The Rising Thunder El Nino and Stock Markets

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THE RISING THUNDER

EL NINO AND STOCK MARKETS:

Tristan Caswell

A Project Presented to

The Faculty of Humboldt State University

In Partial Fulfillment of the Requirements for the Degree

Master of Business Administration

Committee Membership

Dr. Michelle Lane, Ph.D, Committee Chair

Dr. Carol Telesky, Ph.D Committee Member

Dr. David Sleeth-Kepler, Ph.D Graduate Coordinator

July 2015

Abstract

THE RISING THUNDER EL NINO AND STOCK MARKETS:

Tristan Caswell

Every year, new theories are generated that seek to describe changes in the pricing of equities on the stock market and changes in economic conditions worldwide. There are

currently theories that address the market value of stocks in relation to the underlying

performance of their financial assets, known as bottom up investing, or value investing.

There are also theories that intend to link the performance of stocks to economic factors

such as changes in Gross Domestic Product, changes in imports and exports, and changes

in Consumer price index as well as other factors, known as top down investing. Much of

the current thinking explains much of the current movements in financial markets and

economies worldwide but no theory exists that explains all of the movements in financial

markets. This paper intends to propose the postulation that some of the unexplained

movements in financial markets may be perpetuated by a consistently occurring weather

phenomenon, known as El Nino. This paper intends to provide a literature review,

documenting currently known trends of the occurrence of El Nino coinciding with the

occurrence of a disturbance in the worldwide financial markets and economies, as well as

to conduct a statistical analysis to explore whether there are any statistical relationships

between the occurrence of El Nino and the occurrence of a disturbance in the worldwide

financial markets and economies. The purposes of this analysis are to discover if there is ii

a relationship and to see how strong of a relationship exists; and the analysis will be considered a success if this paper can conclude if a relationship exists between El Nino’s and fluctuations in financial markets and economies.

iii

Acknowledgements

First, I would like to thank all of the people in my life that motivate me to succeed. Those people include my Mom and Dad, my sister Tasha, my girlfriend

Maribel, and all of my friends.

I would also like to thank Dr. Michelle Lane and Dr. Carol Telesky for helping me tremendously down the stretch. Thanks!

Table of Contents

Abstract ...... ii

Acknowledgements ...... iv

List of Tables ...... vii

List of Figures ...... ix

Introduction ...... 1

What is an El Nino? ...... 2

History of Stock Market Panics in the U.S Coinciding with El Nino ...... 7

Research Question: ...... 9

Hypothesis ...... 9

Part 1: How an Economy is Affected by El Nino ...... 10

Part 2: The Patterns of History...... 12

The Peruvian Guano Economy 1840-1879 and El Nino Related Collapse ...... 13

The Panic of 1857 and El Nino 1857 ...... 16

El Nino 1891 the Baring crisis of 1890-93 and U.S 1893 Panic ...... 19

Part 3: Analysis of El Nino 1997-1998 on Financial Markets ...... 23

El Nino 1997-1998 ...... 23

Financial Crisis 1997-1998 ...... 26

Method ...... 34

Results ...... 39

Entire Database ...... 39

El Nino Time Periods ...... 41 v

Discussion ...... 47

Bibliography ...... 51

Appendix ...... 53

List of Tables

Table 1: List of Panics in the U.S Coinciding with El Nino ...... 8

Table 2: List and Description of Areas of the Globe Impacted by El Nino ...... 24

