Contingency fund: Your first step towards financial wellbeing!

Financial advisors have time and again advised investors on creating a Contingency corpus. In fact, one of the basic financial lessons we have learnt from our parents is, always save for a rainy day.

Unprecedented times of global pandemic, lockdown extending to almost 6 months, business shutdown, increasing job loss, have further demonstrated the requirement of having a contingency corpus. A contingency fund provides you with some cushion to fall back on in case of an unexpected income loss.

So how does one start? The first step is to determine the value of your Contingency fund. The average rule is 6 months of fixed expenses in case of single income household or 3 months of fixed expenses in case of multiple income households. Ensure you include your rent, maintenance, grocery and food bill, the education cost in any. In case of an ongoing EMI ensure at least 6 months provisioning even with multiple income households.

Now, once you have estimated the appropriate size of your contingency corpus, how does one go about saving for it and where should you invest the money? The instruments suited for investing the contingency fund is ideally which have high liquidity and focus on capital protection. As it is not the job of a contingency fund to earn a high return. The job of a Contingency fund is to be there in an emergency and should be easily converted into cash.

Contingency fund needs to be divided into multiple components, rather than treating it as one single chunk of money. Instruments like Liquid funds, Ultra short term fund, Fixed Deposits etc. are few of the avenues one could look at investing.

It is also suggested, to keep around 10 to 15 days of your expenses in hard cash at home. Because the emergency can also come in the form of a natural catastrophe, during which banks can remain shut and several ATMs not working. 

Rest of the funds can be spread across Deposits and Mutual funds. Do remember as per revised SEBI guidelines, Liquid fund have exit load up to 7 days. Also, while selecting a Liquid fund or an Ultra short term fund ensure the fund has a high quality portfolio. A large portion must be in sovereign or AAA-rated papers and is not mandated towards chasing higher returns.

Do remember to de-risk your contingency corpus, reduce bank risk by spreading out your deposit in more banks and mutual funds also by investing in at least two fund houses. 

Since we all know the need for a Contingency fund, why do investors still fail to provide for it or have insufficient funds allocated? One of the major causes is most of the time the investors cannot provide for the entire fund value at one shot and eventually end up not saving enough.

One of the strategy investors should opt for is systematic saving for Contingency Corpus fund too. One can start with a SIP into a liquid fund or ultra-short term fund, many liquid funds have provision for SIP into liquid funds too. Another option is starting a recurring deposit.

Thus, it is never too late to start provisioning for your Contingency fund, you can start small and build up towards the required corpus. Also, now that you are staying at home and are practising social distancing due to COVID-19, you can use this opportunity to save the money you would normally use on things like dining out, travelling to work, vacations etc toward your contingency fund. 

As our Grandmother used to say, like an ant save for the rainy day.


Sana Shaikh
Volunteer – Wealth Management
M. Sc. Finance
NMIMS, Mumbai
Batch of 2020-22
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India’s GDP-GST Deficiency from COVID-19

In this year of the pandemic of coronavirus, the world’s economic activity has been halted for a brief period and it broke the linkages of globalized trade which has impacted every countries’ economic growth. In the financial year of 2020-21, the majority of developed countries and emerging economies have reported negative economic growth. Which has been worse than the 2008 crisis. 

In India, it has been reported that the GDP growth rate in Q1 of the financial year 2020-21 has fallen by 23.9% compared to the same Q1 of financial year 2019-20. If compared with the other countries, this is worse than in European countries. One of the reasons may be that India had imposed one of the most stringent lockdowns in the world according to Oxford which was something unpredictable for a country with over 1 billion people. And due to this lockdown, economic activity came to a standstill which gave rise to its second problem, which was GST revenue. In the first quarter of the financial year 2020-21, GST revenue had fallen by 41%.

Both problems are closely linked to each other and how both of them from COVID have created the crisis in India will be explained in this article.

Economic decline

The release of Q1 GDP growth for the financial year 2020-21 stated that there was a decline of GDP by 23.9% compared to the previous year’s Q1. The only sector which has seen positive growth is in agricultural sector which had reported 3.4% growth, better than previous year’s Q1 which recorded 3%. The worst sectors are construction and non-financial service sectors which have reported -50.3% and -47% respectively. Whereas the manufacturing sector witnessed a 39.3% decline in growth.

The nationwide lockdown which was imposed on 25 March 2020 till 31 May 2020, halted the majority of economic activity in one night and had caused a great disturbance. Barring essential services, all others were unable to work normally. After which employees had no work, and businesses were witnessing lower orders leading to a decrease in incomes for every citizen. Unlike the 2008 crisis which was caused due to failure of financial institutions and limited countries had faced effectively, this crisis is from the pandemic which has covered almost the world. The lockdown, which was seen necessary to control the pandemic caused a disturbance in the supply chain of goods and services which further affected the income of people.

In this lockdown, many people had to face problems with no or less income. Workers had stopped receiving wages, employed people were facing salary cuts, self-employed people didn’t get new orders. This all leads to a decrease in income for households which further reduced their consumption. 

Source –

In the above photo from the article of Mint in April describes the consumption behavior across all classes based on their occupation. In this picture, casual labour in both rural and urban areas are going to be affected the most, and overall, the poorest families had to suffer from this lockdown. The reason for the casual labours to be affected the most is because of no employment that has stopped the income for them. Now with no income, these people will shift to poverty which can affect India’s growth. After casual labour, it is salaried and self-employed families which will be affected by COVID lockdown. Although they would be less affected than casual labour, they are still at risk due to chances of salary cuts and a decrease in turnovers.

Moreover, household expenditure contributes around 60% of total GDP. This is an important point which the government needs to see to revive the economy. They need to make policies in the favour of household expenditure which will help in increasing their consumption and increasing economic growth.

After 2 months of strict lockdown, the central government had started to unlock the country from June but in a phased manner. This has made relief for firms to normalize its functioning but still, it has not normalized to its previous levels. Some manufacturing facilities are not working in its full capacity. One of the reasons is the hindrance in inter-state transportation and another is the absence of migrant workers who left their place of work and moved to their native places which are far away.

In this period of lockdown, the change in consumption patterns is also one of the reasons for the decline in GDP growth. With the decrease in income, people are spending on essential needs and are avoiding non-essential consumption. Although restaurants and malls have opened, people are avoiding going there and they are preferring to spend only on those things which they need the most. Today, many things such as marriage functions, amusement parks, theatres, transport have still not opened and opened, it is still limited.

This can be seen in the consumption of fuel in India, in August 2020, fuel consumption has seen a decrease of 16% compared to the previous month. In diesel, the main indicator of economic activity, decreased by 12%, from the previous increase by 5% in July.

GST Revenue fall

Due to the lockdown and economic decline. India has to face another problem which is a fall in GST revenue. In the first Quarter of the financial year 2020-21, the GST revenue has fallen by 41% to ₹1.54 lakh crore compared to ₹3.14 lakh in the previous year. Although Monthly GST revenue has improved from June onwards, compared with its previous year’s revenue, it has reduced. But the point which should be noted id that the firms having an annual turnover of fewer than ₹5 crores is getting relaxation to file GST returns till September 2020.

Source –

The problem with the reduced revenue has caused a deficit in both state and central’s balance sheet. They are facing a scarcity of funds to fight against pandemic and expenditure on health and other social expenditures. The situation has so much worsened that the states have to adopt austerity measures. So, the finance ministry had proposed credit schemes for states to borrow for meeting their deficits.

In the first option, the Central government will provide a loan of ₹97000 crores through RBI special window. This option only takes into account shortfall due to GST implementation. This loan will not be considered in the state debt and can be repaid from the compensation cess fund. The central government will also pay for the yield up to 0.5% through subsidy if the cost of borrowing goes higher than the G-sec yield.

In the second option, the states will get a loan of ₹2.35 lakh crore (including ₹97000 crores from option 1) by borrowing from the market through the help of the Central government and RBI. This borrowing considers both the shortfall from GST implementation and the impact of COVID lockdown. But, this borrowing (2.35 lakh crore – 97000 crore = 1.38 lakh crore) will be considered in their respective state’s debt. 13 states have opted for option 1 whereas, only 1 state (Manipur) has opted option 2.

With GST falling, state and central government have resort to those items which are not under GST and both of them have autonomous power to impose Excise duty on them. These items are fuels and liquor. So, for a brief time, some states imposed a special COVID tax on liquor. While most of the states increased VAT and the central government increased excise duty on the sale of fuel, which stabilized the price of petrol and diesel even though the price of crude oil had decreased.

Now let’s see the breakup of GST revenue by slabs. There are 5 GST rate slabs and the items Under it are as follows:

Source –

The majority of items are in 18% slab followed by 288 items under 5% slab. Now, how much revenues come from these slabs; it is given in the chart below:

Source –

18% GST slab contributes nearly 60% of total GST revenues. Now under 18% slab, the items include petroleum products, services in hotels & restaurants, and metal items. In the 12% GST slab, the items included are FMCG and agro-based items. And white goods in 28% GST. So, in April 2020 when the GST revenue had fallen by 72%, the main reason was a decrease in e-way bills and providing moratorium to small businesses till September.

Due to the closure of hotels, amusement parks, theatres, and people not spending on white goods which comes mainly under 18% and 28% slab, GST revenue is going be lower than the previous year in the coming months.

Government response

Central and state governments started to unlock the country in a phased manner starting from June to restart its economy. During the lockdown, the central government in May 2020 announced a ₹20 lakh crore stimulus package that focused on land, Labour, Liquidity and Laws. This package mainly focused on rural, labours, MSMEs and working-class people. For MSMEs, the definition was revised and the collateral-free loan is going to be provided. Global companies will be barred from participating in government tenders upto ₹200 crores. For employees, contribution to EPF has reduced from 12% to 10%. For migrant labours and poor families, free rations, rental accommodation under PM Awaas Yojna and loans to street vendors will be given.

