Current Issue : January-March Volume : 2023 Issue Number : 1 Articles : 6 Articles
Data is an extremely important asset in a modern scientific and commercial society. The life force behind powerful artificial intelligence (AI) or machine learning (ML) algorithms is data, especially lots of data, which makes data trading significantly essential to unlocking the power of AI or ML. Data owners who offer crowdsourced data and data consumers who request data blocks negotiate with each other to make an agreement on data assignment and trading prices via a data trading platform; consequently, both sides gain profit from the process of data trading. A great many existing studies have investigated various kinds of data sharing or trading as well as protecting data privacy or constructing a decentralized data trading platform due to mistrust issues. However, existing studies neglect an important characteristic, i.e., dynamics of both data owners and data requests in trading crowdsourced data collected by IoT devices. To this end, we first construct an auction-based model to formulate the data trading process and then propose a near-optimal online data trading algorithm that not only resolves the problem of matching dynamic data owners and randomly generated data requests but also determines the data trading price of each data block. The proposed algorithm achieves several good properties, such as a constant competitive ratio for nearoptimal social efficiency, incentive compatibility, and individual rationality of participants, via rigorous theoretical analysis and extensive simulations. We further design a decentralized data trading platform in order to construct a practical data trading process incorporating the proposed data trading algorithm....
Investors’ attention to information plays an important role when investors make investment decisions. Individual investors analyze and judge the information that attracts their attention and adjusts their investment behavior, which leads to temporary pricing deviation. This paper uses abnormal trading volume as an indicator to measure the degree of investors’ attention to individual stocks, and uses panel data model and vector autoregressive model (VAR) to study the relationship between investors’ attention driven trading and stock return volatility. The empirical study finds that investors’ attention has a significant positive impact on the current stock returns. When the investors pay more attention to the specific stocks, it will cause the net buying behavior, and the stock price will rise, leading to the increase of the current stock yield. Unlike the short-term effect of investors’ attention on stock returns, the impact period of stock returns on investors’ attention is relatively long. The influence period of stock returns on investors’ attention lasts about three weeks according to the empirical study....
To answer these questions in light of the MRIO model, this paper presents a study of environmental injustice affecting the global economy. Practical ideas and lifespan measurements are often used in studies of the embodied carbon industry. The input-output table method is an important method for industrial embodied carbon research, which can be divided into the regional inputoutput table method, bilateral input-output table method, and multiregional input-output table method. Bilateral and multistakeholder consultations are more accurate than regional proposals. Therefore, when studying the carbon industry implied by the two countries, the input-output table of the two countries is usually used, and the multilateral input-output table is more reliable for determining the input-output calculation. Therefore, when studying local problems, it is advisable to adopt a variety of display strategies. The results show that in 2010, the carbon content of the carbon industry was 26,593 thousand tons, down 34.6% from 17,383 thousand tons in 2011, calculated at 2%. From 2012 to 2018, the carbon content grew from 31,051 tons in 2014 to 84,248 tons in 2018, with an average annual increase rate of 18%. The experimental results show that there is a large incidence of carbon emissions in the bilateral trade between China, the United States, and Japan. The expansion of export industries is the main reason for the increase in carbon emissions between the two industries. The role of technology has narrowed this difference to some extent....
Trend following strategy is a popular strategy that investors often use in trading around the world. Stocks are bought during an upswing and sold during a decline, the two main phases of the trend-following trading technique. This research evaluates the performance of the trend-following strategy in the Chinese commodity market by systematically employing quantitative methods to trade and get back test results for performance evaluation. The main trading indicator for this research is DMAC (Dual Moving Average Crossover) with a trend indicator called ADX for adjustment. As a kind of technical analysis, so-called “Dual Moving Average Crossovers” are often cited as providing reliable signs for discerning future stock price movements. By employing these indicators and systematically backtesting on 21 commodity futures for ten years, the research discovers that DMAC does not perform well (negative annualized return and sharpe ratio) from 2011 to 2021. By refining the strategy which is to replace DMAC with MACD (Moving Average Convergence/Divergence) and abandon ADX, the backtest result performs much better. The refinement suggests that the utilization of different trading signals/indicators will lead to a different performance of the trend following strategy in the Chinese commodity market. The research concludes that the trend following strategy is worthy of exploring in the Chinese commodity market in terms of using different trading indicators....
The rise in contemporary risk and the resultant corporate failures has necessitated the need for the required attributes of risk committee that would minimize risk of firms. To this end, this study was set to find out the effect of risk committee effectiveness (RCE) on financial successes of quoted banks in selected three African countries. The study spanned from 2009 to 2018. The study focused specifically on risk committee diligence, committee composition, committee diversity, committee expertise, committee size and return on equity (ROE) of the countries selected from Africa namely Nigeria, South Africa and Ghana. More so, we controlled for financial leverage. Ex post facto research design was adopted for the study and panel data in relation to the study were sourced from the annual reports of the chosen banks in the selected countries. The study patterned after the fixed effect model (FEM) since the Hausman test supports the FEM. The FEM reported that the effect of RCE diligence and RCE compositions on bank performance in Nigeria, South Africa and Ghana is highly significant statistically at 5% level. Hence, the study concludes that RCE vis-à-vis risk committee diligence, committee compositions and leverage factors should be pivotal to the formulation of risk management committee of organisations....
In the context of the fourth global industrial revolution, the trading environment of international trade is undergoing profound changes. In today’s deep integration of globalization, the teaching mode of economic application courses—international trade practice situational teaching mode—is bound to change. This article sorts out the problems existing in the curriculum from the perspectives of the integration of the practical curriculum, the cohesion of the trade process, and the teaching concepts and methods. On the basis of deep learning, a situational teaching design process of international trade practice is constructed....
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