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)

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

Connect with Neil on LinkedIn

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)

Connect with Chandreyee on LinkedIn

The Curious Case of Quiescent Inflation & Negative Yielding Junk Bonds

One of the most important questions being asked in financial media today appears to be, “Is the Phillips Curve Dead?”

Before I go jump into the analysis of whether that is the case and what impact will it have on the future course of monetary & fiscal policy, let me give me a brief explainer about the concept of The Phillips Curve.

A.W. Phillips stated that there was a trade-off between unemployment and inflation in an economy. He implied that as the economy grew, unemployment went down, this lead to tighter labor markets. Tighter labor markets warranted higher wage increases. Companies, in order to maintain their margins, would pass this higher input cost to the consumers which would then be reflected in the CPI (Consumer Price Index- a gauge of inflation) that we refer to.

You could say this was the case before the 1980s, however, since then, the relationship between the two seems to have hit a “rough patch” or flattened out.

Source: Bank for International Settlements (BIS)

The above chart regresses PPI Inflation (%) with the growth in Unit Labour Costs (%). As defined by OECD,

“Unit labor costs (ULC) measure the average cost of labor per unit of output and are calculated as the ratio of total labor costs to real output.

A rise in an economy’s unit labor costs represents an increased reward for labor’s contribution to output. However, a rise in labor costs higher than the rise in labor productivity may be a threat to an economy’s cost competitiveness, if other costs are not adjusted in compensation.”1

Just from the chart, one can infer that the slope of the regression, R2 or the link between PPI inflation and ULC growth has flattened significantly when you compare the data pre and post 1985.

Hence, I would like to devote a major portion of this article on exploring the structural changes in world economies that have led to this compelling phenomenon.

Lower bargaining power emanating from a declining share of income that accrues to labor

  • A June 2018 research paper titled, “Productivity and Pay: Is the link broken”2 suggests, that post-industrialization (or since the 1980s), median compensation grew by only 11% in real terms, and production workers’ compensation increased by a meagre 12%, compared to a 75% increase in labor productivity. Since 2000, average compensation has also begun to diverge from labor productivity.
  • Apart from the weaker link between the above two variables, the continued sluggishness in wage growth can largely be attributed to productivity growth being far weaker than it was before the crisis.3

Globalization & The Threat of Production Relocation

  • The increased integration of production and complex supply chains connecting advanced economies with emerging market economies, outsourcing along with the relatively smooth and easy flow of money and information across borders have forced workers in rich countries to compete with those in poorer ones
  • The IMF World Economic Outlook (2017) attributes about 50% of the fall in labor share in developed economies to technological advancement, with the fall in the price of investment goods and advances in ICT encouraging automation of routine tasks

Declining Path of Unionisation

As unionization declines, the collective bargaining power of employees starts diminishing. For example, in the United States % of employees enrolled in a trade-union membership has steadily declined from 20% to 10% over the past few decades.

This makes it more difficult for the workers to capture a larger share of the productivity gains enjoyed by the firm as a whole.

Hence, we observe that wage growth in real terms has hardly seen a meaningful increase.

The shift from manufacturing to service economies and the era of automation

  • With the heightened contribution of artificial intelligence and automation in the manufacturing process, firms are able to substitute labor with capital and even the high-quality blue-collar jobs are at stake.
  • From an economic efficiency standpoint, it makes sense for a firm to get more work done for the same or lower cost than to waste resources in hiring and training employees. This could partly explain the delinking of productivity and wage growth.
  • With global PMIs crashing into contraction territory across the world economies due to a host of factors such as dollar strength seen in 2018 (80% of global bank trade credit is denominated in dollars), uncertain CapEx or investment environment due to trade wars among others, we have seen consumption stayed relatively resilient.
  • This may be partly attributed to the transition of economies reliance from manufacturing to services, as a result, the share of employment in services has also jumped in recent years.

