Can Stock Market performance influence Real Economic Growth?

By Omar Barroso Khodr

Disclaimer: This article is for information and educational purposes ONLY. None of the content and opinions presented in this article should be used for investment decisions.

Source: Unplash

Since the last blog post “ Author’s note: When the Dismal Science Hit Rock Bottom pt. I — Debt, Inequality & Political Decay” had a rather apocalyptical overtone, I decided this time to ease things up for the diligent and curious econ readers and enthusiasts out there. As disastrous the Covid-19 pandemic has been for the world economy, an interesting pattern has emerged this year: the stock market boom within a crisis. In a sense this can be quite an ironic thing to happen, as we have seen unemployment levels skyrocketing, a wider gap in inequality, and the collapse of small business worldwide. With everything going on, is of my curiosity to understand why markets are overpriced right? And Whylarge companies are so optimistic when the main street struggles to make ends meet? This question lurked fiercely in my mind through this year, so in this blog post I’ve decided to organize my thoughts more thoroughly to understand better what is going on with markets in the odd year of 2020.

FYI: You can always skip my personal notes if you don’t feel like picking my brain today. 😅

Overview

Big part of 2020 was marked with volatility, uncertainty and a general mood of pessimism. However, by the end of the third quarter (beginning of October), the world changed its perspective to a positive temper. The optimism was backed by the positive vaccine trials from Pfizer, BioNTech, Moderna and AstraZeneca, as well as the announcements of continuous monetary support from central banks, and change in the U.S. presidential administration. These “hot topics” led to hopes of a period which can possibly cushion the major losses suffered in the prior months of 2020. Investors expect a scenario of progress by the gradual vaccination process against the Covid-19 combined with new stimulus on industries. The new optimism by the new U.S. presidential election also led to a stock market boom in November. We can see below the international performance of stock markets, even by countries like Brazil, Italy, Spain, Mexico and Argentina struggled through most of 2020. This is an interesting pattern to investigate more thoroughly.

There are many reasons behind the recent positive performance in stock markets in 2020. One interesting aspect to look is due to the recent unemployment trend, for example, two of three performers in this list (bottom down) Spain and Brazil presented double-digit unemployment up to Q3: 16.26% and 14.60% respectively. While Italy barely passed the double-digit mark at 9.8% (Tradingeconomics, 2020) So how are markets in these countries so “hot” with so much unemployment going on? One of the hypotheses we can use is due to the level of joblessness suffered this year, which led people to find other methods to make ends meet. Just think about the reason why trading platforms became so mainstream this year, how many ads have you seen pop-up in your YouTube videos lately? This kind of marketing is able to influence people that look for a quick cash opportunity and cannot apply for a formal job (since labour markets became rigid because of the pandemic). In Brazil alone, from 2019 to 2020 there was an increase of 81.70% in the number of participants in the local stock market (B3, 2020). In order to understand why the stock market “fever” has blossomed in this year, we will need to check some new school macroeconomics.

We can say that the recent increase in the market participants it’s correlated with the monetary manoeuvres used by Central Banks to stimulate the economy in the 2020 crisis. One famous example is the Quantitive Easing (QE) methodology most popularly used by the United States, United Kingdom, Japan and the European Union in times of crisis. In the U.S. example, The Federal Reserve System on March 15, 2020, purchased US$ 700 billion worth of government debt bonds and mortgage-backed securities from a domestic financial institution (IMF, 2020). The QE program is used to increase a Central Bank’s (CBs) balance sheet or the money supply and lower interest rates in order to inject money to banks so they can expand credit to businesses and consumers. To make things more intuitive let’s think about business, as a manager or banker how can you profit from a scenario of massive unemployment, social isolation, and high rate of internet connectivity? Since the monetary system has granted banks the leeway for cash for financial institutions, we should encourage our customers to engage more and more in the services we can offer, so let’s make them ask for loans and play with stocks. We can assume with so much cash being flown into banks, customers either use that money to invest in the services provided by their banks or to buy other kinds of financial services (e.g., Robin Hood, Trade Station, Interactive Brokers, Etoro, and you name all the others).

However, in economic terms, things are more technical. At the first hand, the stock market booms we have witnessed is a reflection of investors’ expectations are given by the news of expansionary policies by CBs. The other factor is given by the interest rate (I.R.) effect, the I.R. cuts tend to influence decisions by companies which choose to benefit from the lower borrowing costs and expand their businesses. Again, some psychological factors in the game, investors see these patterns and decide to take more risks in order to find stronger returns, which increases stock market prices (Ross, 2020). In practice, economics can be more art than science sometimes when the Fed announced the series of emergency rate cuts in March 2020, markets didn’t take the news really well as they interpreted the cuts as an act of desperation (this can be seen with the drop of the S&P 500 in March). A similar pattern was followed by the European Stocks, as the European Central Bank (ECB) also announced in mid-march plans for emergency cuts. At any rate, this was only a short-term drop, as we can see in June July the stock performance started to improve when compared to late February and march. In other words, we can say it took some time to the investor to “digest” the new reality.

