Introduction

The high-frequency trading (HFT) has significantly replaced the traditional trading among the high-volume trades and impacts the prices of securities in all markets. This form of trading and its role in the volatility of the prices are scrutinised by the regulators across the world. The growth of technology has created a space for algorithms for making HFTs which has caused significant growth in the number of transactions as well as securities. The HFT has a significant impact on the turnover of the shares which results in increased market value of the shares and leads to significant financial crash in the market. The algorithmic trading (AT) is traced from its evolution and is energising itself with technology to pose significant growth in the near future. The trades are set off automatically based on algorithms which give lesser pressure on the transactions and higher rewards for the risk taken by the investors.

This trading form has caused vital crash in the financial markets which has led to several measures to regulate AT. The trading using the algorithms places orders based on the pre-determined values which can be attributed to significant returns. The usage of algorithms has significantly changed the pattern among the traders and it is causing significant noise in the regular market activities based on the leveraging effects. The number of firms dealing in the HFT has got considerable rise in the country and the growth of these companies reflects the need. The transactions are channelised through algorithms which most of the time enables the investors to return with lesser risk.

The study aims to assess the present status of HFT in stock markets in India and across the world to identify subjective areas that can be used for carrying out research in HFT in relation to stock markets operating in India. The study will help to bring out significant areas of research gap which can help to regulate the HFT without much impact on the retail investors by reducing the leveraging effect of the prices. The study has used major studies that have highest citations during the previous ten years in the area of HFT. This will help classify the areas that have significant presence of research carried out and identify various potential areas of research that can improve the knowledge towards the HFT. The research carried out so far in HFT can be discussed in the following structure.

High-frequency Trading in Stock Market

In recent years, rapid technical advances, and their widespread use, notably in equities markets have fuelled the growth of HFT. A thorough knowledge of HFT’s impacts, as well as the possible hazards and possibilities it may bring in terms of market performance such as volatility, liquidity, pricing efficiency, and price discovery, are explored in this study. HFT and AT may have a number of positive benefits on markets, despite widely held unfavourable beliefs. However, under some conditions, this form of trading might lead to market instability. To resolve issues in the near term, well-chosen regulatory actions are required. Considering the many uncertainties and data gaps, further research is required to better inform long-term policy decisions as per the research stud (Linton & Mahmoodzadeh, 2018). Due to time priority rule, resources are allocated based on tick size when price competition is restricted. Three effects of speed competition are shown. The one single penny tick size has a greater impact on price competitiveness in lower-priced equities with a large market capitalisation.

With regards to both the financial and real sectors, these advances may have a detrimental influence because of distortions such as misinformation, market speculation, and increased volatility as transactions increase velocity (Baron et al., 2014). The performance and competitiveness of HFTs have a substantial influence on stock prices. Latency metrics demonstrate considerable differences in HFT trading performance, which are accounted for by relative delays. HFT firms benefit from increased latency rank because of colocation enhancements (Baron et al., 2014). Market creation and cross-market arbitrage require speed because of the short-lived information and risk management channels that it supplies.

When it comes to speed, it is all about comparison. For the quotation stuffing hypothesis (Biais et al., 2011) established support using NASDAQ channel assignment. According to Gai et al. (2013) classic definition, rivalry in velocity but not price causes externalities. HFT is either helpful or bad for the markets, according to experts.

The impact on the lowest tercile of stocks is the opposite of what one would expect from a rise in the average AT intensity (Ma & McGroarty, 2017). This article examines dark pools, which are stock trading platforms with no pre-trade transparency. Trading in dark pools has steadied at or below 10% and is consistent across stock groups from different countries. In major financial markets throughout the world, technical, institutional, and market trends have all adopted HFT trading, which leverages prices (Shafi et al., 2019). Both these incidents and the extent to which HFT tactics have been discovered on Asian regional stock exchanges exhibit some striking similarities (Kauffman et al., 2015). HFT and algorithm trading in Indian Stock Markets are based on Grounded Theory. HFT has also had an impact on financial market connections as a result of financialisation. The VIX Index, a measure of volatility derived from SPX option prices, has an inverse relationship with SPX option prices that most traders are unaware of. To better understand how index options interact with the high-order moment models that replicate their behaviour, this study is being conducted. Future theory development may benefit from an awareness of the logic vs. perception issue in option pricing theory (Shafi et al., 2019). Limit up Limit down rule (LULD) and HFT behaviour in connection to the price limit are the subject of this research. This research investigates five different hypotheses, including trade interference, volatility spillovers, and delayed price discovery. On maker-taker markets, magnet effects and HFT function around a price restriction. In maker-taker and inverted markets, when a subset of sample stocks is moving above and below the $3 barrier as prices approach their upper and lower limits. Although trading is disturbed, volatility is decreased in the near term without delaying the discovery of price. Due to the impending price limit, traders encounter a ‘magnet effect’. There was a decrease in HFT trading activity on the maker-taker market following the trading halt, but no change on the inverted market (Lin, 2018).

