Influences of sentiment from news articles on EU carbon prices

Abstract In this paper, we calculate the carbon tone index that reflects sentiment in news articles through a self-built dictionary. We study the effect of carbon tone index on carbon price return in the period from Sep, 192,017 to Oct, 092020. In addition, we employ the Latent Dirichlet Allocation (LDA) method to explore the differential influences of different topic carbon tone indexes on carbon prices. The market confidence boosted by the approved Market Stability Reserve (MSR) policy led to a continuous increase in volume in 2018. Using two subsample periods divided by the implementation of MSR, we explore the problem whether the increased high volume changed the speed of information absorption in carbon market. Quantile regression with control variables (coal, oil, natural gas, electricity and stock prices) is used to test the robustness of the estimated results. The empirical results show that carbon tone index is closely associated with changes in carbon prices and the efficiency of carbon market is improved after MSR. Finally, we use all carbon tone indexes at the 10% significance level in eight predictive models and show the economic value of the optimal predictive model. In summary, carbon tone index has a strong predictive power for carbon prices.

[1]  S. Trück,et al.  The Relationship between Carbon, Commodity and Financial Markets: A Copula Analysis , 2011 .

[2]  Feng Li.  Do Stock Market Investors Understand the Risk Sentiment of Corporate Annual Reports? , 2006 .

[3]  Qing Zhou,et al.  Predicting carbon market risk using information from macroeconomic fundamentals , 2018, Energy Economics.

[4]  Alessandro Provetti,et al.  A sentiment analysis approach to the prediction of market volatility , 2020, Frontiers in Artificial Intelligence.

[5]  Cindy K Soo.  Quantifying Sentiment with News Media across Local Housing Markets , 2018 .

[6]  Sonja Peterson,et al.  Price and Market Behavior in Phase II of the EU ETS: A Review of the Literature , 2014, Review of Environmental Economics and Policy.

[7]  M. Pahle,et al.  Allowance prices in the EU ETS -- fundamental price drivers and the recent upward trend , 2019 .

[8]  Xueping Tan,et al.  Dependence changes between the carbon price and its fundamentals: A quantile regression approach , 2017 .

[9]  Julien Chevallier,et al.  Carbon futures and macroeconomic risk factors : a view from the EU ETS , 2009 .

[10]  Measuring news sentiment , 2020 .

[11]  Empirical Asset Pricing via Machine Learning , 2020 .

[12]  Francesco Poli,et al.  Building News Measures from Textual Data and an Application to Volatility Forecasting , 2017 .

[13]  Michael W. Brandt,et al.  Macro fundamentals or geopolitical events? A textual analysis of news events for crude oil , 2019, Journal of Empirical Finance.

[14]  Ottmar Edenhofer,et al.  Causes of the EU ETS price drop: Recession, CDM, renewable policies or a bit of everything? - New evidence , 2014 .

[15]  Julien Chevallier,et al.  Price drivers and structural breaks in European carbon prices 2005–2007 , 2008 .

[16]  P. Phillips.  Testing for a Unit Root in Time Series Regression , 1988 .

[17]  Lei Sun,et al.  The Influence of Investor Sentiment on Stock Return and its Volatility under Different Market States , 2012, 2012 Fifth International Conference on Business Intelligence and Financial Engineering.

[18]  Tim Loughran,et al.  Textual Analysis in Accounting and Finance: A Survey: TEXTUAL ANALYSIS IN ACCOUNTING AND FINANCE , 2016 .

[19]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[20]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[21]  Duc Khuong Nguyen,et al.  What explains the short-term dynamics of the prices of CO2 emissions? , 2014 .

[22]  Marshall S. Smith,et al.  The general inquirer: A computer approach to content analysis. , 1967 .

[23]  Julien Chevallier.  Macroeconomics, finance, commodities: Interactions with carbon markets in a data-rich model , 2011 .

[24]  Jie (Michael) Guo,et al.  Can financial media sentiment predict merger and acquisition performance? , 2019, Economic Modelling.

[25]  Julien Velcin,et al.  Sentiment analysis on social media for stock movement prediction , 2015, Expert Syst. Appl..

[26]  Colm Kearney,et al.  Textual Sentiment in Finance: A Survey of Methods and Models , 2013 .

[27]  Francis R. Bach,et al.  Online Learning for Latent Dirichlet Allocation , 2010, NIPS.

[28]  Tim Loughran,et al.  When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks , 2010 .

[29]  Kaijian He,et al.  A multiscale analysis for carbon price drivers , 2019, Energy Economics.

[30]  Elie Bouri,et al.  Return and volatility linkages between CO2 emission and clean energy stock prices , 2018, Energy.

[31]  Srikumar Krishnamoorthy,et al.  Sentiment analysis of financial news articles using performance indicators , 2017, Knowledge and Information Systems.

[32]  Maria Joana Soares,et al.  Carbon financial markets : a time-frequency analysis of CO2 price drivers , 2014 .

[33]  S. Heston,et al.  News Versus Sentiment: Predicting Stock Returns from News Stories , 2015 .

[34]  N. Sinha,et al.  Underreaction to News in the US Stock Market , 2010 .

[35]  Paul C. Tetlock.  Giving Content to Investor Sentiment: The Role of Media in the Stock Market , 2005, The Journal of Finance.

[36]  Stephen P. Ferris,et al.  The Effect of Issuer Conservatism on IPO Pricing and Performance , 2012 .

[37]  Sofus A. Macskassy,et al.  More than Words: Quantifying Language to Measure Firms' Fundamentals the Authors Are Grateful for Assiduous Research Assistance from Jie Cao and Shuming Liu. We Appreciate Helpful Comments From , 2007 .

[38]  Neda Todorova,et al.  Dynamics of China’s carbon prices in the pilot trading phase , 2017 .

[39]  J. Keynes,et al.  The General Theory of Employment, Interest and Money. , 1936 .

[40]  Kent D. Daniel,et al.  Presentation Slides for 'Investor Psychology and Security Market Under and Overreactions' , 1998 .

[41]  D. Duffie.  Asset Price Dynamics with Slow-Moving Capital , 2010 .

[42]  Daniel Andrei,et al.  Investor Attention and Stock Market Volatility , 2015 .

[43]  A. Smeaton,et al.  Influences from the European Parliament on EU Emissions Prices , 2015 .

[44]  Wei Shang,et al.  Text-based crude oil price forecasting: A deep learning approach , 2019, International Journal of Forecasting.

[45]  Tim Loughran,et al.  Assimilation of Oil News Into Prices , 2019, International Review of Financial Analysis.

[46]  Darrell Duffie,et al.  Presidential Address: Asset Price Dynamics with Slow‐Moving Capital , 2010 .

[47]  Anne Toppinen,et al.  Price determination in the EU ETS market: Theory and econometric analysis with market fundamentals , 2013 .

[48]  L. Summers,et al.  Noise Trader Risk in Financial Markets , 1990, Journal of Political Economy.

[49]  Piia Aatola,et al.  Informational efficiency of the EU ETS market – a study of price predictability and profitable trading † , 2014 .