Forecasting Oil Price Trends with Sentiment of Online News Articles

AbstractWith the rapid development of the Internet and big data technologies, a rich of online data (including news releases) can helpfully facilitate forecasting oil price trends. Accordingly, this study introduces sentiment analysis, a useful big data analysis tool, to understand the relevant information of online news articles and formulate an oil price trend prediction method with sentiment. Three main steps are included in the proposed method, i.e., sentiment analysis, relationship investigation and trend prediction. In sentiment analysis, the sentiment (or tone) is extracted based on a dictionary-based approach to capture the relevant online information concerning oil markets and the driving factors. In relationship investigation, the Granger causality analysis is conducted to explore whether and how the sentiment impacts oil price. In trend prediction, the sentiment is used as an important independent variable, and some popular forecasting models, e.g., logit regression, support vector machine, decision tree and back propagation neural network, are performed. With crude oil futures prices of the West Texas Intermediate (WTI) and news articles of the Thomson Reuters as studying samples, the empirical results statistically support the powerful predictive power of sentiment for oil price trends and hence the effectiveness of the proposed method

A Dynamic Level Technical Indicator Model for Oil Price Forecasting

Investment in commodities and stock requires a nearly accurate prediction of price to make profit and to prevent losses. Technical indicators are usually employed on the software platforms for commodities and stock for such price prediction and forecasting. However, many of the available and popular technical indicators have proved unprofitable and disappointing to investors, often resulting not only in ordinary losses but in total loss of investment capital. We propose a dynamic level technical indicator model for the forecasting of commodities prices. The proposed model creates dynamic price supports and resistances levels in different time frames of the price chart using a novel algorithm and employs them for price forecasting. In this study, the proposed model was applied to predict the prices of the United Kingdom (UK) Oil. It was compared with the combination of two popular and widely accepted technical indicators, the Moving Average Convergence and Divergence (MACD) and Stochastic Oscillator. The results showed that the proposed dynamic level technical indicator model outperformed MACD and Stochastic Oscillator in terms of profit

How Consumer Confidence, Corruption and Credit Rating Effect the Exchange Rate: Emerging Market Perspective

The paper empirically examines whether the international rating influences the rate of exchange of an economy in the long run? The paper employs Autoregressive Distributed Lag (ARDL) Bounds testing methodology on the exchange rate of China and contemporary international rating, using time series data from 1996Q1 to 2016Q4. The empirical analysis confirms the presence of a cointegration relationship between country rating and the exchange rate. To be more specific; corruption index, credit rating, and inflation are significantly and negatively cointegrated with the exchange rate of China. Conversely, consumer confidence is uncorrelated with the exchange rate over the long run. The paper focuses only on the exchange rate of CNY-USD; this may limit the generalizability of results for exchange rate with other nations. Nevertheless, the results add to the exchange rate determinants literature by including country-rating indicators in the analysis. Prior literature documents that there is some relationship between inflation and exchange rate. This research is novel in the application of robust ARDL and bounds testing to examine the long and short-run association of country rating of China with its exchange rate, after controlling for inflation

Assessing causality among topics and sentiments: The case of the G20 discussion on Twitter

Although the identification of topics and sentiments from social media content has attracted substantial research, little work has been carried out on the extraction of causal relationships among those topics and sentiments. This article proposes a methodology aimed at building a causal graph where nodes represent topics and emotions extracted from social media users? posts. To illustrate the proposed methodology, we collected a large multi-year dataset of tweets related to different editions of the G20 summit, which was locally indexed for further analysis. Topic-relevant queries are crafted from phrases extracted by experts from G20 output documents on four main recurring topics, namely government, society, environment and health and economics. Subsequently, sentiments are identified on the retrieved tweets using a lexicon based on Plutchik?s wheel of emotions. Finally, a causality test that uses stochastic dominance is applied to build a causal graph among topics and emotions by exploiting the asymmetries of explaining a variable from other variables. The applied causality discovery process relies on observational data only and does not require any assumptions of linearity, parametric definitions or temporal precedence. In our analysis, we observe that although the time series of topics and emotions always show high correlation coefficients, stochastic causality provides a means to tell apart causal relationships from other forms of associations. The proposed methodology can be applied to better understand social behaviour on social media, offering support to decision and policy making and their communication by government leaders.Fil: Fonseca, Mauro. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Delbianco, Fernando Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Soto, Axel Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

Three Essays on Energy Markets

This dissertation includes three essays investigating topics relevant to the energy markets. The first essay employs a new dataset to measure the impact of investor sentiment regarding oil prices on the U.S. inflation premium. The empirical analysis relies on Structural Vector Autoregression (SVAR) and out-of-sample forecasts. The results indicate that a one standard deviation positive shock to overall investor sentiment regarding oil prices results in a significant increase in the U.S. inflation premium by approximately 1.2% over the subsequent 10 weeks. Compared to individual investor sentiment, institutional investor sentiment regarding oil prices has a larger impact on the U.S. inflation premium. Finally, the study finds out-of-sample evidence that the overall investor sentiment regarding oil prices has predictive power on the U.S. inflation premium. The second essay uses sequential energy inventory announcements to shed new light on the informational efficiency of financial markets. The findings provide clear evidence of inefficiency in oil futures and stock markets. This inefficiency can be exploited by sophisticated traders. The study further examines the effect of market conditions, such as liquidity and oil attention, on the efficient incorporation of information in this setting. It also constructs a predictor that can predict inventory surprises and pre-announcement returns in-sample and out-of-sample. Finally, it develops a combination forecast that can be used as a proxy for market expectations of oil inventory announcements. The third essay examines the impact of oil shocks on sovereign credit default swaps (CDS) for the G10 countries and major oil-exporting countries. The results show that oil demand shocks have a uniformly negative impact on CDS spreads. In contrast, oil supply shocks increase the spreads of the G10 countries, but reduce the spreads of oil-exporting countries. Using quantile regressions, the study finds that oil demand shocks affect spreads across the conditional distribution, while oil supply shocks mostly influence the upper quantiles of spread changes. Furthermore, a two-state Markov-switching modeling confirms a significant non-linearity in the impact of oil shock