Timorous evidence of “Contagious Effect” after Dow Sell-Off.

The stock market seems to be returning to the old normal of higher levels of volatility. I suggested on Tuesday that former Fed Chairman Alan Greenspan’s comments could have brought back volatility by triggering the Dow Sell-off on Monday, February 5th. As I wrote early in the week, I believe that we should observe some panic manifestation of economic anxiety because of Mr. Greenspan’s comments. In this Blog Post, I will show two Person Correlation tests that may allow inferences as to how investors’ fear has grown since Mr. Alan Greenspan stated that the American Economy has both a Stock Market Bubble and a Bond Market Bubble. The Pearson correlation tests show that the correlation has strengthened during the last seven days (First week of February 2018), suggesting there might be symptoms of Contagious effect.

I correlate two variables taken from two different time frames for the fifty states. First, the natural logs of the search term “Inflation”; and, the natural logs of the search term “VIX” (Volatility Index). Second, I correlate the natural logs of the same searches for both the last seven days period and the previous twelve months period. By looking at the corresponding coefficients, one may infer that the correlation increased its strengthen after Mr. Greenspan Statements -which reflects on the last seven days data. The primary goal of this analysis is to gather enough information so that analysts can conclude whether there is a Contagious Effect that could make things go worst. Understanding the dynamics of economic crisis starts by identifying the triggers of them.

What is Contagious Effect?

I should say that the best way to explain the Contagious Effect is by citing Paul Krugman’s quote of Robert Shiller (see also Narrative Economics), “when stocks crashed in 1987, the economist Robert Shiller carried out a real-time survey of investor motivations; it turned out that the crash was essentially a pure self-fulfilling panic. People weren’t selling because some news item caused them to revise their views about stock values; they sold because they saw that other people were selling”.

Thus, the correlation that would help infer a link between both expectations is inflation and the index of investors’ fear VIX. As I mentioned above, I took data from Google Trends that show interest in both terms and topics. Then I took the logs of the data to normalize all metrics. The Pearson correlation tests show that the correlation has strengthened during the last seven days, suggesting there might be symptoms of Contagious effect. The over the year Person correlation coefficient is approximate to .49, which is indicative of a medium positive correlation. The over the week Person correlation test showed a stronger correlation coefficient of .74 which is indicative of a stronger correlation. Both p-values support evidence to reject the null hypothesis.

The following is the results table:

February 1st – February 8th correlation (50 U.S. States):

February 2017 – February 2017 (50 U.S. States):

It is worth noting the sequence of the events that led to these series of blog posts. On January 31st, 2018 Alan Greenspan told Bloomberg News: “There are two bubbles: We have a stock market bubble, and we have a bond market bubble.” And, on February 5th, 2018, Dow Jones index falls 1,175 points after the trading day on Monday. As of the afternoon of Friday 9th, the Dow still struggle to recover, and it is considered to be in correction territory.

The Missing Part of the Dow Jones and Stock Market Sell-off Analysis.

The stock market keeps on sending signals of correction as the Dow Jones struggle to rebound from Monday’s 5th of February sell-off. Economic analysts began early in the week to point out to fear of high inflation due to an upward trend in workers compensation. News reports were mostly based on strong beliefs and arguments over the so-called Phillips Curve. However, instead of focusing exclusively on the weak relationship between wages and inflation, I suggest a brief look at the textbook explanation of the link between the stock market and economic activity. In this blog post, I frame the current market correction phenomenon under the arbitrage argument. If one were to consider the arbitrage argument to explain the correction, it would lead analysts to make firm conclusion not only over monetary policy but also over fiscal policy. The obvious conclusion is that Monetary Policy (Interest rates) will most likely aim at offsetting the effects of Fiscal Policy (Tax cuts).

The Arbitrage Argument (simplified):

Market sell-offs unveil a very simple investment dilemma: bonds versus stocks. In theory, investors will opt for the choice that yields higher returns. Firstly, investors look at returns yield by the interest rates, which means a safer way to make money through financial institutions. Secondly, investors look at returns yield by companies, in other words: profits. If such gains yield higher returns than saving rates, investors will choose to invest in the former. In both cases, agreements to repay the instrument will affect the contract and the financial gains, but that is the logic (Things can get messier if one includes the external sector).

