Topic
Algorithmic trading
About: Algorithmic trading is a research topic. Over the lifetime, 6718 publications have been published within this topic receiving 162209 citations. The topic is also known as: algotrading & Algorithmic trading.
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TL;DR: In this article, the authors investigated the effects of listing on the U.S. exchanges on trading volume for stocks listed on the two Canadian stock exchanges: the Toronto Stock Exchange (TSE) and the Vancouver stock exchange (VSE).
Abstract: This study investigates the effects of listing on the U.S. exchanges on trading volume for stocks listed on the two Canadian stock exchanges: the Toronto Stock Exchange (TSE) and the Vancouver Stock Exchange (VSE). The results show substantial differences between the two samples. When a TSE security is cross-listed, both trading volume and stock turnover, the number of shares traded as a percentage of number outstanding, almost double their pre-listing levels. In contrast, when a VSE stock is cross-listed, there is only a slight increase in trading volume and a sharp decline in turnover. The TSE is also able to maintain its pre-listing levels of trading volume in cross-listed securities, whereas the VSE loses about half the trading volume in these stocks to the U.S. exchanges. Even after controlling for the firm-specific factors, the Canadian exchange-specific factors remain the dominant factors in explaining the cross-sectional variation in liquidity effects. Neither the differences in trading costs nor in listing and disclosure requirements between the two exchanges explain these results.
40 citations
TL;DR: Two different ways to represent the discrete states of the environment are proposed and the two proposed models outperformed the Buy-and-Hold and Decision-Tree based trading strategy in terms of profitability.
Abstract: Trading strategies play a vital role in Algorithmic trading, a computer program that takes and executes automated trading decisions in the stock market. The conventional wisdom is that the same trading strategy is not profitable for all stocks all the time. The selection of a trading strategy for the stock at a particular time instant is the major research problem in the stock market trading. An optimal dynamic trading strategy generated from the current pattern of the stock price trend can attempt to solve this problem. Reinforcement Learning can find this optimal dynamic trading strategy by interacting with the actual stock market as its environment. The representation of the state of the environment is crucial for performance. We have proposed two different ways to represent the discrete states of the environment. In this work, we trained the trading agent using the Q-learning algorithm of Reinforcement Learning to find optimal dynamic trading strategies. We experimented with the two proposed models on real stock market data from the Indian and American stock markets. The proposed models outperformed the Buy-and-Hold and Decision-Tree based trading strategy in terms of profitability.
40 citations
TL;DR: In this paper, the authors investigated the profitability of technical trading rules in a wide variety of markets, and many of them found positive profits, but despite positive evidence about profitability and improvements in testing procedures, skepticism about technical trading profits remains widespread among academics mainly due to data snooping problems.
Abstract: Numerous empirical studies have investigated the profitability of technical trading rules in a wide variety of markets, and many of them found positive profits. Despite positive evidence about profitability and improvements in testing procedures, skepticism about technical trading profits remains widespread among academics mainly due to data snooping problems. This study tries to mitigate the problems by confirming the results of a previous study and then replicating the original testing procedure on a new body of data. Results indicate that in 12 U.S. futures markets technical trading profits have gradually declined over time. Substantial technical trading profits during the 1978-1984 period are no longer available in the 1985-2003 period.
40 citations
TL;DR: In this article, the authors consider a CO 2 cap-and-trade model where trading costs develop endogenously as a function of the market size, and the pre-trade allocation of permits determines whether the market can be strongly influenced by expectations that have a role because of adjustment costs.
40 citations
TL;DR: Wang et al. as mentioned in this paper investigated price discovery in the newly established stock index (CSI300) futures market in China and revealed new evidence that the CSI300 index futures market played a dominant role in the price discovery process about one year after its inception.
Abstract: Using high-frequency data, this study investigates price discovery in the newly established stock index (CSI300) futures market in China. Our empirical results reveal new evidence that the CSI300 index futures market play a dominant role in the price discovery process about one year after its inception and new information is disseminated more rapidly in the stock index futures market than the stock market. This is different from findings in the previous literature. Our results also imply that the index futures market has evolved and can be used as a price discovery vehicle. Thus the CSI300 stock index futures market plays an important role in the capital markets in China.
40 citations