HDFC BANK STRATEGY

Retail loans have kept HDFC Bank’s books clean for 24 years. But they may not be in focus for long.
The resignation of Paresh Sukthankar, presently deputy managing director and a CEO probable, has raised many eyebrows, with a possible shift in HDFC Bank’s lending strategy — from retail to corporate — being seen as a reason.
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Madhavankutty G
21 Aug 2018
 PARESH SUKTHANKAR, OUTGOING HEAD OF CREDIT RISK, HDFC BANK; PHOTO BY ABHIJIT BHATLEKAR/BLOOMBERG/GETTY IMAGES
It is ‘too big to fail’ and its combined market capitalisation exceeds that of all public-sector banks put together. Its assets exceed 2% of India’s GDP, earning it the tag of a domestic systemically important entity. That’s HDFC Bank for you.

Apart from an enviable balance sheet, it has the DNA of a retail lender, and infrastructure lending was never its priority. Well, things could change soon. But more on that later.

Let’s get on to the leadership aspect of the story first.

It came as a surprise to many when Paresh Sukthankar, its deputy managing director and a CEO probable, resigned recently.

Sukthankar was a member of the founding team at HDFC Bank and has been with the lender since its inception in 1994. During his 24-year association, he has handled key portfolios, including HR, credit, and risk management. He has been widely acknowledged as a hardcore risk manager who played a key role in the bank’s growth story. Being 55, he had a long tenure ahead, and was expected to take up the CEO’s role someday.

Hence, many link Sukthankar’s departure with a possible shift in the bank’s lending strategy.

But before we probe that further, let’s take a look at the bank’s fundamentals:


 Sequential profit growth of 20%-25%
 Enviable asset quality with delinquency levels below 2%
 A current and savings account (CASA) ratio of 45%
 Net interest margin above 4%
 Return on assets of close to 2% even in the current challenging environment
 There could be three reasons behind Sukthankar’s resignation.

The USD3 billion link
Sukthankar’s resignation came soon after HDFC Bank raised more than USD3 billion in capital. A back-of-the-envelope calculation shows that with this capital infusion, incremental credit deployment should be at least INR2 lakh crore. Notably, 70% of the banks (read public-sector banks) is capital starved and 11 of them, accounting for a fifth of the credit market, are under the prompt corrective action (PCA) framework of the Reserve Bank of India (RBI).

This is a huge opportunity for HDFC Bank to take up the slack and expand its corporate-credit portfolio. A recent RBI study has shown industry capacity utilisation slowly inching up from 70% to 75% levels — evidence that demand for growth capital is indeed on an upswing.

Till now, HDFC Bank has had a strong retail thrust. The corporate-retail mix has been in the ratio of 55:45 most of the times. Sukthankar is said to have built the retail focus of the lender. According to sources, he was not in favour of infrastructure financing, a reason why the bank has a superior asset quality compared to its peers.

But having raised over USD3 billion, a retail push alone would be insufficient to achieve the desired incremental credit growth. Big-ticket corporate and infrastructure funding would need to be given a major thrust. This would require a gradual shift from the bank’s highly retail-centric business model.

An executive from the Indian Banks’ Association tells ET Prime on the condition of anonymity, “Sukthankar is a hardcore risk manager and has not been in favour of infrastructure lending. Maybe he was under pressure to do more of corporate lending. This would have created discomfort.”

To be sure, HDFC Bank’s term-loan profile in its overall corporate book has risen to 30%. Term loans as a share of total loans has also seen a steady rise from 55% during 2013-14 to 70% by the end of March 2018The investor link
In its latest fundraising round, the bank raised INR8,500 crore from its parent, HDFC, through a preferential allotment. It also launched a qualified institutional placement or QIP, and an American depository receipt (ADR) to raise INR15,500 crore on July 31, 2018, according to stock exchange filings.

In June 2018, the government permitted 74% foreign direct investment (FDI) in the bank. So, essentially it is a foreign-owned entity. Since the investment is in the FDI format, most of the investors would be strategic about their interest in the day-to-day functioning of the bank.

Capital Group, Fidelity Investments, Soros Fund, and Highbridge Capital — the major investors in the ADR issue — are focused on sectors like healthcare, industrials, IT, and energy for their portfolio funds. Being FDI players, they might not be amenable to an overly retail-focused approach. Also, investors may not like the fact that a board member from the bank (Sukthankar in this case) is not comfortable with this proposition.

An e-mail sent to HDFC Bank seeking comments on new investors and their sectoral approaches did not elicit any response.

The external-candidate link
Reportedly, Sukthankar was told that he was indeed one of the candidates for the CEO’s post but external candidates would also be considered. A senior executive at HDFC Bank says, “This was more than enough to hint that Sukthankar might not be in the race for the post. Moreover, nowadays, it is very rare to see upward mobility from within the ranks and somebody making it to the top post. He has been with the bank for 24 years. Promoters might want fresh ideas and, hence, they could be looking for an external candidate. Also, after a point, those at CXO levels exit to pursue their own ventures or passions.”

The banking landscape is indeed changing fast and to withstand competition while maintaining profitability and margin growth, focus on niche areas is a necessity. This requires a strong grip over emerging segments such as fintech, artificial intelligence, cloud, cybersecurity, and blockchain technology. The promoters and investors could be thinking along these lines as well while scouting for the next CEO.

The decision to abolish the post of deputy managing director, too, assumes significance. While the post was presumably created as a recognition for Sukthankar’s contributions, his replacement will now be an executive director, to be announced by the end of this month.

The potential successor for the CEO position is proposed to be identified by October 2019 and the candidate will be seasoned for a year before ultimately taking over the reins. Though the name of executive director Kaizad Bharucha is doing the rounds, for an internal candidate well versed with the nuances of the bank, such a lengthy seasoning might not be necessary. This once again strengthens the possibility of an external candidate as the CEO.

If an internal candidate has a chance to make it to the corner office, it is logical to assume his induction to a post apparently senior to that of executive director, like chief operating officer in the case of ICICI Bank. But Sukthankar’s replacement would be another executive director.

On its part, the bank maintains that it has not yet constituted a committee to identify potential candidates for the top post. An e-mail sent to the bank regarding the appointment of an external agency for identification of potential candidates did not receive any response.

What’s next?
Speculation is rife that Sukthankar will head another private lender, Axis Bank. Shikha Sharma’s term as CEO will come to an end by December 2018, just a month-and-a-half after Sukthankar’s 90-day notice ends at HDFC Bank. Axis is in dire need of a hardened risk manager as it is beset by power-sector and infrastructure woes. Sukthankar’s risk-management capabilities could come in handy.

There are also rumours that he could head ICICI Bank, but it has a culture of promoting from within the ranks.

While founding HDFC Bank, both Aditya Puri (current CEO) and Sukthankar came from Citibank. Considering the robust systems and processes that HDFC Bank has built over the years, a candidate from a foreign bank is likely to make the cut as the next CEO. Some of the possible candidates could be Pramit Jhaveri, CEO, Citi India, Zarin Daruwala, CEO of Standard Chartered India, and Ravneet Gill, CEO of Deutsche Bank, India.

These banks unarguably have some of the best systems and processes. They have also embraced fintech and emerging technologies in a bigger way, which HDFC Bank needs to adopt to become future ready.

