There are quite a few reasons why Indians would want to invest overseas. Education, retirement and emigration are frequently cited as top priorities. In the past, the only way to do this was through the Liberalised Remittance Scheme (LRS) route. However, with Indian mutual funds finally waking up to increasing demand from investors, does investing in international public market securities through this process still make sense?
Liberalised Remittance Scheme
The Indian Government, through its various regulatory and enforcement arms, have traditionally tried to keep Indians from sending money abroad. The problem has always been that populist policies used to win elections end up choking growth and stoking inflation. This leads to investors pulling funds away from India – aka, capital flight. One way to stem the tide is to try and trap Indian capital within India.
We use the word “try” because we are all aware about the hawala network that thrives to this day because of these policies. Thankfully, the process of liberalization has slowly, in baby steps, opened the doors for Indians to legally remit funds abroad.
The Reserve Bank of India (RBI) sets the rules governing these fund transfers that banks need to follow. And banks are supposed to report and track these transactions both at the individual and aggregate levels. Given the paperwork involved, most banks require you to make a trip to a “designated” branch office and execute the instruction in-person. The whole process is cumbersome, requires paperwork and takes an hour or two to complete.
Not only is LRS is painful, it is also expensive.
Most people fixate on bank fees and GST but that’s only part of the story. The biggest scam is the exchange rate given by the bank – it is the worst possible rate that they can give you while still being compliant with rules & regulations. If you compare the “google” rate with the final transfer rate, you’ll find that the drag is about 3%
So, why do it?
More Choice, Less Cost
The US ETF market went through a decade-long price war that drove vanilla cap-weighted fees to almost zero.
For example, if you want to invest in the S&P 500 index, then Vanguard’s VOO ETF charges you 3bps for the privilege whereas Motilal’s index fund charges 50bps. If you consider the tax differential and the transfer knee-cap, you break-even by year 7. So, if you are a passive, buy & hold investor with a long enough time horizon, LRS makes more sense.
While costs are important, so are choices. You can access strategies beyond what Indian mutual funds deign to offer in the local market. For example, there are a ton of factor strategies available through ETFs that are probably never going to be launched in India.
Indian policy makers love to ape Western European policies without giving a second thought to its appropriateness given our stage of growth. One such self-goal has been the STT – Securities Transaction Tax – that taxes transactions rather than profits. And yes, we tax both short-term and long-term capital gains. Sort of like a dare: We’ll see how you’ll make money trading.
Fortunately, the US has avoided shooting itself in the foot so far.
Lets say, you fall in the 30% income tax slab. You have a trading strategy that makes 20% returns in both markets. The strategy turnsover the portfolio “x” times. Given 0.01% STT in India and zero brokerage in the US, what is “x” for you to be indifferent in Year 1?
If you turn over your portfolio more than 60 times, you would be better off deploying that strategy in the US rather than India.
If you set the gross returns to zero, the required turnover drops to 30.
However, if you only trade infrequently, then LRS may not the best way to go. For example, a 40x turnover strategy will need 3 years to be indifferent.
Basically, if you are going to trade frequently, doing it in the US makes a lot more sense.
Caveats
LRS ensures that you will need a Chartered Accountant to do you taxes. You need to account for the dividends you have received, report your net personal assets, etc. So this route doesn’t make sense for small accounts.
If you don’t share your trading and banking passwords with your next-of-kin, then you need to be worried about US Estate laws. The U.S. has jurisdiction over U.S.-situated assets and requires executors for nonresidents to file an estate tax return if the fair market value at death of the decedent’s U.S.-situated assets exceeds $60,000. Directly investing in U.S.-situs assets as a non-U.S. investor creates potential U.S. estate tax liabilities.1
Conclusion
We feel that the LRS route is attractive both for a buy & hold investor as well as an active investor with an edge. However, one should get into it knowing the trade-offs involved.
If you are looking for simple, pre-canned investment strategies to invest in the US, check out freefloat.us
For the longest time in the US, actively managed mutual funds ruled the roost. Then came Jack Bogle with his index fund and the ceaseless mantra of “costs and taxes matter” and the dynamic shifted, slowly at first and then suddenly, in favor of indexing. It was only a matter of time before people figured out the tax loophole of ETFs and now, there are over 2500 ETFs listed in the US.