Recently the government will give half a salary to those people who lost their jobs in the COVID pandemic and lockdown. An estimated 40 million people will benefit from this scheme but it will be for those who have a monthly salary up to ₹21000 and who are under ESIC (Employees’ State Insurance Corporation).

For migrant and rural workers, the central government launched “Garib Kalyan Rozgar Abhiyaan”, similar to MNREGA, which is implemented in states where migrant labours have returned, mainly in BIMARUO states. This will provide labour jobs for migrants who have come back to their native places in infrastructure projects and rural development schemes. This will help the government to complete their pending projects and the beneficiaries will get income to sustain themselves. 

By providing these relief plans, the government needs to check their debt levels and avoid cross the limit of thresholds to avoid country to go in debt crisis.

Looking forward

Although the government has started to unlock its economy in a phased manner, it is still not enough because India still has stringent lockdown according to Oxford. Transportation such as railways is still not running to its full potential. This can be seen in diesel consumption in the august month which has decreased by 14% compared to the previous month.

With announcing unlocks, the central government has transferred power to states or declaring lockdowns in their respective states. This is a bad idea because if some states impose lockdown every now and then which is still happening, it will impact in the production cycles of factories and supply chain which will cause disturbance in working capital and expenditure on restarting the factories. This is happening in some states where they impose weekend lockdown, week lockdown and weekday lockdown which is causing big impact in normal functioning of state economy.

With the rural economy thriving during the pandemic, India’s economic downturn may revive in the coming months but time will say. As rural areas are more responsive in the unlocking phase, it is solving unemployment problems as people have started working in the new sowing season and participating in government employment schemes.

In urban areas, some of the things have still not opened such as theatres, amusement parks, and other mass gathering places in the time when festive seasons are coming which will likely remain the same. With these ongoing restrictions, the coming months which is an important period for India due to the number of festivals will go dull. People are still restricting their spending on un-necessary things mostly white goods and spending on those things which are necessary.

With this type of situation currently prevailing in the country, the GDP growth in the next quarter will be better than its previous quarter but it will be worse than the previous year’s same quarter. And overall GDP growth in this financial year 2020-21 will be negative. The sector which will affect most in this situation will be the service sector. Similarly, in GST revenue collection, it will improve compared to the previous month but is will be worse when compared with the previous year’s same month. With this, the government’s fiscal deficit will widen. Until the vaccine for COVID-19 doesn’t come, people will not freely move around and spend on other things which is essential for the economy to grow.

The Solution

One of the solutions for reviving the economy is to make policies for the rural economy first. As urban areas are prone to pandemic due to high population density, the government needs to start the rural economy first for time being as they are less prone to the pandemic and can be easily restarted. Moreover, pending infrastructure projects due to the absence of migrant workers can be replaced my nearby local people to complete the projects which will help local people to earn income and restart the economy. Moreover, providing agricultural infrastructures such as water supply, cold storage, and warehouses can increase efficiency and reduce economic losses for rural people. Rural areas will be prosperous with these policies which will make them self-sufficient and also reduce migration which will further reduce pressure on urban infrastructure. As rural migrant workers may be reluctant to go back to work in other places, the government should provide permanent employment opportunities to these people by promoting rural and cottage industries. If the rural economy is in good condition, most of India’s economic problems will be solved as 65% of the total population in India lives in rural areas.

Other solution is to redefine GST slabs. Current average of tax rate is lower than the tax rate which was prevailing before implementation of GST. Current average weighted tax rate is 11.6% compared to 16% before GST as mentioned by RBI. The tax slabs should be reduced and tax rate on some goods and service should be change in order to reduce complications and increasing the revenues. Other than GST, state governments should avoid imposing temporary lockdowns now and then. This will help in smooth functioning of activity and firms wont have to bear the loss in shut-downs.

Another solution for solving these problems is to promote MSMEs. MSMEs, if supported by government policies can be a strong backbone for India’s Economy. MSMEs in India is the second-largest employer employing 124 million people according to CII. This can help in reducing income inequalities and increasing middle-class households by encouraging entrepreneurship. It can also generate employment easier than other policies. Moreover, they contribute to 45% of India’s total exports. MSMEs can be of great help for the Aatmanirbhar Bharat mission as they can easily set up to make indigenous products. Also, if there is a healthy MSMEs, it can be a strong forward linkage to other companies therefore making smooth economic growth. For that government needs to streamline finance, regulations, and special clusters for these industries.


Siddharth Dholaria
M. Sc. Finance
NMIMS, Mumbai
Batch of 2019-21
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The Economic Impact of COVID-19 on different sectors in India

Impact On The Indian Economy

In the wake of the coronavirus pandemic, India has gone into complete lock-down since 25th March 2020, the largest in the world, restricting 1.3 billion people.

This corona-induced lock-down has only heightened the economic slump India was already going through over the last few months. In the third quarter of the 2019-2020 fiscal year, the economy grew at a six-year low rate of 4.7%. The low rate of expansion in the economy seen in the December quarter was mostly an extension of weak manufacturing, falling exports, and weak consumer demand and private investment. several stimulus measures have been taken to bring back the economy on a growth path. There was a strong hope of recovery in the last quarter of the fiscal. However, the new coronavirus epidemic has further pulled down growth prospects in India. and has presented fresh challenges for the Indian economy now, causing a severely disruptive impact on both demand and supply-side elements.

The demand Side Impact of the pandemic has been first seen in tourism, Entertainment (cinema halls, restaurant and hotels), and Aviation sectors as they are among the worst affected sectors that are facing the maximum brunt of the crisis even the retail sector is affected by impacting consumption of both essential and discretionary items. Spending is getting impacted due to job losses and a decline in income levels.  job-destruction caused by the nation-wide lock-down in India is worse than anything that has ever known. According to a report by Mint, 136 million jobs are at risk in post corona India. This has to lead to the demand side getting severely impacted as people are postponing their purchase decisions because of uncertain employment conditions.

On the supply side, There have been severe disruptions in the supply chain because of shutdown of factories and the resulting delay in the supply of goods from China has affected many Indian manufacturing sectors. Some sectors like automobiles, pharmaceuticals, and electronics which heavily rely on imports are facing the brunt. Not only are the imports getting affected by the lock-downs around the world but it is even hampering India’s exports.

With the major shock to the demand and supply India, Major financial institutes lower India’s growth prospects which can be seen in the graph below.

Source: Deloitte

Greater uncertainty about the future course and repercussion of Covid-19 has also made the financial market extremely volatile, leading to huge crashes and wealth erosion. In just weeks, the Coronavirus pandemic has shaved off nearly a third of the global market cap. One of the major slides in the domestic equity markets was seen on March 12, when following the trend of the global equity markets, both the BSE Sensex and NSE Nifty crashed by more than 8% in a single day. The BSE Sensex dropped over 2,919 points – its biggest one-day fall in absolute terms while the NSE Nifty dropped by 868 points. An estimated Rs 10 lakh crore of market cap was reportedly wiped off due to this single-day fall.

Currently, a partial relaxation was announced for 60% of the economy in ‘green zone’ districts and lock-down restrictions are slowly eased, The financial markets have been showing a positive trend after this free fall but even after this the economy will likely struggle to normalize, as companies will have to deal with labour, raw materials, and demand shortages.

This will eat into the corporate profits and bankruptcies will rise which will inevitably impact jobs and consumption.

Sectorial Impact:


There has been an unprecedented decline in passenger traffic, internationally which is something never seen in history.

Source: ICAO

Globally the aviation sector has been the first industry to be hit. It is among the worst-affected sectors amidst the Covid-19 crisis that has taken the scale of a pandemic. According to the International Air Transport Association (IATA) which accounts for 82% of airlines around the world, airlines globally can lose in passenger revenues of up to $113 billion due to this crisis.

India’s aviation sector is one of the worst-hit globally, with the suspension of international and domestic travel many believe, this crisis is a greater threat than the financial meltdown of 2008-09 along with travel restrictions, grounded fleets, benched staff, schedule uncertainties, ticket liabilities and cash burn, it faces questions around its very survival.

The Coronavirus pandemic is expected to bring not only the Indian aviation but the global aviation industry to a halt as many as 29.32 lakh jobs are likely to be at risk in India’s aviation sector during the year 2020.

The report also said that the revenue of the sector in India may fall by $11,221 million this year compared to 2019. Further, passenger demand is likely to fall by 47% in the country.

Usually, the number of Indian travellers to both domestic and international destinations peak during March and April. However, this time around nearly 90% of bookings of hotels and flights for the peak time has been cancelled.

IndiGo, the biggest airline in the country is also feeling the hit and announced a pay cut for its staff of 20%. CEO of IndiGo Ronojoy Dutta said that the virus’s impact on the aviation sector has been particularly severe and the company must reduce costs in line with the fall in revenues. He believes that airlines have to pay careful attention to cash flows so that they do not run out of cash

India has already faced a casualty concerning the aviation industry and there will be more casualties if governments do not step in urgently to ensure airlines have sufficient cash flow to tide them over this period.

The government of India has already started taking steps for a recovery in the aviation sector and is planning a rescue package of up to $1.6 billion which would be in the form of tax cuts to aid airlines battered by coronavirus.

Way Forward

  • This pandemic is going to change how we travel forever, safety and hygiene assurance is needed to be given by airlines for it to encourage people to continue flying.
  • The fall in fuel prices is godsend for the aviation sector, therefore better credit policies for fuel given to the airlines will help them survive.
  • Financial aid in terms of reduction in airport charges, overflight fees and instead levy fees from passengers to maintain safety and hygiene standards.


