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Sina Weibo and Twitter are the top microblogging platforms with billions of users. Accordingly, these two platforms could be used to understand the public mood. In this paper, the authors want to discuss how to generate and compare the public mood on Sina Weibo and Twitter. The predictive power of the public mood toward commodity markets is discussed, and the authors want to solve the problem that how to choose between Sina Weibo and Twitter when predicting crude oil prices.

An enhanced latent Dirichlet allocation model considering term weights is implemented to generate topics from Sina Weibo and Twitter. Granger causality test and a long short-term memory neural network model are used to demonstrate that the public mood on Sina Weibo and Twitter is correlated with commodity contracts.

By comparing the topics and the public mood on Sina Weibo and Twitter, the authors find significant differences in user behavior on these two websites. Besides, the authors demonstrate that public mood on Sina Weibo and Twitter is correlated with crude oil contract prices in Shanghai International Energy Exchange and New York Mercantile Exchange, respectively.

Two sentiment analysis methods for Chinese (Sina Weibo) and English (Twitter) posts are introduced, which can be reused for other semantic analysis tasks. Besides, the authors present a prediction model for the practical participants in the commodity markets and introduce a method to choose between Sina Weibo and Twitter for certain prediction tasks.

The purpose of this study is to investigate empirically the pattern of co-movement between prices and implied volatility in the future markets for crude oil.

The tool of non-parametric quantile regression is applied to daily price returns and implied volatility changes from 2007 to 2018.

For the total sample period, the link between price returns and forward-looking volatility expectations is contemporaneous, negative and asymmetric, and it exhibits an (approximately) inverted U-shaped pattern suggesting that: the pricing of implied volatility is heavier for large (in absolute value terms) changes relative to small ones and it is lighter for large positive changes relative to large negative ones. The pattern of co-movement, therefore, appears to be in line with the theoretical postulates of fear, exuberance and loss aversion. The main characteristics of the relationship are present in some (but not in all) sub-periods, which are also considered in this study.

Less than a handful of works have assessed the link between implied volatility and prices for commodity ETFs. This is the first one relying on flexible non-parametric quantile regressions.

The purpose of this paper is to investigate herding behavior around the crude oil market and the stock market and the possible cross-herding behavior between the two markets. The analysis examines also the herding behavior during financial turmoil and includes the investor sentiment and market volatility.

The authors use a modified version of the cross-sectional standard deviation and the cross-sectional absolute deviation to include investor sentiment, financial crisis and market volatility.

The authors find that the volatility of the stock market reduces the herding behavior around the oil market and boosts that around the stock market. However, the investors’ sentiment reduces the herding around the stock market and boosts that around the crude oil market. Consequently, the authors can conclude that the herding behavior around the two markets moves inversely and the herding in each market is enhanced by the lack of information in the other market.

This paper is limited to the herding of stocks around the crude oil market and ignores the possible herding of commodities around the oil market.

The originality of the paper rests on the study of the possible cross-herding behavior between the oil market and the stock market especially during financial turmoil.

John McGee's 1958 paper, “Predatory Price Cutting: The Standard Oil (NJ) Case,” has had an astonishing influence on both antitrust policy in the United States and economic lore. McGee argued that predatory pricing is irrational and his analysis of the Standard Oil Company Matter, decided in 1911, led him to conclude that the Record in this case does not show that Standard Oil engaged in predatory pricing. This single publication appears to serve as a foundation of the U.S. Supreme Court's position on the issue of predatory pricing, as well as the assertion by many economists that predatory pricing is irrational and rarely occurs.

Numerous arguments have been advanced during the past 25 years that predatory pricing can be a rational strategy. As to McGee's empirical findings, there has been no re-examination of the Record of the Standard Oil case to determine the validity of his finding that the trial “Record” does not support the claim that Standard Oil engaged in predatory pricing.

We examined this Record and have found that the trial Record contains considerable evidence of predatory pricing by Standard Oil. Therefore, the Record does not support McGee's conclusion that Standard Oil did not engage in predatory pricing.

Thus, the decisions of the Supreme Court in recent years, as well as the opinions of many economists, concerning predatory pricing are not consistent with either current theory or the empirical record.

This study aims to investigate the dynamic relationships between oil price shocks and Indian stock market.

The study used daily data for the period starting from January 2001 to March 2013. In this study, Johansen's cointegration test, vector error correction model (VECM), Granger causality test, impulse response functions (IRFs) and variance decompositions (VDCs) test have been applied to exhibit the long-run and short-run relationship between them.

The cointegration result indicates the existence of long-term relationship. Further, the error correction term of VECM shows a long-run causality moves from Indian stock market to oil price but not the vice versa. The results of the Granger causality test under the VECM framework confirm that no short-run causality between the variables exists. The VDCs analysis revealed that the Indian stock markets and crude oil prices are strongly exogenous. Finally, from the IRFs, analysis revealed that a positive shock in oil price has a small but persistence and growing positive impact on Indian stock markets in short run.

The study would enhance the understandings of the interaction between oil price volatilities and emerging stock market performances. Further, the study would enable foreign investors who are interested in Indian stock market helps in understanding the conditional relationship between the variables.

This study aims at investigating how energy strategies of China impact its energy shipping import through a strategic maritime link, the Straits of Malacca and Singapore (SOMS).

Vector error-correction modelling (VECM) is applied to examine the key energy strategies of China influencing crude oil and liquefied natural gas (LNG) shipping import via the SOMS. Strategies investigated include oil storage expansions, government-setting targets to motivate domestic gas production, pipeline projects to diversify natural gas import routes and commercial strategies to ensure oil and gas accessibility and cost-effectiveness.

For the crude oil sector, building up oil storage and diversifying oil import means, routes and sources were found effective to mitigate impacts of consumption surges and price shocks. For the LNG sector, domestic production expansion effectively reduces LNG import. However, pipeline gas import growth is inefficient to relieve LNG shipping import dependency. Furthermore, energy companies have limited flexibility to adjust LNG shipping import volumes via the SOMS even under increased import prices and transport costs.

As the natural gas demand of China continues expanding, utilisation rates of existing pipeline networks need to be enhanced. Besides, domestic production expansion and diversification of LNG import sources and means are crucial.

This study is among the first in the literature using a quantitative approach to investigate how energy strategies implemented in a nation impact its energy shipping volumes via the SOMS, which is one of the most important maritime links that support 40% of the global trades.