So, what about Brazil? In the first graph of this article, we saw that the Latin American country was in the second spot of stock market performance in November 2020. Well, things work in a different manner in Brazil whenever it is compared to Europe and the U.S. as Brazil did not use any QE tools in the early days of the pandemic. Although, it’s fair to mention that a Brazilian version of the QE has been highly contemplated among Brazilian policymakers, economists and the economics/finance academia. So how are Brazilian markets so “hot” right now? Well, in a nutshell like many countries Brazil decided to engage in fiscal measures to aid the impacts of the pandemic, the most famous measure was the creation of the “war budget” where includes a temporary support to venerable income classes, employment support, increases in federal to state government to support higher health spending and expansion of credit line for businesses and households to support working capital, payroll costs and investment. In the monetary end, the Brazilian Central Bank (BCB) followed the same pattern to cut I. Rs that was lowered to historical levels at 2%. The liquidity measures used in Brazil were by the reduction of reserve requirements (from 25% to 17%) and the creation of a facility to provide loans to financial institutions which are supported by private corporate bonds as collateral. Capital requirements for small financial institutions were also eased, and banks were allowed to reduce provisions for contingent liabilities if they are directed towards small to medium-sized enterprises (IMF, 2020).

In essence, the QE used in Europe and the U.S and the fiscal/monetary stimulus in Brazil can be main drivers which lead to this “stock market bonanza”. We could also add to this argument the systemic incentives which Spain created with the € 40 billion new lines for credit which promotes investment activity (IMF, 2020). However, we believe that things have started to make sense to readers at this point, let’s move to to the next topic.

So, the cherry on top of the cake might be over in this section, as we will try to decipher the influence of fiscal and monetary maneuvers used by governments and CBs are potential drivers for the connection between stock market performance and economic growth. For starters, it might be too soon to make bold assumptions, since the Covid-19 pandemic is still underway and we don’t know if countries will be required to create or maintain new tools in order to contain the future necessity of new expenditures. The best we can measure are the short-term effects of such policies, compare them to the present economic stimulus and somehow relate them to the recent stock market appreciation. We saw in the last section, that markets didn’t take the initial rate cuts really well, but, this was soon backed by a general mood of optimism (or adaptability) by market players.

Even countries that struggled in the first semester of 2020 (Brazil, Spain, and Italy) already started to show economic stimulus by the third quarter of 2020: Brazil presented a GDP increase of 7.7%; Spain grew by 16.7%; Italy grew by a record of 15.9%. Furthermore, this general trend was repeated by most of the G20 countries, since most countries followed a similar pattern of rigid lockdowns followed by the re-opening of the economy. In qualitative terms we can assume that people became “braver” to consume in restaurants, pubs, and physical stores, which stimulated business and industrial demand. But, what’s the weight of stock markets in this equation? As we have seen in November 16 and 24 the Dow Jones Industrial Average (DJIA) hit record highs at 29,950.44 and 30,046.24 respectively. Is this a true indicator for real economic stimulus like politicians like to advertise? In order to find if this type of commentary is relevant to a real economic, we decided to check the correlation between GDP growth and the variation in prices of the DJIA.

The enigma between economic growth and stock market performance is not as easy as it seems. According to Mladina (2016), the equity returns from the DJIA and real GDP from 1995 to 2015 don’t yield promissory correlation results. The explained variation (R2) of the variables was effectively zero, which does not indicate strong evidence between long-term GDP growth and equity returns (Mladina, 2016). Inspired by Mladina (2016), we decided to test our own parameters, however, we change the DJIA and GDP growth to a quarter-by-quarter basis from January 01, 2001 to December 09, 2020. We used the classic Person correlation method (to not complicate things too much). Where we defined x as GDP quarterly Growth (%) and y as the DJIA quarterly growth (%), all the calculations were made with aid of the statistical software R studio.

In the end we reached a similar dead end as Mladina (2016). Our 𝝆 (rho or correlation coefficient) was barely approved with -0.05, which indicates a small negative (or inverse) correlation. Which means in theory that both variables move in opposite directions, let’s say if GDP growth decreases, stock market returns increase by the “same magnitude”). Which, in intuitive terms, it makes sense, since through Q1 and Q2 of 2020 most countries experienced GDP contraction, in the case of the U.S. there was a decrease in GDP by 1.3(Q1) and 9%(Q2). But, as we can see below after the brief crash in March, the DJIA started to see increases again while U.S. GDP plunged (similar to the S&P 500 and Euro Stoxx we saw earlier).