Table 3: Anova Table S&P 500 vs El Nino 1950-2015...... 53

Table 4: Anova Table Mexico Stock Market vs El Nino 1991-2015 ...... 53

Table 5: Malaysia Stock Market vs. El Nino 1994-2015...... 54

Table 6: Hang Seng Index vs. El Nino 1987-2015 ...... 54

Table 7: S&P 500 vs. El Nino 2006-2007 ...... 55

Table 8: Mexico Stock Market vs. El Nino 2006-2007 ...... 55

Table 9: Malaysia Stock Market vs. El Nino 2006-2007...... 56

Table 10: S&P 500 vs. El Nino 1997-1998 ...... 56

Table 11: Mexico Stock Market vs. El Nino 1997-1998 ...... 57

Table 12: Hang Seng Index vs. El Nino 1997-1998 ...... 57

Table 13: Brazil Stock Market vs. El Nino 1997-1998 ...... 58

Table 14: Nikkei 225 Index vs. El Nino 1997-1998 ...... 58

Table 15: S&P 500 vs. El Nino 1986-1988 ...... 59

Table 16: England Stock Market vs. El Nino 1986-1988 ...... 59

Table 17: Nikkei 225 vs. El Nino 1986-1988 ...... 60

Table 18: S&P 500 vs. El Nino 1982-1984 ...... 60

Table 19: S&P 500 vs. El Nino 1972-1973 ...... 61

Table 20: S&P 500 vs. El Nino 1968-1970 ...... 61

Table 21: S&P 500 vs. El Nino 1957-1959 ...... 62

vii

Table 22: S&P 500 vs. El Nino 1952-1954 ...... 62

viii

List of Figures

Figure 1: Normal Westward Flow OF Wind And Water ...... 5

Figure 2: El Nino Related Reverse Of Westward Flow ...... 6

Figure 3: ENSO SST Departure From Normal In Degrees Celsius...... 25

Figure 4: Stock Market of Thailand 1993-2000 ...... 27

Figure 5: Currency of Thailand 1993-2000 ...... 27

Figure 6: Thailand Imports 1993-2000 ...... 29

Figure 7: Thailand Exports 1993-2000 ...... 29

Figure 8: Stock Market of Philipppines 1993-2000 ...... 31

Figure 9: Philippines Imports 1993-2000 ...... 31

Figure 10: Philippines Exports 1993-2000 ...... 32

Introduction

Economic recessions and stock market selloffs occur on the average of once every

three to seven years. In the economy of every nation, there are periods of economic

growth and periods of decline, called recessions. During periods of growth, domestic

production increases, business revenues and profits increase, banks remain stable, stock

markets increase, and unemployment decreases. (Sherman, 1991) The growth economy is

very satisfying and contributes towards increasing people’s income, job retention, the

value of assets, and many other economic and financial factors. Recessions, however,

include decreases in business revenues and profits, increases in bankruptcy of business,

increases in unemployment, banks become unstable, stock markets decline, and the

spillover effect has undesirable societal impacts, such as an increase in mental and

physical illness of unemployed persons and their families. (Sherman, 1991) According to

The Business Cycle: Growth and Crisis under Capitalism, Howard Sherman notes that

“During recessions you can expect that increased unemployment will cause increases in

alcoholism, divorce, child abuse, crime, and even suicide.” (Sherman, 1991, p. 4)

Severe, regularly occurring weather events, known as El Nino’s occur on average

of once every three to seven years. During this phenomenon, normally occurring weather

patterns are disrupted by warmer than average sea surface temperatures in the Pacific

Ocean off the coast of South America. El Ninos bring destructive floods, droughts, and temperature, air pressure and rainfall anomalies all across the world. The unexpected changes bring disruptions in the supply of goods as well as changes in economic

consumption patterns due to the weather itself. My goal of this analysis is to determine if there is a causal link between this regularly occurring weather phenomenon, and the activity of the global economy, particularly with respect to the value of stock markets worldwide.

What is an El Nino?

The term El Nino refers to a disruptive weather anomaly that causes floods, droughts, excessive heat and cold, and many other influences. To understand how this phenomenon may affect things like the value of the stock market we must first understand more about what El Nino is, how it is caused, and what its effects are.

