Speeding Out of India: The HFT & Algo Trading Debate Quickens

Stock brokers trade in brokerage firm in Kolkata

High-frequency trading can be a tough topic to tackle. Regulators around the globe are scrambling to ensure their markets are fair and orderly while drawing in liquidity and lessening spreads. Greeksoft discusses Indian regulator Sebi’s new proposals targeted at algorithmic and high-frequency trading, and what it could mean for the nation’s burgeoning market going forward.

In North America and Europe, high-frequency trading (HFT) has been put under the microscope. Flash crashes—the most recent of which befell the British pound on October 7—price volatility, heaps of cancelled orders, and the belief that the financial markets are rigged, notably outlined in Michael Lewis’ Flash Boys, has led regulators to reexamine the potential (some might say “theoretical”) benefits of the practice: added liquidity, tighter spreads, and decreased costs.

But one region of the world that has been quick to embrace HFT in order to bring in foreign expertise and liquidity is Asia. To varying degrees, Japan, Singapore, Australia and Hong Kong have seen their exchanges build out their trading platforms and networks to entice low-latency traders to try out their markets. While it’s too early to judge the long-term success or failure of these efforts, there’s clearly an appetite for this type of trading in this region.

Another market that is poised to join this group is India. For those unfamiliar with India’s marketplace, the second-most populous country in the world has two stock exchanges: the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE).

The topic of HFT brings about its own drama. India’s regulator, the Securities Board and Exchange of India (Sebi), hopes to address those concerns. As such, any conversation about the merits of HFT will first have to begin with how best to regulate algorithmic trading and co-location.

HFT and Algo Differentiation

Earlier in the year, the Indian regulator issued a discussion paper, Strengthening of the Regulatory Framework for Algorithmic Trading & Co-location.

The nine-page report lists seven measures designed to address concerns relating to market quality, market integrity and fairness due to the increased usage of algo trading and co-location in the Indian securities market. By the end of August, Sebi had collected feedback from the industry, including from broker-dealers, investors and intermediaries, among others.

Sebi chief Upendra Kumar (UK) Sinha recently said the regulator would consult all stakeholders—including the Reserve Bank of India (RBI) and technology providers—before making a final decision on implementing new rules targeted specifically at HFT.

There is a concern among HFT proponents that some of the measures proposed will be detrimental to the overall trading landscape in India. According to data provided by Sebi, algorithmic trading and HFT strategies account for about 40 percent of the country’s trades. However, that estimate lumps HFT and algo trading together. While all high-frequency trading strategies are conducted using algorithmic platforms, not all algorithmic trading constitutes HFT. Even in the US, regulators have given up on trying to define an exact latency that constitutes HFT, but there still needs to be some differentiation between HFT and simple algorithmic trading, notes one asset management firm’s risk manager.

 New Ideas

In the paper, Sebi states that algo trading and HFT drew regulatory attention due to price volatility, market noise, costs imposed on other market users, and it often presented limited opportunities for regulatory intervention. The regulator is examining several measures to “allay the fear and concern” of unfair and inequitable access to the trading systems of the exchanges.

Potential measures to curb high-frequency trading include a minimum resting time for orders; frequent batch auctions; random speed bumps or delays in order-processing or matching; randomization of orders received during a given period; maximum order message-to-trade ratio requirements; separate queues for co-located and non–co-located orders; and a review of tick-by-tick data feeds.

One big concern, though, is that Sebi has not clearly identified the specific problem it is trying to solve. Without proper clarification, implementing those measures without addressing a specific problem could be detrimental, adding complexity to the overall market structure in India.

Rather than target HFT specifically, cracking down on specific cases would deter any intentional misconduct.This then leaves more options on how to ensure a fair and competitive marketplace. Continuous market supervision or guidelines and enforcement of pre-trade checks and market-abuse monitoring have been proven successful in other markets and could also prove useful here.

But the concern is that the measures that Sebi has proposed will likely make a complicated situation even more complex and confusing. As a vendor, we think India and Korea are some of the most challenging countries in terms of compliance. Regulators in India have always been allowed to review the code of algorithmic firms, which causes intellectual property issues, given that the written code is confidential. This is an issue also being debated in the US with the Commodity Futures Trading Commission’s (CFTC’s) recent source-code provision of Regulation Automated Trading (AT), which stipulates that trading firms have to turn over their source codes to a repository, as opposed to handing over the code after being served a subpoena.

Resting Times

Despite the discussion paper being closed for feedback, Sebi is still consulting with industry bodies and market participants on the proposals as the body looks to get more clarity on the specific issues it intends to solve. And people are happy that they’re taking their time—at least for right now—but the concern of unintended consequences looms.

As an example, one of Sebi’s proposals is to implement a minimum-resting time for orders of 500 milliseconds; the idea is to eliminate “fleeting orders,” or orders that are put in and then cancelled within a short amount of time. But Sebi also noted that no other regulator currently mandates the resting-time mechanism.

