The Heavier they are the Further they Deviate: New Evidence on Non-Linear Adjustment of Law of One Price Deviations and Physical Characteristics of Goods

2007 ◽  
Author(s):  
Martin Berka
Keyword(s):  
Physical Characteristics ◽  
Law Of One Price ◽  
Non Linear ◽  
New Evidence

scholarly journals Forecasting stock price movement: new evidence from a novel hybrid deep learning model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Zhao ◽  
Zhonglu Chen
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Hybrid Model ◽  
Stock Price ◽  
Linear Time ◽  
Price Movement ◽  
Content Type ◽  
Non Linear ◽  
New Evidence ◽  
Deep Learning Model

PurposeThis study explores whether a new machine learning method can more accurately predict the movement of stock prices.Design/methodology/approachThis study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.FindingsThe hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.Originality/valueThis study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

scholarly journals Health Impacts of Exposure to Gaseous Pollutants and Particulate Matter in Beijing—A Non-Linear Analysis Based on the New Evidence

2018 ◽  
Vol 15 (9) ◽  
pp. 1969 ◽  
Author(s):  
Yunfei Cheng ◽  
Tatiana Ermolieva ◽  
Gui-Ying Cao ◽  
Xiaoying Zheng
Keyword(s):  
Air Pollutants ◽  
Respiratory Diseases ◽  
Meteorological Data ◽  
Positive Association ◽  
Gaseous Pollutants ◽  
Air Pollution Control ◽  
Control Plan ◽  
Short Term Exposure ◽  
Non Linear ◽  
New Evidence

This paper aimed to estimate health risks focusing on respiratory diseases from exposure to gaseous multi-pollutants based on new data and revealed new evidence after the most stringent air pollution control plan in Beijing which was carried out in 2013. It used daily respiratory diseases outpatient data from a hospital located in Beijing with daily meteorological data and monitor data of air pollutants from local authorities. All data were collected from 2014 to 2016. Distributed lag non-linear model was employed. Results indicated that NO2 and CO had positive association with outpatients number on the day of the exposure (1.045 (95% confidence interval (CI): 1.003, 1.089) for CO and 1.022 (95% CI: 1.008, 1.036) for NO2) (and on the day after the exposure (1.026 (95% CI: 1.005, 1.048) for CO and 1.013 (95% CI: 1.005, 1.021) for NO2). Relative risk (RR) generally declines with the number of lags; ozone produces significant effects on the first day (RR = 0.993 (95% CI: 0.989, 0.998)) as well as second day (RR = 0.995 (95% CI: 0.991, 0.999)) after the exposure, while particulate pollutants did not produce significant effects. Effects from the short-term exposure to gaseous pollutants were robust after controlling for particulate matters. Our results contribute to a comprehensive understanding of the dependencies between the change of air pollutants concentration and their health effects in Beijing after the implementation of promising air regulations in 2013. Results of the study can be used to develop relevant measures minimizing the adverse health consequences of air pollutants and supporting sustainable development of Beijing as well as other rapidly growing Asian cities.

Sign in / Sign up

Export Citation Format

Share Document