Indian pharma industry enjoys an important position in the global pharmaceutical industry. The Indian pharmaceutical market is the third-largest in terms of volume and thirteenth-largest in terms of value. It’s amongst the global leaders in providing quality generics to the world and supplies drugs to the developed economies such as the US, EU and Japan.

There is a general myth regarding the fact that the pandemic is beneficial for the pharma sector but that is not the case as even though the Indian pharmaceutical industry is not as severely impacted as some of the other sectors since its exempted from the lock-down that does not mean it is not negatively impacted by the pandemic.

India has been facing stiff competition from China in the pharmaceutical sector because of China’s lowest cost APIs (Active Pharmaceutical Ingredients) which is a key ingredient in making any drug.

To benefit from low-cost APIs made in china, India has increased its import from China tremendously. Currently, India’s import dependency on China is nearly 70% of its total requirement and not only this but in the case of intermediates of stages before APIs and key starting materials (KSMs) which are the building blocks for drugs, wherein, in some cases, China is the exclusive supplier.

This is alarming because as a result of lock-down and factory closures in China, India is facing disruptions in its supply chain which have caused significant shortages of essential drugs and are increasing their cost. The cost of paracetamol has gone up from Rs 250-300 kg to 400-450 kg. Similarly, the prices of vitamins and penicillin have also increased by 40-50% in India.

Supply chain disruptions are so serious that a committee has been formed by the Department of Pharmaceuticals to regularly review the availability of stocks of API and the government has restricted exports of certain medicines to deal with the situation. The government is also planning to grow in the API sector in India in the future by encouraging domestic manufacturing of APIs so that the reliance on china is reduced.

Even though the production partially resumes in china, the logistics between the two countries are still impacted making imports costlier. This is a problem as many drug prices are controlled by the government in India therefore the margins of these companies will be impacted.

Inter-state transport challenges are also a major issue. It has become difficult for companies to reach retailers. The distributers are also facing transportation issues for supplying medicines in other states. The government eased rules as part of its latest set of efforts to supply goods and services during the coronavirus-induced lock-down. Problems regarding the non-availability of labour and social distancing have also hampered the production volumes in the sector.

There is also the potential for negative impacts of both a medium- and longer-term nature on R&D and manufacturing activities, as well as a delay on projects not related to the core supply chain/data management operations.

Way Forward

  • Address the labour shortage issue by providing the means to commute, so that they can commute and not inconvenienced during the lock-down period.
  • Reduce dependence on China for the import of raw materials and create partnerships with other countries.
  • The promotion of E-Pharma companies will help boost customer reach.


The automobile sector is one of the largest employers in the country, employing about 37 million people, directly and indirectly. The automotive industry, moves in sync with the economy of India and accounts for more than 7% of the country’s GDP has been in the grip of an intense slowdown since a year and was dealing with idle capacity, low demand, and high cost of production has only been exacerbated by the coronavirus-induced lock-down. The industry was witnessing a revenue loss of Rs 2,300 crore per day.

The pandemic has affected the industry in many ways even before the virus entering India, China accounts for 27% of India’s automotive part imports. Owing to the closure of the factories of these companies, there had reportedly been a delay in the production and delivery of vehicles.

As things begin to normalize in China, the problem with disruption in the supply chain is expected to be solved.

A great slump in demand exists since this segment is significantly impacted by economic sentiments, and consumer purchasing power, and with a shutdown of all non-essential services, the demand for commercial vehicles has further plummeted to such an extent that the entire industry has reported zero sales in April.

To add to that the automotive value chain is highly complex, integrated, and interdependent, if any element in any segment does not commence operations, the value chain will not be able to restart and with problems regarding the availability of contract labour for operations and support functions can be an issue even after the lock-down is lifted also the continued cash flow tightening will impact the market further.

The auto industry collectively has asked the government to allow them to restart their entire value chain immediately as the lock-downs have seriously affected the industry and many MSMEs in the sector are struggling to stay afloat.

Even though the government has announced relaxations in the ‘green and orange zone’ but it’s not enough as there have been no relaxations in the red zones hence making it difficult for the industry to restart completely as segments of the industry’s value chain operate in those zones.

In a report published by Bloomberg, experts believe that once stay- at – home orders are lifted, there could be a surge in car sales around the world. Sales have already rebound in China (which has been indicated in the graph below) as consumers are purchasing personal vehicles to ensure their safety to avoid traveling in crowded public transports.

This is in contradiction to what is generally believed as post lock-down there would be a severe cash crunch faced by the consumer and they would not have enough cash to invest in a car.

In India, the two-wheeler segment might see a rebound but the future of four-wheelers remains bleak.

The graph below indicates China’s weekly car sales which have shown to be rebounding as China slowly restarts its economy.

 Source: Bloomberg

Way Forward

  • Consumer attractiveness by allowing income tax deduction on the auto loan
  • Giving incentives in the form of rate cut resulting in a reduction in interest rates for retail customers.

Information Technology (IT)

India is the largest exporter of IT in the world this is because of its cost competitiveness in providing IT services, which is approximately 3-4 times more cost-effective than the US, this continues to be its unique selling proposition in the global sourcing market. This makes the IT industry heavily influenced by the change in the global market and any recession globally will negatively impact the IT sector in India.

This is why it is feared that Covid-19 will significantly impact the $180-billion Indian IT sector, and the impact may be worse than that of the 2008 global financial crisis.

This fear is justified as the IT sector in India relies heavily on exports, their exports form more than 80% of the revenue with countries like the USA, UK and Europe accounting for most of it, considering the US and Europe, are among the worst affected geographies by the pandemic. Clients could significantly reduce their IT spending this year.

The global IT services industry is predicted to report a revenue decline by 3-4% because of the economic slowdown induced by the pandemic and with India being at the forefront of IT services it is expected to take a hit.

A decline in IT sector is predicted, this will be because of IT companies that are exposed to industries that are highly impacted by the pandemic such as travel, hospitality, manufacturing and retail where projects will be put on hold. Projects from other industries are also likely to be stalled as companies will be forced to revisit their IT spending and will even negotiate their existing contracts, However, on the positive side, business-critical IT such as core banking, call centres and e-commerce will continue to operate and may witness a surge in short-term demand.

The pandemic has caused significant displacement in the operating model as travel restrictions are already delaying the execution of existing projects and hurting the ability of IT companies to ramp up projects and close deals. Further, pricing pressure will lead to lower deal wins and renewal.

IT giants TCS and Infosys suspended promotions and freeze salary hike to deal with the pandemic but are going to honour all new job offers.

In retrospect, the IT sector is not directly or as severely impacted by the pandemic as some of the other sectors but its impact is correlated to the other sectors. Even though the IT sector faces a temporary setback because of the pandemic, it’s future is bright.

Way Forward

  • It is evident that in the long run, that there will be an environment where businesses are done virtually and where technology will play a big role in innovations and designing the infrastructure and applications of this new reality.
  • With e-commerce and online schooling becoming the new norm, the IT sector is going to benefit from it.

Beyond COVID-19

One of the few things that seem fairly certain is that the current downturn is fundamentally different from recessions we have seen in the past. This is not just another turn of the business cycle, but a shakeup of the world economic order. While countries and companies continue to comprehend the scale of this pandemic, it is certainly undeniable that we are staring at more permanent, structural changes to the way we live, work, and play.

The collective experience of going through this common crisis will lead to questioning of fundamental assumptions and priorities which will be both a challenge and an opportunity.

We need to focus on ideation and actions to create the difference and emerge stronger and enable actions to seize opportunities in the new normal.

Here are some ways in which we can embrace the new normal:

  • With customer focus shifted to online, more efforts in e-commerce will be crucial in building a long- term customer base.
  • Good management of cash flows to ensure that any business disruption does not affect employees or outsourced workers.
  • Supply chain resilience is key and growing a decentralized supply chain can provide stability to operations and reduce external dependencies.
  • Even when things start going back to normalcy, several people will lose their purchasing power or be more conscious of making purchases. Hence, customers will prefer brands that promise value for money, and meet customer expectations during dire conditions.
  • With the shift to rural retail, and with the newly associated customer base, retailers should focus on a rebalance that strengthens rather than weakens their position in the market.
  • On a positive note, Indian manufacturers have the opportunity to establish themselves as manufacturing hubs and leverage the void created in Chinese manufacturing.

To conclude, COVID – 19 is likely to lead to a new normal, and being aware of and preparing for these shifts will help businesses and economies navigate in the post COVID-19 world.






Pratik Jaju
Departmental Co-head – FinTech
M. Sc. Finance
NMIMS, Mumbai
Batch of 2019-21
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Tarini Patesaria
M. Sc. Finance
NMIMS, Mumbai
Batch of 2019-21
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Radhika Sharma
M. Sc. Finance
NMIMS, Mumbai
Batch of 2019-21
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What Are Your Actual Returns From Mutual Funds?

If you have the stomach for stocks but neither the time nor the inclination to do the homework, invest in equity mutual funds

-Peter lynch

When we look at any investment avenue, we mostly consider three aspects–

  1. Historical Returns
  2. Risk / Volatility
  3. Cost

Historical returns

We usually analyze the historic returns in short term (6months to 1-year), medium-term (1-3 years) and long term (>3 years) depending on the investment class and investment horizon. This analysis helps us to form the general expectation of returns over the holding period, disregarding the random walk theory.

Risk / Volatility

Here we compute the standard deviation or movement of returns around the average returns of an investment avenue. Consider two investment avenues, Investment A provides average returns of 15% with standard deviation of 5% and Investment B provides average returns of 20% with standard deviation of 15%

Which amongst the two is riskier?