Quantum Pricing & Long Term Inflation Expectations

  • The traditional theory states that wages are stickier than prices. If so, profit margins should ideally rise if demand increases. However, after studying firm-level behavior we observe that they tend to abstain from margin expansion for the sake of higher market share. Also, firms unable to generate sufficient sales tend not to reduce prices proportionately to avoid losing cash to meet their rising debt and interest burdens (which explains why we saw inflation falling less than expected during the GFC).
  • Firms have since been engaging in “Quantum Pricing” where firms may change the quality or composition of their products to adjust for production cost volatility instead of increasing prices across the board. This, in turn, makes prices stickier while keeping margins stable. It becomes increasingly complex for mainstream macroeconomic models to capture such structural shifts in pricing affecting inflation.4
  • In a nutshell, all the above factors along with weak cyclical pressures drag longer-term inflation expectations lower (as observed by the 5Y5Y forward breakeven inflation, etc). Lower expectations through their negative feedback loop anchor inflation lower to some extent.5

Low Rates, Asset Price Inflation & The Lure of Negative Yields: Glimpse

Markets have set their expectations in stone for rates being “lower for longer” due to the inflation dynamics stated above, secular stagnation going forward and maybe even price level targeting by central banks.

In an environment where markets will pounce on anything with a positive real yield, there may be a real risk of financial instability arising from irrational bidding of risk assets which cannot be more prominently observed than from the negative-yielding junk bonds.

You are essentially paying companies with significant credit risk for (the privilege of) borrowing funds from you!

It may sound absurd but what if I tell you that this negative-yielding Japanese/European debt may in certain cases provide you with a dollar yield that is even higher than the positive yield that you get in treasuries? In other words, (for example) -0.1% (¥) > 2.5% ($)

I shall follow up on the mechanics of how this kind of sorcery is possible (along with the risks associated with the same) in part II of this article.

References:

  1. Retrieved from https://stats.oecd.org/glossary/detail.asp?ID=2809
  2. Stansbury, A. M., & Summers, L. H. (2017). Productivity and Pay: Is the link broken? (No. w24165). National Bureau of Economic Research
  3. IMF World Economic Outlook, April 2019
  4. https://www.bis.org/events/ccaresearchconf2018/rigobon_pres.pdf
  5. IMF Blog “Euro Area Inflation: Why Low For So Long?”
Author
Harsh Shivlani
Team Leader– Fixed Income & Derivatives
(M.Sc. Finance, NMIMS – Mumbai. Batch 2018-20)

Connect with Harsh on LinkedIn

Impact of MSME on Indian Economy

INTRODUCTION TO MSME

The Micro, Small and Medium Enterprises (MSME) sector has emerged as a highly vibrant and dynamic sector the Indian Economy over the last 5 decades. MSME Sector has been one of the most focused sectors in prospects of Investments and has contributed significantly for our country’s Social Development as well as Economic development. MSME has also promoted women empower and has helped in generating largest employment opportunities at lower capital cost, next only to agriculture. It has helped abundantly by promoting the term ‘Entrepreneurship’. MSME have merged as complementary to large industries as ancillary units and they are widening their domain across all sectors of the Indian Economy as well as producing a range of Products and Services which will help to meet the needs of not only domestic market but International markets also. Government of India has never failed to support MSME in all ways possible and have promoted MSME sectors by starting a number of Schemes and other Incentives for them. The Ministry of MSME runs Various Schemes aimed at financial assistance, Infrastructure development, technology assistance and Upgradation, skill development and training, enhancing competitiveness and market assistance of MSMEs.

GOVERNMENT SUPPORT TO MSME

The ministry of MSME is doing its best to help MSMEs reaching new high and contributing more and more to The Indian Economy. The ministry recently came up with some Policy Initiatives like:

  • Ease of Registration Process of MSMEs- Udyog Aadhaar Memorandum
  • Framework for Revival and Rehabilitation of MSMEs
  • MSME Data Bank
  • MyMSME
  • Direct Benefit Transfer in the M/o MSME
  • GST rollout & Ministry of MSME
  • Digital Payments
  • Grievance Monitoring
  • MSME Samadhaan: To Address Delayed Payments to MSEs
  • MSME- Sambhandh
  • Technology Centre Systems Programme(TCSP)
  • Partnership with Industry
  • International MoUs
  • MoU with NSIC for provision of services for MSMEs
  • Swachhta Pakhwada by Ministry of MSME
  • National Scheduled Caste / Scheduled Tribe Hub

These Policies are being formulated to help MSME reach new heights and contribute more in Economic and Social Development of the Country. The Schemes by Government help MSMEs Financially/in-kind for their betterment. Government of India has Supported and Promoted MSME Sector not only on Domestic Levels but in International Markets also. The contribution made by MSME in development of Economy and Social Life in backward areas has been spectacular.