PRICING AYUSHMAN BHARAT

Private Hospitals Find Modi’s Health Insurance Pricing Unviable
• Ridhima Saxena@Ridhima__Saxena
21 August 2018, 12:16 PM21 August 2018, 6:06 PM
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Calling the pricing unviable, private hospitals threatened to stay away from the Prime Minister’s health insurance scheme that will cover more than half-a-billion Indians.
The Indian Medical Association, a doctors’ lobby that agreed to bring small and medium hospitals on board, said the rates of medical procedures under the scheme were “unscientific, non-viable and will compromise on patient safety”, according to a June 22 letter to NITI Aayog. The prices calculated by the association were up to 84 percent higher than what the government fixed.
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Also Read: Most States Opt For Trusts Amid Rush To Roll Out Modi’s Health Insurance
“The existing package rates are at least 25 percent lower than even the break-even point for small and medium hospitals,” said Dr. RV Asokan, chairman at IMA Hospital Board of India that represents small hospitals. “If hospitals are to provide treatment at such unsustainable rates, they will end up shutting shop because of losses,” he said, adding that cost calculation needs to be transparent.
Participation of private hospitals is vital for the Pradhan Mantri Jan Arogya Yojana. That’s because half the households in the country, according to the National Family Health Survey published in July last year, don’t use state-run facilities because of poor access and quality. Moreover, India has one government doctor for every 11,082 people, according to the National Health Profile. That compares with the World Health Organization’s suggested ratio of 1:1,000.
Prime Minister Modi, in his Independence Day speech, announced the rollout from Sept. 25 to cover at least 10 crore families for Rs 5 lakh a year each. More than 8,800 hospitals have already expressed interest, according to Indu Bhushan, chief executive officer of the National Health Agency, the implementing authority. Nearly half of them, he said, are private.
Ravi Wankhedkar, national president of Indian Medical Association, countered that. They are purely expressions of interest, he said. “These are not conclusive and unless the issue of low package rates is resolved, no private hospital is likely to come on board.”
The association, which represents 33,000 hospitals across the country—including 15,000 small and medium—said a team should be constituted to fix costs of at least 150 common medical procedures. Low rates, according to IMA, would lead to poor quality of healthcare as hospitals would cut costs to break even.
Also Read: Beds In Rural Areas, Pricing Biggest Challenges To Ayushman Bharat Scheme
‘No Time’ To Study Costs
The IMA and the government, Wankhedkar said, had agreed that two representatives from the association will assist the Department of Health Research to form a consultative pricing group led by the NITI Aayog—which designed the scheme.
But no cost study was carried out. “We are really stretched in terms of rolling out the scheme and such a study can’t be done before the implementation begins,” Bhushan said. A comprehensive analysis of rates will be done in December-January, according to Bhushan, as it will take time to agree on a methodology, collect relevant data and discuss it.
The existing prices of 400 most-used medical packages fixed by NITI Aayog are based on Directorate General of Health Services’ analysis of insurance programmes offered by states, according to Bhushan. The pricing, he said, will vary with states, which have the freedom to revise them.
Yet, lack of a consultative approach has led to confusion and ambiguity, according to Girdhar Gyani, director general at Association of Healthcare Providers (India), which represents 40,700 large and about 8,000 small and medium hospitals. There is lack of clarity on claim settlement and even the rollout date, he said.
Also Read: 65 Lakh Families Identified For Modi’s Health Insurance Missing
‘Underfunded’
The government has allocated Rs 2,000 crore for 2018-19. “A scheme envisaging Rs 5 lakh coverage for 50 crore people should have at least Rs 1,16,000 crore allotted for adequate capitalisation. Only Rs 10,000-12,000 crore are expected to be the fund allotment,” IMA said in a dossier outlining its concerns. “Such gross underfunding will lead to a collapse of the scheme itself.”
India’s public expenditure on health is about 1 percent of its GDP, compared to the 1.4 percent spent by low-income countries and also below the global average of 5.9 percent, according to the World Health Organization’s Global Health Expenditure Database. The nation’s private sector contributes almost 74 per cent of this expenditure, according to a report by India Brand Equity Foundation citing RNCOS, Grant Thornton, LSI Financial Services and OECD. Without the participation of private hospitals, the prime minister’s scheme won’t be as effective.
“If the prime minister’s scheme has to run through public hospitals, which already provide free healthcare, what is the need of a universal insurance scheme in the first place?” Gyani asked.  “I don’t think that any private hospital would formally sign up even by Sept. 25, the official launch date.”
Most States Opt For Trusts Amid Rush To Roll Out Modi’s Health Insurance
• Ridhima Saxena@Ridhima__Saxena
01 August 2018, 1:32 PM01 August 2018, 4:59 PM
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Most states have opted to set up subsidy pools to fund Prime Minister Narendra Modi’s health insurance scheme that will cover more than half of Indians, as insurers stayed away because of pricing concerns and officials rush to meet the Aug. 15 deadline.
Twenty out of the 36 states and union territories will create non-profit trusts to pool in subsidy contributed by central and state governments, according to the National Health Agency, the implementing authority. Seven, including two union territories, opted for tying up with insurers—only one, Nagaland, has done so. Eight opted for the mixed model—payments below Rs 50,000 will be covered through insurance and the rest will be paid from the corpus.
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Odisha decided to stay out. Seven have still not officially joined but indicated preference for trusts, the agency said.
“It’s a quick fix,” said Anirudh Jain, who heads insurance at the financial services firm Centrum Group. Forming public-private partnerships with insurers takes time, and given the launch must happen on Independence Day, the trust model seems to be the only option left on the table, he said.
Indians pay over three-fourths of all healthcare costs out of pocket, according to a study by the Public Health Foundation of India published in May. Nearly 5.5 crore people were pushed below the poverty line because of healthcare expenses, of which 3.8 crore became poor only because they had to bear medicine costs, it said. Modi’s scheme targets such households. The Ayushman Bharat National Health Protection Mission will cover about 17 crore families a year through a cashless cover of Rs 5 lakh each.
It will subsume all existing plans in participating states, increasing the coverage from the initial 10 crore families. Only four—Andhra Pradesh, Telangana, Karnataka and Assam—followed the trust model.
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“It cannot be called insurance, which is more than just pooling of funds,” said Nidhesh Jain, financial services analyst at Investec Capital Services. The subsidy pools don’t involve price discovery through actuarial valuation, risk assessment, creation of reserves for expected claims or reinsurance, he said. “That could mean increased government liabilities, delay in claim settlement and more frauds in the long run.”
Joydeep Roy, insurance leader at PwC India, said the health insurance plan will yield better results if implemented in a public-private partnership. An insurer would ensure that claims management, technology, service delivery and fraud detection are robust to ensure profitability, he said.
The head of implementing authority, however, said states that opted for trusts “might have some distrust in insurance agencies” and felt that these pools can provide more effective service in a short period. Agreeing that the “government’s risk will be unlimited”, Indu Bhushan, chief executive officer of Ayushman Bharat, said in an emailed response that they were inspired by the good performance in Andhra Pradesh and Telangana.
The trust will get subsidy from a designated escrow account at Rs 500 for each family in the 60:40 ratio from the central and state governments. Bhushan said, “We will review the scheme after six months and based on the actual costs, the premium will be revised.”
Low Premium
An insurer that quotes the lowest premium is selected to join the scheme. The government pays it the premium to settle claims. Apollo Munich won the Nagaland bid at Rs 444 per family a year.
But for India’s largest general insurer, pricing is a concern. Ramesh Nag, chief manager (government health business) at state-run New India Assurance Company, cited the example of Rajasthan, where premium had to revised from Rs 370 to Rs 1,263 a family for a coverage of Rs 3 lakh a year.
“We are not making profits even at a higher premium as the claims ratio is over 200 percent,” he said. “How can we then quote anything less than Rs 2,500 a family for a scheme that has a sum assured of Rs 5 lakh?”
Jain of Investec Capital concurred. Calling the pricing “unsustainable”, he said insurers’ experience with the previous Rashtriya Swasthya Bima Yojana doesn’t inspire confidence. There were long delays in disbursal of premium subsidy while insurance companies had to pay the claims immediately, he said.
Moreover, the claims ratio of group health insurance—the closest in structure to the Prime Minister’s scheme— in general stays above 100 percent: that is insurers pay more to policyholders than they collect as premium.
Vinod Kumar Paul, member at NITI Aayog who was involved in planning the scheme, however, said there’s no pre-determined budget or premium, and the government will keep providing funds to trusts based on the claims received.
Hospitals Yet To Sign Up
Selecting hospitals to offer healthcare services hasn’t started yet. The implementing authority is initially looking to enrol 6,000—half of them state-run, Bhushan said. “We expect to have more than 10,000 hospitals enrolled by Aug. 15.”
Hospitals are, however, seeking better rates to provide quality services. Competitive package rates for hospitals would ensure better services and fewer frauds, according to Sanjay Datta, chief of underwriting and claims at ICICI Lombard General Insurance. “There is a high chance that private healthcare providers would stay away from the scheme if the present medical package rates are not made competitive. This could also lead to capacity constraints and substandard medical infrastructure—some of the issues companies faced in the initial stages of RSBY.”
Beds In Rural Areas, Pricing Biggest Challenges To Ayushman Bharat Scheme
• Tamanna Inamdar@TamannaInamdar
15 August 2018, 7:33 PM16 August 2018, 12:43 PM
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The biggest challenge to the Narendra Modi government’s ambitious plan to provide healthcare to 10 crore families will be the availability of hospital beds in tier-II and tier-III cities and in rural areas, said Indu Bhushan, chief executive officer of Ayushman Bharat.
The prime minister announced the roll-out of a pilot project on Sept. 25 under the scheme, also known as the National Health Protection Mission, during his Independence Day address on Wednesday. Ten states and union territories have been selected for the pilot run.
The scheme is essentially a Rs 5-lakh cashless family floater insurance covering all members of the household for a year. Bhushan, in an interview with BloombergQuint, said the private sector will be incentivised to open more hospitals “in lagging areas”. The pilot project, he said, will initially involve only government-run hospitals.
We are targeting 50 crore people. If, on average, 2 percent of them need hospitalization, we would have 1 crore admissions. And if we say that each hospital can provide 200 bed days we would need 2 lakh beds.
Indu Bhushan, CEO, Ayushman Bharat National Health Protection Mission
Bhushan admitted that a full roll-out—that will bring in approximately 50 crore Indians—in 40 days will be challenging. We have, from the hospitals that have come onboard, 60,000 beds, he said. He estimated that at least 2 lakh beds will be required if the scheme is to run at full capacity.
Pricing of health packages has been another pain-point, with the Indian Medical Association questioning the viability of providing healthcare at the proposed low costs. Bhushan said that this has been a “bone of contention” but assured that treatment rates under the scheme are “the median” and that states have the flexibility to change them.
We are hoping and expecting that we’ll find a middle ground. This is just the beginning and we’re putting a system in place for cost methodology.
Indu Bhushan, CEO, Ayushman Bharat National Health Protection Mission
BloombergQuint
65 Lakh Families Identified For Modi’s Health Insurance Missing
• Ridhima Saxena@Ridhima__Saxena
10 August 2018, 1:29 PM11 August 2018, 11:16 AM
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Even before Prime Minister Narendra Modi’s health insurance scheme is rolled out for more than half a billion people, India found about 6 percent families missing during surveys conducted to curb the possibility of ghost beneficiaries.
The National Health Agency didn’t find 65 lakh of the 10.74 crore households that will be covered for Rs 5 lakh a year, Indu Bhushan, chief executive officer of the implementing authority, said in an emailed statement to BloombergQuint. The beneficiaries were selected based on the seven-year-old socioeconomic caste census.
The agency conducted field surveys across the country to verify families to be covered under the Ayushman Bharat National Health Protection Mission. Uttar Pradesh and Bihar contributed 70 percent of the missing families, Bhushan said. “Some families have either migrated to other cities or some of their members are no longer alive, causing a mismatch.”
India ranks 81 in Transparency International’s Corruption Perception Index of 180 nations. Pilferage of welfare benefits has been rampant. To counter that, Modi has pushed ahead with the Aadhaar biometric ID-based direct benefit transfers—rolled out towards the end of his predecessor Manmohan Singh's term—to plug subsidy leakage in everything from cooking gas to food security and rural jobs guarantee programmes.
Also Read: Most States Opt For Trusts Amid Rush To Roll Out Modi’s Health Insurance
For the health insurance scheme, identifying families during field surveys is the first step. The government is considering a stringent claim-settlement process for those who couldn’t be verified. These families will have to present government-authorised personal and family identification, Bhushan said. “Aadhaar details are preferred but other IDs will also be accepted for the first time.”
The scheme will eventually be bigger than originally planned as beneficiaries covered by states but not in the central scheme will also be included. That would take the total number of insured families to 17 crore.
The prime minister, who reviewed the progress ahead of the Independence Day announcement, sought a strong information technology system to reduce fraudulent claims, Bhushan said. There will also be other checks and balances, such as identification, limiting fraud-prone medical packages to public hospitals, medical audits and feedback, he said.
Maintaining Caution On ModiCare: ICICI Lombard