Answer is investment B- Even though investment B provides higher returns, the potential risk is much higher. A standard deviation of 15% indicates that there is a higher probability of returns lying somewhere in the range of 5% to 35% whereas the returns of investment A can range from 10% to 20% which might be better for a risk-averse investor. The probability of generating returns below 10% is very low in case of investment A.

Apart from the standard deviation, R squared and Beta are also risk indicators that can be used.


Cost includes tax, transaction cost, brokerage etc.

Taxes on mutual funds can be classified into Stamp duty, Income tax and Security transfer tax

  • Consider equity- the Stamp duty is 0.015% and 0.003% for deliverable and non-deliverable securities respectively.
  • Income tax depending on tenor of investment there will either attract Short term Capital gains tax and Long-term capital gains tax.
  • STT i.e. security transfer tax which is applied on sale of security

Brokerage fees depends on broker person subscribes and services they require.

Costs – And how various costs are affecting your investments over the time. What strategies could use to maximize your returns from mutual funds?

Costs attached to mutual funds are –

  1. Entry load
  2. Exit load
  3. Expense ratio
  4. Taxes

To explain effect of this cost on your portfolio let’s take hypothetical example-

Initial Capital of Rs. 1, 00,000, average annual return of 15%, time horizon of 1, 3, 15, 40

1)      Entry Load

It is charge you pay to buy/enter in mutual fund scheme. Which could be up to 2.25%. Entry load is now abolished by SEBI.


Entry Load2.25% 
1 Year11241312.41%
3 Years14866614.13%
15 Years79539814.83%
40 Years2618366214.93%

We can see that effect of entry load on investment in short term and long-term investment horizon. In short run it has more impact than in the long run.

2)      Exit Load  

Exit load is basically the load for redeeming your units and booking your gains before completing a fixed duration. It is like a penalty charged on exiting, before completing a fixed investment tenor or lock in period. Essentially an exit load in effect is not different from entry load in terms of returns. But unlike the example given above, in practice the exit load is leviable only for a fixed lock in period for which the mutual fund requires the funds. Hence exiting is recommended only after completion especially when the NAV returns are high and expense ratio is low. Usually liquid funds and guilt funds have zero to negligible exit loads given their portfolio and name

3)      Expense Ratio:

It is the annual charges levied on the invested capital for financing all expenses.

AUM  (Billion Rs)Equity-oriented Others (excl. Index funds, ETFs & FOFs)
0 – 52.252
5 – 7.521.75
7.5 – 201.751.5
20 – 501.61.35
50 – 1001.51.25
100 – 500Reduction of 0.05% for every increase of 50 billion AUM/ part thereofReduction of 0.05% for every increase of 50 billion AUM/ part thereof
Total expense ratio of mutual fund scheme Source: SEBI Press release

Regular Plans:  

Distribution plays a very important role in last-mile delivery of product and that is true for Mutual funds as an investment product too. It also requires proper distribution for it to reach its target customer. If they are sold through distributors, then there are extra fees levied on investors every year which act as commission for distributor.

Expense heads% charge
Investment management fees1.25
Trustee Fees0.1
Audit Fees0.01
Custodian Fees0.01
Registrar & Transfer Agent Fees0.15
Marketing and Selling Expenses0.75
Investor communication cost and fund transfer cost0.15
Other Expenses0.08
Total Expenses2.5
Example of breakup of typical mutual fund’s expense ratio of regular plan

Direct Plans:

A direct plan allows investors to invest directly in mutual funds without any intermediary. The benefit of it being that investors don’t pay extra distribution and marketing fees every year.

 “SEBI has mandated mutual funds to compulsorily launch a direct plan for direct investments, i.e., investments not routed through a distributor, from 01 January 2013. Such a separate plan has a lower expense ratio excluding distribution expenses, commission, etc., and no commission is to be paid from such plans. The plan also has a separate NAV.”


Illustration – Breakup of any generic Regular Mutual fund

These are expenses that are deducted every year from assets under management. When we look at the above table, we can see that 0.75% is what you pay extra for regular plans every year. Passive funds are usually direct funds

When we look for expense ratio in short term it might not look major expense but small increase in expenses ratio will have drastic effect on your invested capital. We can observe that by below:

Invested Capital ₹ 1,00,000
Regular plan Expenses Ratio2.50%
Direct Plan Expense ratio1.75%

 YearsRegular value ₹HPR %Direct ValueHPR %Difference
11,12,12512 1,12,98813863

This illustration clearly demonstrates benefits of investing in direct plan rather than investing through distributors.

The statistics given below show the growth in investments through a direct plan.

Some example of Mutual funds Regular vs Direct plan –

Axis Bluechip1.700.431.27
ICICI Prudent Bluechip1.791.100.69
Mirae Asset Large Cap1.660.621.04
Kotak Bluechip Fund
Source: Value Research    

In all plan investor is better off investing in direct plan.

4) Tax

Taxes related to mutual funds are

  • STCG of 15% for investment horizon less then or equal to 12 months
  • LTCG of 10% for investment horizon more then 12 months
  • STT Security Transfer Tax

STT on various Investment avenues-

Security TypeTransaction TypeSTT RateSTT Levied On
EquityBuy (Delivery)0.100%Purchaser
EquitySell (Delivery)0.100%Seller
Derivatives- FutureBuyNil
Derivative-Option (When Option is exercised)Sell0.125%Purchaser
Equity Mutual FundsBuyNil
Equity Mutual Funds- Close Ended/ ETFSell0.001%Seller
Equity Mutual Funds- Open EndedSell0.025%Seller
Equity Mutual Funds-Intraday (Non-Delivery)Sell0.025%Seller

Conclusion –

These are few points that should be kept in mind before investing in mutual funds. Other relevant aspects highlighted in bold over the article along with information on types of mutual funds and their costs will be explained in the following articles.

Harsh Shah
Team Member-
Alternative Investment
(M.Sc. Finance, NMIMS – Mumbai. Batch 2019-21)

Connect with Harsh on LinkedIn


Don’t you think the 21st century is more synonymous with Environment depletion, pollution and, degradation? ” A growing number of investors wish to make profits and do good at the same time. They want their portfolios, or part of their portfolios, to be “ESG” – that is to support environmental social, and governance causes.” With efforts taken to “GREEN” the financial system there lies the concept of GREEN BONDS. Source


In its most basic form Green Bonds function by generating funds from investors to develop environmental or eco-friendly projects, in which environmental outcomes are potentially achieved and then investors are paid with interests.


A capital and an energy-intensive company can use a green bond to fund the company’s use of WHRS (Waste Heat Recovery System). The WHRS harnesses waste heat from exhaust gases discharged in industries and converts it into a source of electrical energy. Over here use of WHRS has a prolonged cost saving which is linked to its bond repayment and meeting its environmental objective by minimizing carbon emissions.

Attaching Green Bond with Pay Performance?

There always has to be a third party when it comes to an enterprise undertaking sustainable objectives. In the example above the use of WHRS will require third party contractors who build infrastructure and install the technology. These contractors have the power to ensure that projects meet performance expectations, more than the company who administered the bond. The Bond issuer should link the WHRS contractor’s financial incentives to the bond repayment structure to ensure the achievement of sustainability through innovative ways and new technology.


“India has the potential to be a large market for green finance which will have a positive impact on both the Indian economy and environment,”

– India & UK working group on Green Finance.Source

A new green trading platform has been developed earlier this year as the Bombay Stock Exchange (BSE)’s international arm, INX India. GSM Green serves as a platform for fundraising and trading green, social, and sustainable bonds exclusively. 

“… with a dedicated green platform, issuers, investors and traders will find it more convenient to list and trade green, social and sustainable bonds,”

CEO V Balasubramaniam said. Source

Over the last few years, a lot of renewable energy companies have shown interest in issuing green bonds. Recently, Urja Global Limited has received approval to raise green bonds of up to $500 million to fund its environmental oriented renewable projects and Electric Vehicles (Evs). Azure Power Solar Energy Private Limited has also announced that it would issue a green bond offering of $350 million (~25 billion). The bond is expected to mature in 2024 with an expected US Dollar coupon of 5.65%. The Hyderabad-based Greenko group with a $950 million green bond, made its biggest contribution to the global green bond market.

Adani Green Energy Chief Financial Officer Mr.Ashish Garg said – ” We are excited that a platform like Global Securities Market with a dedicated green segment is being offered now at India INX in India’s very own International Financial Services Centre. This was a long pending gap and will encourage more green financing in the country.”   Adani Green Energy Limited (AGEL), the renewable energy arm of Adani Group has raised $ 362 million by selling green bonds with a tenure of 20 – years, the company informed exchanges on Friday i.e 4th October. The bond will bear interest at the rate of 4.625 percent a year, payable semi-annually. They will be listed on the Singapore Exchange Securities Trading platform. In August, Adani Green had signed an agreement with Essel Infra to buy its 205 megawatts (Mw) of solar assets for Rs 1,300 crore. The acquisition of 205 Mw of operating solar assets has strengthened Adani Green Energy as one of India’s major renewable power producers. Adani Green Energy is a forerunner for a potential dollar bond as Prime Minister Narendra Modi announced the more ambitious plans.


This is how AGEL stock prices got affected by the Green Bond announcements.


India has set a target to reduce the ’emissions intensity’ of its GDP by 33-35% by 2030 from the 2005 level. The Capital requirement is to be fulfilled primarily by the private sector. With the target investment of $370 billion on infrastructural development, a paper supporting notions of executives of the major Investment Banks stated that – 

“Indian government should think of providing tax incentives to mutual funds and their investors for investing in local green bonds. A debt fund where more than 80% of the assets are invested in green paper, can benefit from tax incentives for its investors – where effectively the tax rates are reduced from the current applicable tax rate on income arising from such investments.”



