The Cost of Algorithmic Trading

Institutional investors currently use algorithmic trading as one of the most popular and developing trading strategies on the Indian stock market. It is a form of trading where systems are programmed with predetermined rules and instructions to execute transactions at a high rate of speed and accuracy that is hard for human traders to achieve manually. Many retail traders and market regulators have opposed algorithmic trading because of its quick execution. Although algorithmic trading has taken a battering for unintended volatility and blocking market quality by adding large volume at specific levels in accordance with their system or strategy, the evidence relevant to its drawbacks has not yet been established. This paper moves in the right way by supporting algorithmic trading and giving it the credit it deserves for raising market quality. This study analyses the National Stock Exchange's (NSE) stock market to directly identify algorithmic trading. It then aims to identify the primary benefits of algorithmic trading and express the rationale behind its expansion not just in India but also on the international market.

2019

The Indian securities market has been growing very rapidly with a large amount of capital flowing in from the foreign institutional investors. Algorithmic trading (AT) has also gradually increased in National Stock Exchange (NSE) after the introduction of co-location trading in 2010. More than 50% percent of the trading occurs through AT in India. In this paper, we conduct an analysis of volatility for the pre-AT and the post-AT period of the leading stock indices in NSE by using various methods of volatility calculation. We also provide a compare analysis of the volatility for the pre-AT and post-AT period. Our findings indicate that the volatility has significantly reduced for the post-AT period in comparison to pre-AT period, thus making the markets more efficient. The results were corroborated through primary data collection by conducting focused interviews of people with expertise in algorithmic trading. For further research, data analysis to calculate volatility for the Pre-AT...

Institutional Investors, 2001

Recent ICT developments, however, have changed competitive conditions significantly. Whereas the telephone and telegraph had substantially increased economies of scale and scope, bringing natural monopoly features to the predominant national exchange and raising sunk cost barriers to new entry, computerization has had a much more complex effect. Whereas computerization has increased the potential for exploiting network externalities, and hence for concentrating trading, it has also introduced the possibility of product differentiation and dramatically reduced sunk cost barriers to entry. 2.1. Product Differentiation Computerization has facilitated new modes of trading which reduce the costs of different types of trading strategies. The continuous electronic auction system, first introduced by a brokerage firm (Instinet) in the 1970s, is now employed by every stock exchange in Europe. This mode of trading allows traders to place limit orders directly on an open order book via a remote computer terminal, and to execute market orders directly against these limit orders without intermediation by a dealer, floor broker, or exchange specialist. Electronic call market trading was first introduced in 1991 (by the Arizona Stock Exchange), allowing patient traders to pool their orders for execution at a pre-set time. Such systems execute all qualifying orders at a single price, determined either within the system itself (a call auction) or on other markets (a crossing network). In the US, the Arizona Stock Exchange (AZX) operates a call auction after the close of NYSE trading, Instinet offers an after-hours closing-price cross, and Posit runs five set-time intra-day crosses based on the contemporaneous Consolidated Quotation System (CQS) mid-quote. In 1998, OptiMark will introduce an electronic trading system based on massive parallel processing or supercomputers. The system, which can be run continuously or on a call auction basis, will process orders in three dimensions. Traders will be able to specify not only how many shares they wish to buy or sell at a given price or better, but also to specify preference rankings of such pairings in graph format. Even when run on a call auction basis, trades will execute at different prices depending on the pricing "aggressiveness" of each order (unlike with the previous generation of two-dimensional call auction systems, which execute all trades at a common clearing price). 2.2 Contestability Contestability theory (Baumol, Panzar, and Willig, 1988) highlights the theoretical and regulatory significance of potential competition. It demonstrates that allocative efficiency is achievable in an industry marked by natural monopoly production, provided that the monopolist faces the credible threat of entry by a lower cost producer. Since it had previously been generally assumed that

Journal of Economic Perspectives, 2013

Financial markets have undergone a remarkable transformation over the past two decades due to advances in technology. These advances include faster and cheaper computers, greater connectivity among market participants, and perhaps most important of all, more sophisticated trading algorithms. The benefits of such financial technology are evident: lower transactions costs, faster executions, and greater volume of trades. However, like any technology, trading technology has unintended consequences. In this paper, we review key innovations in trading technology starting with portfolio optimization in the 1950s and ending with high-frequency trading in the late 2000s, as well as opportunities, challenges, and economic incentives that accompanied these developments. We also discuss potential threats to financial stability created or facilitated by algorithmic trading and propose “Financial Regulation 2.0,” a set of design principles for bringing the current financial regulatory framework ...

Operations Research Proceedings 2008, 2009

Investors which trade in financial markets are interested in buying at low and selling at high prices. We suggest to solve this type of problem with an online algorithm. This active trading algorithm is based on reservation prices. The effectiveness of the algorithm is analyzed from a worst case and an average case point of view. We also compare the average case and the worst case bounds using simulation on historical data. Moreover, we want to give an answer to the question if the suggested active online trading algorithm shows a superior behaviour to buy-and-hold policies.