scholarly journals Forecasting with Business and Consumer Survey Data

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 113-134
Author(s):  
Oscar Claveria
Keyword(s):  
Machine Learning ◽  
Analytical Techniques ◽  
Consumer Survey ◽  
Economic Forecasting ◽  
Machine Learning Methods ◽  
Forecasting Performance ◽  
Consumer Surveys ◽  
Predictive Content ◽  
Consumer Survey Data ◽  
Tendency Surveys

In a context of growing uncertainty caused by the COVID-19 pandemic, the opinion of businesses and consumers about the expected development of the main variables that affect their activity becomes essential for economic forecasting. In this paper, we review the research carried out in this field, placing special emphasis on the recent lines of work focused on the exploitation of the predictive content of economic tendency surveys. The study concludes with an evaluation of the forecasting performance of quarterly unemployment expectations for the euro area, which are obtained by means of machine learning methods. The analysis reveals the potential of new analytical techniques for the analysis of business and consumer surveys for economic forecasting.

scholarly journals Exchange Rate Forecasting with Advanced Machine Learning Methods

2021 ◽  
Vol 15 (1) ◽  
pp. 2
Author(s):  
Jonathan Felix Pfahler
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Exchange Rate ◽  
Random Walk ◽  
Predictive Power ◽  
Exchange Rate Forecasting ◽  
Forecasting Models ◽  
Machine Learning Methods ◽  
Forecasting Performance ◽  
Out Of Sample

Historically, exchange rate forecasting models have exhibited poor out-of-sample performances and were inferior to the random walk model. Monthly panel data from 1973 to 2014 for ten currency pairs of OECD countries are used to make out-of sample forecasts with artificial neural networks and XGBoost models. Most approaches show significant and substantial predictive power in directional forecasts. Moreover, the evidence suggests that information regarding prediction timing is a key component in the forecasting performance.

scholarly journals Improving CAT bond pricing models via machine learning

2020 ◽  
Vol 21 (5) ◽  
pp. 428-446
Author(s):  
Tobias Götze ◽  
Marc Gürtler ◽  
Eileen Witowski
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Linear Regression ◽  
Random Forests ◽  
Regression Models ◽  
Linear Regression Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Forecasting Performance ◽  
Asset Classes

Abstract Enhanced machine learning methods provide an encouraging alternative to forecast asset prices by extending or generalizing the possible model specifications compared to conventional linear regression methods. Even if enhanced methods of machine learning in the literature often lead to better forecasting quality, this is not clear for small asset classes, because in small asset classes enhanced machine learning methods may potentially over-fit the in-sample data. Against this background, we compare the forecasting performance of linear regression models and enhanced machine learning methods in the market for catastrophe (CAT) bonds. We use linear regression with variable selection, penalization methods, random forests and neural networks to forecast CAT bond premia. Among the considered models, random forests exhibit the highest forecasting performance, followed by linear regression models and neural networks.

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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