Firms that have adopted green bonds benefit from both positive financial and environmental outcomes. Green bonds have grown rapidly over the last decade. The green bond market is largely dominated by three countries. China with $83 billion worth of green bonds issued over the last decade. The United States with worth $58 billion and France worth $57 billion. India still lags behind these countries but is one of the fastest-growing green bond markets in Asia with worth $5.2 billion for the year 2018. Commentators often see green bonds as a promising tool to address climate change, following the issuance of green bonds companies can reduce their CO2 emissions and achieve a higher environmental rating.


Apart from “HOW DARE YOU” motions this is where we can contribute to creating a sustainable world.  Where our government can frame guidelines for mutual fund houses and insurance companies to encourage investments in green bonds as at present it has a limited investor base. With the pick-up in green bonds floating and annual issuance, a certain long-term minimum investment level can be encouraged or mandated. Where industries have an added advantage to take up environmental friendly methods of business, we end up minting GREEN. 


Radhika Sharma
Team Member-
Fixed Income
(M.Sc. Finance, NMIMS – Mumbai. Batch 2019-21)

Connect with Radhika on LinkedIn

The Way Forward

The policy makers for Government must understand the problem the Indian economy is facing right now is people do not have enough money to spend. There is a demand issue in the economy not the supply, and private investment is not going to come until and unless the demand issue is corrected in the economy. Recently government reduced the corporate tax for the industries which is a welcome move, it will help Indian industries to compete in international market and increase our export, but it is not going to solve the demand issue in the economy. There is slow down in 7 out of 9 core sectors. The government might argue that auto sector is facing slowdown due to people are waiting to buy bs 6 vehicles or people are waiting for better electric vehicle option and it might be true to some extend but government cannot deny the fact that FMCG industry is also facing the slowdown.

For Hindustan Unilever ltd, the country’s biggest FMCG company, there was a 7-percentage point dip in volume growth between the June quarter this year versus the same period last year. Britannia industries, India’s second largest biscuit company, also recorded a 7-percentage point drop while for Dabur India, the slide in volume growth on a year-on-year basis during the April June quarter was 15 percentage points. The dip in sales is mostly contributed by the rural India which are still facing the farm distress if government is serious about the economy then it must address the farm distress without addressing the farm distress, we cannot expect the rural demand rising.

Here are some ways through which government can revive growth in the economy:-

New tax code

The government must immediately accept the new tax code which suggests new tax rate of 5%, 10%, 20%, 30% and 35%. This means that the formal salaried class which mostly earns between 5 to 10 lakhs has to pay 10% income tax instead of 20% and people earning between 10 t0 20 lakh which come from upper middle class has to pay 20% income tax instead of 30% which will leave more disposable income in the hands of people and will lead to greater demand and consumption in the economy, and in future leading to greater indirect tax collection.

As far as revenue shortfall is concerned it can be covered by letting go the fiscal deficit target which is well under control and India can afford right now to let it go beyond 3.3% and government has to look at to the larger picture of riving the domestic consumption which will lead to growth and if domestic consumption is corrected then private investment will correct itself this way government can fire the two main growth engines.

Auto Industry Campaign

One of the main reasons why there is a slowdown in auto industry is because of people are trying to delay their purchase. People are uncertain whether to buy the new vehicle now or wait for the bs 6 vehicle or wait for the electric vehicle. So to tackle the uncertainty all the industry players should run a media campaign mainly through T.V. advertisement informing the consumers smartly about the benefits of buying the vehicles now and assuring customers there would be no harm from the government policies, informing price benefit they get with the bs 4 vehicles with the same features. In a price sensitive market like India consumers will surely get motivated and start buying again. Industry players would also not feel the pinch of media publication cost because it is getting divided among the whole industry players.

Export oriented economy

Another mistake the government does that we overly get dependent on the domestic market for consumption and growth. Due to this for years we didn’t think of exports seriously. But if we want to attend the double-digit growth, we must increase our exports like china did. China took advantage of domestic market as well as of international market thus giving double thrust to the economy and growing in the double digits and lifting millions out of poverty.

Surely, the tax cut will help Indian economy to increase its exports by making our products cheaper.

Low cost credit and stable environment for business

Government of India must work with RBI to make credit cheaper in the economy so business can utilize to their advantage and invest more. And government should also make sure that they provide stable environment to do business and it can’t disrupt the economy with the moves like demonetization in the near future.

Kedar Kore
(B.Com (Hons), NMIMS – Mumbai. Batch 2017-20) Connect with Kedar on LinkedIn

2019 Indian auto industry slowdown – a complex problem

General Information

The Automotive Industry is one of the major drivers of India’s growth. Currently, it is the 4th largest market in the world. Having a valuation of $93 Billion, it contributes around 7.5% of the GDP and nearly half of the manufacturing GDP. Many known international automotive companies have setup their manufacturing units in India and some of them export also. There are currently 21 international and 18 Indian automotive companies.

Macro outlook

Being a driver of India’s Economic growth, it has the world’s largest two-wheeler and 4th largest four-wheeler market. Moreover, India also exports $14.5 Billion worth of automobiles, comprising 2.2% of total exports and growing fast. It is also one of the largest employers where 37 Million people are employed directly and indirectly. With the recent growth in the middle-income households, the auto sales have crossed 26 million in 2018, surpassing Germany. It is also a major supporter of labour-intensive domestically ancillary units which is dominated by small and medium scale enterprises.

The slump

This year i.e., 2019 has witnessed the worst slowdown of automobile sales after December 2000. The sales have been decreasing for the last 10 months. In July, due to a decrease in sales, around 2.3 lakh jobs have been lost in this sector and 300 dealerships have been closed. Auto sales in August have decreased by 23.5% compared to the previous year. Talking about the segments, the commercial vehicle is worst affected by the decrease in sales by 38.71%, followed by 31.57% in commercial vehicles and 22.24% in two-wheelers. But, the exports in this year has increased marginally by 2.3%.


The first slowdown which was recorded after SIAM (Society for Indian Automobile Association) was formed was in the year of 2000 where the auto sales had reduced by 35%, where passenger car was worst sufferers suffering reduction by 23.1%.

The recent slowdown is going on since November 2018 and no hope for revival is seen. The trigger was started with the IL&FS crisis, where not only them but also other NBFCs were taken with it to the trouble. This led to a shortage of funding and their loan disbursement were decreased by 30% in the first quarter of this financial year. Similarly, NPAs in banks were multiplied by 4-times in 4 years which discouraged bank to sanction more loans.

Second reason is the new ruling by the supreme court on pollution control. Supreme court has given the deadline of 1 April 2020 to all automobile companies to comply with BS-VI norms. Maruti-Suzuki has decided to stop its diesel model production due to high-cost and lack of expertise on adaptation of BS-VI norms. Also, the potential buyers have held its decision due to the low resale value of BS-IV models in future.

Third reason is the announcement of electric cars and emphasising on it has confused consumers on whether to buy internal combustion cars or electric cars. Government is lacking its vision on the policy of electric cars.

Fourth reason is an increase in third-party insurance. This has increased the cost of auto maintenance which has backed off consumers from purchasing automobiles.

Current Scenario

Maruti-Suzuki, the largest automobile company in India has seen a decrease in the production by one third and they had to shut down the production for 2 days. Till now, ₹80,000 crores have been invested by auto companies behind BS-VI norms and it is uncertain that if the sales would pick up.

A report by Reserve Bank of India in May has rejected the reason for credit shortage on slow auto sales. Instead, RBI says that increase in fuel prices and exogenous policy changes has reduced auto sales. The increase in third-party insurance premium, registration fees (which has taken back) has discouraged buyers to purchase cars.

If we see the decline of auto sales by category, two-wheeler and commercial vehicles, especially tractors have declined which indicates that there is a decrease in the spending of consumers especially in rural areas where these vehicles are popular. This may indicate that the slowdown of the economy which is currently going on.

To tackle the slowdown, there is demand for GST cut rates and availability of easier credit for automotive vehicles. Most of the auto companies are demanding a GST rate to cut down from 28% to 18%. On the other side, some companies are introducing new schemes to make their way from slowdown. One such company, Mahindra has introduced subscription service for some of its models where the subscriber has to pay a subscription fee and deposit in advance which includes insurance premium and maintenance charges. The subscriber after registration has to take a plan ranging from one to four years and have to pay monthly fees accordingly. After the plan expires, the subscriber can either return the car to the company, purchase the same car at a discounted price or take a new plan for a different model.

Government on the rescue

Recently, finance minister, Nirmala Sitharaman has announced capital infusion of ₹70,000 crore in the PSU banks to increase the liquidity in the economy. She also has lifted the ban on purchasing new vehicles for government administration which will provide a short-term demand. Moreover, the validity of BS-IV will be valid for the entire period of registration done today even after April 2020. An additional depreciation of 15% is allowed, taking it to 30% on vehicles purchased today till April 2020. The Government is also planning with a temporary reduction in GST rates to reduce the prices and fully implemented scrapping policy.

The silver lining

Despite the slowdown in the auto sales going on for the last ten months, the sales of the new models launched in this year is cruising through its sales and the demand is more than expected. The new players in the Indian auto market, MG motors and KIA motors have recently launched their first models, Hector and Seltos respectively. Hector has got so many bookings that MG motors have to close its bookings and the waiting time is a minimum of 6 months. Seltos, complied with BS-VI since its introduction is still not have delivered its model but they have received bookings up to 32000 in august with the waiting time of 4 months. Jeep’s compass has the waiting time for 45 days. Tata motors, facing slowdown has its saviour, Harrier which still has waiting time for 6 weeks. This shows that the new models with the latest features are favourite among young consumers and old players now have to innovate their automobiles and give that features that fulfil the value of which the consumers are paying.