Unlike in India, where mutual funds are “pass through,” US mutual fund investors pay capital gains tax on assets sold by their funds. When there are large-scale redemptions, say, during a market melt-down, funds are forced to sell their holdings. This generates capital gains taxes, meaning that investors have to pay tax on assets that had fallen sharply in value1.
ETFs, on the other hand, don’t have to subject their investors to such harsh tax treatment. ETF providers offer shares “in kind,” with authorized participants serving as a buffer between investors and the providers’ trading-triggered tax events.
A Plethora
Of the ETFs that survive today, the number of launches every year has trended higher.
While Equity ETFs dominate launches, the share of fixed income, alternatives, etc. has increased as well.
Most of the AUM resides in “vanilla” strategies – typically market-cap based.
The winner HAS taken all
Plot the assets of each ETF, in billions, in log-scale and you can tell that this is a game of scale.
Of the total 2577 ETFs, 2022 (78.5%) have less than a billion dollars in assets. You need to filter for $10 billion and up to just see the x-axis.
The top 3 issuers: Blackrock, Vanguard and SSGA manage ~80% of all ETF assets.
Where there is an ETF, there’s an Index
Until last year, ETFs were supposed to be a “passive” entity. There were no “actively managed” ETFs. In order to be passive, an ETF needed to follow an index. And indices had to be rules based – however convoluted the rules. And issuers needed a third-party to provide the index.
The rise of ETFs (and passive investing, in general) put index providers in the middle of all the a action. They became a crucial cog in world finance that can make or break entire economies. So powerful, in fact, that China blackmailed MSCI to include its domestic stocks in its Emerging Markets Index, which is tracked by close to $2 trillion in assets2. And India has been working on inclusion of Indian sovereign bonds in global bond indices3.
We can see industry consolidation here as well. The top 5 index providers control ~75% of ETF AUM (more if you include index funds.) S&P Global and MSCI are as close to “pure-play” index providers as you can get and their stock market performance is off-the-charts.
Fee Squeeze and Innovation
The problem with index ETFs/funds is that buyers only care about two things: expense ratio and tracking error. This resulted in a massive fee war that saw the vanilla-passive industry consolidate around Blackrock and Vanguard. For example, Vanguard’s S&P 500 ETF’s expense ratio is 3bps.
So, what next?
International ETFs
The first wave was ETFs providing international diversification. However, the “home-bias” is pretty strong with AUM under international ETFs barely making a quarter of the total.
On a weighted average basis, these ETFs charge about 30bps. However, since these are mostly cap-weighted, the fee-war is just as intense here.
Leveraged/Inverse ETFs
Many investors have mandates that prevent them from trading derivates outright. This is especially true for Indian investors taking the LRS route to invest in the US. However, Wall Street has your back.
Leveraged ETFs give you 2x or 3x the daily returns of a benchmark index like the S&P 500 or the Nasdaq 100. Feeling bearish? Inverse ETFs do the opposite.
Caveat: These are NOT buy-and-hold investments and are more suitable for day-traders. The discussion requires a separate post.
On a weighted average basis, these ETFs charge about 100bps. While lucrative, they are mostly niche.
Active ETFs
An ETF’s tax-free wrapper make it an order of magnitude more attractive than an identical mutual fund. New issuers/managers have taken advantage of this and launched actively managed ETFs.
On a weighted average basis, active ETFs charge about 50bps. These are still early days for this category – they barely make 5% of total ETF assets. Liquidity and tracking errors during market crisis are yet to be tested.
Conclusion
There is a plethora of choices when it comes to ETFs in the US. If you plan to wander away from the plain-vanilla stuff, please take the time to read the prospectus and understand how it works.
If you are looking for simple, pre-canned investment strategies to invest in the US, check out freefloat.us
Define Governance: the act or process of governing or overseeing the control and direction of something (such as a country or an organization).
In this article, I will focus on whether any organization can have decentralized governance, and what does that even mean? And how is this related to cryptocurrencies. Let’s start with a very basic organization, and see whether it can be governed in a decentralized way.
