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This study examines the impact of the $60 billion tariff announcement of the US government on the Chinese exporting firms. In particular, it focuses on the firms whose revenues are highly dependent on the US economy.

This study uses an experimental analysis and the event study methodology. The sample includes firms listed in mainland China and Hong Kong Stock Exchanges that have the highest revenues from exporting to the USA. The data are obtained from China Stock Market and Accounting Research (CSMAR) and DataStream.

The authors find that the tariff announcement has significantly negative impacts on stock performance both before and after the announcement, and the impacts are heterogeneous across all sample firms. For A shares listed in Mainland China, firms with more revenues from the US experience greater price drops on the announcement day, regardless of being in the targeted industry or not. But such finding is absent from H shares listed in Hong Kong. The authors also find that for all the firms, greater pricing power can alleviate the impacts of the tariff announcement.

The results provide implications to investors, policymakers and regulators on the further US-China cooperation in the future.

This is the first study documenting the heterogeneity of the impact of the tariff announcement and thus contributes to the prosperous studies on the varied firm-level responses in the Chinese stock market, and to the burgeoning literature by filling the gap of the financial market responses to the protectionist policy announcement.

This study aims to find the impact of the trade war between the USA and China on Asian economies. Apart from macroeconomic variables associated with trade, this study explicitly creates a trade war scenario and trade war participant dummies. Using the neural network multilayer perceptron, this study checks for the causal linkages between the predictors and target output for the panel of Asian economies and the USA.

A conceptual model of the after effects of trade war in a quadrant is developed. Variables related to trade and tariffs are included in the study for a panel of 19 Asian economies. The feedforward structure of neural network analysis is used to identify strong and weak predictors of trade war.

The hidden layers of the multilayer perceptron reveal the inconsistency in linkages for the predictors’ services exports, tariff measures, anti-dumping measures, trade war scenario dummy with gross domestic product. The findings suggest that to curtail the impact of the trade war on Asian economies, predictors with neural evidence must be paid due weightage in policy determination and trade agreements.

The study applies a novel and little explored AI/ML technique of Neural Network analysis with training of 70% observations. The paper will provide opportunity for other researchers to explore techniques of AI/ML in trade studies.

The pattern of US–China relations can be analyzed through the two opposite experiences – chilling bitterness and friendly accommodations. For a long time, China was neglected by the United States and treated as an enemy due to ideological considerations. The nature of bilateral relations between Washington and Beijing is a very complex one. The US–China economic relationship has reached a critical juncture. Over the few years, the United States has imposed tariffs of $250 billion worth on Chinese imports, and in return, China also raised high tariffs on US exports. The basic objectives of this research are to investigate the causes and potentiality of China–US bilateral trade. It has used the content analysis method and observation method in this study. The result of the study will be manifested based on recent trade restrictions and economic sanctions to each other. The impact of the recent Sino-American trade war resulted in a negative impact on not only both countries’ economies but also on the world economy.

The paper aims to investigate the impact of economic policy uncertainty (EPU) (i.e. uncertainty due to government policies) on fine wine prices.

The paper uses the Baker et al. (2016) monthly news-based measure of EPU for the leading wine markets: the USA, the UK, France, Germany and China in conjunction with monthly fine wine pricing data from the London International Vintners Exchange (Liv-ex). The wine sub-indices used are the Liv-ex 500 (Bordeaux), Burgundy 150, Champagne 50, Rhone 100, Italy 100, California 50, Port 50 and Rest of the World 50. The Prais–Winsten and Cochrane–Orcutt regressions are used for our analyses to correct for effects of serial correlation. Time lags are chosen based on the appropriate information criterion.

Changes in EPU levels negatively impact changes in the Liv-ex 500 index for all our leading wine markets except France, the Champagne 50 index for the UK and the Burgundy 150 and the Rhone 100 indices for Germany, with the effects being significant for at least up to a quarter before EPU is detected. The authors did not find significant results for the EPU of France.

The paper aims to provide insights into whether EPU creates opportunities or threats for investors and wineries.

A forward-looking news-based EPU measure is used to gain insights into how the different Liv-ex sub-indices react to increases in uncertainty centered around government policies across a sample of different countries.

Circulation In the main library the university has installed a Plessey Satellite system for circulation control of normal loan stock, operating in batch mode and recording the data direct onto ½ inch magnetic tape. The transactions for the previous day are processed every morning, the library not having secure access to sufficient computer time to make online operation a feasible proposition. At the moment, for the great majority of items held by the library there is no bibliographic record available to the circulation system, though this is gradually being remedied and it is intended to use the mag. tape as part of the LOCAS service (see below) to build up automatically a short‐title file of all additions to stock.