Siddharth Dholaria
Team Member- Alternate Investments (M.Sc. Finance, NMIMS – Mumbai. Batch 2019-21)

Connect with Siddharth on LinkedIn

Deep Learning Network Portfolio: Building a Minimally Correlated Portfolio Deploying Network Analysis


In this article, we have used hedgecraft‘s approach to portfolio management. However, unlike hedgecraft, we have used a sub-graph centrality approach.This sub-graph centrality approach is what makes our approach unique. Using insights from Network Science, we build a centrality based risk model for generating portfolio asset weights. The model is trained with the daily prices of 31 stocks from 2006-2014 and validated in the years 2015, 2016, 2017, 2018 & 2019. As a benchmark, we compare the model with a portfolio constructed with Modern Portfolio Theory (MPT). Our proposed asset allocation algorithm significantly outperformed both the Sensex30 and Nifty50 indexes in every validation year with an average annual return rate of 26.51%, a 13.54% annual volatility, a 1.59 Sharpe ratio, a -21.22% maximum drawdown, a return over maximum drawdown of 6.56, and a growth-risk-ratio of 1.86. In comparison, the MPT portfolio had a 9.63% average annual return rate, an 18.07% annual standard deviation, a Sharpe ratio of 0.41, a maximum drawdown of -22.59%, a return over maximum drawdown of 2.2, and a growth-risk-ratio of 0.63.


In this series, we play the part of an Investment Data Scientist at Bridgewater Associates performing a go/no go analysis on a new idea for risk-weighted asset allocation. Our aim is to develop a network-based model for generating asset weights such that the probability of losing money in any given year is minimized. We’ve heard down the grapevine that all go-decisions will be presented to Dalio’s inner circle at the end of the week and will likely be subject to intense scrutiny. As such, we work with a few highly correlated assets with strict go/no go criteria. We build the model using the daily prices of each stock in the Sensex. If our recommended portfolio either (1) loses money in any year, (2) does not outperform the market every year, or (3) does not outperform the MPT portfolio—the decision is no go.

Asset Diversification and Allocation

The building blocks of a portfolio are assets (resources with the economic value expected to increase over time). Each asset belongs to one of seven primary asset classes: cash, equity, fixed income, commodities, real estate, alternative assets, and more recently, digital (such as cryptocurrency and blockchain). Within each class are different asset types. For example stocks, index funds, and equity mutual funds all belong to the equity class while gold, oil, and corn belong to the commodities class. An emerging consensus in the financial sector is this: a portfolio containing assets of many classes and types hedges against potential losses by increasing the number of revenue streams. In general the more diverse the portfolio the less likely it is to lose money. Take stocks for example. A diversified stock portfolio contains positions in multiple sectors. We call this asset diversification, or more simply diversification. Below is a table summarizing the asset classes and some of their respective types.

An investor solves the following (asset allocation) problem: given X rupees and N, assets find the best possible way of breaking X into N pieces. By “best possible” we mean maximizing our returns subject to minimizing the risk of our initial investment. In other words, we aim to consistently grow X irrespective of the overall state of the market. In what follows, we explore provocative insights by Ray Dalio and others on portfolio construction.

The above chart depicts the behavior of a portfolio with increasing diversification. Along the x-axis is the number of asset types. Along the y-axis is how “spread out” the annual returns are. A lower annual standard deviation indicates smaller fluctuations in each revenue stream, and in turn a diminished risk exposure. The “Holy Grail” so to speak, is to (1) find the largest number of assets that are the least correlated and (2) allocate X rupees to those assets such that the probability of losing money any given year is minimized. The underlying principle is this: the portfolio most robust against large market fluctuations and economic downturns is a portfolio with assets that are the most independent of each other.

Visualizing How A Portfolio is Correlated with Itself (with Physics)

The following visualizations are rendered with the Kamada-Kawai method which treats each vertex of the graph as a mass and each edge as a spring. The graph is drawn by finding the list of vertex positions that minimize the total energy of the ball-spring system. The method treats the spring lengths as the weights of the graph, which is given by 1 – cor_matrix where cor_matrix is the distance correlation matrix. Nodes separated by large distances reflect smaller correlations between their time-series data, while nodes separated by small distances reflect larger correlations. The minimum energy configuration consists of vertices with few connections experiencing a repulsive force and vertices with many connections feeling an attractive force. As such, nodes with a larger degree (more correlations) fall towards to the center of the visualization where nodes with a smaller degree (fewer correlations) are pushed outwards. For an overview of physics-based graph visualizations see the Force directed graph drawing wiki.

In the above visualization, the sizes of the vertices are proportional to the number of connections they have. The color bar to the right indicates the degree of dissimilarity (the distance) between the stocks. The larger the value (the lighter the color) the less similar the stocks are. Keeping this in mind, several stocks jump out. Bajaj Finance, ITC, HUL, and HeroMotoCorp all lie on the periphery of the network with the fewest number of correlations above Pc = 0.325. On the other hand ICICI Bank, Axis Bank, SBI, and Yes Bank sit in the core of the network with the greatest number connections above Pc = 0.325. It is clear from the closing prices network that our asset allocation algorithm needs to reward vertices on the periphery and punish those nearing the center. In the next code block we build a function to visualize how the edges of the distance correlation network are distributed.


  • The degree distribution is left-skewed.
  • The average node is connected to 86.6% of the network.
  • Very few nodes are connected to less than 66.6% of the network.
  • The kernel density estimation is not a good fit.
  • By eyeballing the plot, the degrees appear to follow an inverse power-law distribution. (This would be consistent with the findings of Tse, et al. (2010)).

Intraportfolio Risk

We read an intraportfolio risk plot like this: ICICI Bank is 0.091/0.084 = 4.94 times riskier than Maruti Suzuki (MSPL). Intuitively, the assets that cluster in the center of the network are most susceptible to impacts, whereas those further from the cluster are the least susceptible. The logic from here is straightforward: take the inverse of the relative risk (which we call the “relative certainty”) and normalize it such that it adds to 1. These are the asset weights. Formally,

Next, Let’s visualize the allocation of 100,000 (INR) in our portfolio

Subgraph Centrality-Based Asset Allocation

Bajaj Finance receives nearly 12.58%, Bajaj Auto gets about 12.58%, HUL 8.15%, Infosys 4.52%, and the remaining assets receive less than 0.5% of our capital. To the traditional investor, this strategy may appear “risky” since 60% of our investment is with 5 of our 31 assets. While it’s true if Bajaj Finance is hit hard, we’ll lose a substantial amount of money, our algorithm predicts Bajaj Finance is the least likely to take a hit if and when our other assets get in trouble. Bajaj Finance is clearly the winning pick in our portfolio.

It’s worth pointing out that the methods we’ve used to generate the asset allocation weights differ dramatically from the contemporary methods of MPT and its extensions. The approach taken in this project makes no assumptions of future outcomes of a portfolio, i.e., the algorithm doesn’t require us to make a prediction of the expected returns (as MPT does). What’s more—we’re not solving an optimization problem—there’s nothing to be minimized or maximized. Instead, we observe the topology (interrelatedness) of our portfolio, predict which assets are the most susceptible to the subgraph centrality of volatile behavior and allocate capital accordingly.

Alternative Allocation Strategy: Allocate Capital in the Maximum Independent Set

The maximum independent set (MIS) is the largest set of vertices such that no two are adjacent. Applied to our asset correlation network, the MIS is the greatest number of assets such that every pair has a correlation below Pc = 0.325. The size of the MIS is inversely proportional to the threshold Pc. Larger values of Pc produce a sparse network (more edges are removed) and therefore the MIS tends to be larger. An optimized portfolio would therefore correspond to maximizing the size of the MIS subject to minimizing Pc . The best way to do this is to increase the universe of assets we’re willing to invest in. By further diversifying the portfolio with many asset types and classes, we can isolate the largest number of minimally correlated assets and allocate capital inversely proportional to their relative risk. While generating the asset weights remains a non-optimization problem, generating the asset correlation network becomes one. We’re really solving two separate problems: determing how to build the asset correlation network (there are many) and determining which graph invariants (there are many) extract the asset weights from the network. As such, one can easily imagine a vast landscape of portfolios beyond that of MPT and a metric fuck-tonne of wealth to create. Unfortunately, solving the MIS problem is NP-hard. The best we can do is find an approximation.

Using Expert Knowledge to Approximate the Maximum Independent Set

We have two options: randomly generate a list of maximal indpendent sets (subgraphs of such that no two vertices share an edge) and select the largest one, or use expert knowledge to reduce the number of sets to generate and do the latter. Both methods are imperfect, but the former is far more computationally expensive than the latter. Suppose we do fundamentals research and conclude Bajaj Finance and HUL must be in our portfolio. How could we imbue the algorithm with this knowledge? Can we make the algorithm flexible enough for portfolio managers to fine-tune with goold-ole’ fashioned research, while at the same time keeping it rigged enough to prevent poor decisions from producing terribe portfolios? We confront this problem in the code block below by extracting an approximate MIS by generating 100 random maximal indpendent sets containing Bajaj Finance and HUL.

The generate_mis function generates a maximal independent set that approximates the true maximum independent set. As an option, the user can pick a list of assets they want in their portfolio and generate_mis will return the safest assets to complement the user’s choice. Picking Bajaj Finance and HUL left us with Sun Pharma, Hero Moto Corp amongst others. The weights of these assets will remain directly inversely proportional to the subgraph centrality.