However, in another perspective, the GDP increase in Q3 was backed by the DJIA showing record highs in November (as we mentioned earlier). This means that may exist other types of stock market variables that can influence GDP growth. We can theorize some: like the rebound in industrial stimulus for specific sectors or the improved economic expectations by certain agents which bounced back the business cycle in the second semester.

Back Test & End Notes

(If you are not interested in the statistical analysis of this article, feel free to skip to the last part of this section).

The famous discourse used by politicians, “look at how great I’m doing, stocks have soared because of my administration” is not one to be taken seriously, in the end, is all political marketing. As we have seen, by the example of Mladina (2016) and the Pearson test, the negative correlation is not a strong one to make such a bold assumption. However, the statistical correlation between GDP growth and stock market returns may require more sophisticated tools. Although, this type of mathematical manoeuvre can be a “catch 22” which may require much effort by the researcher, with not so many promissory results. One of the first incongruences as we notice by the small correlation between the DJIA returns and GDP growth, was by the possibility of both variables to not display a normal distribution. To do this we ran a couple of backtests, which can be seen in the image below.

If we look at the right-hand corner, we can see that both variables (GDP growth and DJI variation) are approximate to the reference line, which we can assume for normality. Whereas, in the left-hand corner we can see that the density plot for GDP growth makes a sort of takes a leptokurtic distribution shape, where the values are located in the tails rather than the mean. The shape of the density plot for the DJIA changes is a rather curious one as it almost takes an exponential pattern where the average might be somewhere at 0.1, which makes sense as the returns of stock markets usually occur at a constant average (in the long run). We also performed a univariate Shapiro-Wilk test (SWT) it gave us a p-value of p < 2.2e-16 for the percentage change of the DJIA which gave us a significant result by a typical threshold of 0.05. However, the SWT for GDP growth was higher than 1, which means that probably a type I error occurred, so we decided to leave this analysis behind.

We also performed a Kendall Tau ( 𝝉 ) correlation method, to estimate a rank-based measure of association between both variables.

The Kendall 𝝉 is able to show us how strongly the two variables are monotonously related. In other words, if the size of the DJIA percentage variation increases as GDP growth increase also increases and vice-versa. Our Kendall 𝝉 gave us 𝝉 = 0.000999001, which indicates a really low correlation between the DJIA and GDP growth.

(you can start reading from below here if the statistical analysis is not of your interest)

The correlationbetween stock market performance and GDP growth, should not be taken with much relevance as the financial press and the political debate sell off to the public. There may exist certain types of positive correlations that can be measured or manipulated by different variables which can indicate a strong relationship between stock market performance and economic growth (e.g., an increase of a stock price for a specific sector and that region’s economic development). However, as the economic literature suggests the correlation between stock market growth and real GDP growth remains either low or negative (indirect). In the other hand, we must note that by the use of statistical manipulation with econometric models like the Vector Error Correction Model (VECM), there may exist relevant significance between the impulse-response functions of variables with stock market performance and Gross Fixed Capital Formation (GFCF). This has experimented with the Brazilian stock index called the “Ibovespa”, the methodology suggested that a shock on the Ibovespa stock index had a direct effect on the variation of GFCF in 2020 (Wilher, 2020). As we can see it is possible to find clues as to whether real economic growth can be influenced by the stock market stimulus.

Sceptics (like the author) and experienced investors doubt the stability of the stimulus which stock markets experience in an uncertain scenario. We used the logarithm properties to calculate the log return of the DJIA, we were able to identify some of the parameters of the DJIA this year.

With aid of the statistical software Rstudio, we built this graph to observe the variations of the returns of the DJIA price series in 2020.

We can notice the high drop in returns around March with the initial impacts of the Covid-19 pandemic and by the scepticism of markets on the emergency fiscal and monetary policies used to contain the crisis. After the initial stress, we can see a trend which is extended until now (December 2020), with small drops but nothing too significant. We can also assume that the boom in stocks is due to a lot of free cash being thrown around, which creates short-term enthusiasm. In conclusion, the recent “booms” in stock markets are not yet a reason to hope for economic stability, there exist more systemic variables that require attention so we can return to pre-pandemic economic levels. This analysis will probably require a supplemental article, we didn’t touch the reasons why markets are overpriced, but I will leave this cliffhanger to the next article.

In the following days, I will be uploading my Github updated with the codes used in this article for the creation of the graphs and statistical analysis. Please give me a couple of days so I can organize everything.

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Originally published at https://www.studentsofeconomics.com.