Over the past 18 months, the Indian economy has witnessed dramatic swings, largely due to the twin government initiatives of demonetization (November 2016) and implementation of the new national Goods and Services Tax (GST) (July 2017). These structural reforms have been vital cogs in the wheel for the current government in reforming the Indian economy.
Here, I will focus on the financial impacts of the unexpected demonetization. By combining stress testing analysis with an ex-ante risk model, we can evaluate the conditional performance of a fund in the event of a similar shock scenario.
A Sudden and Sharp Drop in Liquidity
With demonetization, the Indian economy witnessed a large-scale depletion of liquidity from the markets in the form of a withdrawal of approximately 86% of the legal tender overnight. This, in turn, resulted in massive disruptions on the supply side as well as a sharp fall in the aggregate consumption level.
Demonetization impacted the performance of the larger economy across various sectors, leading to a sharp reversal in the performance of the equity markets. The markets saw large capital gains neutralized within a span of four to six months following the announcement, although they recovered quickly once liquidity returned to normal.
To assess the impact of any hypothetical acute cash shortage on my portfolio, I used the Reserve Bank of India’s (RBI) measure of narrow money (M0) as the exogenous factor in my model. With the demonetization, India’s money supply fell by 50%, creating a classic liquidity crunch.
Breaking Down the Portfolio Impacts
To illustrate the market impact of this contraction, I built an open-ended portfolio with a greater exposure tilt toward small & mid cap companies, benchmarked to the Nifty Midcap 100. I then applied a shock in the form of a 50% decline in the money supply, similar to the impact of the demonetization.
Using an event-weighted stress test, this shock reduced the portfolio total return by approximately 3%. This translates to a 6 billion rupee decline in the portfolio’s market value.
By dissecting the portfolio into sector buckets, we see that the Financial Services sector (which includes banks, which would have borne the brunt of the shock) and the cash-intensive Manufacturing sector experienced the greatest drop in market value from the shock.
Compared to the benchmark, the portfolio gained relative to the benchmark’s bets in the Consumer, Healthcare, Energy and Commodities sectors. On the other hand, the portfolio losses in the Financial Services and Manufacturing buckets exceeded those of the benchmark, by a huge margin. This was primarily because of the steep overweight in these two sectors coupled with the shortage of liquid cash driving output and new orders lower in the cash-intensive manufacturing sector.
Taking a step further, we can drill down into the effects of the liquidity shock at the asset level. It’s not surprising that the four stocks with the biggest declines were all banks, with Indian Bank seeing the biggest drop in market value among the stocks in the sample portfolio.
Additionally, looking at the individual contribution from each of the model’s factors on an ex-ante basis (using Barra’s India-based INE2 model), the country factor and the size factor saw the biggest drops in market value as a result of the shock.
The large negative impact of the country factor on our portfolio returns reflects the prevailing bearish sentiment in the Indian equity market. The size factor reflects a portfolio’s exposure tilt toward either large cap- or small cap-oriented stocks whereas the mid-capitalisation factor indicates the portfolio’s exposure to mid-cap stocks. For our portfolio, we see that the liquidity shock results in a drag across market cap bins, with relatively larger drags on the small cap and large cap chunk of stocks as compared to the mid-cap stocks.
With this analysis, we can assess the effects of a large, uncharacteristic shock on a portfolio to gain additional insights from our portfolio positioning with respect to the risk factors. This can help us to evaluate the potential upside / downside risk of the portfolio in absolute as well as relative terms.
Mrudul joined FactSet in 2014 and is based out of Mumbai. In his current role, Mrudul is responsible for managing and servicing the requirements of analytics oriented clientele in the India Region, as well as bringing best practices and insights to client organizations.