Allocating Shares to the Deep Learning Network Portfolio

In this section we write production (almost) ready code for portfolio analysis and include our own risk-adjusted returns score. The section looks something like this:

We obtain the cumulative returns and returns on investment, extract the end of year returns and annual return rates, calculate the average annual rate of returns and annualized portfolio standard deviation, compute the Sharpe Ratio, Maximum Drawdown, Returns over Maximum Drawdown, and our own unique measure: the Growth-Risk Ratio.

Finally, we visualize the returns, drawdowns, and returns distribution of each model and analyze the results the performance of each portfolio.

Visualizing the Returns

Pictured above are the daily returns for Deep Learning Network MIS (solid green curve), Deep Learning Network (solid blue curve), and the Efficient Frontier portfolio (solid red curve) from 2015 to 2019. The colorcoded dashed curves represent the 50 day rolling averages of the respective portfolios. Several observations pop: (1) Deep Learning Network MIS significantly outperformed Deep Learning Network Portfolio, (2) Deep Learning Network Portfolio substantially outperformed the Efficient Frontier, (3) Deep Learning Network MIS grew 158.4% larger, falling below 0% returns 0 out of all the trading days, (4) Deep Learning Network grew 139.3% larger and (5) the Efficient Frontier grew 49.8% larger. Next, let’s observe the annual returns for each portfolio and compare them with the market.

In comparison, the Nifty50 had a -4.1%, 3%, 28.6%, 3.2%, -1.03% (YTD) annual return rate in 2015, 2016, 2017, 2018, & 2019 (YTD) respectively. Deep Learning Network Portfolio and Deep Learning Network MIS substantially outperformed both the market and the Efficient Frontier. Both Deep Learning Network portfolios grew at an impressive rate. Deep Learning Network MIS grew 19.1% larger than Deep Learning Network Portfolio and 108.6% larger than the Efficient Frontier, while Deep Learning Network Portfolio grew 89.5% larger than the Efficient Frontier. What’s more, Deep Learning Network MIS’s return rates consistently increased about 25% every year, whereas the return rates of Deep Learning Network Portfolio and the Efficient Frontier were less consistent. Deep Learning Network Portfolio MIS clearly has the most consistent rate of growth. We’d expect this rapid growth to be accompanied with a large burden of risk—either manifested as a large degree of volatility, steep and frequent maximum drawdowns, or both. As we explore below, the Deep Learning Network portfolios’ sustained their growth rates with significantly less risk exposure than the Efficient Frontier.

Visualizing Drawdowns

Illustrated above is the daily rolling 252-day drawdown for Deep Learning Network MIS (filled sea green curve), Deep Learning Network (filled royal blue curve), and the Efficient Frontier (filled dark salmon curve) along with the respective rolling maximum drawdowns (solid curves). Several observations stick out: (1) the Deep Learning Network portfolios have significantly smaller drawdowns than the portfolio generated from the Efficient Frontier, (2) both Deep Learning Network portfolios have roughly the same maximum drawdown (about 22%), (3) Deep Learning Network on average lost the least amount of returns, and (4) Deep Learning Network’s rolling maximum drawdowns are, on average, less pronounced than Deep Learning Network MIS. These results suggest the subgraph centrality has predictive power as a measure of relative or intraportfolio risk, and more generally, that network-based portfolio construction is a promising alternative to the more traditional approaches like MPT.

Deep Learning Network and its MIS variant dramatically outperformed the Efficient Froniter on every metric (save Deep Learning Network MIS’s annual volatility). These results give credence to the possibility that we are on to something substantial here as we have passed the criteria of our go/no go test. Outperforming MPT by these margins is no simple feat, but, the real test is whether or not Deep Learning Network Portfolio can consistently beat MPT on many randomly generated portfolios. To wrap up this notebook, let’s take a look at how the returns for each portfolio are distributed and move to the conclusion of Deep Learning Network Portfolio Optimzation.

Visualizing the Distribution of Returns

Above are the returns distribution for each portfolio: Efficient Frontier (in red), Deep Learning Network (in blue), and Deep Learning Network MIS (in green). The Efficient Frontier algorithm somewhat produced a portfolio with a normal distribution of returns; the same can’t be said of the Deep Learning Network portfolios as they’re heavily right-skewed. The right-skewness of the Deep Learning Network portfolios is caused by their strong upward momentum, that is, their consistent growth. In general, we’d expect a strong correlation between the right-skewness of the returns distribution and the growth-risk-ratio.

It’s important to emphasize that deviation-based measures of risk-adjusted performance implicitly assume the distribution of returns follows a normal distribution. As such, the Sharpe ratio isn’t a suitable measure of performance since the standard deviation isn’t a suitable measure of risk for the Deep Learning Network portfolios.

While Deep Learning Network had less pronounced maximum drawdowns it was more frequently bellow 0% returns (1.59% of the time) than its MIS variant (0.53% of the time). These values dwarf that of the Efficient Frontier, which painfully experienced negative returns a third of the time. It’s interesting to note that the maximum loss of the Deep Learning Network portfolio is an order of magnitude smaller than their maximum drawdowns. This relationship is in contrast to the Efficient Frontier’s maximum loss which is on the same order of magnitude as its maximum drawdown. It’s also interesting to point out that Deep Learning Network has a lower probability of falling below its rolling 30, 50, and 90 averages than its MIS variant. Taken together, Deep Learning Network’s smaller average rolling maximum drawdown and smaller probabilities of falling below the above rolling averages indicate its growth is more consistent than its MIS variant. On the one hand, Deep Learning Network’s growth is more consistent than its MIS variant while on the other hand, the MIS variant has a more consistent growth rate. Stated another way: Deep Learning Network’s “velocity of returns” is more consistent than that of the MIS variant’s, whereas Deep Learning Network MIS’s “acceleration of returns” is more consistent than that of the Deep Learning Network portfolio.

Future Portfolio Allocation & Conclusion

A similar analysis as displayed above was repeated for generating the optimal portfolio and the subsequent allocation. Following were the results of the same:

HUL: 11.73% , ITC: 11.73% , Bajaj Auto: 11.73% , Sun Pharma: 11.73% , ONGC: 11.73% , Asian Paints: 11.73% , NTPC: 7.6% , PowerGrid: 7.6% , Tech Mahindra: 3.88% , Infosys: 3.88% TCS: 2.76% HCL Tech: 2.76% HeroMotorCorp: 0.87%

Thus, Sector Allocation proposed by our Deep Learning Network Algorithm is as follows: FMCG: 23.46% , Automobile: 12.6% , Pharma: 11.73% , IT: 13.28% , Energy: 26.93% , Paints & Varnishes: 11.73%


In this article, we built a novel algorithm for generating asset weights of a minimally correlated portfolio with tools from network science. Our approach is twofold: we first construct an asset correlation network with energy statistics (i.e., the distance correlation) and then extract the asset weights with a suitable centrality measure. As an intermediate step we interpret the centrality score (in our case the subgraph centrality) as a measure of relative risk as it quantifies the influence of each asset in the network. Recognizing the need for a human-in-the middle variation of our proposed method, we modified the asset allocation algorithm to allow a user to pick assets subject to the constraints of the maximal independent set.

Both algorithms (Deep Learning Network and Deep Learning Network MIS, including the benchmark Efficient Frontier) were trained on a dataset of thirty-one daily historical stock prices from 2000-2014 and tested from 2015-2019. The portfolios were evaluated by cumulative returns, return rates, volatility, maximum drawdowns, risk-adjusted return metrics, and downside risk-adjusted performance metrics. On all performance metrics, the Deep Learning Network algorithm significantly outperformed both the portfolio generated by the Efficient Frontier and the market–passing our go/no go criteria.

Harsh Shivlani
Team Leader– Fixed Income & Derivatives
(M.Sc. Finance, NMIMS – Mumbai. Batch 2018-20)

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Neil Jha
Team Leader – Fintech
(M.Sc. Finance, NMIMS – Mumbai. Batch 2018-20)

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On the 30th of August, 2019, Finance Minister (FM), Nirmala Sitharam announced the merger of 10 major public sector banks (PSBs) to reduce the number of players in the banking scenario from a whopping 27 to 12. This news comes in wake of the disappointing news that India faced a 5% GDP growth in the preceding quarter. It is expected that the merger will increase the CASA (Current to Savings Account Ratio) and enhance lending capacity. These reforms were deemed necessary to foster the idea of India becoming a $5 trillion economy. Illustrated below shall be the expected scenario if the mergers are proven successful:

Merger between

Rank (based on size)

Number of Branches

Total Business Size

(Rs in lakh crore)

Punjab National Bank (A), Oriental Bank of Commerce and United Bank – Merger I



17.95 (1.5 times of current)

Canara Bank (A) and Syndicate Bank – Merger II



15.2 (1.5 times of current)

Union Bank of India (A), Andhra Bank and Corporation Bank – Merger III



14.59 (2 times of current)

Indian Bank (A) and Allahabad Bank – Merger IV



8.08 (2 times of current)

(A) Anchor Bank

It was also announced that Rs 55,250 crore of capital infusion will take place to ease credit growth and regulatory compliance. Now we’ll look at the capital infusion expected to take place to aid the mega mergers:


Recapitalization (Rs in crore)

Punjab National Bank


Union Bank


Bank of Baroda


Canara Bank


Indian Bank


Indian Overseas Bank


Central Bank


UCO Bank


United Bank of India


Punjab and Sind Bank


FM also announced multifarious administrative reforms to increase accountability and remove political intermediation. Bank management is made accountable as the board will now be responsible for evaluating the performance of General Manager and Managing Director. It is mandatory to train directors for their roles thus improving leadership in the PSBs. The role of the Non-Official Director is made synonymous to that of an independent director. In order to attract talent, banks have to pay competitive remuneration to Chief Risk Officers.