Ridhima Saxena @Ridhima__Saxena
22 June 2018, 6:46 AM22 June 2018, 6:46 AM
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Bidding for India’s health insurance scheme, dubbed Modicare, may’ve commenced, but ICICI Lombard General Insurance Ltd. prefers to wait and watch.
“We’ll have to see whether the pricing and the bidding that is happening is sustainable for a longer period of time,” said Bhargav Dasgupta, the company’s chief executive officer and managing director, told BloombergQuint on the sidelines of the launch of India’s first ‘Wellness Index’. “We’ll take a call on participating later.”
Based on experience from other schemes in the past, we’ve realised there is a lack of data, understanding and inefficient pricing in the initial period of implementation, Dasgupta told BloombergQuint. “In some such schemes the pricing becomes completely unviable from the insurer’s perspective and when that happens on a sustained basis, the schemes fail.”
The company has so far not taken part in the bidding process for the Prime Minister Rashtriya Swasthya Suraksha Mission (PMRSSM), dubbed Modicare.

RUPEE DATA BEARISH

India’s GDP Fracas Could Hurt Modi and Maul Rupee Bears
• Andy Mukherjee
21 August 2018, 10:13 AM21 August 2018, 5:49 PM
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(Bloomberg Opinion) -- Just as some analysts were starting to believe in a reset for the Indian rupee, with the government becoming more relaxed about letting it weaken, history got in the way.
The history in question is past GDP data, which haven’t been officially released after India reworked its calculation methodology in early 2015. But the report of a subcommittee tasked to suggest ways to link the old and the new GDP series was recently made public. The panel’s calculations suggest growth averaged a tad above 8 percent in the 10 years through fiscal 2014, compared with 7.4 percent since then.
The opposition Congress Party lost no time in pouncing on the report. India’s economy, it said, had fared better under its last Prime Minister Manmohan Singh than it has in the four years of Narendra Modi’s leadership.
How does this history lesson tie in with the outlook for the rupee? Before the GDP back-series, some officials and analysts were beginning to shrug off this year’s 8.3 percent depreciation of the currency, the worst in Asia, as par for the course. The Reserve Bank of India would “let go” of the rupee, Bank of America Merrill Lynch wrote in an Aug. 15 note, “because there is only so much they can do to limit the sell-off.”

Technically, a hands-off approach may be the right position. The currency may still be overvalued, based on India’s inflation differential with trading partners and rivals. Besides, the nation has already spent $24 billion defending the rupee since April, which is when President Donald Trump’s administration added India to the U.S. Treasury’s monitoring list of potential currency manipulators. While there’s little chance of a country with a current-account deficit actually being punished for manipulation, with Trump you can’t be sure.
Politically, though, Modi can ill afford a laissez-faire stance. Could he allow the Congress Party to claim that the rupee at 80 to the dollar (it’s around 70 at present) is a symbol of the prime minister’s failure to deliver the “good days” he promised in 2014? Modi might have bitten the bullet if he could give his social-media warriors the cover of GDP data that make his performance look good while rendering the previous regime’s record indecipherable.
Now that a direct, unflattering comparison with the past has suddenly sprung up, however, my bet is he can’t let the rupee go. Every milestone in that weakness – 70, 71, 72, 75, 80 – would make him vulnerable to ridicule on Twitter, Facebook and WhatsApp. And that’s one thing no Indian politician who has to fight reasonably fair elections can endure.