The corresponding consequences are the market expectations about the economy. On the one hand, currently investors expect monetary policy to tighten. On top of jobs reports and previous announcement about rate increases, fears of inflation lead to the conclusion that the Federal Reserve Bank will most likely accelerate the pace in rising interest rates for its ten years treasury bond. Such policy will decrease the amount of circulating money, thereby making it harder for business to get funds because, following the arbitrage framework, investors will prefer to invest in safer treasury bonds. On the other hand, investors expect fiscal policy to have an impact on the economy as well. Recent corporate tax cut bolster the expectation for a higher level of profits from the stock market. Such policy may allure investors to believe that financing companies through Wall Street will yield higher returns than the bond market. Thus, sell-offs unveil the hidden expectations of investors in America.

Expectations and the Economy:

Once expectations seem formed and clear concerning declared preferences, (meaning either continuing the correction path for other indexes, or a rebound), investors begin evaluating monetary policy adjustments. They all know the Federal Reserve dual mandate as well as the Taylor Rule. The question is how the Federal Reserve would react to the market preferences based on other leading economic indicators. Will the Fed accommodate? Or will the Fed tighten? As of the first week of February, all events suggest that the Federal Reserve Bank will most likely tighten to offset and counterbalance the recent tax cut incentives and its corresponding spillovers.

Recent Narratives of Stock and Bond Bubbles.

On February 5th, 2018, Dow Jones index fell 1,175 points after the trading day. Four economic scenarios are being analyzed in the news as of the first week of February 2018. First, there are indeed both Stock Market and bonds Bubbles. Second, the Monday Dow’s selloff is just an anticipated correction move on the investor’s side. Third, the stock market returns to the old normal of higher levels of volatility. Fourth, Trump economic effect. I need not to cover the concerns on the US economy nowadays in this blog post. Hence, the analysis that I think is needed currently is the ruling out of a contagious effect from the narratives created around the Dow’s selloff on Monday. Indeed, I believe that such narrative, if any, can be traced back to former chairman Alan Greenspan’s comments when he stated on January 31st that America has both a bond market and stock market bubbles. By discarding the contagious effect in current narratives, I side with analysts who have asserted that the Dow’s fall was just an anticipated market correction.

Can economists claim there is some association between Alan Greenspan’s comments and the Monday fall of the Dow Jones? I may not have an answer for that question yet, but We can look into the dynamics of the phenomenon to better understand how narratives could either deter or foster an economic crisis in early 2018. If there is room for arguing that Mr. Greenspan’s comments triggered the Dow Selloff on Monday, I believe we should be observing some sort of panic or manifestation of economic anxiety. By looking at data from Google Trends, I spot on breakouts that may well be understood as “spreading” symptoms. In other words, if there is any effect of Mr. Greenspan’s comments on the Dow’s selloff on Monday, we should expect to see an increase in Google searches for two terms: first “Alan Greenspan”, and second “Stock Market Bubble.” The chart below shows google trends indexes for both terms. Little to nothing can be said about the graph after a visual inspection of the data. It is hard to believe that there are narratives of economic crisis fast-spreading, nor have Mr. Greenspan’s comments had any effect on the Dow’s sell-off.

How did things occur?

Economists are lagging on the study of narratives, hence the limited set of appropriate analytics tools. Robert Shiller wrote early in 2017 that “we cannot easily prove that any association between changing narratives and economic outcomes is not all reverse causality, from outcomes to the narratives,” which is certainly accurate whenever time has passed as empirical evidence become obscure. However, on February 1st of 2018 mainstream media reported extensively a couple of statements made by Alan Greenspan about bubbles. In the following two days, several market indexes closed with relatively big loses. In detail, the events occurred as follows:

  1. On January 31st, 2018 Alan Greenspan told Bloomberg News: “There are two bubbles: We have a stock market bubble, and we have a bond market bubble.”
  2. On February 5th, 2018, Dow Jones index falls 1,175 point after the trading day on Monday.

Whenever these events happen, we all rush to think about Robert Shiller. As Paul Krugman cited Shiller today February 6th, 2018, “when stocks crashed in 1987, the economist Robert Shiller carried out a real-time survey of investor motivations; it turned out that the crash was essentially a pure self-fulfilling panic. People weren’t selling because some news item caused them to revise their views about stock values; they sold because they saw that other people were selling”. In other words, Robert Shiller’s work on Narrative Economics is meant for these types of conjectures. Narratives of economic crisis play a critical role in dispersing fear whenever economic bubbles are about to burst. One way to gauge the extent to which such a contagious effect occurs is by looking at google trend search levels.