What is an organization anyway?
Say some people want to pool their money and use it for charity. We have ourselves a rudimentary organization. During the organization’s inception, the founders make some bylaws – for example: for any charitable donation to happen, say 2/3rd of the remaining capital in the pool has to approve it. These bylaws are written down formally in a “human language” (the language being a “human language” is important). The organization will register itself with the government of that geographical area (let’s say, a country). In case disputes arise in the future, the courts of that country will interpret the bylaws of the organization, apply the relevant common laws of that country, and with the threat of force, ask the members of the organization to abide by the court’s judgment. We kind of get how this works.
I will call this “centralized governance”, because the dispute resolution is adjudicated by a centralized authority. In an ideal world, this centralized authority is fairly appointed by representatives of the people who were fairly elected by the people to carry out such appointments.
Enter Smart Contracts
If the bylaws were precisely written down in an unambiguous computer language, and deployed on a distributed computer that could not be stopped, or taken over by any single authority – we have a decentralized organization. It’s governance is encoded in the program that was deployed on the distributed computer. Ideally, once deployed, the program cannot be changed, and can be arbitrarily run by anyone forever. Who are the members of this organization? Let’s say the program has a function that accepts money as input, and gives out an equivalent valued token – anyone who makes such a function call is a member of this organization, as they have a stake in the program. Do disputes arise in such an organization? No. To see why the answer is “no”, we have to understand that this system adheres to the maxim: “Code is Law”. The program does exactly what it was programmed to do – there is no randomness or discretion or uncertainty in the execution. This faithful execution of the program obsoletes the idea of dispute resolution.
Ethereum smart contracts are such programs. They are deployed and run on Ethereum, which is a distributed network of computers that ideally cannot be censored or stopped. Ethereum has a richer programming language, along with the notion of a smart contract having monetary deposits, and other arbitrary data. Using this setup, one can write a smart contract that represents the charitable organization that we saw earlier. In fact, back in 2016, when Ethereum was still in its infancy, exactly such an organization was deployed as a smart contract on it. It was called The DAO, or the decentralized autonomous organization. It could accept funds from anyone, and with token holders voting for projects, would fund these projects from the collective pool of funds. Venture capitalists thought that the DAO would disrupt the VC industry itself, and added their own funds into the pool. At its peak, the DAO had 14% of all of ETH pooled inside it (ETH is the native currency of the Ethereum system). I didn’t read the code of the DAO, and am not sure how a project got actual funding – was some ETH moved to the recipient’s address? How would the DAO verify that the recipient actually produced something of value, if that artifact was not native to the blockchain itself? In the cryptocurrency space, it’s important to ask these questions – as the answers are not obvious, and often times hide red flags that indicate possible scams.
But as it turned out, this DAO program itself had a software bug, and that allowed a clever hacker to drain the uninvested funds into their own control. To “fix” this “hack”, people who had enough social clout in the Ethereum ecosystem managed to undo history, and start an alternate timeline where this hack never happened.
What?!?!
How does one undo history and make alternate timelines?
Let’s start with an example. Let’s say your credit card is stolen, and is used to buy strange things in strange lands. You call your credit card issuer and ask them to undo history, and start an alternate timeline where the theft never happened, and you have a clean slate of your own previous transactions and new transactions. Where did the thief’s transactions go? Turns out that they were never “settled”. In the traditional finance world, very very few transactions are actually “fully settled”. Transactions between countries, or between large banks, or those that are brokered by central banks are considered settled for good, and are truly irreversible. The rest of the world’s transactions can be reversed, if the right people are convinced.
In Ethereum, where code is supposed to be law – alternate timelines should not have been possible. The hacker took out the pooled funds from the DAO because the smart contract allowed that to happen. That’s the bylaws of the contract, and the hacker is playing by the rules. There shouldn’t be a discretionary voice that says “But that’s not the spirit of the law”. Smart contracts are only supposed to respect the word of the law, and not the spirit of the law. Ethereum, in its early days at least, believed that the spirit of the law mattered more than the word of the law, and allowed the DAO hack to be “bailed out”.