Five years ago, Team Modi succeeded with great aplomb in nailing the Singh government with simple but lethal social-media memes around anemic growth, high gasoline prices, a weak rupee and a string of corruption scandals. Now, the Congress Party wants to return the compliment in next year’s elections by using the same narrative of expensive gasoline, a wilting currency and alleged improprieties in the purchase of Rafale fighter aircraft from France’s Dassault Aviation SA.
In the absence of reliable employment data (the Modi administration has stopped surveys), all that was missing from the Congress Party’s attack was clinching evidence that the prime minister’s policies were growth-unfriendly. The subcommittee on past GDP has given it that weapon.
Never mind that the new GDP series is still full of holes. At least now the opposition can stop blaming Modi for fudging data and instead rush to take credit for the 10.8 percent expansion it apparently delivered in fiscal 2011. As for the 5 percentage-point decline in growth on its watch in the two subsequent years, the shocks are reverberating even now. Firms that made bold, debt-financed investment bets in the expectation of rapid economic growth are now being dragged through bankruptcy courts; the banking system is grinding its way through more than $200 billion in stressed corporate loans.
However, that story is too complicated for Modi’s supporters to tell in WhatsApp groups. Good luck to any finance minister who tries to explain on Facebook the not-so-minor detail that world output grew by more than 4 percent in four of the 10 years of Singh’s tenure, while the fastest expansion in the global economy during the Modi years was 3.15 percent in 2017.

Recovering from a major loss in a battle of perceptions isn’t easy. Slapping a note on a published report saying figures aren’t final and should “not be quoted anywhere” – after they’ve been quoted everywhere – only helped highlight the government’s embarrassment. There’s no way Modi can give his detractors still more ammunition by shrugging off a weak rupee.

The age-old theory that fear of losing money drives financial decisions is under fire

The age-old theory that fear of losing money drives financial decisions is under fire

Loss aversion is a central idea in behavioural finance. Two academics have challenged it and kicked up a storm between hedge-fund managers and finance professionals.

VCG/VCG/GETTY IMAGES

Avanti Feeds is one of the favourite stocks in the mid-cap segment. It has given extremely high returns to its investors: 350% over the last three years.

The dream run ended in the beginning of June, when the stock lost 38% in 10 days. (It also recovered rapidly: 38% in two days.)

The fall hit loyal investors hard. “Those who purchased this stock four years ago and saw multiple growth will now be pained ten times more,” says a fund manager.

He is hinting at the phenomenon of loss aversion.

Most investors like stocks that give lower but relatively certain returns. They are willing to pay a premium for such stocks. But a high-growth stock with relatively high uncertainty of performance is something that investors with serious loss-aversion bias tend to avoid.

Or do they?

Challenging a core belief of behavioural economics
Since January, the mid-cap index has fallen 13%. It was up 140% over the last four years. Conventional wisdom would suggest that for mid-cap investors with loss aversion, the pain of the last eight months would have overriden the pleasure from the gain. In general, the theory of loss aversion states that losses cause twice the pain compared with the pleasure of gains.

Now two finance professors are challenging this theory, which is one of the key pillars of behavioural economics. They say there is little evidence that investors focus more on the possibility of loss rather than expectation of gain while making decisions.

In a recent review published in the Journal of Consumer Psychology, David Gal and Derek Rucker say loss aversion is a fallacy. According to them, people do not rate the pain of losing USD10 to be more intense than the pleasure of gaining USD10.

Gal is a professor of marketing at the University of Illinois, and Rucker teaches marketing at Northwestern University.

Essentially, what the two professors have done is challenge the prospect theory, which was developed by Daniel Kahneman and Amos Tversky, two psychologists who created the field of behaviorial economics. The prospect theory posits that people make decisions based on the probabilistic value of losses and gains rather than the final outcome.

“What is most fascinating to me about the premise that Gal and ... Rucker … have pushed forward is around the meta-concept that challenging the status quo is an uphill battle. They are on to something here, though surely they recognize that Daniel Kahneman and Amos Tversky’s famous theory was itself not accepted for a long time. Kahneman and Tversky’s ground-breaking 1979 paper was an assault on the status quo at the time, and it took decades before their thesis was assimilated into psychology and economics”, says Barry Ritzoltz, co-founder and CIO of Ritzoltz Wealth Management LLC in a Bloomberg article.What does it say? Pain of losing is twice as powerful as the pleasure of gaining.

A real-life example: Economist Paul Samuelson asked a colleague whether he would be willing to accept the following bet: a 50% chance to win USD200 and a 50% chance to lose USD100.

§  The colleague turned down that bet, but announced that he was happy to accept 100 such bets.

§  Here is what the colleague offered as his rationale for turning down the bet: "I won’t bet because I would feel the USD100 loss more than the USD200 gain.” A string of 100 bets, the colleague felt, would give a better chance of gains.

Effect of loss aversion

§  Researchers say it explains the equity premium puzzle, which says stock returns sometimes appear to demonstrate a high degree of risk aversion.

§  It gives rise to the disposition effect, in which investors tend to sell off winners too early and hold on to losers for too long.

The challenge and the response to it
Gal and Rucker argue that loss aversion as a concept took wings because contradictory evidence was dismissed, ignored, and explained away, while ambiguous supportive evidence was magnified.

According to them, the phenomenon of sunk-cost effect, where people keep repeating an activity or an idea that doesn’t give any returns, is attributed to loss aversion. This is wrong.

“While the sunk-cost effect might reflect a reluctance to recognise losses, this is not relevant to loss aversion, which requires [that] a comparison be made between losses and gains”, Gal wrote in Scientific American.

The article stirred much debate on Twitter, with some well-known investors sinking their teeth into the subject. Financial philosopher Nassim Nicholas Nassim Taleb tweeted that loss aversion is fiction, citing Gal and Rucker.

Drew Dickson, CIO and managing partner at Albert Bridge Capital, took an opposing position. Gal and Rucker say that risky choices, such as buying a lottery ticket, are gain-seeking behaviour. Dickson says: “They haven’t considered that the buyer doesn’t believe his payoffs are symmetric. If you think betting USD1 gives (what you – albeit mistakenly – believe to be) a reasonable chance of making USD1,000,000; you aren’t risk averse.”

He tweeted, “So, sure, a billionaire will not distinguish between a USD100 loss and a USD100 gain as much as Taleb’s at-risk baker with a child in college; but add a few zeros, and the billionaire will start caring,” and “Losses (that are significant to the one suffering the loss) feel much worse than similarly sized gains feel good. Just do the test on yourself”.

Cliff Asness, founding partner of hedge-fund giant AQR Funds, tweeted: “The new study doesn't make me doubt loss aversion very much. Though a different question is how much such biases affect prices/returns. Always have to separate those two things. Market efficiency doesn't rely on each individual being a Vulcan.”Bugbear of value investors
Yes. Market efficiency doesn’t rely on each individual being a Vulcan. It depends on the market’s collective smartness. Do the biases of individual investor affect prices and returns? That is the question that requires serious research.

Loss aversion will have an effect on prices or returns for some time. Ask value investors. Their first rule of investment is not to lose money. They work real hard to preserve capital. Ideally, they are ultra aware of loss aversion. But only the smart ones can turn this into an opportunity. They can spot a scenario where the prices of some stocks fall so much that the risk of falling further is nullified. These investors buy such stocks, whose intrinsic value is infinite. But such opportunities are few.

