Diversifying Into Emerging Market Funds With A Quantitative Approach by Aayush Kadakia & Rutvij Thakkar
By: Dogs Of Dalal Street
Tags: Quant, Stocks, Investing, Emerging Markets

The Underlying Fundamentals:

The factors for buying into this fund are mostly macroeconomic, hence the name of the fund: Emerging Markets. VWO holds numerous companies that aren’t based primarily in the US, with a concentration/bias for Chinese and Indian tech companies from Alibaba, NetEase, Pinduoduo, and JD to Reliance, Tata, and Infosys. On top of these rather large companies, this fund has a fair amount of speculative picks as well that may pick up its growth down the line. Even if the speculative picks don’t work out though, the overall fund should see tremendous gains as the economies of most of these companies are those of monopolies (Alibaba is basically the Amazon of China except with a greater potential customer base, and Reliance/Tata/Infosys are also huge conglomerates that are “too big to fail” and they’re still too small to not grow further due to their growing markets). The regulative structure of both India and China is also very protectionist which allows for some degree of competitive asymmetry for their respective countries. Anti free trade policies historically aren’t always good, however, and these concerns are true more for India. Thankfully India is a growingly progressive democracy that’ll continue opening its doors to free trade in the near future which’ll be a net positive for their companies in most economists’ view. The recovery for emerging markets haven’t been distributed fully yet, and the quantitative analysis will support this evidence as well but even fundamental analysis points to more growth as Lazard reported: “Looking more closely at the third quarter, just as in the US, the performance of a few e-commerce and information technology stocks drove emerging markets. Out of nearly 1,400 securities, just two, Alibaba and Taiwan Semiconductor, accounted for more than 40% of the index’s returns”. This shows that returns are still very unevenly distributed and the rest of the emerging markets indexes (Both VWO and EEM) have room to run because only ~22% or so is in tech for both of them. The rest of the funds are in financials, consumer discretionary, materials, etc. and some of these foreign banks are still well below their highs due to a lack of stimulus from their countries. Returns between growth stocks and value stocks with high dividends are at their widest point right now, meaning the collective index is in for a sector rotation very soon. These underlying fundamentals will set up emerging markets to return more than their domestic counterparts. Securities based in foreign markets can also be great to diversify your portfolio, so it’s an especially safe bet if your portfolio is concentrated primarily in domestic securities as the more uncorrelated assets you own, the better you’re hedged against future volatility.

Quantitative Analysis on VWO-EEM:

The paradigm of ETFs in pairs trading strategies, VWO-EEM creates a spread that records levels of stationarity and passes the CADF test at 99% certainty and rejecting the null hypothesis through the Johansen Eigenvalues at a 99% certainty. In the past week, the VWO and EEM pair has demonstrated the importance of a global perspective on quantitative analysis. VWO, Vanguard’s Emerging Markets ETF, is one of the largest ETFs in the world, focused on assets from Asia and the Pacific. EEM, MSCI Emerging Markets exchange-traded fund, takes mid-ranged and large corporations in emerging regions such as Taiwan, Hong Kong, India, and Brazil. Superficially, these assets seem to overlap in their underlying equities and management patterns, however, in the past week, VWO and EEM have created quite the ripple in quantitative-fund pairs-trading algorithms. Why should this pair be the focal point of portfolios in the coming weeks?

But before we dive too deep into a quantitative take of these East Asian Emerging Markets ETFs, lets recap the basics of pairs trading. Essentially, in pairs trading, the investor formulates a third, hypothetical asset from the spread, or difference, between the two asset prices. Creating a new derivative which functions as a two dimensional indicator for gauging shorts and longs on each asset in a pair, the trader creates figurative bands or ratios in order to measure when to sell or buy. Say for example asset A and asset B create a spread of S. When S varies from its mean by an ‘nth’ standard deviation, an investor would know to sell A since it could be said to be overvalued in perspective of its mean and buy B since it is undervalued. Spread mean-reversion allows investors to quantitatively trade assets in the blink of an eye, in the matter of nano-seconds, longing one and shorting the other. Therefore, if the spread of two assets consistently mean-reverts then the investor can cycle through shorts and longs of one single equity pair. It’s an intuitive trading pattern that can be exploited by simple quant-manipulation.

