scholarly journals Analyzing Vaccination Priority Judgments for 132 Occupations Using Word Vector Models

2021 ◽  
Author(s):  
Atsushi Ueshima ◽  
Hiroki Takikawa
Keyword(s):  
Resource Allocation ◽  
Knowledge Representation ◽  
Vector Space ◽  
Division Of Labor ◽  
Prediction Accuracy ◽  
Scarce Resource ◽  
Psychological Dimension ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
High Degree

Most human societies conduct a high degree of division of labor based on occupation. However, determining the occupational field that should be allocated a scarce resource such as vaccine is a topic of debate, especially considering the COVID-19 situation. Though it is crucial that we understand and anticipate people’s judgments on resource allocation prioritization, quantifying the concept of occupation is a difficult task. In this study, we investigated how well people’s judgments on vaccination prioritization for different occupations could be modeled by quantifying their knowledge representation of occupations as word vectors in a vector space. The results showed that the model that quantified occupations as word vectors indicated high out-of-sample prediction accuracy, enabling us to explore the psychological dimension underlying the participants’ judgments. These results indicated that using word vectors for modeling human judgments about everyday concepts allowed prediction of performance and understanding of judgment mechanisms.

Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach

2020 ◽  
Vol 53 (4) ◽  
pp. 513-554
Author(s):  
Daniel V. Fauser ◽  
Andreas Gruener
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Risk Prediction ◽  
Prediction Accuracy ◽  
Explanatory Power ◽  
Machine Learning Algorithms ◽  
Out Of Sample ◽  
Corporate Social Irresponsibility ◽  
Out Of Sample Prediction ◽  
Corporate Social

This paper examines the prediction accuracy of various machine learning (ML) algorithms for firm credit risk. It marks the first attempt to leverage data on corporate social irresponsibility (CSI) to better predict credit risk in an ML context. Even though the literature on default and credit risk is vast, the potential explanatory power of CSI for firm credit risk prediction remains unexplored. Previous research has shown that CSI may jeopardize firm survival and thus potentially comes into play in predicting credit risk. We find that prediction accuracy varies considerably between algorithms, with advanced machine learning algorithms (e. g. random forests) outperforming traditional ones (e. g. linear regression). Random forest regression achieves an out-of-sample prediction accuracy of 89.75% for adjusted R2 due to the ability of capturing non-linearity and complex interaction effects in the data. We further show that including information on CSI in firm credit risk prediction does not consistently increase prediction accuracy. One possible interpretation of this result is that CSI does not (yet) seem to be systematically reflected in credit ratings, despite prior literature indicating that CSI increases credit risk. Our study contributes to improving firm credit risk predictions using a machine learning design and to exploring how CSI is reflected in credit risk ratings.

Generalizability of Convolutional Encoder–Decoder Networks for Aerodynamic Flow-Field Prediction Across Geometric and Physical-Fluidic Variations

2020 ◽  
Vol 143 (5) ◽  
Author(s):  
Kaustubh Tangsali ◽  
Vinayak R. Krishnamurthy ◽  
Zohaib Hasnain
Keyword(s):  
Flow Field ◽  
Prediction Accuracy ◽  
Informed Choice ◽  
Flow Regimes ◽  
Training Data ◽  
Out Of Sample ◽  
Flow Features ◽  
Out Of Sample Prediction ◽  
Convolutional Encoder ◽  
Geometric Variation

Abstract The generalizability of a convolutional encoder–decoder based model in predicting aerodynamic flow field across various flow regimes and geometric variation is assessed. A rich master dataset consisting of 11,000+ simulations including cambered, uncambered, thin, and thick airfoils simulated at varying angles of attack is generated. The various Mach and Reynolds number (Re) chosen allows analysis across compressible, incompressible, low, and high Re flow regimes. Multiple studies are carried out with the model trained on datasets that are categorized based on the aforementioned parameters. In each study, the loss of prediction accuracy by training the model on a larger dataset (generalizability), versus a smaller categorically sorted dataset, is evaluated. Largely disparate flow features across the Re range lead to a 25.56% loss, while the generalization across Mach range led to an average of 23.95% loss. However, flow-field changes induced due to geometric variation exhibited a better generalization potential, through an increased accuracy of 12.4%. The encoder–decoder architecture allows extraction of relevant geometric features from largely different geometries (geometric generalization) providing a better out-of-sample prediction accuracy in comparison to physics-based generalization. It is shown that, through user-informed choice of training data (removal of geometrically similar samples), computational costs incurred in generating training data can be reduced. This is important for the application of such methods in the design optimization of platforms and components that require the analysis of the fluid flows.

scholarly journals Optimizing Scarce Resource Allocation During COVID-19: Rapid Creation of a Regional Health-Care Coalition and Triage Teams in San Diego County, California

2020 ◽  
pp. 1-7
Author(s):  
Asha Devereaux ◽  
Holly Yang ◽  
Gilbert Seda ◽  
Viji Sankar ◽  
Ryan C. Maves ◽  
...  
Keyword(s):  
Public Health ◽  
Health Care ◽  
Resource Allocation ◽  
San Diego ◽  
Scarce Resource ◽  
Successful Implementation ◽  
Regional Health ◽  
Incident Command ◽  
Regional Health Care ◽  
Crisis Care