Back in March 2013, the Australian Securities and Investment Commission (ASIC) asked for feedback on implementing a similar measure to address concerns market operators and participants had over the effect of HFT and dark-pool trading. However, ASIC later decided against it, saying that such an implementation would only affect about 1 percent of order amendments and 2.26 percent of order deletions. In total, that represented approximately 1.25 percent of all order flows, including executed orders on the Australian market. So they concluded that the reward of such a bold move would not be worth the added market complexity.

“The proposed minimum resting time rule would affect only a small portion of HFT operators. In ASIC Report 331, it is estimated that HFT accounts for 46 percent of orders and 32 percent of trades in the Australian equities market, with around 25 percent to 35 percent of small fleeting orders attributable to high-frequency traders,” according to Capital Markets Consulting, which was commissioned by the Financial Services Council of Australia to conduct research on the impact of technology on capital markets.

Risk manager says implementing a minimum resting time for orders would definitely hurt HFT players, but the source also questions the need for that kind of speed in the first place. “It is such a small time period that the normal institutional investor wouldn’t have a problem with this. But 500 milliseconds is like an eternity to HFT traders. What are you doing that requires that kind of speed and why would you be cancelling orders in half a minute?” he says.

Questions Abound

Another proposed measure is the random speed bumps or delays in order processing and/or matching, similar to what IEX in the US has implemented. IEX introduced a two-way 350 microsecond delay on communications from its members and its trading system.

Sebi said in its discussion paper that this type of mechanism could discourage latency-sensitive strategies, which would drastically affect HFT but would not deter non-algo order flow. The intent behind it is to “nullify the latency advantage of the co-located players to a large extent,” it said.

As for randomization of order matching for co-located orders and non–co-located orders, implementing such measures may result in shifting the problem rather than resolving it.

While Sebi mulls over whether it should implement some, if any, of the proposed measures, there is no doubt that any of these measures would affect liquidity in India’s market—the question is, whether the impact would be positive or negative.

We understand that at least some of the proposals will be implemented, since Sebi put out the discussion paper. The industry understands that Sebi is under pressure to implement measures to solve what it perceives to be a problem. These proposals, if implemented, may impact volumes and, hence, liquidity.

The Liquidity Issue

Whether in Asia, North America or Europe, the HFT battle often comes down to the question of liquidity. Access to liquidity in Asia has always been complicated because the region is both fragmented—each country has its own market structures/rules and the differentiation isn’t always that clear—and homogenous, as most markets have only one or two exchanges. Other than Japan and China, markets in Asia are really small. US markets trade around $250 billion a day while markets in Asia trade only about a third of that. Trying to trade a $50 million block in India is very hard; you would exhaust liquidity and end up pushing up prices. So the more liquidity you have, the better.

This is a big reason why countries like Japan, Singapore and Australia have tried to entice HFT firms, which boosts volumes.

The main benefit of HFT would be the creation of more liquidity, if it is hard to buy and sell, then institutional investors will see the risk of being stuck with positions longer than they want. This is definitely not the only factor in question for encouraging institutional investment, but anything that helps tighten spreads and improve liquidity is important.

Salient Points

  • The Securities and Exchange Board of India (Sebi) is mulling implementing measures to address concerns around HFT.
  • Some industry participants have criticized Sebi for not specifying the exact problem it is trying to solve, and they believe that implementing any of the proposed measures would adversely affect liquidity in India.
  • For all the talk of HFT, the practice has seemed to have plateaued in Asia, so is it best to let the market work itself out before adding in any new rules that could bring about unintended consequences and complexity.

Note: As we are writing this blog, it was reported by Mint, a business newspaper, that Sebi will start a second round of consultation—following the August consultation paper—and it will drop some of its earlier proposals, though details of what will be removed were not released as of deadline.

 

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Algorithmic trading in India: Current State, Challenges and Future

Algorithmic trading in India

We posed the following question in-front of our experts in financial markets:

How do you think the Algorithmic trading is performing in India? And how you foresee it vis-a-vis the algorithmic trading in US?  The challenges facing the algorithmic trading in India, and its future?

But before we come to our question, here is a brief about Algorithmic trading and High frequency trading (HFT):

Algorithmic trading is the use of algorithms to generate orders based on certain conditions. In last 3 yrs, algorithmic trading has gained prominence in Indian Markets.

High frequency trading involves the use of these algorithms in placing orders in real time in stock exchange and utilising market inefficiencies for one’s benefit. Mostly arbitrage opportunities help HFT traders make small profits and since the volumes are high, even small profits help these HFT traders make huge gain!

Now, coming back to our question, in our view:

  • Algo trading in India is still in nascent stage. US and UK markets are way ahead of Indian markets.
  • The sentiment of Indian brokers/dealers towards high frequency/ algorithmic trading needs to change. They find it difficult to adapt to the new technology and do not want to give up their traditional way of trading.
  • All the exchanges require the algorithms to get an approval after which a broker can execute those algorithms. Among the Indian exchanges, getting approval from MCX is toughest and from BSE is easiest.