Back to EEM and VWO, the underlying equities only contribute to the superficial market for cointegration. The management method solely affects dividend and internal asset control. What is of greater interest, is the oscillation of the spread formed by the two assets and how one can manipulate Bollinger Bands to maximize returns. Linear-Regression, the simple function of drafting a line of best fit through a given set of points, is a commonly used and understood concept. When pairs-trading, a more critical attribute of stock prices is stationarity. But for once, simple one-dimensional linear regression has trumped the efficacy of stationarity, as can be seen by VWO and EEM’s volatile yet indicative behavior in the past week.

Having an average of 207 minutes before mean reversion, the pair perfectly mixes optimal volatility with strong cointegration. Coupled with a stationary spread, rejecting the null hypothesis at 95% certainty, VWO and EEM paint a picture of strong intuitive and mathematical cointegration. The rising technique of linear regression, however, factors in a new indicator known as equivalent oscillation. When we combine two assets we take “Spread”, an indicator of both prices and something much more important, difference. If the spread has points that equivalently orient themselves on either side of the mean then while the individual assets might not be stationary, their spread mean reverts over the period of time. If an equal number of points orient themselves equidistant from the mean value, not only do they indicate stationarity and therefore mean-reversion, we can also place a quantitative measurement for comparison on this. Equal points equidistant from a mean create a straight line of best fit yield a slope of 0. So therefore, the absolute value of the spread of a group of stocks provides a measurement of our ability to slap Bollinger bands on a pair. EEM and VWO in the past week had a regression slope value of -0.00008, infinitely close to 0. What this means is that the past week observed the spread of EEM and VWO to vary by 0.0008 percent of the original mean.

Current Application for Quant-Trading:

This pair comes to focus for us due to multiple speculative reasons of interest for future consideration and exploration. One, the Covid-19 competition equalizer. Theorizing that the pandemic allowed for temporary setbacks in all equities under these ETFs therein created similar performance and a limitation in individual asset diversity. Another idea that may contribute to the past weeks astounding levels of cointegration is the effect of an individual-cofactor. South Korea is not allocated under the VWO exchange transfer fund while it is under EEM. Having a large impact on the ETF as it is the second largest holdings allocation for EEM, a single country’s economic performance and market, can directly affect EEM while having a converse, ripple effect on the assets managed under Vanguard’s VWO. Note that the linear regression continuance is not guaranteed by these events, merely reinforced. Returns on a 10k investment account solely on basic pairs trading through Bollinger Bands for this pair can be seen below.

The output above measures the returns of EEM and VWO when placed against the market in the past week with an initial investment of 10,000 USD. The first column represents profits, the next column shows the Bollinger band standard deviation coefficient, the next column shows the lookback window, and the last two show the pair ticker. The mean reversion pattern described above and tested on a Bollinger band backtester returned just more than 14 percent only in the past week. Each cascading parameter after that observes a decreasing return but still a very high and noteworthy ROI. Over the period of one trading week, VWO and EEM created a spread that required just under 124 trades. Factoring commission, slippage and lookback biases, the pair would have yielded 12.5% ROI in under one full week.

As investors become more and more accustomed to the introduction of linear regression to cointegration, in specific, pairs trading, ETF pairs like VWO and EEM will be, under the right conditions, more sought after. For the future, this pair is one for serious consideration to integrate into your portfolio even for those non algorithmic traders as well, since standard deviation limits can be placed on larger scales, and if such cointegration prevails, then simple investing through third party brokerages when stocks hit certain limit prices is possible. All in all, Vanguard’s Emerging Markets and MSCI Emerging Markets are great vantage points to begin quant-pairs trading with.