In most cases, value investors miss out on good opportunities due to loss aversion. They know their subject so well that they are not ready to take even calculated risks.

A case in point, as we earlier wrote, is that value investors will not invest in an Avenue Supermart or a Bandhan Bank because they will give a lot of importance to that one negative point that might cause pain to these companies in the near future. For them, these stocks need to fall massively from their issue price to eliminate loss aversion.

They are willing to let go of the possibility of manifold gains in these stocks because they know that the pain they will experience if they don’t perform will be extremely high.

Warren Buffett avoids investing in technology stocks precisely for this reason. He is worried about that one technology upstart that will come and wipe out the existing order. He did not invest in Amazon. The company did not pass through the filters of his value parameters. Today, Buffett says he regrets the decision. In a CNBC interview in May 2017, he said it was out of stupidity that he did not invest in Amazon.

Smart investors understand that investment is a psychological game, and while investment models can capture the intrinsic value of a stock, it is the emotional aspect of how much to buy and how long to hold the stock that is an art.

This is where loss aversion comes in. The most informed value investor will suffer more damage emotionally than the one who is taking relatively blind risks. And this is the problem that Gal and Rucker have not been able to address properly.

They have generalised all people into one basket. If investors are more invested into a particular topic, they will feel more pain if their idea doesn’t work. This is where the biases of sunk-cost fallacy gets explained through loss aversion.

So loss aversion does exist. Investors who have burnt their fingers in the dotcom bust are still scared to enter the stock markets. Those who purchased infrastructure stocks in 2007 are still licking their wounds. But truly seasoned investors are able to look beyond the pain of loss and continue investing.

Ask yourself this simple question: Do you concentrate too much on that one loss-making stock, or does the overall gain in your portfolio excite you more? If the odd bleeding investment wipes out the thrill of seeing your money grow, Gal and Rucker have work to do.

The dawn of EV charging in India No infrastructure. Low demand. Apathetic government. In the middle of this wilderness, a handful of companies are investing in building electric-vehicle charging stations in India. We bring you a ground report

"Oh shit" is so 2010. The new anthem of urban angst is “low battery”.

I felt a whole new dimension of it last week, riding around in a Mahindra eVerito. After about an hour on Delhi roads, the car started flashing the warning in bright red. I knew I was safe: It was the middle of the day, and the whole point of the ride, which had been arranged by the Finnish company Fortum, was to experience the electric car’s charging process. Still, the creeping sense of unease was hard to shake off.

Presently, we steered down to Scope Complex on Lodhi Road, which houses a phalanx of government offices. In a parking lot inside the complex, two electric-vehicle (EV) charging stations have been set up, one by Fortum and the other by Gurugram-based Exicom Power Solutions. We plugged the eVerito into the self-service Fortum Charge & Drive public network. The LED indicator at the charging point turned blue, meaning occupied.

Having thus satisfied ourselves with the colour of relief, we took shelter in the National Thermal Power Corporation building next door.

Then, we waited.

When dead-dino fuel meets the future: India’s rocky relationship with EVs
Fortum came to India in 2012. Since then, it has punctuated India’s sparse EV-charging landscape with two AC smart-charging stations in Delhi and four in Hyderabad (with two more in the works).

For the latter, it’s piggybacking on the existing infrastructure owned by the country’s largest fossil-fuel marketer, Indian Oil.

“Fortum will set up charging stations at 50 retail outlets of Indian Oil on an exclusive basis in Hyderabad. Each station will be [powered by] DC-type fast chargers of 10kWh or 12kWh capacity,” says SSV Ramakumar, director (R&D) and board member, Indian Oil.

“The Indian market is different from the Nordic and European markets,” says Awadhesh Kumar Jha, who leads Charge & Drive at Fortum India. “What we have observed is that Indian customers appreciate the quality of service (read: the self-service culture in vogue in the West will require significant customer education to take off in India). We are deploying Made in India chargers in our network. On our app, our customers can locate the chargers and their availability as well as pricing details. After charging their car, they can pay digitally.”

Pune-based PlugInIndia started out in the charging-station business a year after Fortum’s entry. In collaboration with Tata Power, the company has set up a charging point at Vikhroli, an office hub in Mumbai. Another charger has been installed at Trinetri Petrol Pump in the hill town of Lonavla, serving holidaymakers as well as commuters on the busy Mumbai-Pune route.

What these companies are doing is a very early precursor to the ultimate EV dream: Imagine the roadside chai stall or dhaba on your drive up your vacation spot or on a highway no longer serves you just a bowl of masala Maggi and ginger tea, but also a plug point to charge your car.

But before that utopia, there’s reality.

EVs have had an on-again, off-again relationship with India, marked by extremes. In 2017, the government decided to turn the country into an EV-only market by 2030, sparking laudatory coverage in the global media. However, it was a matter of months before the government had to walk back its aggressive stance and clarify that there were no such plans.

All conversations on the sluggish uptake of EVs have been dominated by a chicken-and-egg problem: Whither the charging infrastructure to drive your car in peace? Meanwhile, charging-station operators have been anxious about putting the cart before the horse, seeing the low demand for EVs. We wrote about the arduous journey ahead for India’s EV sector here and here.

Given all this, what is the future of the charging-station business in India?

The mechanics of charging stations
Charging stations can follow one of two models.

Smart charging: This is the model commercial charging stations follow. The EV and the charging station communicate the charging requirement between themselves without human interference. The user needs to flash an RFID code at a scanner installed in the station to begin charging. The time needed for charging depends on the type of charger used.

 AC (slow charging): 6-10 hours for a car, one hour for a two-wheeler.
 DC (fast charging): 50%-60% charge in an hour for a car.
“The present fleet of cars being manufactured in India are sub-100V systems, which can be charged using 10kW-15kW chargers in DC fast mode, whereas they can be charged at home or at the workplace using a 15 Amp socket in the AC mode. High-voltage-system cars would require higher-capacity chargers both in DC and AC mode,” says Jha.

Community charging: Community charging stations are set up by EV users themselves. Their services can be paid for or free.

On its website, PlugInIndia, which helps set up community charging stations, explains: “… companies and the government can only do so much when it comes to installing charging stations. The beauty of an electric vehicle is that any home/business/hotel can install EV charging stations. The idea is for businesses/resorts/vacation homes, etc., which are at a distance of 40km-70km around cities, to set up normal 15Amp charging stations.”

Anyone can set up a community charging station and pin it on PlugInIndia’s app, RE:CHARGE India. The company says so far are a little over 200 community charging stations have been set up in India.

“The good part about going to these stations is you get to meet interesting people who are also interested in EVs,” says a PlugInIndia spokesperson.

Locations such as shopping malls, restaurants, offices, stadiums, underground or multi-level parking lots, movie theatres, hotels, and airports, where there is an unhindered supply of power along with ample parking space, are suitable for setting up charging stations.

“All parking lots should have a minimum 20% of their space earmarked for charging stations,” says Jha. “The earmarked spaces should be given free of cost to potential charging-service providers initially, for a couple of years. Alternatively, the government can adopt the Transport for London (TfL) model, where TfL secured the sites and provided upgraded electricity-grid infrastructure.” A Mahindra eVerito at a Fortum charging station in Lodhi Road, Delhi; photo by Kanika SaxenaThe economics of charging
For commercial players, scaling the business will be a long grind. Hearteningly, the early movers don’t seem worried about making money just yet. Fortum, for instance, is offering free charging to EV owners. It wants to develop the market first and the revenue model later.

PlugInIndia says the cost of charging at its stations could range between INR50 and INR75 per hour. Some of PlugInIndia’s stations are powered by solar energy and offer free charging. One such station has been set up at Wai, on the way to the hill station of Mahabaleshwar in Maharashtra.