 

 

No signs of fast-spreading economic crisis narratives:

Despite the ample airtime coverage, there is little to none evidence of a market crash and economic crisis. In the wave of fast pace breaking news announcing crisis and linking them to political personalities, markets seem just to be having an expected correction after an extended period of gains. The best way to conclude such correction is by looking at the firm numbers reported lately on jobs markets as well as to investigate the collective reaction to fear and expectations. Thus, four economic scenarios are being analyzed as of the first week of February. First, there are Stock Market and bonds Bubbles. Second, the Monday Dow’s selloff is just an anticipated correction move on the investor’s side. Third, the market returns to the old normal of higher levels of volatility. Fourth, Trump effect. None of the scenarios seem plausible to me. First, the selloff appears not to have dug into the investors and people’s minds, thereby avoiding the contagious effect. Second, despite the unreliability of winter economic statistics, jobs reports on January 2018 seem optimistic (I think they will revise those number low). Third, claiming volatility is back to the stock market is like claiming Trump is back into controversy. Therefore, the only option left to explain Monday’s selloff is the argument of a market correction.

The overuse of the word “Strong” in economic news.

The US economy added 228,000 new jobs in November of 2017 and analysts rush to assess the state of the economy as “STRONG.” Although the job reports are indeed good indicators of the performance of the US economy, one should not simplify the job report as the snapshot of the economy that allows for those “strong” conclusions by and in itself. In this post, I show that despite the existence of a cointegration vector between unemployment rate data and the word-count of the word “Strong” in the Beige Book, journalists indeed overuse the word “Strong” in headlines. Although interpreting cointegration as elasticity goes beyond the scope of this post, I think that by looking at the cointegration relation it is safe to conclude that the current word count does not reflect the “strong” picture showed by the media, but somewhat more moderate economic conditions.

To start, let me go back to the first week of December of 2017. Back then, news outlets had headlines abusing the word “strong.” Some examples came from major newspapers in the US such as the New York Times, Reuters, CNN and the Washington Post. The following excerpts are just a sample of the narrative seen those days:

“The American economy continues its strong performance” (CNN Money).

“The economy’s vital signs are stronger than they have been in years” (NY Times).

“Strong US job growth in November bolsters economy’s outlook” (Reuters).

“These are really strong numbers, which is pretty exciting…” (Washington Post).

Getting to know what is happening in the economy challenges economists’ wisdom. Researchers are constrained by epistemological limits of data and reality, and so are journalists. To understand economic conditions, researchers utilize both quantitative and qualitative data while journalists focus on qualitative most of the times. Regarding qualitative data, the Beige Book collects anecdotes and qualitative assessments from the twelve regional banks of the Federal Reserve system that may help news outlets to gauge news statements and headlines. The Fed studies business leaders, bank employees, among other economic agents to gather information about the current conditions of the US economy. As a Researcher, I counted the number of times the word “strong” shows up in the Beige Book starting back in 2006. The results are plotted as follow:

If I were going to identify a correlation between the word count of “strong” and its relation to the unemployment rate, it would be very hard to do so by plotting the two lines simultaneously. Most of the times, when simple correlations are plotted, the dots show any relation between the two variables. However, in this case, cointegration goes a little deeper into the explanation. The graph below shows how the logs of both variables behave contemporarily over time. They both decrease during the Great Recession as well as they increase right after the crisis started to end. However, more recently both variables began to divert from each other, which makes it difficult to interpret, at least in the short run.

Qualitative data hold some clues in this case. Indeed, the plot shows a decreasing trend in the use of the word within the Beige Book. In other terms, as journalists increase its use in headlines and news articles, economists at the Federal Reserve Bank decrease the use of the word “strong”. If I were going to state causality from one variable to the other, first I would link the word “strong” to some optimism for expected economic outcomes. Thereby, one should expect a decrease in the unemployment rate as the use of the word “strong” increases. This is a classic Keynesian perspective of the unemployment rate. Such relation of causality might constitute the cointegration equation that the cointegration test identifies in the output tables below. In other words, the more you read “strong,” the more employers hire. By running a cointegration test, I can show that both variables are cointegrated over time. That is, there is a long-term relationship between both variables (both are I(1)). The cointegration test shows that at least there exists one linear combination of the two variables over time.

