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Better Alternative Trading System (BATS) Definition
The Better Alternative Trading System (BATS) is a stock exchange founded in 2005. BATS is based in the United States, it offers foreign exchange services to investors and traders as well as equities and options. The better alternative trading system was approved by the Securities Exchange Commission (SEC) and it has its operations in the U.S and Europe.
A Little More on What is the Better Alternative Trading System (BATS)
The BATS became an actual stock exchange in 2008 when it entered the European market but it was founded in 2005. after entering the market in 2008, a BATSexchange was launched in both London and the U.S. the offerings of the BATS include options equities and foreign exchange. Given the uniqueness of the BATS and the services it offers, it was renamed as BATS Global Markets and regarded as an alternative trading platform by investors.
As a reputable stock exchange, BATS competed with established exchanges such as the Nasdaq and the New York Stock Exchange (NYSE) and by 2016, BATS was ranked the second-largest equity exchange in the U.S. BATS operates in both Europe and the United States. Investors and traders can trade options and stocks in this exchange. In October 2008, BATS BZX was launched as a registered exchange while BYX two years after. BATS is considered innovative amongst other exchanges which was why it acquired many exchanges between 2011 and 2015 including Chi-X Europe and Hotspot ECN.
Despite its innovative nature, BATS has encountered many challenges over the YEARS. After an effort to go public in 2012, BATS in 2013 encountered an error which led to multiple trades executed below the best bid and offer leading investors to engage in short-sellings. As of 2017, BATS was acquired by CBOE Holdings.
Reference for “Better Alternative Trading System (BATS)”
Academic research on “Better Alternative Trading System (BATS)”
Technology’s Latest Market Manipulator-High Frequency Trading: The Strategies, Tools, Risks, and Responses, Bhupathi, T. (2009). Technology’s Latest Market Manipulator-High Frequency Trading: The Strategies, Tools, Risks, and Responses. NCJL & Tech., 11, 377. The development of high frequency trading technology has created significant controversy in the financial markets, especially in light of the increased use of tools such as naked access, flash orders, and co- location. This recent development argues that the SEC is correct in both banning naked access, because it increases risk of market detriment, as well as eliminating flash orders, due to their potential to aid in market manipulation. Further, the SEC’s lack of regulatory response to high frequency trading and co-location should be maintained. Since neither mechanism presents a risk of market detriment or manipulation on its own, and both seem to be criticized solely because they break from traditional market fundamentals, it would unnecessarily stifle technological development to insist on banning or minimizing the use of these strategies.
Distance-based high-frequency trading, Felker, T., Mazalov, V., & Watt, S. M. (2014). Distance-based high-frequency trading. Procedia Computer Science, 29, 2055-2064. The present paper approaches high-frequency trading from a computational science perspective, presenting a pattern recognition model to predict price changes of stock market assets. The technique is based on the feature-weighted Euclidean distance to the centroid of a training cluster. A set of micro technical indicators, traditionally employed by professional scalpers, is used in this setting. We describe procedures for removal of outliers, normalization of feature points, computation of weights of features, and classification of test points. The complexity of computation at each quote received is proportional to the number of features. In addition, processing of indicators is parallelizable and, therefore, suitable in high-frequency domains. Experiments are presented for different prediction time intervals and confidence thresholds. Predictions made 10 to 2000 milliseconds before a price change resulted in an accuracy that ranged monotonically from 97% to 75%. Finally, we observed an empirical relation between Euclidean distance in the feature space and prediction accuracy.
High-frequency trading and the flash crash: structural weaknesses in the securities markets and proposed regulatory responses, Poirer, I. (2012). High-frequency trading and the flash crash: structural weaknesses in the securities markets and proposed regulatory responses. Hastings Bus. LJ, 8, 445.
Will high-frequency trading practices transform the financial markets in the Asia Pacific Region?, Kauffman, R. J., Hu, Y., & Ma, D. (2015). Will high-frequency trading practices transform the financial markets in the Asia Pacific Region?. Financial Innovation, 1(1), 4. High-frequency trading (HFT) practices in the global financial markets involve the use of information and communication technologies (ICT), especially the capabilities of high-speed networks, rapid computation, and algorithmic detection of changing information and prices that create opportunities for computers to effect low-latency trades that can be accomplished in milliseconds. HFT practices exist because a variety of new technologies have made them possible, and because financial market infrastructure capabilities have also been changing so rapidly. The U.S. markets, such as the National Association for Securities Dealers Automated Quote (NASDAQ) market and the New York Stock Exchange (NYSE), have maintained relevance and centrality in financial intermediation in financial markets settings that have changed so much in the past 20 years that they are hardly recognizable. In this article, we explore the technological, institutional and market developments in leading financial markets around the world that have embraced HFT trading. From these examples, we will distill a number of common characteristics that seem to be in operation, and then assess the extent to which HFT practices have begun to be observed in Asian regional financial markets, and what will be their likely impacts. We also discuss a number of theoretical and empirical research directions of interest.
DARK POOL APPLICATION IN THE STOCK EXCHANGE, BAĞCI, H. DARK POOL APPLICATION IN THE STOCK EXCHANGE. ACADEMIC RESEARCH IN SOCIAL, HUMAN AND ADMINISTRATIVE SCIENCES-I, 297. The dark pools are the markets that started to enter the world market since 1980. Development and active use began in 2007. The dark pool is a market where the identity of the investor can not be determined in the purchase and sale transactions in the frame of confidentiality, the transaction volume is unknown, transparency and depth are not found.The purpose of the dark pools is to encourage small investors to open up their investments and make high volume investments. Because there is no secrecy in other markets, the profile of the investor is known and the large and institutional investors who are trusted by this profile are preferred. The concept of dark pool in this study mentioned development both in Turkey and in the world. Purpose of the study is with general knowledge about these concepts to describe the dark pools and dark pool in the world and to explain the development in Turkey. The study ended with explanations about the importance of the dark pool, its contributions to the whole world, and its negative aspects.
Modelling and Simulation for the Analysis of Securities Markets, Hu, R., Mazalov, V., & Watt, S. M. (2014). Modelling and Simulation for the Analysis of Securities Markets. SYMBOLIC COMPUTATION IN SOFTWARE SCIENCE, 23. Many financial markets today are dominated by automated high-frequency trading. According to some studies, this has accounted for more than half the volume of US equity markets in recent years. High-frequency trading strategies typically adopt powerful computers and communications infrastructure and a variety of algorithms to process a large number of orders at high speed, attempting to profit sometimes a fraction of a cent on every trade. While this has led to significant theoretical and applied research, the area still presents many important challenges. These arise both in the strategy modelling phase, where accurate and efficient prediction of the price movement of securities is required, and in the evaluation phase, where strategies must be examined in a variety of market conditions before being launched in real markets. We investigate these two areas. Our modelling approach is based on clustering in a space of technical indicators, using a weighted Euclidean distance in a manner similar to certain handwriting recognition algorithms. Our evaluation environment is a market simulator that uses historical data or live data and agents to reproduce the fine-grained dynamics of financial markets. This paper outlines our approach to modelling and simulation and how they work together.
The long and short of it: The Securities and Exchange Commission should reinstate a price restriction test to regulate short selling, Horvatich, R. A. H. (2009). The long and short of it: The Securities and Exchange Commission should reinstate a price restriction test to regulate short selling. Creighton L. Rev., 43, 593.