Ethereum is just one such “network computer” (blockchain, to keep up with the times) that runs such code-is-law smart contracts. There are other blockchains that claim to do the same, and have varying degrees of centralization that allows the powers-that-be to “bail out” certain contracts if shit his the fan. On the other hand, Bitcoin doesn’t even allow such powerful smart contracts, and the rudimentary smart contracts that it does allow, have never been reversed because some people lost their money. I think it’s an important distinction that makes Bitcoin the most (if not the only) credible blockchain in existence, but that’s just me.
Governance, through code
Coming back to Ethereum smart contracts which act as decentralized autonomous organizations, how can governance rules be changed if all token holders agree to it? We now get into some of the more sophisticated governance models for smart contracts, which can all be coded into the initial smart contract itself. Here’s one popular model:
In our original charity smart contract, we had the initial bylaw that 2/3rds of the total pool had to apply every new donation. Let’s say we want to change this rule to have 3/4 instead of 2/3. While writing the initial smart contract, this particular constant (2/3) is delegated to a different smart contract that is deployed first, and the main smart contract calls this other smart contract to perform it’s actions. In software programming, this is either called “delegation” or “forwarding” or “a pimpl – pointer to an implementation”. The difference between a classic software program that does this, vs a smart contract that does the same thing – is that in a smart contract with decentralized governance, the change in implementation of a functionality has to be voted by token holders. This is how it looks:
The initial smart contract is written in such a way that the following steps are supported.
Someone (doesn’t matter who) codes a new piece of functionality and deploys it on the blockchain. For now, this is dead code, as no one is executing it. But everyone can see what it does.
Someone (again, doesn’t matter who) makes a proposal in the original contract that they would want to call a vote for this new functionality from step (2) to replace the equivalent step in the original code.
There is a timeline for token holders of the smart contract to vote for this proposal. Votes are tallied. The result is known.
If the governance change is approved, there is an additional time window before it comes into effect. Token holders who are unhappy that this change was made can withdraw their capital from the pool by returning or burning the tokens.
The governance change is affected by changing the smart contract implementation of this functionality from the original to the new.
Many smart contracts on Ethereum have the so called “governance token” that allows token holders to change the rules of the smart contract if enough such token holders vote for it.
Uniswap, the popular decentralized exchange on Ethereum, has its own governance token UNI, which allows UNI holders to vote for governance changes like increasing or decreasing the fee taken by the protocol per exchange trade.
Compound, a smart contract for credit issuance on Ethereum, has its own governance token COMP, which allows COMP holders to affect governance changes – like how they recently voted to change their price oracle.
MakerDAO, the smart contract behind the stable coin DAI, has its own governance token MKR, which allows MKR holders to change the parameters of the DAI stablecoin, and how it maintains its 1:1 peg against the USD.
In my naïve unqualified opinion, these governance tokens can sometimes pass the Howey test, and could qualify as securities under some regulatory regime.
What’s in it for me?
Many tokens/coins are available to buy on many cryptocurrency exchanges.
Some are native coins of their own blockchains – like BTC/ETH. Many of these native coins are centralized, issued to investors first, and dumped on the general public later.
Some are ERC-20 tokens on the Ethereum blockchain. They represent governance rights on protocols, and thereby generate cash flow.
Some are tokens on other blockchains. Most blockchains’ native currencies themselves are worth nothing. Tokens that are launched on these blockchains are even trickier.
Some are even more complex tokens issued by smart contracts that govern other smart contracts.
Some tokens are blatantly pointless, and are valuable just as collectibles: remember NFTs?
Some tokens have a point, but are still worth nothing.
Some tokens have a point, and might be worth something.
To keep life simple, one can just buy Bitcoin. If that’s too conservative (it’s not), maybe add ETH to the mix (don’t).
Gary Antonacci created the Global Equities Momentum (GEM) model that applied dual momentum to stock and bond indices. It toggles between stocks and bonds using 12-month trailing returns. And when it toggles to “stocks,” it chooses between US equities and International (ex-US) equities based on whichever posted higher returns in the previous 12-months. The model uses the S&P 500 index as a stand-in for US equities and the WORLD ex USA index for international stocks.