And now, let’s see what our experts have to say:

“Expect high sophisticated ALGO development, but likely focused on a relatively small number of liquid stocks. LIQUIDITY will define success of the effort. Regulatory issues could mushroom”

 “The key is to approach each market separate and tune the algorithms to specifically perform for that market.”

They further state that the approach to Indian market would consist of:

  1. Identifying the universe of stocks that drives the market and study over all the Indian market
  2. Speak to experienced “old school” traders and extract valuable information
  3. Create specific market rules which will drive the algorithms on the macro scale
  4. Build tailor made algorithms per each stock for the frequently traded stocks
  5. Build in local anti gaming techniques

It is evident that through algorithmic trading, investors will be able to customize algorithms and automate their trading strategies to serve best their objectives. This will allow them to access liquidity at an optimal price, alongside being able to reducing market impact and signaling risk.

But the move to introduce algo trading in Indian stock markets is being faced with many questions like the increasing volatility and the cost of its setting being a deterrent. But the shift to algorithmic trading will surely be better for the broader market, if not a few.

The Dynamics of Algorithmic Trading

Dynamics of Algorithmic Trading

Using Algorithmic Trading and High Frequency Trades could boost your trading options and perhaps your profitability too. In fact it is attracting increasing attention among market players ever since the regulator SEBI has permitted its use on exchanges .Lets analyse how technology could help you gain an edge on the markets.

Do you remember all of those slick Hollywood movies like Sneakers, Virtuosity or The Net where people use technology to gain an edge; usually monetary; over their rivals? The other day we got a peek into just such a world that at the very least left us breathless! But, at the end of the day remember that technology is a tool and it is only as good as you are!

Algorithmic Trade per se is nothing but a reflection of what happens in our brains. Algorithmic trading, also called automated trading, black-box trading, or Algo trading, is the use of electronic platforms for entering trading orders with an algorithm which executes pre-programmed trading instructions whose variables may include timing, price, or quantity of the order, or in many cases initiating the order without human intervention.

God has given us the ability , we only do this with computer procedures in a more organised or swifter manner than what God has given us . Within this broader genre exist the option of High Frequency Trades. This is basically trading that is done several times in one day, Intraday traders are High Frequency Traders. Some traders trade over a thousand times a day, and it is here that you need high speed programmes to generate your trades.

The simplified process in High Frequency Trades would start from Trade generation, through trade routing till the final step of trade execution.

You may not get the trade at the price that you expect, but you would get it at the best price in the market, this explains about the dynamics of the equity markets vis-à-vis High Frequency Trades.

Here is where the high-tech dazzle comes in. The speed of trades (in exchanges) is such is that if an exchange offers space in the exchange for your server, then you have a time advantage. Thus even the time that is taken to bounce a (trade) signal off a satellite can be avoided. While this may not be a huge issue if you were to trade say just 500 or so times a day, equations change if you trade say a million times a day.

So if there are two exchanges where there is a differential in speeds then an arbitrage opportunity exists to make money. “Arbitrage opportunities exist across time and space,”

High Frequency Trades is about volumes and not margins. Basically it is about thousands of trade and the arbitrage opportunities that lie thereof. At a personal level we feel that High Frequency Trades help in improving efficiencies in markets.

But again as said, we need to use the human mind and not technology to make money here. In High Frequency Trades you trade very fast to make money, while in Algorithmic Trading you use strategy to make money. All High Frequency Trades is Algo Trading, but not all Algo Trading is High Frequency Trades.

Algo Trading is thus, a mathematical model to trade, i.e. the timing, submission and the management of trade orders. In Dubai for example this model supports some 65% of the trading activity and in India this accounts for some 20% of trading activity in the equities arena.

And just in case one felt that the Algo model resulted in volatility in the equities segment, we must not forget that this mode of trading is just a tool and that computers and their systems just obey orders that are placed by humans. Algorithmic trading is not just a facility but and aid.

So you can use the Algorithmic system either to automatically execute a trade or as a decision support mechanism. While Algorithmic trading gives you freedom to trade, it does not replace fundamental research. It only enhances trading efficiency.

Eliminating Human Error !!!

quant-fund

Technology has become central to our lives. It is also increasingly driving the way we invest. Quant funds use algorithmic, or algo, trading, in which computer software takes buy/sell decisions on the basis of preset formulae after extensive data crunching.

The process involves use of advanced mathematical models-based on parameters such as price movement, volume, earnings, financial ratios and growth-to take decisions in the market.

Quant funds have a data-driven approach. Typically, there is a model that automatically selects stocks based on various data inputs that may or may not include fundamental data.

Quant funds are at a nascent stage in India. Quantitative funds work best when you have a large liquid market, particularly for long-only quantitative investing. India has about 100 stocks with large trading volumes. This will increase as liquidity improves.