ABSTRACT Successful management of an event where health-care needs exceed regional health-care capacity requires coordinated strategies for scarce resource allocation. Publications for rapid development, training, and coordination of regional hospital triage teams to manage the allocation of scarce resources during coronavirus disease 2019 (COVID-19) are lacking. Over a period of 3 weeks, over 100 clinicians, ethicists, leaders, and public health authorities convened virtually to achieve consensus on how best to save the most lives possible and share resources. This is referred to as population-based crisis management. The rapid regionalization of 22 acute care hospitals across 4500 square miles in the midst of a pandemic with a shifting regulatory landscape was challenging, but overcome by mutual trust, transparency, and confidence in the public health authority. Because many cities are facing COVID-19 surges, we share a process for successful rapid formation of health-care care coalitions, Crisis Standard of Care, and training of Triage Teams. Incorporation of continuous process improvement and methods for communication is essential for successful implementation. Use of our regional health-care coalition communications, incident command system, and the crisis care committee helped mitigate crisis care in the San Diego and Imperial County region as COVID-19 cases surged and scarce resource collaborative decisions were required.

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.

scholarly journals Modeling and Predictability of Exchange Rate Changes by the Extended Relative Nelson–Siegel Class of Models

2018 ◽  
Vol 6 (3) ◽  
pp. 68
Author(s):  
Hokuto Ishii
Keyword(s):  
Exchange Rate ◽  
Prediction Accuracy ◽  
Factor Model ◽  
Five Factor Model ◽  
Model Fitting ◽  
Extended Model ◽  
Out Of Sample ◽  
Exchange Rate Changes ◽  
Relative Spread ◽  
Future Exchange

This paper investigates the predictability of exchange rate changes by extracting the factors from the three-, four-, and five-factor model of the relative Nelson–Siegel class. Our empirical analysis shows that the relative spread factors are important for predicting future exchange rate changes, and our extended model improves the model fitting statistically. The regression model based on the three-factor relative Nelson–Siegel model is the superior model of the extended models for three-month-ahead out-of-sample predictions, and the prediction accuracy is statistically significant from the perspective of the Clark and West statistic. For 6- and 12-month-ahead predictions, although the five-factor model is superior to the other models, the prediction accuracy is not statistically significant.

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.

scholarly journals An approach to predict Spanish mortgage market activity using Google data

2019 ◽  
Vol 8 (4) ◽  
pp. 209
Author(s):  
Marcos González-Fernández ◽  
Carmen González-Velasco
Keyword(s):  
Prediction Accuracy ◽  
Mortgage Market ◽  
Sample Analysis ◽  
Market Activity ◽  
Out Of Sample

The aim of this paper is to use Google data to predict Spanish mortgage market activity during the period from January 2004 to January 2019. Thus, we collect monthly Google data for the keyword hipoteca, the Spanish expression for mortgage, and then, we perform a regression and an out-of-sample analysis. We find evidence that the use of Google data significantly improves prediction accuracy.

scholarly journals SEQUENCE-BASED PREDICTION OF PROTEIN B-FACTORS WITH DUAL SUPERVIZED LEARNING MODELS

2010 ◽  
Vol 22 (05) ◽  
pp. 385-391
Author(s):  
Yu-Cheng Liu ◽  
Shien-Ching Hwang ◽  
Yu-Feng Huang ◽  
Win-Li Lin ◽  
Yen-Jen Oyang ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Prediction Accuracy ◽  
Active Sites ◽  
Pearson Correlation ◽  
Experimental Results ◽  
Structural Flexibility ◽  
Dual Model ◽  
Functional Sites ◽  
High Degree ◽  
Model Approach

The B-factor, which is also known as temperature factor or Debby–Waller factor, is an important structural flexibility index of the ground-state protein conformation. In particular, the B-factors associated with a segment of residues, reflect the local flexibility of the corresponding protein tertiary substructure. Recent studies have shown that, for certain families of proteins, there exists a high-degree of correlation between the B-factors and the protein functional sites, including antigenic regions, enzyme active sites, and nucleotide binding sites. This paper presents a sequence–based predictor of B-factors with a dual-model approach.  The design of the dual-model approach has been aimed at exploiting the bi-modal distribution of B-factors in order to achieve higher prediction accuracy. In this paper, the prediction accuracy is measured by Pearson correlation coefficient. Experimental results show that the dual-model predictor proposed in this article is capable of delivering superior correlation coefficient in comparison with two predictors reported in two latest papers.  Though experimental results show that the dual-model proposed in this paper really works more effectively than the conventional approach, it is of interest to continue investigating more advanced designs since there exists a strong correlation between B-factors and protein functional sites. In this respect, identifying additional physiochemical properties that are related to structural flexibility deserves a high-degree of attention.

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.

Sign in / Sign up

Export Citation Format

Share Document