2020 ◽  
Author(s):  
David Easley ◽  
Marcos López de Prado ◽  
Maureen O’Hara ◽  
Zhibai Zhang
Keyword(s):  
Machine Learning ◽  
Market Efficiency ◽  
Predictive Power ◽  
Explanatory Power ◽  
Futures Contracts ◽  
Machine Age ◽  
Price Process ◽  
Out Of Sample ◽  
Asset Classes ◽  
Out Of Sample Prediction

Abstract Understanding modern market microstructure phenomena requires large amounts of data and advanced mathematical tools. We demonstrate how machine learning can be applied to microstructural research. We find that microstructure measures continue to provide insights into the price process in current complex markets. Some microstructure features with high explanatory power exhibit low predictive power, while others with less explanatory power have more predictive power. We find that some microstructure-based measures are useful for out-of-sample prediction of various market statistics, leading to questions about market efficiency. We also show how microstructure measures can have important cross-asset effects. Our results are derived using 87 liquid futures contracts across all asset classes.

scholarly journals Application of bivariate negative binomial regression model in analysing insurance count data

2017 ◽  
Vol 11 (2) ◽  
pp. 390-411 ◽  
Author(s):  
Feng Liu ◽  
David Pitt
Keyword(s):  
Ridge Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Third Party ◽  
Negative Binomial Regression Model ◽  
Shrinkage Methods ◽  
Binomial Regression ◽  
Out Of Sample ◽  
Selection Operator ◽  
Out Of Sample Prediction

AbstractIn this paper we analyse insurance claim frequency data using the bivariate negative binomial regression (BNBR) model. We use general insurance data on claims from simple third-party liability insurance and comprehensive insurance. We find that bivariate regression, with its capacity for modelling correlation between the two observed claim counts, provides both a superior fit and out-of-sample prediction compared with the more common practice of fitting univariate negative binomial regression models separately to each claim type. Noting the complexity of BNBR models and their potential for a large number of parameters, we explore the use of model shrinkage methodology, namely the least absolute shrinkage and selection operator (Lasso) and ridge regression. We find that models estimated using shrinkage methods outperform the ordinary likelihood-based models when being used to make predictions out-of-sample. We find that the Lasso performs better than ridge regression as a method of shrinkage.

scholarly journals Selecting The “Best” Prediction Model: An Application To Agricultural Cooperatives

1992 ◽  
Vol 24 (1) ◽  
pp. 163-169 ◽  
Author(s):  
Alicia N. Rambaldi ◽  
Hector O. Zapata ◽  
Ralph D. Christy
Keyword(s):  
Credit Scoring ◽  
Scoring Function ◽  
Poor Performance ◽  
Agricultural Cooperatives ◽  
Agricultural Cooperative ◽  
Out Of Sample ◽  
Farm Credit ◽  
Rank Transformation ◽  
Out Of Sample Prediction ◽  
Credit Worthiness

AbstractA credit scoring function incorporating statistical selection criteria was proposed to evaluate the credit worthiness of agricultural cooperative loans in the Fifth Farm Credit District. In-sample (1981-1986) and out-of-sample (1988) prediction performance of the selected models were evaluated using rank transformation discriminant analysis, logit, and probit. Results indicate superior out-of-sample performance for the management oriented approach relative to classification of unacceptable loans, and poor performance of the rank transformation in out-of-sample prediction.

Machine Learning Methods for Demand Estimation

2015 ◽  
Vol 105 (5) ◽  
pp. 481-485 ◽  
Author(s):  
Patrick Bajari ◽  
Denis Nekipelov ◽  
Stephen P. Ryan ◽  
Miaoyu Yang
Keyword(s):  
Large Data ◽  
Demand Estimation ◽  
Large Data Sets ◽  
Linear Functions ◽  
Data Sets ◽  
Science Literature ◽  
Data Set ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Scanner Panel

We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regressors; scales easily to very large data sets; combines model selection and estimation; and can flexibly approximate arbitrary non-linear functions. We illustrate our method using a standard scanner panel data set and find that our estimates are considerably more accurate in out-of-sample predictions of demand than some commonly used alternatives.

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