Investors can use the ETFs SPY/VOO for the S&P 500, SCHF for World ex-US DMs and AGG for bonds while replicating this strategy.
The best part about this strategy is its simplicity. It takes just 3 inputs and anybody can set it up on Google Sheets. Execution is as simple as it gets because at any given point in time, it is long just one ETF. Also, given that it uses a 12-month look-back, it is less prone to whiplashes, resulting in a lower trading frequency.
Specifications & Expressions
When you automate systematic strategies, you need to nail down its exact specifications. In this case, they are mainly: inputs, look-back periods and traded instruments.
The original version of the Strategy uses the S&P 500 and World ex-US both for inputs and as proxies for the traded instruments. However, there is no reason why they both should be the same. Also, what is so magical about a 12-month look-back period anyway? Why can’t it be 6 -months, a month or an average of the last 6-months?
The Strategy only describes a broad idea with one set of Specifications and Expressions out of a multitude. It can (and should) be adapted to fit one’s risk profile and investment horizon.
The Momentum Expression
The easiest tweak to the original strategy is to swap out the traded equity instruments with their momentum counterparts.
At the final step, when it comes to executing the trade, you can use MTUM, the US Momentum ETF instead of SPY/VOO and IMTM, the DM ex-US Momentum ETF instead of SCHF.
Long-only momentum ETFs are highly correlated to their market-cap counterparts but have the potential to juice returns in bull-markets. Since we are trend-following anyway, why not go a step further up the risk-curve and embrace momentum as well?
Picking a look-back for trend-following strategies is fraught with data-mining bias. One could potentially test 100s of periods and pick one that gave the best results historically. The data-mined look-back could even work in forward tests but inexplicably, and suddenly, fail in real portfolios.
The safest thing to do would be to not change the look-back periods outlined in the original research. However, the world would’ve changed since its first publication. How do you strike a balance between the two?
Long look-backs are slow at reacting to rapidly changing markets. Some might say that this is a bug while some might argue that this is a feature. Shorter look-backs, on the the other hand, can react faster but are prone to head-fakes and whiplashes.
The second version of our GEM strategy tries walk the fine line by taking the average of 6- through 12-month returns. It tries to hew close to the original research while acknowledging that the world has gotten faster since it was first published.
No Free Lunch
While the strategy adapts to the broad, slow-moving macro theme of US equity under-performance vis-à-vis rest-of-the-world (were it to occur,) it is not immune to getting whiplashed due to short and steep market dislocations like the COVID crash of March 2020. The strategy got into bonds just when the equity markets were recovering and stayed there until well-after. It is simply not possible to avoid all landmines when it comes to investing.
While we ran our back-tests, we tried a fair amount of permutations and combinations. Some where discarded in spite of having better risk-adjusted returns because they lacked internal consistency. While some slipped into data-mining territory in spite of our best efforts to avoid it. Readers interested in the process and the code can read through our GEM Collection.
Popular indices, like NIFTY 50 & MIDCAP 150, are useful if you are benchmarking long-only portfolios. However, if you have a long-short portfolio, then you need a long-short benchmark.
When Are Contrarian Profits Due To Stock Market Overreaction? (Lo, MacKinlay, 1990) describes a naïve portfolio construction process that is fit for purpose.
For momentum, portfolio weights are in proportion of excess returns over an equal-weighted index and for mean-reversion, they are the inverse.
For example, if you subtract the returns of each of the components of the NIFTY 50 index with the returns of NIFTY 50 EQUAL-WEIGHT index and divide by 50, you end up with the portfolio weights for the next day. Each look-back period used to calculate returns will produce a different set of weights (and a different synthetic index.)
As impractical as constructing such a portfolio may seem, they are useful as a benchmark for long-short mean-reversion/momentum portfolios. Here are index returns since April 2020 with 20- and 50-day look-backs.
This is especially interesting if you are looking at market dislocations and subsequent recoveries. Here are indices since June 2019 with 5-, 20- and 50-day look-backs.
Counter-intuitively, naïve mean-reverting long-short seems to out-perform momentum.