What does the economics of charging look like?
The cost of setting up a charging station varies based on the type of power supply. Whereas an AC smart-charging point could cost roughly between INR30,000 and INR1,50,000, a DC smart-charging point could require an outlay of INR3 lakh-INR9 lakh.

For users, EV charging is barely a pinch compared with conventional fuel.

In Delhi, for instance, the cost of electricity is INR1.45/unit. This means to charge an EV, for example the Mahindra E2O, which requires 10 units to completely juice up its 10kWh battery, the total cost of electricity would be a paltry INR14.50. (In comparison, the Tesla Model S P100D has a 100kWh battery. So, to charge that car in Delhi, the electricity cost would be INR145. Still a bargain compared with gas.)

But power is only one variable in price calculations. The other variable is time.

The amount of time taken to completely charge an EV varies from five to seven hours. In an hour, a car charges about 24% using slow AC chargers, which can last through a 20km drive. It takes about four hours to charge up to 80%-85%. The remaining part of the battery takes about two hours to charge. This is how batteries work, and this is where the price calculation gets tricky.

The charging process, Jha explains, doesn’t involve a simple sale of power but a full suite of value-added services. Some the cost heads that get loaded up as a result are

 The cost of equipment to keep the car safe during charging
 The cost of converter for DC charging and connectors
 The cost of space for parking the car during charging
 The cost of beaming real-time charging-station data (location, occupancy, rates, etc.)
 “You don’t bring your EV to a charging station to buy electricity but to get its battery charged in a safe and reliable manner, and to learn about the condition of the battery from the diagnostic tools available at the station,” Jha says. It’s a full package — not the stop-fill-go transaction we are used to at conventional fuel pumps.

Darkness before dawn?
To cynics, all that might sound like bells and whistles, even as the EV market grapples with fundamental problems. The lack of infrastructure, coupled with the limited range and high price of EVs, has long stymied the market’s potential.

The government’s one-step forward, two-steps back attitude hasn’t helped.

“The adoption of EVs in the Nordic countries, particularly in Norway, was initiated through government support,” Jha says. “That helped create demand for EVs, which in turn imparted a push to the development of public charging infrastructure. In India, the government has indicated that the sector will not be subsidy-driven. That throws its own interesting challenges.

“In the long term, the industry could be more sustainable if OEMs are able to reduce the cost of EVs in the initial years. In principle, support from the government is required till the time cost of battery EVs comes on a par with ICE vehicles. However, we understand from media reports that the government is contemplating withdrawing subsidy to individual and fleet operators. If that is true, this will be a big setback to EV adoption in India.”

A few state governments have come up with their own policies and schemes to enable ease of business for EV-infrastructure providers — Telangana, for instance, explaining the emergence of state capital Hyderabad as an early adopter. But a huge amount of work still needs to be done.

“The government of India has announced an amendment to the Electricity Act 2003, according to which no separate licence is required for charging stations and charging would be treated as service, not sale of electricity,” says Ramakumar of Indian Oil.

“However, respective state governments are yet to issue any policy guidelines. The EV policy of the Telangana government is still in a draft stage and yet to be announced. There is no clarity on the electricity tariff applicable to EV charging. There are different standards for EV chargers. The government is yet to announce standards to be adopted by EV manufacturers to bring uniformity in charging infrastructure.”

Red to green
Our car stayed docked to the charging point for about an hour, good enough to reach its next destination. If the wait to fully charge your car bores you, you can choose to stop any time by pressing a button on the app.

It’s a nifty feature for users, but cheerleaders of EVs in India will hope that the powers that be don’t pull the plug midway.

(Graphic by Ankita Mehrotra)

AM FM DATA

Advances in Monetary and
Financial Measurement (AMFM) AMFM Home |  US Data |  Augmented |  International |  Library
Divisia Monetary Data for the United States:
rigorously founded in economic aggregation and index-number theory.

Federal Reserve Official Monetary Aggregates

Extensive published results demonstrate that the best monetary aggregate for almost all uses was the Federal Reserve’s former broadest aggregate, L, but only if computed as a properly weighted index number, such as the Divisia or Fisher-ideal index. The second-best monetary aggregate was the former next-broadest aggregate, M3, properly constructed as an index number. In contrast, when computed as simple-sum accounting numbers, disconnected from economic aggregation theory, M3 and L were among the worst monetary aggregates and were inconsistent with elementary principles of economic measurement. Narrow aggregates, such as M1 and M2, give no weight to many highly-liquid substitutes for money. That truncation is hard to justify. More...

Figures Provided on This Page

We are displaying on this page three, new, Divisia, monetary-quantity aggregates, and their corresponding, aggregation-theoretic interest-rate aggregates.

The primary one is what we are calling Divisia M4. It is a broad aggregate, including negotiable money-market securities, such as commercial paper, negotiable CDs, and T-bills. DM4's components are similar to those of the monetary aggregate once called L, but modernized to be consistent with current market realities.a
The aggregate DM4- (to be called "M4 minus") is DM4 minus Treasury bills.
Divisia M3 excludes those money-market securities not issued by financial intermediaries, such as commercial paper and Treasury bills, but does include negotiable CDs and repurchase agreements.
We are providing narrower aggregates, such as Divisia M1 and M2, only on a spreadsheet and through a link to the St. Louis Fed’s source. We do not include those narrower aggregates among these featured charts. More...
More Information Below

The Methodology
Data Table
Additional Data, Not Displayed on This Page
Download in Excel
Bloomberg Terminal Access
Key Takeaway Points
CFS Money Supply for July
(Released August 22, 2018)
Monetary & Financial Data Release
Monetary Notes & Views
Previous Releases and Views
CFS Divisia Release Schedule
Featured Charts

Hover over chart to see a larger version, or click on the title for an even larger full-page version.

Select start date of charts: 

Divisia Growth
Divisia Level, Normalized to Equal 100 in the 1967
Interest-Rate Aggregate

Divisia Data Table

Levels normalized to equal 100 in Jan. 1967
Select a different start year for the table below: 