The banks were merged on three criteria – the CRR should be greater than 10.875%, the CET ratio should be above 7% (which is above the Basel norms) and the NPAs should be less than 6%. However, Syndicate and Canara bank have not been able to meet the criteria.

Post consolidation facts and figures:

  • Total Business Share
  • Ratios (all amounts in %)




United Bank of India


CASA Ratio















CRAR Ratio





Net NPA Ratio






Canara Bank

Syndicate Bank


CASA Ratio












CRAR Ratio




Net NPA Ratio





Union Bank

Andhra Bank

Corporation Bank


CASA Ratio















CRAR Ratio





Net NPA Ratio






Indian Bank

Allahabad Bank


CASA Ratio












CRAR Ratio




Net NPA Ratio





  • Economies of scale.
  • Efficiency in operation.
  • Better NPA management.
  • High lending capacity of the newly formed entities.
  • Strong national presence and global reach.
  • Risk can be spread over and thus will be minimized.
  • Lower operational cost leading to lower cost of borrowing.
  • Increased customer base, organic growth of market share and business quantum.
  • Banking practices reform announced to boost accountability and professionalism.
  • Appointment of CRO (Chief Risk Officer) to enhance management effectiveness.
  • Centralized functioning promoting a central database of customers.


  • The slowdown witnessed by the economy coupled with the dangerously low demand in the automobile sector will maintain the existing situation pessimism.
  • The already existing exposure of NBFCs in the individual constituent banks will be magnified as the merged entities shall have more than 10% loan exposure to NBFCs and thus, in effect, the liquidity pressure that comes along with it.
  • As history dictates, the merger of these eminent banks will cause near-term problems with respect to restructuring, recapitalization, operation, flexibility and costs.
  • Near-term growth shall be hindered and core profitability may suffer.
  • Compliance becomes a huge barrier.
  • Difficult to merge human resources and their respective work cultures post-merger – this will in turn lead to low morale and inefficient workforce


The mergers were announced with a very noble idea in mind; however, the timing is a bit unfortunate. During these times of economic slowdown, India needs its bankers devoting their time to boost the economy. With the merger happening, the banks will be more pre-occupied with the integration process rather than enhancing the economic growth. Merely combining banks will not help enhance credit capacity, it is also important to see whether synergies in reality will be created (or if it is merely on paper).

The share of assets of the top three or four banks account for only 30%-32%. Thus, the banks still remain fragmented for a major part – systemic risk or contagion effect shall not be a problem as of now. Although this is the case, out of the four mergers not one of them can be said to be financially strong. This is a phenomenon of blind leading the blind; it cannot be expected that two financially weak banks can merge into one financially strong entity. “A chain is only as strong as its weakest link.”

This announcement comes at a time when even the results of the previous mergers (e.g. Bank of Baroda) have not yielded any fruit and the PSBs have recently jumped back from a long stress scenario. It seems as if there is no common theme in the mergers (i.e. retail, corporate or SME), no particular skill-set that has been emphasized upon. Rather, it was just assumed that all the banks fall under the same template and a haphazard combination was made – in such a case, there is a slim chance of synergy creation. Also, with no major theme in hand the multifarious objectives will confuse the banks with respect to the pressing matters at hand.

According to technical experts, it might take around three to four years to integrate the existing IT systems of the banks. Although all of the use the CBS, heavy customization is required, mobile apps need to be in sync, backend functions have to be centralized effectively.

As for the case of resolution of NPAs, it might actually become easier and faster. Earlier, the bankers had to talk to their counterparts, the approach the senior management to come to a resolution. Now, with these institutions merging and with lesser levels to report to, a solution plan can be implemented at the earliest with considerably less effort. Apart from this, now that the banks will have a common database and a larger network, they can increase the services offered at a higher level at lower costs – this might show an increment in the fees earned and in turn, the profitability. It is expected that the Anchor banks will be benefitted more from the mergers as the swap ratio will be in their favour.

Chandreyee Sengupta
Team Member- Equity Research & Valuation
(MSc Finance, NMIMS Mumbai. Batch 2019-21)

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CATASTROPHE BONDS – Fortune From The Disaster

Catastrophe Bonds simply were known as the “Cat Bonds” is a financial instrument where the issuer issues bonds for re-insurance against the natural disaster or a catastrophe. The insurance company issues bonds as collateral against the catastrophe insurance. Cat bonds have a high yielding feature with a duration of 2 years to 5 years. Cat bonds transfer the risk of insurance into the capital market.

History for development of cat bonds can be traced back in the 1990s when the claims filed by clients against hurricane Andrew couldn’t be acknowledged and the insurance industry suffered humongous losses. Many insurance companies that earlier provided catastrophe risks decided to leave the insurance sector and about eleven insurance companies filed for bankruptcy. Therefore, there was a need to cover the capital by catastrophe insurance-linked bonds.

Working of the CAT Bond:

As this bond transfers the risk from insurance company to the financial markets. The amount which is pooled out from the investors is transferred to the Special Purpose Vehicle (SPV). There is a reinsurance agreement between the SPV and the insurance company which dictates the terminology and clauses for the amount to be paid during the catastrophe. The SPV invests it into the capital market and to manage the security. The returns from the financial market are further passed to investors of cat bonds. They are mostly invested in money market instruments with low risk. They are high yield debt instruments. These SPVs fulfill the claims of the risk carrier i.e. insurance company if any catastrophe occurs or as the terms of an agreement are fulfilled.

For instance, a family living in Florida where hurricanes are most likely to happen they approach for Hurricane insurance from the General Insurance Company. The insurance company will provide such insurance since they get good premiums but still hang back because if the hurricane occurs they will have to pay a huge amount as indemnity. The solution to the problem is by issuing cat bonds they won’t incur huge losses. If the event is not triggered at the maturity then the collateral account by SPV will be liquidated and the proceeds will be returned to the investor. But if the event triggers then the collateral is liquidated where some or all the proceeds are passed on to the sponsor.

Figure 1: Process of CAT Bonds

Investor’s Perpective:

A cat bond is a lookalike corporate bond with a pre-determined coupon rate. These bonds are not related in any way to the global markets. A financial crisis has nothing to do with the trigger of a natural disaster or catastrophe. They are built on floating rates notes where the investor benefits the return not only from the risk premium of the cat bond sponsor but also the returns from the money market where the pooled amount is invested. Since these bonds are not linked with capital markets, investors view such bonds to diversify their portfolios to minimize the risk related to markets. Over the years the cat bonds have shown great growth and seemed to be a lucrative investment option. Performance of cat bonds Index, Insurance-Linked Securities-Hedge Fund (ILS-HF), Equities and Bonds Index is shown below. Figure 2 to Figure 4 shows why cat bonds are considered to diversify their portfolio and have been alluring over the years.

Figure 2: Performance of Cat Bond Index versus other Financial Instruments Index










Total Return










Annualized return





Sharpe Ratio





Figure 3: Comparing Returns and Volatility ( Source )








Cat Bond Index*





ILS HF Index**















Figure 4: Correlations ( Source )

Benefit for the Economy:

It is next to impossible to bear the shock of catastrophe alone by the insurance companies. The financial markets are stronger and capable to bear the economic effect of the catastrophe. So, to benefit the quantum of financial markets for the effect of catastrophe, was when the establishment of catastrophe bonds came into existence after Hurricane Andrew 1992.

The use of cat bonds is mainly to protect and manage risk associated with the disaster. The development of cat bonds is growing rapidly over the years for developing economies as well. Countries and regions in the risk-prone areas are many a time not insured or is backed by government funding for the upliftment of the economy.

This new insurance-linked product has led the World Bank providing a framework for the same known as the “MultiCat Program”. This has given aid to Mexico’s Caribbean islands to issue cat bonds by structuring themselves using the framework provided by the World Bank. The intrinsic value of these bonds is to provide for the recovery of the loss incurred and transfer the risk to those willing to take the risk. Financial investors have turned around to this investment option as an asset class with higher returns and low or no correlation with the financial markets. But today cat bonds are proving themselves as a social-driven investment instrument and new breed for this cat bonds are coming are known as the pandemic bonds which will help to combat the life-threatening diseases.

Indian Scenario about Cat Bonds:

When the world is booming and progressing on different financial products India cannot step back but indeed tries to be in the race. Yes, it is trying to come up with the debutant of its cat bonds in the Indian Economy. General Insurance Corporation of India (GIC), is the country’s foremost reinsurer that has come upon the thought of issuing cat bonds on the wakeup call of the Uttarakhand floods in 2012. GIC had to pay approx. 2000 crores of claims settlement from their treasure chest. E.g. If GIC issued cat bonds worth 1000 crores in 2011 with the maturity of three to five years, on triggering of the event they would have to shed only 1000 crores.

India being a developing economy, many parts of the country are risk-prone areas like aforesaid floods, cyclones, landslides and very rare symptoms of earthquakes in the regions of Rajasthan, etc. Let’s assume India agrees to pay at 12% – 14% coupon on cat bonds in India, it would likely get the subscription of Pension Funds, Hedge funds or high net worth individuals since they are attracted to benefiting from high-interest yields over the short tenure of the bonds. The government should try and come out with such bonds and mitigate the losses for its own.

Thus, Catastrophe Bonds a savior to the economy by passing on the risk to the risk bearing financial investors.

Lorretta Gonsalves
Team Member- Alternate Investments (M.Sc. Finance, NMIMS – Mumbai. Batch 2019-21)

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