ROLE OF MSME IN INDIA

The MSMEs have been a great contributor to the expansion of entrepreneurial endeavours through business innovation. Since past 9 Years MSME have contributed around 29% in GDP of India(Source: CSO, Ministry of Statistics & Programme Implementation). The Gross Value added by MSMEs in contribution to Indian Economy as on 2015-16 was INR 1,24,58,642 Crs.

In India 324.88 Lakhs MSMEs are located in Rural Areas whereas 309 Lakhs MSMEs are located in Urban Areas. Shockingly 630.52 Lakhs of these MSMEs falls under Micro Sector whereas 3.31 Lakh MSME falls under Small sector and only 0.05 Lakh falls under Medium Sector(Data as per MSME Annual Report 2017-18). MSMEs have a big impact on Micro Sector helping small entrepreneur’s achieving their dreams.

Not only was these, it also seen that 22.24% of the ownership of these Enterprises in rural areas were of female. In urban areas Female ownership of these enterprises came around 18.42%. MSME have led a movement in supporting Female entrepreneurs and have helped them in achieving their dreams. One more interesting fact is that 50% of MSMEs in India have ownership of OBCs followed by 12.45% of SCs and ST having ownership of 4.10%. In total ~66% of MSMEs in India are owned by Socially Backward Groups.

Estimated number of MSMEs (Activity Wise) is as follows:

Activity Category

Estimated Number of Enterprises (in Lakh)

Share(%)

RURAL

URBAN

TOTAL

Manufacturing

114.14

82.50

196.65

31

Trade

108.71

121.64

230.35

36

Other Services

102.00

104.85

206.85

33

Electricity*

0.03

0.01

0.03

0

ALL

324.88

309.00

638.88

100

*Non-captive electricity generation and transmission and distribution by units not registered with the Central Electricity Authority (CEA)

MSMEs have helped women entrepreneurs, socially backward groups in excelling and have been a major player in generating employment. Truly Micro Sector has been a major contributor in Social and Economic development of our nation.

Following Table shows how MSME helped in Employment Generation:

Activity Category

Employment (in Lakh)

Share(%)

RURAL

URBAN

TOTAL

Manufacturing

186.56

173.86

360.41

32

Trade

160.64

226.54

387.18

35

Other Services

150.53

211.69

362.22

33

Electricity*

0.06

0.02

0.07

0

ALL

497.78

612.10

1109.89

100

*Non-captive electricity generation and transmission

Interestingly, out of the total Estimated Employment Generated around 97% are generated by Micro sector which shows how it has been aiding in development of our nation and shaping a bright future.

MSME Sector has always been supported by Government and Big industries in every ways possible and MSME have returned the favour.

“No dream is too big and no dreamer is too small” these saying have been proved right as the smallest of enterprises have supported millions of peoples dream by providing them with employment.

*The figures were taken from the government MSME Annual Report of 2017-2018

Author
Aditya Majmudar
Volunteer- Equity Research & Valuation
(MSc Finance, NMIMS Mumbai. Batch 2018-20)

Connect with Aditya on LinkedIn

Fintech in India- A Global Growth story

FinTech has emerged as a relatively new industry in India. The year 2018 was a big one for the Indian financial technology and financial services ecosystem. With $2.34 billion being raised across 145 deals, fintech finally unseated ecommerce from the top of the list after years of dominance.

So, what really is FinTech?

In short, it is an industry that comprises of companies(such as insurance, asset management, payments) that use technology to offer financial services. Initially, FinTech started its trial by setting its operating base in the banking industry. But over the last five years, it has seen tremendous development and has expanded to insurance and asset management companies as well. 
By leveraging machine learning, FinTech companies are looking to analyze customer expectations and their responses.

