Category: Investing Insight

Investing insight to make you a better investor.

Lumpsum vs. Dollar Cost Averaging (SIP)

Among Indian investors, SIPs (Systematic Investment Plans) are the rage right now. The total amount collected through SIP during May 2018 was ₹7,304 crore according to AMFI. SIPs are great for investors with a regular income – it matches the frequency of savings with the frequency of income. Structural discipline is always a welcome thing. However, for investors who have lumpy incomes or a windfall, it is often a dilemma whether to invest as a lumpsum or to setup an STP (Systematic Transfer Plan.)

Both SIPs and STPs are a form of DCA (Dollar Cost Averaging) where you average into an investment over a period of time (the accumulation phase.) The thing about DCA is that it ends up under-performing a lumpsum in markets that are trending up. Intuitively, you want to buy more when the price is low (in the beginning) and less when the price is high (at the end.) So, if the market is going up, then it makes no sense to spread a lumpsum over a period of time – you are guaranteed to make the later buys at a higher level, reducing your overall returns.

In the case of equity markets, the expectation is that they tend to go up over time. So if you are looking at a 10-20 year time horizon, then you are better off investing in one shot. To put this intuition to test, we modeled the returns of NIFTY, MIDCAP and GOLD as a Generalised Lambda Distribution (this works better than a normal distribution because these returns have significant skews and kurtosis) and ran a 10,000 path simulation to get a sense of the probability distribution of DCA vs lumpsum investments.

Roughly, this is like assuming that the weekly return distribution is going to be the same across 10,000 different worlds. So you pick set of random weekly returns from the same distribution 10,000 different times and see how DCA and lumpsum perform over those worlds. When you plot the density of those returns, you get an idea of how they compare.

To keep things simple, lets compare NIFTY MIDCAP 100 and GOLD. First, the price charts:

And now the simlulated cumulative return densities of MIDCAP and GOLD, modeled with data after 2010:

The area to the left of zero is that of negative returns. Lumpsums have a longer left tail compared to DCA so probability of a large negative outcome is higher for the former.
However, the total area under zero is higher for DCA in MIDCAPs so the probability of negative outcomes in general is higher for DCA/SIP.
Lumpsums have a fat right tail for both MIDCAPs and GOLD so the probability of large positive outcomes is higher for lumpsums.
“Average” DCA returns are less than “average” lumpsum returns but they occur with a higher probability.

For a prudent investor, it is the left tail that matters the most. Even though lumpsums hold out hope for higher returns (fat right tails,) they have a small probability of a big loss that is greater than that for DCA (longer left tails.) In conclusion, a prudent investor should convert a windfall into an STP and a risk-seeker should do a lumpsum.

For readers curious about the code and for additional charts with longer time periods, visit github.

Relative Momentum Back-test

Daily data of over 35,000 global indexes published by NASDAQ OMX including Global Equity, Fixed Income, Dividend, Green, Nordic, etc. are available for free download on Quandl. Out of which about 1000 indices are USD denominated Total Return (TR) indices. We were curious to find out the result of applying our Relative Momentum algorithm on this subset. We also wanted to know if the rank of the Indian TR Index within this subset can be used to time NIFTY or MIDCAP investments. To know more, download the pdf Relative Momentum Back-test.

The charts in the document are low-res. Whatsapp us on +918026650232 for the high-res ones.

Trend Following vs. Trend Prediction, Part II

We are finally past the 100-day milestone on our machine-learning trend-following models. Here is how it compares against our other momentum models:

The ML algos out-performed a majority of traditional momentum algorithms. “NN” here stand for Neural Network and “ML” for models that use a SVM under the hood. It will be interesting to see how these models look under the 200-day lens as the short-term “luck-factor” evens out.

You can check out these models here.
The first post in this series is here.

Benchmarking against a Momentum Index

When we first launched our momentum strategy in India back in 2013, we were one of the few to openly talk about momentum as a systematic strategy. Even the thematic indices that were later launched by the NSE focused on value and beta. This resulted in momentum strategies being forced to inappropriately benchmark against market-cap weighted indices. Thankfully, that is not the case anymore.

S&P BSE Momentum Index

The BSE came out with a Momentum Index last year which can now be used to benchmark momentum strategies. An obvious flaw in this index is that it is rebalanced only once in 6 months whereas most academic research on momentum assume a monthly rebalance. However, if you look past that, it is a better alternative.

Here is how our Momo Relative Momentum strategy compares against the index:

Our risk-managed momentum strategy has out-performed the momentum index even after transaction costs.

The Dao of Collusive Trading

When people talk about crypto-currencies, the primary focus so far has been the price of bitcoin, ethereum, etc. However, that is only scratching the surface of what is possible. When you dig a little deeper, you find yourself sucked into a rabbit-hole. One such rabbit-hole is the DAO.

Decentralized Autonomous Organizations

DAO stands for Decentralized Autonomous Organization. A traditional corporation is structured in a top-down hierarchy where decisions are taken by CxOs and handed over to people down the hierarchy to execute. But what if you replace CxOs and workers by code and replace the decisions making mechanism by votes? You get a DAO.

In a DAO, participants buy DAO tokens and vote on what tasks need to be performed. The set of tasks is defined in code. The DAO then uses “smart contracts” to perform those tasks. As long as the output can be digitally verified, the whole thing can be distributed (does not need a central server) and anonymous.

The ability to anonymously create and participate in a DAO could open up a whole can of worms when it comes to securities market regulation. Take synchronized and circular trading, as an example for collusive trading.

Synchronized and Circular Trading

A synchronised trade is a transaction where the buy and sell orders are identical, and are put through at exactly the same time on a stock exchange. A circular trade is where a set of brokers buy and sell shares frequently amongst themselves. The intention could either be to artificially influence price, trading volume or tax avoidance.

Surveillance systems red flag these transactions and SEBI follows up by proving collusion and intent. The latter is possible because there is always a paper-trail and it is possible to draw a relationship diagram between market participants under the lens. But a DAO can upend that.

The DAO can be setup anonymously. The crypto that is required by the DAO can be acquired and traded anonymously. Participants can vote on the stock to be manipulated anonymously. The DAO can execute the code (“smart contract”) that trades these stocks autonomously.

So even if a surveillance system red flags these transactions, it becomes next to impossible to prove collusion. The trades would occur between participants who have not even interacted with each other before – just like what occurs naturally in a stock exchange.

Please tell me that some version of this is not possible.

Links:
DAOs, DACs, DAs and More: An Incomplete Terminology Guide
DACs VS the Corporation
What is a DAO?
How Do Ethereum Smart Contracts Work?