Divisia M4
Including Treasuries (M4) Divisia M4
Excluding Treasuries (M4-) Divisia M3 (M3)
Date Divisia M4
Including
Treasuries1 Y/Y Pct.
Change2 Divisia M4
Excluding
Treasuries1 Y/Y Pct.
Change2 Divisia M31 Y/Y Pct.
Change2
Jan 2015 1,320.3 2.92% 1,252.2 3.69% 1,247.2 4.07%
Feb 2015 1,329.2 2.78% 1,261.8 3.83% 1,256.6 4.14%
Mar 2015 1,332.6 2.70% 1,266.9 3.90% 1,262.3 4.21%
Apr 2015 1,330.1 3.03% 1,262.5 3.38% 1,258.9 3.84%
May 2015 1,333.0 2.70% 1,263.3 2.91% 1,261.2 3.46%
Jun 2015 1,336.3 2.80% 1,266.9 2.96% 1,262.1 3.35%
Jul 2015 1,350.5 3.75% 1,278.9 3.89% 1,271.6 4.04%
Aug 2015 1,356.5 4.13% 1,289.0 4.60% 1,282.4 4.90%
Sep 2015 1,351.4 3.60% 1,285.6 4.23% 1,279.1 4.54%
Oct 2015 1,349.1 3.18% 1,292.2 4.34% 1,285.9 4.63%
Nov 2015 1,363.7 4.34% 1,295.2 4.40% 1,288.3 4.70%
Dec 2015 1,367.5 3.76% 1,297.8 3.84% 1,291.7 4.00%
Jan 2016 1,373.1 4.00% 1,302.8 4.04% 1,297.3 4.01%
Feb 2016 1,383.4 4.08% 1,312.2 4.00% 1,306.5 3.97%
Mar 2016 1,392.7 4.51% 1,319.5 4.15% 1,310.3 3.80%
Apr 2016 1,395.9 4.94% 1,322.9 4.79% 1,315.4 4.48%
May 2016 1,401.6 5.15% 1,328.3 5.14% 1,321.7 4.80%
Jun 2016 1,414.1 5.82% 1,338.9 5.69% 1,333.3 5.64%
Jul 2016 1,415.5 4.81% 1,338.3 4.65% 1,334.5 4.95%
Aug 2016 1,422.0 4.83% 1,341.9 4.10% 1,340.4 4.52%
Sep 2016 1,423.6 5.35% 1,340.7 4.28% 1,339.8 4.75%
Oct 2016 1,425.2 5.64% 1,338.4 3.58% 1,338.9 4.12%
Nov 2016 1,434.3 5.17% 1,345.2 3.86% 1,346.2 4.49%
Dec 2016 1,437.3 5.10% 1,350.1 4.02% 1,347.7 4.33%
Jan 2017 1,439.2 4.82% 1,352.5 3.81% 1,351.8 4.20%
Feb 2017 1,438.7 4.00% 1,355.7 3.32% 1,355.2 3.73%
Mar 2017 1,446.2 3.85% 1,364.4 3.40% 1,362.9 4.02%
Apr 2017 1,456.9 4.37% 1,370.8 3.62% 1,371.3 4.25%
May 2017 1,463.0 4.38% 1,376.2 3.61% 1,376.4 4.14%
Jun 2017 1,467.3 3.76% 1,379.8 3.05% 1,380.3 3.53%
Jul 2017 1,473.3 4.09% 1,384.9 3.48% 1,385.7 3.84%
Aug 2017 1,480.4 4.11% 1,393.4 3.84% 1,393.3 3.95%
Sep 2017 1,492.2 4.82% 1,402.0 4.57% 1,400.1 4.50%
Oct 2017 1,496.2 4.98% 1,405.8 5.03% 1,404.7 4.91%
Nov 2017 1,503.5 4.83% 1,412.0 4.97% 1,411.0 4.81%
Dec 2017 1,507.2 4.86% 1,415.2 4.82% 1,413.5 4.88%
Jan 2018 1,509.9 4.91% 1,416.4 4.73% 1,413.9 4.59%
Feb 2018 1,511.3 5.04% 1,416.8 4.50% 1,414.5 4.38%
Mar 2018 1,519.6 5.07% 1,421.5 4.19% 1,419.4 4.14%
Apr 2018 1,523.8 4.59% 1,425.3 3.98% 1,423.2 3.79%
May 2018 1,529.1 4.51% 1,429.9 3.90% 1,427.7 3.73%
Jun 2018 1,537.9 4.81% 1,437.9 4.21% 1,435.7 4.02%
Jul 2018 1,542.9 4.73% 1,442.4 4.15% 1,440.3 3.94%
1Level, normalized to equal 100 in Jan. 1967
2Year-over-year percentage growth rate. We are displaying year-over-year growth rates rather than monthly growth rates, since the volatility of the monthly growth rates masks information that is more evident from the smoother year-over-year growth rates. But users who prefer monthly growth rates can compute those month-over-month growth rates directly from the levels that we are providing. All component quantities that are not seasonally adjusted by the data source are being seasonally adjusted by the CFS using the Census X-12 program. As a result, any volatility in the monthly growth rates is not a consequence of seasonality.
3Interest-rate aggregate, percent per year. The formula and theory relevant to interest rate aggregation can be found in the AMFM’s data sources document.

Federal Reserve Official Monetary Aggregates

Extensive published results demonstrate that the best monetary aggregate for almost all uses was the Federal Reserve’s former broadest aggregate, L, but only if computed as a properly weighted index number, such as the Divisia or Fisher-ideal index.

The second-best monetary aggregate was the former next-broadest aggregate, M3, properly constructed as an index number.  See, e.g., Barnett (1982), “The Optimal Level of Monetary Aggregation,” in the AMFM Library. In contrast, when computed as simple-sum accounting numbers, disconnected from economic aggregation theory, M3 and L were among the worst monetary aggregates and were inconsistent with elementary principles of economic measurement.

Narrow aggregates, such as M1 and M2, give no weight to many highly-liquid substitutes for money.  That truncation is hard to justify.  At the other extreme, the broad, simple-sum aggregates give equal weight to distant substitutes for money as to currency.  That far worse weighting cannot be justified at all.  Recognizing the distortions produced by improper weighting within the broad, simple-sum, monetary aggregates, the Federal Reserve has rightfully discontinued publication of both simple-sum M3 and L.

Figures Provided on This Page

We are displaying on this page three broad Divisia monetary aggregates. The primary one is what we are calling Divisia M4. It is a broad aggregate, including negotiable money-market securities, such as commercial paper, negotiable CDs, and T-bills. DM4's components are similar to those of the monetary aggregate once called L, but modernized to be consistent with current market realities.a

The aggregate DM4- (to be called "M4 minus") is DM4 minus Treasury bills. In aggregation theory, DM4 should always be preferred to DM4-, since there is no good reason to give a weight of zero to T-bills in DM4. Nevertheless, for some research purposes, it might be useful to separate the effects of monetary from fiscal policy. Including T-bills in DM4 produces an overlap. The supply of T-bills can change either from monetary-policy open-market operations or from fiscal-policy changes in debt financing.

Divisia M3 excludes those money-market securities not issued by financial intermediaries, such as commercial paper and Treasury bills, but does include negotiable CDs and repurchase agreements. Divisia M3 may be closer than Divisia M4 to Federal Reserve actions in the policy transmission mechanism, so could be useful under some circumstances.  But aggregation theory provides no reason to impute weight of zero to the highly liquid money-market securities included in DM4, but not in DM3.

Simple-sum or arithmetic-average monetary-quantity aggregates are inconsistent with best practice economic measurement. For the same reasons, linear approaches to price aggregation are also unacceptable. The price of a monetary asset is its user-cost price, measuring the opportunity cost of holding the asset in terms of foregone interest. In continuous time, the real user-cost price of a Divisia monetary asset is the difference between the real rate of return on pure capital investment, R, called the “benchmark rate,” and the asset’s interest rate. We are providing as figures on this page the interest-rate aggregates, but not the aggregation-theoretic user-cost price aggregates, since there can be differences of opinion on how to compute the real rate of return on pure capital. However, we are providing our user-cost price aggregates on a spreadsheet for the benefit of experts. In our work, we are using the benchmark rate measure recently proposed by Akiva Offenbacher at the Bank of Israel.b

Interest rate aggregates, unlike quantity and price aggregates, are based on accounting conventions, rather than on deep aggregation and index number theory. The formulas and theory relevant to economic user-cost price aggregation and to accounting interest-rate aggregation are provided in the AMFM data sources document.

The Methodology

We are not supplying simple-sum M3 and L, since we agree with the Fed that those aggregates were severely defective by grossly overweighting distant substitutes for money.  In addition, constructing broad simple-sum aggregates, even as accounting numbers, cannot be based directly upon available data, since the Fed is no longer providing the former consolidated components.  Those consolidated components netted out overlaps, such as the overlaps among negotiable CDs and money market funds.  Adding up non-consolidated components produces double counting and other violations of accounting conventions.  But the need remains for the very best monetary aggregates --- M3 and L, produced as competently weighted index numbers.

We do so, using the highly-regarded Divisia monetary-index formula, as first derived and produced by Barnett (1980), “Economic Monetary Aggregates: An Application of Aggregation and Index Number Theory.” Consolidated components are not needed for that purpose.  Index number theory, being based on microeconomic theory, rather than on accounting conventions, uses market data.  Economic index numbers aggregate over assets and over economic agents in terms of demand for the imperfectly-substitutable services of the market assets and do not just add up the components.  No mathematical-economics training is needed to understand that “you can add apples and apples, but not apples and oranges.” For extensive background on how we are “getting it right,” see Barnett’s (2012) book, Getting It Wrong.