In today’s digital economy, a whole new generation of FinTech’s, including nimble new start-ups with cutting edge technology have boomed alongside behemoths and are now valued more than many traditional banks and financial services firms.
We are witnessing specialization in many of the global fintech centres – London emerging as a hub for investments into open banking solutions, while China has become well known for facial recognition associated with biometric technology and Israel, a centre for cybersecurity.
India, however is yet to find a niche to focus on or a specialization, despite the mass availability of smart-technology talent.
As different hubs emerge around the world and technologies become more mature, specialization will be key to further develop India’s fintechs and continue to lure billions of dollars in investments and talent. Being the jack of all trades in different technologies and solutions, from mobile payments to credit scoring, digital banking and peer-to-peer lending, may have worked at the initial stages of fintech development, but the future will be dominated by specialists. 

To answer the many queries we have about fintech, its utilization and growth in the future, I decided to go ahead and take a personalized approach about this. I prepared some questions I had in mind about FinTech in India, and figured the ideal approach to know more would be to ask people who themselves are part of this industry.

I went ahead and interviewed two people, one who works as an Analyst in ‘Advanced Analytics’ division of MasterCard, and the other who is a Head of Sales and Alliances at Airpay. Both had a lot to say about matters relevant to this field, since a lot of work is being done in India to promote FinTech as a rising industry. Here is what they had to say about it.

Me-

How is the growth of fintech being supported in India?

MC-

India is doing pretty good in terms of fintech and it’s mostly coming from start-ups. Though one of the concerning facts is that most of them are eventually bought by foreign investors so won’t be completely fair to say that India has its in-house fintech innovation or technology. But with Indian government taking great initiatives like launching apps BHIM and UPI interface for P2P payments, it’s a great step towards our contribution to the fintech world.

AP-

  • A large unbanked and untapped market in India has been driving in private players willing to bet on it.
  • The Digital India initiative by the GoI has broadly laid down the framework for going digital and you cannot truly be a digital economy without Fintech.
  • The PMJDY led to financial inclusion of the masses, which has been leveraged by Fintechs to grow.
  • The exponential growth of smartphone adoption, along with some of the lowest mobile data rates have brought the next round of Indians online, which will further fuel digitisation of payments, insurance, etc.
  • The NPCI has been actively involved in churning out cutting edge payment methods like the UPI, which has again spurred adoption of digital means.

Me

There are namely 4 spheres of Fintech: In the systems sphere, in the B2B sphere, in the B2C sphere and B2G sphere. How do you think these are getting impacted, whether interdependently or not?

MC-

I think we are experiencing a major shift in our focus from P2P to B2B or B2C. The P2P space is quite evolved now unlike B2B or B2C space which has great potential. And, when it comes to B2B space, it’s both Corporates and Small Business where we need to focus because one is much more higher in revenue and the latter one is much more in number. There is still a lot of informal lending when it comes to business space and there is a lot of potential to not only improve payments systems in that space from both cost and speed perspective but also, come up with new technology to encourage small business to start using digital ways of money transfer.

B2C is also evolving everyday where every business is trying to value their customer much more than ever and give as personalised recommendations or offers as possible.

AP-

There are definite overlaps in the various spheres and a huge opportunity to create a unified platform. The effort has already begun with the BBPS infrastructure, which brought C2B and C2G payments on a single platform.

Me-

FinTech is reshaping categories and disrupting incumbents across a number of financial services sector categories. These include ‘Savings, Personal Finance, Investment & Wealth Management, Insurance, Block chain & Crypto, Lending & Unsecured Credit, and most significantly, Payments. Could you highlight more on the payments system, considering how big it is, not only in India, but all across the globe?

MC-

Gone are the days when people used to stand in long queues in banks to deposit/withdraw money for their personal use or to transfer it to somebody else. We are also trying to come out of the cheques world which takes 2-3 days for payment processing. This is the age of fast and quick technology, we want everything simple, fast and secure. You press the button while you’re sitting at home and you can transfer money from 1 part of the world to another. This is already embraced by P2P payments world and companies are coming up with efficient and cheap solutions for Small Business and even large corporates. We are yet to capture the full potential of corporate space but fintech is headed in the right path and card acceptance in Small Business and Corporates is a start of it. So yes, payments system has been modified highly not just for ease but also to make it more secure.

AP-

As they say, data is the new oil. Payments are the final layer to any business activity. It is your final signature when doing business with an entity. This payments data generated through digital payments is a rich source for creating patterns and understanding consumer behaviour. We see Fintech as an enabler or a partner for the existing ecosystem, helping them reach out to untapped markets and helping them to create a consumer profile using alternate data points.

