Purpose The purpose of this paper is to identify and describe the drivers of trading company strategy that explain trading company success in international business. Design/methodology/approach The strategy tripod that results from combining the industry-, resourceand institution-based views, each of which proposes specific drivers of strategic success, was used as the framework for investigati...

although in principle the benefit and loss resulted from partnership is to be taken into consideration in private relationships proportionate to the parties’ shares, prediction of stipulating more benefit or exempting all or part of the loss in the company’s contracts, with respect to the existing necessities and the need for maximum participation, is a common practice. whereas, the article 575...

We analyze the location of stock trading for firms with a US cross-listing. The fraction of trading that occurs in the United States tends to be larger for companies from countries that are geographically close to the United States and feature low financial development and poor insider trading protection. For companies based in developed countries, trading volume in the United States is larger ...

This paper is to explore the possibility to use alternative data and artificial intelligence techniques to trade stocks. The efficacy of the daily Twitter sentiment on predicting the stock return is examined using machine learning methods. Reinforcement learning(Q-learning) is applied to generate the optimal trading policy based on the sentiment signal. The predicting power of the sentiment sig...

In the course of technological evolution security markets offer low-latency access to their customers. Although latency figures are used as marketing instruments, only little research sheds light on the means of those figures. This paper provides a performance measure on the effect of latency in the context of the competitive advantage of IT. Based on a historical dataset of Deutsche Börse’s el...

Noise traders as one of the key elements of the market play a significant role in determining the market volatilities, returns, and stock market mispricing. Hence, this study attempts to scrutinize the role of noise trading in capital asset pricing. Therefore, by using daily data, samples including 14105 data of 200 companies listed on stock exchange were selected and noise trading index was es...

This paper evaluates the use of an artificial neural network within a stockmarket trading strategy. The neural network was previously developed by the same authors, and has been trained using fundamental, company specific data. This study sites the neural network within a trading context, and demonstrates it is capable of producing economically significant results after accounting for costs.

We propose a mean-risk decision model for a steel company facing emission limits and trading with emission allowances. The model is calibrated using data of a real-life steel company and is subsequently solved for five different scenarios of demand and different levels of risk aversion. It is found that while the limits are never reached, permit trading influences the decision to a great extent...

Stock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior. For each stock, we characterize the daily trading relationship among its investors using a trading ...