Time-series coefficient variation in value-relevance regressions: a discussion of Core, Guay, and Van Buskirk and new evidence

2003 ◽  
Vol 34 (1-3) ◽  
pp. 69-87 ◽  
Author(s):  
S.P Kothari ◽  
Jay Shanken
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Zhao ◽  
Zhonglu Chen

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.


2019 ◽  
Vol 36 (4) ◽  
pp. 616-636
Author(s):  
Murad Harasheh ◽  
Andrea Amaduzzi

Purpose This paper aims to investigate the value relevance of the European Emission Allowance (EUA) return and volatility on the equity value of the top listed European Power Generation Firms for the three trading phases of the European Emission Trading Scheme. Design/methodology/approach The authors use the multifactor financial market model over the period 2005-2016 on daily basis for the return relevance relationship, whereas time series models such as autoregression moving average and generalized autoregressive conditional heteroskedasticity are applied on a weighted average portfolio of the sample firms to test serial correlation and volatility of returns. Findings The findings are novel in which a positive and significant relevance of EUA return on equity return is shown; however, a vanishing effect is seen as one moves to further trading phases. Another remarkable finding is that the return relationship remains constant until a certain level in EUA price then inverts. Finally, the authors present that EUA is considered a systematic factor as firm and country-specific features are not statistically significant. Practical implications At policy level, these findings signal policymakers for an appropriate design of the future trading phases in which they achieve the balance between public interests, as climate risk mitigation by reducing emissions, and the private interests of the market players to support innovative changes. Originality/value To the authors’ knowledge, this study would be the first to offer recent and comprehensive findings on the economic and financial implications of the European Emission Trading Scheme for the three trading phases. Additionally, the research offers time series robustness check besides the standard regression analysis and shows that there is an optimal EUA price that triggers polluters’ decision on emission and generation.


2000 ◽  
Vol 176 ◽  
pp. 461-462
Author(s):  
C. Barban ◽  
E. Michel ◽  
M. Martic ◽  
J. Schmitt ◽  
J. C. Lebrun ◽  
...  

AbstractThe aim of this paper (further developed in Barban et al. 1999) is to present new evidence of the possible stellar origin of the observed excess power in the power spectrum of Procyon A presented in Martic et al. (1999) by comparing these observational data with theoretical predictions and numerical simulations.


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