Reading data: “Corrections” have become a convenient way of coming over data aberration

Published: January 22, 2019 1:14:38 AM

The deepening of the financial markets and the global integration of the economies is creating challenges for the financial sector, and the volatility in the market is slaughtering economic predictions like a juggernaut.

Inflation, on the other hand, has moved quite off the central bank’s expectations; so have been other macro numbers.

By Sharad Kumar

The world of finance and economics is getting interesting by the day. The deepening of the financial markets and the global integration of the economies is creating challenges for the financial sector, and the volatility in the market is slaughtering economic predictions like a juggernaut.

Who would not like to see the future and lay out plans accordingly, be it liquidity, forex rate, inflation, employment or other macro numbers, but the dynamic financial environment we are working in makes things uncertain. It is not only becoming difficult to peek into the distant future, but also in the immediate one. This may be on account of various factors—some of which are geopolitical in nature, some guided by political motives, few pure economic, and some even due to the frequent statements made by global bigwigs on trade and foreign policies, resulting in changing dynamics of demand and supply, besides others. Gone are the days of ‘ceteris paribus’.

Let us take the case of the year’s first release, i.e. the US Nonfarm payroll data. In the past few months, the actuals have been quite distant from the consensus, with revisions following later on. The variations are manifest over the last four months, which may signal impact of the trade war. It is by easy understanding that a better-than-anticipated payroll data results in strengthening of the dollar as it indicates economy to be growing fine. However, the revisions in the data later creates an anomaly. For instance, as against the first release of the US Nonfarm payroll data of 201,000 (against estimate of 191,000), the third revision increased the figure to 286,000. For November 2018 also, the second revision at 176,000 is higher than the first release of 155,000 (against a consensus of 200,000). Value judgements go for a swirl on these frequent changes.

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Now, take the case of other critical indicators like oil price movement, the dollar and even domestic inflation levels. Although looking at the historical data on crude oil, no major correlation was found between the oil price and rupee level, but that the post-August 2018 slide in the rupee was triggered squarely due to the hardening of oil prices and the correlation of over 0.50 (up to the first week of January 2019) supports this argument as against a negative correlation, which was there in the past decade.

When the rupee was losing out to the dollar, few even talked about the rupee touching 80 levels—guided by the forward premium in the non-deliverable forward (NDF) market—suddenly bringing export-oriented bigwigs out of slumber, warranting the central bank’s intervention, further leading the think tanks across the country to scratch through the ice and find out ways to stabilise the sliding rupee. When we take a look at last 14 years’ data, it has been found that the correlation of 0.94 between the rupee and forwards (12-month NDF) has been moving down and has touched 0.80 levels if data points of past couple of years are taken.

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The NDF, which signalled further slippage of the rupee, might have been guided by the US sanctions on Iran supposedly kicking on from November 4, 2018 (which actually was waived off by the US for some countries, including India and China). However, the softening of the rupee has belied tracking of (only) the NDF for forecasting rupee in the absence of any other supporting factor. However, the rupee, after touching 74.39 to a dollar on October 9, 2018, eased around the 70-level as a response to easing international crude oil prices, restoration of capital flows, etc. The 10-year benchmark, an indicator of systemic liquidity and balance, has also eased after breaching 8% levels during the same period. However, the crux is the change in the swing again. Oil looks moving up again, and so is the dollar. Will it again change the matrix?

Inflation, on the other hand, has moved quite off the central bank’s expectations; so have been other macro numbers. The fiscal turbulence on account of hardening crude suddenly eased, and so have been trade deficit numbers, quelling debates on fiscal imbalances and slippage. The extent of this disconnect can be understood by the fact that even the relatively-low GDP advance estimates announced on January 7, 2019, failed to trigger the market either way, signalling the numbness of the market towards these numbers.

The issue that impairs the criticality of these numbers is the frequent revisions, and these too, at times, by a decent margin. If revisions were not causing enough confusion, these are topped up by corrections in the numbers, which also, of late, have become a trend. Take the latest IIP numbers. The growth in IIP for November 2018 has come to 0.5% (year-on-year). This is a sharp reversal from the growth of 8.4% registered a month ago, in October 2018. To the common man, it is nothing less than “sky has fallen.” In the absence of economic knowledge, to check this number against the previous year’s base (which gives it the real sense), these figures look abysmally low, especially for an economy like India, which seems to be driving growth of emerging market economies (EMEs). Such a drastic reduction reflects the volatility of numbers followed by “corrections,” a word that has become a convenient way of coming over data aberration (against expectations), if any.

These numbers raise an interesting question: Whether volatility itself has become too much volatile? The dynamic economic environment that has catapulted the world economy, especially the EMEs, to grow very fast has become too dynamic to handle.

In the emerging scenario, trade-related rhetoric of world leaders sometimes takes us back to the pre-liberalisation era and makes us wonder the complexity of business. It even forces us to think whether, in such an interconnected and fragile business environment, these numbers truly guide in making any value-judgement and forecast on macroeconomic variables?

The author is economist, Senior Management, State Bank of India. Views are personal

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