High Frequency Trading: Business Redesign through Big Data and Cloud

This monograph explores the operation of HFT traders on the Helsinki Stock Exchange (HSE) and focuses on the viability of the strategies suggested by HFT for other market participants. The author offers a comprehensive analysis of both the institutional context and the related scholarly literature in support of this report. In this monograph, by deemphasizing “need-for-speed” in computerised high-frequency trading without affecting the basic nature of the speed race, the author identified the welfare effect on trade performance. The aim of this organisation is to verify that high frequency trading is not only about speed, but also about the efficient trading strategies used to conduct the trade. Strategies are the technical method used to look for gains from speculation based on events. After discovering and exploring the feasibility of HFT in event-based trading on the Finnish stock exchange, a customised version of the company cloud trading system is thus architected. So that new HFT companies that guess about what tact to exercise for what holding time and are unable to get micro-second news feed favours faster than their opponents can practise this discipline and the proposed alpha-profit generation cloud trading architect.

The findings of this monograph suggest:

First of all, this monograph takes one-month NASDAQ OMX Nordic high frequency limit order and tick data and selects six mostly traded Finnish stocks based on their small order book operations. Basic limit order book activities of all selected stocks are evaluated to ensure that all selected stocks are impacted by high-frequency activities, including and except non-high-frequency activates, so that the result is more reliable. This monograph follows Aldridge’s proposed high frequency trading strategies and respective holding periods (2009). Strategies often succeed not because policies are effective, but because of market inefficiency. This monograph cross-checks the inefficiency of the market with the autoregressive test. Because of tick data and a very short time period between the two findings, the current return indicates a strong impact of previous returns and past price fluctuations, implying inefficiency in the sector.

Secondly, using distinct comparative ratios, efficiency capability assessment of high frequency trading strategies is performed. Studies find that it is difficult for high-frequency traders to achieve substantial alpha because of the tight spread by trading highly liquid stocks using market making strategy. But with the Sharpe ratio almost equal to the market, they can still produce a positive return. Following this approach, they behave more like market makers. The potential is between (58-75) percent for other high frequency trading strategies. Of all the high frequency trading strategies, statistical arbitration strategies are the best. As a key tool for comparing high frequency and non-high frequency traders, the Sharpe ratio indicates several times higher Sharpe values for high frequency traders relative to non-high frequency traders. For both strategies with long and short positions, Value at Risk (VaR) indicates the likelihood of producing a positive return.

Thirdly, cloud computing in trading and rapid growth of computer hardware and software in the world of big data; cloud computing and high frequency trading (HFT) are unconditionally closely connected as the market is rapidly evolving. This monograph revisits these topics in a novel way and first reveals the distinctive characteristics of Helsinki stock exchange high-frequency trading, suggesting the impression of optimistic event trading recoveries. After discovering and exploring the feasibility of HFT in event-based trading on the Finnish stock market, a customised version of the cloud trading system is then architected. Any academic benefit is a welcome side effect.

Author(s) Details

Arodh Lal Karn
School of Management, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi’an Shaanxi, 710072, P.R. China.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/340

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