The difficulty with the overuse of the word nowadays is that the word is not being used by economists in the Federal Reserve at the same pace as journalist economists do. In fact, the word-count has decreased drastically for the last two years from its peak since 2015. Such mismatch may create false expectations about economic growth, sales and economic performance that may lead to economic crisis.

Why is America’s center of gravity shifting South and West?

Ever since Florida surpassed New York as the third most populous state in the nation, journalists started to document the ways in which the South region of the United States began attracting young sun-lovers enthusiasts. Two factors have been identified as drivers of an apparent migration from the north towards the south. On one hand, real estate prices have been arguably one of the major causes for people heading south. On the other, employment growth and better job opportunities allegedly support decisions on moving out regionally. This article checks empirical data on those two factors to determine the effect on population growth of major cities in the United States. The conclusion, in spite of the statistical model limits, indicates that employment dynamic seems to drive a slightly higher level of influence in population growth when compared to housing costs.

Is it because of real estate prices?

The first factor some prominent people have identified is real estate prices. Professor Paul Krugman highlighted in his NYTimes commentary of August 24th, 2014 that the most probable reason for people heading south is housing costs, even over employment opportunities. From his perspective, employment has little effect on such a change given that wages and salaries are substantially lower in southern states when compared to the north. Whereas, housing costs are significantly lower in southern regions of the country. Professor Krugman asserts that “America’s center of gravity is shifting South and West.” He furthers his argument “by suggesting that the places Americans are leaving actually have higher productivity and more job opportunities than the places they’re going”.

By Catherine De Las Salas

By Catherine De Las Salas

Is it because of employment opportunities?

Otherwise, Patricia Cohen –also from the NYtimes- stresses the relevance of employment opportunities in cities like Denver in Colorado. In her article, the journalist unfolds the story of promising entrepreneurs immersed in an economically fertile environment. The opposite situation to that prosperous environment happens to locate northeast of the United States. Cohen writes that not only “in the Mountain West — but also in places as varied as Seattle and Portland, Ore., in the Northwest, and Atlanta and Orlando, Fla., in the Southeast — employers are hiring at a steady clip, housing prices are up, and consumers are spending more freely”. Her article focuses on contrasting the development of urban-like amenities and how those attractions lure entrepreneurs.

A brief statistical analysis of cross-sectional data.

At first glance, both factors seem to be contributing factors for having an effect on migration within states. However, although both articles are well documented, neither of those readings goes beyond anecdotal facts. So, confirming those very plausible anecdotes deserves a brief statistical analysis of cross-sectional data. For doing so, I took data on estimated population growth for the 71 major cities in the U.S. from 2010 to 2015 (U.S. Census Bureau), and regressed it on the average unemployment rate in 2015 (U.S. Bureau of Labor Statistics), median sale price of existing houses for the same year (National Association of Realtors), and the U.S. Census Bureau’s vacancy rate for the same year and cities (Despite that the latter regressor might be multicollinear with sale price of existing houses, its inclusion in the model aims at reinforcing a proxy for housing demand). The statistical level of significance for the regression is a 90 percent confidence interval.

Results.

The results show that, for these data sets and model, the unemployment rate has a bigger effect on population growth than vacancy rate and median home sale prices altogether. The regression yielded a significant coefficient of -2.78 change in population growth as unemployment decreases. In other words, the lower the unemployment rate, the greater the population growth. A brief revision of empirical evidence shows that, once the coefficients are standardized, unemployment rate causes a higher effect on the dependent variable. If we were to decide which of the two factors affects population growth greater, then we would have to conclude that employment opportunities do it largely.

Regression Results.

Regression Results.

By using these data sets and this model, the employment dynamic seems to drive a slightly higher level of influence in population growth, when compared to housing costs. The unemployment rate has a standardized effect of negative 56 percent. On the other hand, median sale price of houses pushes a standardized change effect of 23 percent. Likewise, vacancy rate causes in the model an estimated 24 percent change in population change. Standardized coefficients are a tool meant to allow for disentangling the combined effect of variables in a model. Thus, despite that the model explains only 35 percent of population growth, standardized coefficients give insights on both competing factors.

Limits of the analysis.

These estimates are not very reliable given that population growth variable mirrors a five years lapse while the other variables do so for one year. In technical words, the delta of the regressand is longer than the delta of the regressors. For this and many other reasons, it is hard to conclude that employment constitutes the primary motivation for people moving out south and west. Nonetheless, this regression sheds light onto a dichotomy that needs to be understood .