Me-

38% of the world’s population lack a basic bank account and an even greater proportion lack the simplest of insurance and investment products. Do you think FinTech, particularly in the form of mobile money, is an essential part of the solution for this or it could face barriers?

MC-

If we just talk about India, out of 1.3B population, we have 800M mobile users. With such high penetration of mobile in India, fintech in the form of mobile money is an essential part of the solution. Apps like Paytm which cater to small business and small local merchants making them reach out to each and every person in India in turn, facilitating P2P, B2C, C2B, B2B payments. Anyway, the future is mobile or maybe something even smaller where 1 device could serve the purpose of everything so it does make sense that we are trying to fit in every payment form in there.

AP-

Absolutely! Digital payments give the customer a footprint that banks and insurance companies can leverage. It is redefining the metrics that a traditional bank or an insurance company would use to evaluate a customer and use the alternate data points to curate the best products. Even for existing consumers, Fintechs are helping the large legacy players get down to the brass tacks of profiling and provide products that stay current and relevant. For example, sachet insurance is a great innovation that does away with larger bulky and one-size-fits-all products and gives the consumers exactly what they want and presents the insurer another opportunity for product uptake

Me-

Is it a boon for cyber security or are we just unaware of how much of our personal data is actually out in the cloud? Fintrusion? A survey said online fraud could reach $25.6 Billion by 2020. Views on this? So, is fintech actually increasing cyber security?

MC-

There is definitely new technology coming up for cyber security and it is surely needed when it is so easy to do an identity fraud or any kind of fraud for that matter because technology will have certain limitations too. Definitely, most of the people are unaware of the quantity/quality of their personal data in the cloud and even, if they are have some idea, they aren’t educated enough on its misuse. And with this whole fintrusion, there is growth of the idea of “privacy” and people are becoming more cautious while sharing their personal data with various websites/apps. But fintech is definitely coming up with increased cyber security. Country like India has 2 step authentication which is a good measure to avoid fraud. Not even that, banks are also coming up with different algorithms to identify fraud and mitigate it in the best possible manner. But there is need for more and at much faster rate especially with new payment methods like Card on File, Contactless, etc. coming in.

AP-

Every innovation comes with its own set of risks. We feel the problem here is that the push is mostly on product adoption, rather than consumer education and then eventually adoption. So rather than being reactive, all the stakeholders must be proactive when it comes to data security and consumer awareness.

That said, there is a whole segment of entrepreneurs and technologists working on data security & fraud prevention, so as the industry matures, we should see this challenge being mitigated.

Me-

50.2% globally saying they do business with at least one non-traditional firm for banking, insurance, payments or investment management, with the percentage reaching the highest in Asia-Pacific (58.5%). How do you think the consumer response has been to fintech since its inception?

MC-

Asia Pacific has a lot of developing countries which is one of the major factors as to why they do business with at least one non-traditional firm. People are still stuck to their old ways and will definitely take time to come up to speed in terms of learning new technology. But overall, fintech has been growing quite fast in these countries and even in better way because fintech isn’t just reaching the consumers or large corporates but also small business or local small merchants which are more prevalent again in developing countries rather than developed countries. China has their own payment system and they are closed to the idea of having the same payment system as the rest of the world but that definitely makes them more secure and India has 2 step authentication which is again unique to it but again, mitigates a lot of fraud already. And because there is a lot of untapped potential and cash is still a king in Asia Pacific, the growth of fintech will be the highest here.

AP-

The consumer response till now has been positive, albeit not uniformly. While banks and traditional institutions invoke trust, the generation coming into the workforce (with disposable income in hands and a key drivers of consumption) values speed and customer experience equally, if not more. That said, there is still an entire section of the population that intrinsically thinks of new-age Fintechs as risky. This trust deficit hampers a digital-only strategy. So as a Fintech, our ideal sweet spot is to partner with the traditional institutions to combine the trust that banks inspire with the speed and customer experience that Fintechs offer.

Author
Purnima Dutt
Volunteer- FinTech
(MSc Finance, NMIMS Mumbai. Batch 2018-20)

Connect with Purnima on LinkedIn