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Algorithmic Trading – The Financial super car of next generation

June 27, 2019

1 3 - Algorithmic Trading - The Financial super car of next generation“Speed alone cannot be a mark of success unless backed by the character of good intentions”

The imperative need for speed cannot be denied in any segment. Finance being no exception, the markets have taken leaps and bounds on adding speed to the concepts. The concept of a super car might bring a thrill and a spark to the eyes, but to run the full potentials of this super car, we need super infrastructure, super support system, and a well-controlled regulatory environment. Algorithmic trading and its different facets have already put forth the potentials and resultant dominating volumes are indicating the acceptance of the concept. What is not convincing is the concentration of trade in the hands of few institutions and High net worth investors.

If a market practice is expected to have constructive outcomes, it should be widely accepted and not restricted to few hands. The regulators and exchanges have a regulatory and moral responsibility to penetrate the concept to the vast spectrum of traders.

Traders and investors who are not updated with facets of Algorithmic Trading would soon find the market potentials declining for them, leading to an exit. This could be hazardous over a long term as the concentration of wealth would build-up in hands of few leading to widening of the income disparity levels.

A mature market would seek the interest of all and hence any activity that leads to disparity of income and erosion of confidence should be addressed with utmost importance.

Algorithmic trading turns a blessing in disguise as it facilitates in minimising the mispricing of the assets across the market, hence reduces noise and volatility. Moreover, it also adds liquidity over a short term by increasing the turnaround time of a trade. Amidst all these advantages, what algorithm trading does not address is capital formation, the main objective of capital market in any economy.

It should not be reiterated that capital formation is the key driving force for any economic growth. Capital markets are created to facilitate the mobilization of savings of all surplus economic units and convert them into capital assets (long term assets). Algorithmic trading does not facilitate capital formation as the trades are driven for a very short span.

India’s capital formation rate has been consistently declining at a CAGR of -4.10% in last 9 years. Whereas the volumes in algorithmic trading in last 7 years has been growing at the rate of 5.11%.

Figure 1.1 below indicates the Capital Formation Rate of India from 2008 to 2016

Figure: 1.1

f1 - Algorithmic Trading - The Financial super car of next generation

Source: World Bank

 

Figure 1.2 below indicates the Growth of Algorithm Trading in India from 2010 to 2016

Figure: 1.2

f2 - Algorithmic Trading - The Financial super car of next generation

Source: SEBI

In the light of the above-stated facts algorithmic trading should be acceptable to the market but in controlled numbers. Moreover, the amount of money diverted to capital market should be used for both the purposes, capital formation and liquidity enhancement.

The proposers and supporters of algorithm trading in India are just looking at the market volumes as indicators of growth and success. The statistics on support say that developed countries like USA (having close to 70% of the total trade coming from Algo Trade) as compared to India (having close to 46% of the total trade coming from Algo Trade).

Comparing the algorithm volumes of India with markets like USA and Japan would not be appropriate as these developed markets already have a very high gross capital formation, whereas India’s gross capital formation in absolute scale is not even comparable.

Figure 1.3 below gives a comparative analysis of the gross capital formation of India vis-à-vis other countries.

Figure: 1.3

f3 - Algorithmic Trading - The Financial super car of next generation

Source: Trading Economics

The above statistics indicate that India should concentrate on increasing the capital asset base as a priority and later facilitating the expansion of short term trading strategies.

Among other things, algorithm trading is leading to concentration of income in the hands of few. This could be an alarming sign as already warned by IMF that India and China of facing the social risk of inequality of income distribution.

In its regional economic outlook for Asia and Pacific, IMF said that Asian countries are unable to replicate the “growth with equity” miracle and pointed out that inequality has only increased in the past two and a half decades, lowering the effectiveness of growth to combat poverty and preventing the building of a substantial middle class.

Economic inequality can be measured by Gini Coefficient, which has been showing alarming signs for India.

 

f4 - Algorithmic Trading - The Financial super car of next generation

India’s Gini coefficient rose to 51 by 2013, from 45 in 1990, mainly on account of rising inequality between urban and rural areas as well as within urban areas.

China’s Gini coefficient also rose to 53 in 2013, from 33 in 1990. At a time when inequality has been coming down for most of the world, the average net Gini coefficient for Asia rose to 40 in 2013 from 36 in 1990, the highest among the rest of the world.

Gini coefficient is a widely used measure of inequality and takes into account income distribution among residents of a country. The income, in this case, has been calculated net of taxes and transfers. The higher the Gini coefficient, the greater is the inequality.

 

 

f5 - Algorithmic Trading - The Financial super car of next generation

The above facts clearly indicate that our key focus should be on addressing fundamental issues of our country that would ensure stability and growth in future.

Suggestions for achieving a sustainable long term economic growth

In the light of facts stated above, following suggestions are proposed for Regulators and market participants.

  • The regulator of the market should ensure traders strike a balance between long term trades and short term trades
  • Training and education of algorithm trading and techniques should be disseminated across the market
  • A proportion of earnings through algorithm trades should be used for promoting financial literacy
  • Market wide limits should be defined linked to capital formation rate

The regulator in any market should evaluate the trade-off between short term liquidity and long term growth. The super car, algorithmic trading would turn a blessing for the next generation only when the economy is stable and promising consistent growth backed by capital formation.

Rishi Mehra

Visiting Faculty, ICoFP

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