MINTING “GREEN”

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

GREEN BONDS

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.

CASE

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.

GREEN FINANCE IN INDIA

“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.

STOCK MARKET RESPONDS POSITIVELY TO THE ANNOUNCEMENT OF GREEN BOND ISSUES

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

FISCAL BENEFITS ATTACHED WITH GREEN BONDS

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.”

Source

GREEN BONDS AT MACRO LEVEL

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.

OPPORTUNITY to ‘GREEN’ THE FINANCIAL SYSTEM

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. 

GO GREEN INVESTMENTS !!!

Author
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.

Author
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%.

History

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.

Author
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

Summary

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.

Background

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.

Observations

  • 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%

Conclusion

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.

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

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

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THE PSB MEGA MERGER: AN OVERVIEW

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

2nd

11,437

17.95 (1.5 times of current)

Canara Bank (A) and Syndicate Bank – Merger II

4th

10,342

15.2 (1.5 times of current)

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

5th

9,609

14.59 (2 times of current)

Indian Bank (A) and Allahabad Bank – Merger IV

7th

6,104

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:

Bank

Recapitalization (Rs in crore)

Punjab National Bank

16,000

Union Bank

11,700

Bank of Baroda

7,000

Canara Bank

6,500

Indian Bank

2,500

Indian Overseas Bank

3,800

Central Bank

3,300

UCO Bank

2,100

United Bank of India

1,600

Punjab and Sind Bank

750

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 %)

MERGER – I

PNB

OBC

United Bank of India

Post-Merger

CASA Ratio

42.16

29.4

51.45

40.52

PCR

61.72

56.53

51.17

59.59

CET-I

6.21

9.86

10.14

7.46

CRAR Ratio

9.73

12.73

13

10.77

Net NPA Ratio

6.55

5.93

8.67

6.61

MERGER – II

Canara Bank

Syndicate Bank

Post-Merger

CASA Ratio

29.18

32.58

30.21

PCR

41.48

48.83

44.32

CET-I

8.31

9.31

8.62

CRAR Ratio

11.90

14.23

12.63

Net NPA Ratio

5.37

6.16

5.62

MERGERIII

Union Bank

Andhra Bank

Corporation Bank

Post-Merger

CASA Ratio

36.10

31.39

31.59

33.82

PCR

58.27

68.62

66.60

63.07

CET-I

8.02

8.43

10.39

8.63

CRAR Ratio

11.78

13.69

12.30

12.39

Net NPA Ratio

6.85

5.73

5.71

6.30

MERGER – IV

Indian Bank

Allahabad Bank

Post-Merger

CASA Ratio

34.75

49.49

41.65

PCR

49.13

74.15

66.21

CET-I

10.96

9.65

10.63

CRAR Ratio

13.21

12.51

12.89

Net NPA Ratio

3.75

5.22

4.39

Advantages:

  • 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.

Disadvantages:

  • 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

Outlook:

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.

Author
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
Source

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
Source


 

CAT BOND

ILS HF

EQUITIES***

BONDS****

INDEX*

INDEX**


 


 

Total Return

166.4%

89.9%

124.3%

55.9%

Volatility

3%

3%

15%

5%

Annualized return

7.9%

5.1%

6.50%

3.5%

Sharpe Ratio

2.39

1.69

0.45

0.69

Figure 3: Comparing Returns and Volatility ( Source )


 

CAT BOND

ILS HF

EQUITIES***

BONDS****

INDEX*

INDEX**

Cat Bond Index*

1


 


 


 

ILS HF Index**

0.87

1


 


 

Equities***

0.18

0.1

1


 

Bonds****

0.17

0.14

0.39

1

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.

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

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Is RLLR the long lost saviour?

Before we get into the topic lets know what repo rate is. It is the rate at which the central bank of a country (RBI) lends money to the commercial banks in event of any shortfall of funds.

Now people are excited about the new repo linked lending rate (RLLR) which has come into the market but let’s just roll back a few months back to December 2018. RBI in its fifth bi-monthly monetary policy review on December 5th made a big announcement (by N.S. Vishwanathan – Deputy Governor) that many bank customers were waiting for and that retailed loans will be linked to external benchmarks instead of various internal benchmarks produced by banks.RBI had instructed the banks to start the process the linking the new repo rates from 1st April 2019.

By adopting a single benchmark, the home loans for example which are linked to the marginal cost of funds will now be linked to the repo rates. This makes the banks bound to revise the rates of the home loans instantly as and when there is a change in the RBI repo rate. These changes are welcomed by the customers because it has been long sought out as RBI reduced the repo rates to control the inflation but the benefit never reaches the consumers but now because of this, the consumers will get the benefit of at least some lower interest rate to be paid. The benefit of Repo-rate linked home loan scheme is that it is transparent compared to existing loans linked to marginal-cost-of-fund based lending rate (MCLR). The interest rates on loans will change upwards or downwards in line with the movement of the repo rate announced by RBI.