In the trading market, traders make decisions depending on whether or not a particular phenomenon is widely recognised. Algorithms and HFT are familiar concepts to Indian traders. The current Indian market is hampered by AT and HFT (Chakervarti & Chaitanya, 2016). Investors now have additional alternatives because of technology advancements. As of 2010, the Tokyo Stock Market’s Arrowhead trading platform had been established by the Tokyo Stock Exchange. HFQ has increased from 0% to 36% of trade activity on this platform in the last year. Extreme market circumstances coupled with HFQ might lead to systemic hazards, such as flash crashes. CoVaR and correlations can be used to mitigate the systemic hazards posed by HFQ, but circuit breakers and other limits should be applied to do so (Jain et al., 2016). HFTs and buy-side algorithmic traders (BSTs) use two different types of algorithmic trading tactics (BATs). Trading volumes between BATs and HFTs are quite comparable, although the BATs have a greater within-group similarity than do the HFTs. Similarity in directionality of execution metrics between groups is also apparent. BATs are more likely than HFTs to engage in contrarian trading behaviour, according to a new study. The existence of commonality and contrarian trading among ATs ensures market stability and price discovery in the market (Arumugam & Krishna Prasanna, 2021). Advances in technology and novel concepts have spurred global financialisation. Among the many technological advances developed to keep pace with the financial sector’s rapid evolution and to reduce risk while increasing profit is AT. Despite the widespread use of AT, there is a lack of scientific research on the evidence of its efficacy. No evidence exists to support the assertion that AT and HFT definitions are interchangeable. An understanding of the impact of an ever-increasing number of financial transactions on the world economy must be based on evidence. AT, which we see as a component of financialisation, can be accurately described and identified in the Indian stocks market. There is a lot of interest in how financialisation’s transaction velocity-symbolising AT influences prices (Dubey et al., 2017).

There is no correlation between foreign institutional investments (FIIs) and domestic institutional investments (DIIs) in India, based on the most recent high-frequency data (Iskandar, 2018).

HFT has a significant influence on Tehran Stock Exchange stock returns, causing market shockwaves. Because of the disparities in firm size, the HFT volume and returns for small and large businesses differ (Sarlak & Talei, 2016).

High-frequency Trading and Liquidity

HFT has a significant influence on Tehran Stock Exchange stock returns, causing market shockwaves. The statistics basis includes all Tehran Stock Exchange companies that have traded in the stock market during the past two years. Some large and small companies have assets logarithms that are significantly different from the average. It is difficult to predict the direction of Tehran’s stock market because of its non-linear dynamics and the HFT of significant enterprises. Because of these disparities in firm size, the HFT volume and returns for small and large businesses differ. High-frequency market makers are generally unable to offer stable liquidity as a result of these restrictions (Ait-Sahalia & Saalam, 2017). HFTs are expected to lower their liquidity provision as a result of volatility (Ait-Sahalia & Saalam, 2017). Liquidity is not affected in the same way by internationalisation in all companies and nations (Ma et al., 2016). There were potential repercussions on Bulgarian capital market when new EU rule targeting HFT is put into effect (Stefanova, 2018). Ethical standards are necessary to ensure fair and stable marketplaces in the financial sector (Dalko & Wang, 2018). According to some proponents, HFT is a net liquidity supplier, although this is not the case. HFT significantly affected spoofing and quote stuffing on the market (Wang et al., 2016). During instances of extreme high and low returns, illiquidity has a greater influence (Bhattacharya et al., 2022). In summary, ephemeral orders are not the cause of market illiquidity and so should not be characterised as ‘spoofing’ described under the Dodd–Frank Act (Li, 2018). A stochastic order-driven model with waiting has a major influence on order books that are diverse in nature.

For large-cap equities, there is a decrease in liquidity during the time when HFT activity is strong, but an increase for small-cap stocks (Wang et al., 2016). AT in the Indian equities market has been hindered by the usage of an orders-to-trades ratio charge. The second charge had little or no effect on the order-to-trades ratio or the quality of the market (Aggarwal et al., 2017). Market characteristics including trade time, tightness, depth and robustness may all be measured using liquidity as a metric, according to the literature (Hou et al., 2017).

Traditional market makers are unable to compete with high-frequency market makers in terms of speed and information (Ait-Sahalia & Saalam, 2017). During market collapses, market-aggregate margin trading has a far greater influence on selling and investor order submission tactics than individual margin trading (Hu et al., 2021).