Download CFS Divisia Monetary Data for the United States

Download the data for the Divisia Aggregates in this Excel workbook, Divisia.xlsx. (Updated August 22, 2018)

The Excel workbook includes three worksheets that include:

The broad Divisia monetary aggregates, including DM3, DM4-, and DM4 (Broad tab)
The narrower Divisia monetary aggregates (Narrow tab)
All user-cost price aggregates (User-Cost tab)
The CFS Divisia aggregates are normalized to equal 100 at their first observation (January 1967).
The Center for Financial Stability is now releasing new Credit Card-Augmented Divisia Aggregates each month. See more information about the new Divisia alternatives here.

Download Divisia Documentation

The document describing data sources here.
The document describing data sources for the augmented Divisia here.
The CFS theoretical foundations paper, Barnett and Su (2017), “Financial Firm Production of Inside Monetary and Credit Card Services: An Aggregation Theoretic Approach,” here.
See more information about the supporting data below.

Divisia on Bloomberg Terminals

Bloomberg users can access the CFS data by any of the four options:

{ALLX DIVM }
{ECST T DIVMM4IY}
{ECST} --> 'Monetary Sector' --> 'Money Supply' --> Change Source in top right to 'Center for Financial Stability'
{ECST S US MONEY SUPPLY} --> From source list on left, select 'Center for Financial Stability'
Data Provided on a Spreadsheet, but Not Displayed on This Page

Data and analysis that cannot be replicated are not consistent with the normal standards of science, regardless of any claims of proprietary interest.  We use only component quantity and interest-rate data available to the public. The component data are provided at no cost only to established researchers, who need the data for use in their research for publication in major peer-reviewed journals, such as Macroeconomic Dynamics, edited by CFS Director, Professor William A. Barnett. For such academic research purposes, requests for the component data or the dual user-cost price aggregates should be sent to Professor Barnett.

We do not provide the Federal Reserve’s official simple-sum monetary aggregates.  We consider the Federal Reserve’s simple-sum monetary aggregates to be entirely without merit. The simple-sum aggregates are based on archaic economic-measurement methodology, incompetent since the appearance of Irving Fisher’s (1922) landmark book, The Making of Index Numbers, nearly a century ago. The simple-sum aggregates have been obsolete, since monetary assets began yielding interest. If you nevertheless should wish to see the Federal Reserve’s, official, simple-sum monetary aggregates, you can find them in the St. Louis Federal Reserve’s database, FRED.

While the broadest Divisia monetary aggregates are best for most purposes, some applications exist for which narrower Divisia monetary aggregates are of use.c  The St. Louis Federal Reserve is supplying narrower Divisia monetary aggregates, which the St. Louis Fed calls MSI (monetary services index). At present, the St. Louis Fed is providing most but not all the component data, included now with the MSI data in the St. Louis Fed’s excellent FRED database. Eventually all components are planned to be available within FRED. At that time, the FRED Divisia data will be easy to replicate and use, since FRED contains outstanding software for display and analysis. Although the component quantity and interest rate data are not yet all available on FRED, we are indebted to Richard Anderson at the St. Louis Fed for providing that data to us. As opposed to the Federal Reserve Board’s simple-sum monetary aggregates, which we consider to be entirely without merit, we consider the St. Louis Fed’s MSI (Divisia) narrow monetary aggregates to be an admirable and important contribution to public information.

We also compute and provide narrow Divisia monetary aggregates, such as M1 and M2.  Our series differ from the St. Louis Fed’s only in our use of the Bank of Israel’s benchmark-rate procedure, described above.  But the Divisia monetary-quantity index is highly robust to the choice of benchmark rate, so our narrow Divisia monetary aggregates can be expected usually to track the St. Louis Fed’s MSI aggregates relatively closely.d  Because of the anticipated inclusion of the MSI data in the excellent FRED database, the difference in benchmark rate alone would not have been adequate reason for the CFS to produce those aggregates in a manner nearly redundant with MSI.  But the St. Louis Fed froze its MSI data for over five years, from the start of the housing crises through the financial crisis and Great Recession.  To protect the public from any such future freezes in that data, we are providing our own parallel computations as a backup source — in response to popular demand.

There is one entirely nontrivial difference between our narrow aggregates and MSI:  the dual user-cost aggregate, which is not robust to changes in the benchmark rate.  If you only need the quantity aggregates, there is little reason to prefer our CFS narrow Divisia monetary aggregates over the St. Louis Fed’s, especially when the MSI Divisia monetary aggregates become conveniently available within FRED.  But if you need the dual user-cost aggregates in your research, you may find that you will have good reason to prefer ours.
Key Takeaway Points

The broadest monetary aggregates are almost always the best monetary aggregates, when their components are properly weighted.
When computed as unweighted simple sums, the broadest monetary aggregates are among the worst monetary aggregates ever provided by the Federal Reserve.
The Federal Reserve was right to discontinue publication of the broad, simple-sum, monetary aggregates.
The Fed has hampered computation of simple-sum, broad, monetary aggregates as accounting numbers, by discontinuing publication of their consolidated components.  But there is no good reason to compute simple-sum broad monetary aggregates at all.
Aggregation-theoretic monetary aggregates, such as our Divisia monetary aggregates, use available market data and do not need consolidated components, since those aggregates are based on microeconomic theory, not accounting.
Imperfect substitutes are never given equal weights in economic aggregation theory or in economic index-number theory.  Valid aggregation over imperfect substitutes is the primary focus of the field of economic measurement.
While economic aggregation and index number theory provide the relevant principles for quantity aggregation over monetary assets and for price aggregation over user costs, accounting principles are relevant to aggregation over interest rates. We use accounting principles where relevant and economic theory where relevant, as is consistent with best practice economic measurement. Details are provided in the AMFM data sources document.
CFS Divisia Release Schedule

CFS Divisia Aggregates for the prior month are released at 9:00 AM Eastern time on the following dates:

September 19, 2018,
October 17, 2018,
November 21, 2018,
December 19, 2018,
January 16, 2019,
February 20, 2019,
March 20, 2019,
April 17, 2019,
May 22, 2019,
June 19, 2019,
July 17, 2019,
August 21, 2019,
September 18, 2019,
October 16, 2019,
November 20, 2019,
December 18, 2019
a The Federal Reserve discontinued publication of its broadest aggregate, L, in 1998.  There have been substantial changes in money markets since then.  M4’s components take into considerations those changes.  Similarly the component data for M3, which was discontinued by the Fed in 2006, have been modified to take into consideration market data availability and market structural changes.  For example, the repurchase agreements in our Divisia M3 and M4 are from the New York Federal Reserve Bank and include the total market values, rather than from the narrower definition previously adopted by the Federal Reserve Board in its aggregates, and provided by the St. Louis Fed.

b In discrete time, the foregone interest is discounted to present value from the end of the period to the start of the period, by division by 1 + R.  The real user cost is multiplied by the cost-of-living index to convert to the nominal user cost. If p is the cost-of-living index and ri is the interest rate on monetary asset i, then the asset’s nominal user cost in discrete time is p(R – ri)/(1 + R). For the formal derivation, see Barnett (1980).  The Bank of Israel’s measure of the rate of return on pure investment is the short-term, bank, loan rate.  To protect against the highly improbable risk that a monetary component’s interest rate might occasionally exceeding that loan rate, we incorporate the short-term bank-loan rate into the upper envelope over all of DM4’s monetary-asset component yields.

c For the relevant criterion, since Barnett and de Peretti (2009).

d The Divisia monetary-quantity index computes the growth rate of the aggregate as a weighted average of the growth rates of the component quantities.  The weights are the expenditure shares of the components with user-cost prices in the share computations.  Since the benchmark rate appears symmetrically in all terms in the numerator and denominator of the share weights, those weights are not highly sensitive to variations in the benchmark rate