The RBI had stated that banks should benchmark the rates to either the RBI policy repo rate or Government of India’s 91 or 182 days Treasury bill yields as developed by the Financial Benchmarks India Private Ltd (FBIL) or any other external benchmark developed by the FBIL but still several banks opposed the decision of linking lending rates to an external benchmark, indicating that their cost of funds was not linked to those external benchmarks and delayed the implementation indefinitely.

By March 2019 the only bank to realize this directive was SBI, the largest public sector bank but it too took some time and made it effective from July 2019. Following the same footsteps, Bank of Baroda too introduced RLLR home loan scheme from 12th August 2019 and Syndicate Bank. Allahabad Bank, Canara Bank & Union Bank of India and other banks will announce their plans to launch RLLR soon.

To be eligible for the SBI repo rate linked home loan scheme, the borrower should have a minimum annual income of Rs 6 lakhs and tenure of the loan is up to 33 years. In the case of under-construction projects, the maximum moratorium period up to two years is offered over and above maximum loan tenor of 33 years. So, in such cases, the total loan tenure cannot exceed 35 years.

In this home loan scheme, the borrower needs to repay a minimum of 3 per cent of the principal loan amount every year in equated monthly instalments. If you take a home loan of Rs 50 lakhs, you need to repay a minimum of Rs 1.50 lakhs as principal plus the interest cost every year.

The interest rates in this scheme are not directly linked with the repo rate figure announced by the RBI. The interest on the loan is 2.25% points more than the repo rate. On July 1, the repo rate was 5.75 per cent, so the repo-linked lending rate is 8 per cent. But, the repo-linked lending rate may change effectively from September 1 as we had a repo rate cut of 35 basis points (bps) announced by the RBI in August.

Currently, RLLR is at 8 per cent. Banks will maintain a spread over and above RLLR of 40 to 55 bps. So, the effective rate for home loans up to Rs. 75 lakhs range from 8.4 per cent to 8.55 per cent. For home loans above Rs 75 lakhs, the effective rate is 8.95 per cent to 9.10 per cent (i.e. spread of 95 to 110 bps on RLLR of 8 per cent). With effect from 10th August, the home loan rates linked to MCLR would be 8.6 per cent to 8.85 per cent at SBI, which is more than RLLR.

Similarly, for Bank of Baroda MCLR linked home loan rate starts at 8.45 per cent, while the repo-linked rate starts at 8.35 per cent. At present its 5 bps cheaper than SBI’s repo-linked home loan scheme. Repo rate linked home loan scheme will be beneficial to borrowers with immediate savings when the interest rate goes down.” For instance, with a further 50 bps rate cut as expected in the next year, there will be further savings for borrowers on interest.

Let aside the interest rates alone, if you choose to switch for an RLLR home loan there are more added costs to be noticed, for instance, SBI levy’s transfer and processing charges of 0.35% on the amount of loan plus GST. The minimum fees shall be Rs 2,000 and the maximum can go up to Rs 10,000 plus GST. These charges may vary from bank to bank

One should wait a bit longer as other banks are also coming up with this scheme so one can choose a home loan from the bank of his choice and preference but also take into consideration the charges and extra paperwork, hassle and time to keep a tab on both accounts one home loan and other accounts (savings, joint etc). One has to take into consideration the fact that if you choose another bank apart from your savings bank look at the spread (margin) the bank is charging over and above RLLR. Check the impact of the spread between RLLR and the final rate of interest offered. Stick to the ones which offer the least spread as it reflects RBI’s repo rate policy correctly.

It’s also to be noted that the RLLR is effective from the following month after RBI monetary policy announcement. But, the borrowers also need to be aware and prepared that RBI can increase the repo rate due to the economic factors.

As far as the private banks are concerned; from Axis Bank, Mr Rajiv Anand, executive director for corporate lending said, “It’s not necessary to use only external benchmarks; there are multiple avenues to meet the requirement that the RBI wants us to do… What RBI is essentially looking at is that the rates are being cut and there should be better transmission”. More details on this weren’t revealed whether Axis bank is planning to offer RLLR but he did mention “Axis Bank’s asset-liability committee will take a call on the same.”

Hence, this scheme is to target customers & borrowers who reside in Tier 1 or Tier 2 cities and having an annual steady income of Rs.6 lakh. So before switching your home loan take note of the above points as to charges, the spread between RLLR and final interest rate and also if the central bank may increase the repo rate due to economic scenario.

Author
Rishi Khanna
Team Member- Equity Research & Valuation
(MSc Finance, NMIMS Mumbai. Batch 2019-21)

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