Despite a more thorough analysis revealing that the new situation benefits only HFT, this macro phenomenon disappears in markets containing both institutional investors and HFT, leaving institutional investors even with increased trading expenses (Lachapelle et al., 2016). It is clear that machine-based liquidity provision markets have the potential for systemic instability, and our findings support regulators’ worries (Raman et al., 2015).

HFT and Volatility

The volatility of the market is significantly affected by the operation of HFT which is caused by AT. There are many who argue that it gives an opportunity for traders to calm down and make sensible judgments at times of high volatility in the market. Opponents downplay its importance, calling it a roadblock to a free market in price discovery. The calls for increased market regulation got stronger in the wake of the 2007–2008 Crisis and the 2010 Flash Crash. Because of this, it is doubtful that circuit breakers will go out of use (Sifat & Mohamad, 2019). During the V-shape bounce, there was an initial surge in selling, followed by a surge in purchasing. There were a lot of ups and downs in the market. As a consequence of this catastrophic occurrence, many people are left wondering what caused and aggravated the Flash Crash in the first place (Dalko, 2016). Sociological issues about the connection between investment businesses and society are impacted by illiquidity and dispersed execution (Pitluck, 2011). This publication is one of the first systematic assessments of theoretical and empirical research on the magnet effect as this new sub-discipline evolves (Sifat & Mohamad, 2020).

Market volatility, liquidity shocks and stock returns were found to have a direct correlation with the use of HFT. Faster trading and greater governance, as well as a lack of prohibitions on short sells, all have a role (Ma et al., 2018). There is new evidence that increased automated trading leads to lower intraday liquidity management and a decreased risk of extreme intraday price fluctuations (Aggarwal & Thomas, 2014). The HFT has significant impact on the stock market’s volatility from a variety of angles. Volatility in the stock market and foreign commerce are mutually exclusive, since volatility decreases trade and exacerbates the country’s current and capital account deficits (Bhowmik, 2013). The Securities and Exchange Board of India recently implemented a securities legislation known as the volume limit. It examines existing research on the detrimental effects of high sales volumes on the stock market’s stability. The recent growth of HFT in India is exorbitant. The volume limit control works by decreasing the substantial price implications caused by legitimate transactions. There is insufficiency in the regulations when HFTs use spoofing to manipulate order display (Dalko & Wang, 2019).

Using high-frequency data, authors can identify the precise time intervals impacted by upcoming events (Agarwalla & Pandey, 2012). Semi-martingales based on high-frequency financial returns are the subject of an economics research. The effect of various stock specific and market-wide events on intraday volatility dynamics in the Indian market was thoroughly investigated. The high-frequency asset returns to its basic components (continuous, tiny jumps and big jumps) (dan Rosad, 2015). Investors might use these trends to construct heuristics, which would allow them to recognise probable bubble and herd scenarios before they occur (Ghosh & Kozarevic, 2019).

The research articles studied for the present study purpose and their contribution to literature are explained in Table 1.

Conclusion

The HFT in India and as well across the world is controlled based on various technical regulations and with the advent of technology, the HFTs are able to carry out the transactions. The literature explained above has given vital areas of research in improvising the HFT with minimal impact on the retail investors and crunch on prices of the financial securities. The reviews have also helped to learn various techniques of arbitrages, passive trading and spoofing, which has an impact on the leveraging effect of the prices. The liquidity of the market is having significant impact based on the operation of the HFT which is clearly explained by the market crash in the year 2010. The HFT has the potential to bring down the market based on its operation has significant impact on the liquidity of the market. The world over has gained significant knowledge about the vital impacts created in the areas of liquidity and volatility. The majority of the studies found in the literature that are highly cited are based on multi-national and other world markets. The Indian market related researches are very scarce in the literature. The Indian Stock Market has been affected vitally with the operation of HFT which is explained by the literature. The connecting link between the HFT with the operational areas of liquidity and volatility of stock market is hardly found in the literature. The various measures to eliminate the overcoming effects of spoofing and quotation stuffing are identified as potential area of research based on this synthesis. The regulatory measures and their impact on HFT were found to be an interesting area of research that is left unexplored. This study on whole has provided clear synthesis of HFT in the areas of liquidity and volatility over the decade which has helped to identify significant areas of research related to Indian as well as world stock markets.

Implications of the Study

This research study contributes to the regulators by providing insights to earlier research in HFT so that regulators can concentrate on devising more extensive regulatory policy. This study also contributes to the researchers in the area of HFT as this gives a condensed view of previous research done in studying impact of HFT on various layers of stock markets. This study mainly contributes to the HFTs and institutional investors by specifying what kind of impact it will have in their trading transactions and also the challenges it is posing.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iDs

Jaya Sankar Krishna  https://orcid.org/0000-0001-5555-4930

Renuka Lenka  https://orcid.org/0000-0003-0392-5917