El Nino is defined by the periodic warming of the Pacific Ocean off the coast of

South America and has been a fixture in the way of life of people in South America for many years. (Allan, Janette, & Parker, 1996) The Spanish term El Nino has been used for

centuries by South American mariners and fisherman to define the occurrence of warm,

southward flowing oceanic currents off the coast of Ecuador and Peru around Christmas

time. (Allan, Janette, & Parker, 1996) More recently the term El Nino refers to the

occurrence of extreme events of warmer than normal ocean temperatures in the Pacific

Ocean that last well beyond the seasonal occurrence of warm waters, and in many cases

last over a year. (Allan, Janette, & Parker, 1996) The National Oceanic and Atmospheric

Administration (NOAA) defines an El Nino as “a phenomenon in the equatorial pacific

ocean characterized by a positive sea surface temperature departure from normal…

greater than or equal in magnitude to 0.5 degrees C (0.9 degrees Fahrenheit) over three

consecutive months”. (NOAA, 2015) To understand why a change in ocean temperature

is important to weather patterns we must first present a model of how the ocean currents

work, how they affect the weather, and how that in turn causes changes to the underlying

economic condition of a nation.

The world’s weather patterns follow a set of patterns that make weather relatively constant across similar latitudes of our planet. Due to the position of the earth in relation to the sun, the spin of the earth, and the tilt of the earth’s axis in regards to its rotation around the sun, global macroscale weather patterns develop that control the overall climate of a certain region. Around the equator the weather is relatively stable and benign, with generally warm air temperatures, warm ocean temperatures and winds that blow from the east, in both the northern and southern hemisphere. In this area the easterly trade winds, as they are known, provide a flow of warm moist air that cause regularly occurring summer rains and relatively mild and dry air during the winter time. As you get further north or south of the equator, the winds are persistently from the west and bring cold moist air during the winter that cause winter storms and unstable weather. In the summer, however, the weather around these latitudes can be quite dry and stable. This latitudinal macro scale occurrence, known to meteorologists as the mid-latitude westerly’s, is the primary belt of the jet stream and provides non-equatorial regions with their primary source of rainfall.

It is these opposing winds that set the stage for the dynamics of oscillating ocean currents around the globe. In a normal year the east winds that are prevailing around the equator off the coast of South America push warm water from the surface (the surface

water is warmer than the water below, the variance is called a thermocline) west across the ocean towards the Asian landmasses. These winds cause water on the western edge of the pacific to be very warm and the warm temperatures reach deep below the surface of the ocean. (Caviedes, 2001) Conversely, these winds that are pushing the warm water away from the coast of South America cause upwelling of cold waters that are below the

surface directly off the coast of South America, resulting in cold water along the eastern

part of the Pacific Ocean. (Caviedes C. N., 2001) The westerly winds around the mid- latitudes’ reinforces the trend by pushing cold water to the east across the ocean and

north along the South American coastline. This cold water current pushing north, meets

up with the cold water caused by upwelling along the coast of South America to provide

continuous reinforcement of cold water, which is very important for the stability of the

very active fishing industry on the west coast of South America.

In an El Nino year, the east winds that push the warm water towards the western pacific collapse and die out, to be replaced with a wind from the west. According to

Cesar Caviedes, renowned author and a trusted weather expert, to begin with, the

westward flow of cold water hesitates or is replaced by warm surface water, next the

easterly trade winds die down causing even more warm water to replace the cold water

current, after this occurs the normal area of high pressure and stable air is replaced by a

low pressure air mass that is unstable and precipitates many storms. (Caviedes, 2001) It

is this invasion of warm water in one concentrated part of our earth that seems to cause

weather changes to occur on nearly every part of the planet. Below, in the figures

provided, there is an illustration of a normal weather pattern represented by Figure 1, and

5 an El Nino weather pattern represented by Figure 2. In Figure 1, as shown below, the winds push the warm water westward towards the West Pacific, and causes upwelling along the East Pacific. Figure 2, directly below figure 1, shows an El Nino impacted time period evident by the reversal of the westward flow of water and strong storms along the

South American coast.

Figure 1: Normal Westward Flow OF Wind And Water

Source: http://www.pmel.noaa.gov/tao/elnino/nino_normal.html#nino

Figure 2: El Nino Related Reverse Of Westward Flow

Source: http://www.pmel.noaa.gov/tao/elnino/nino_normal.html#nino

During the majority of El Nino’s that have been recorded, the warmer than normal

water temperatures have tendencies to alter the weather pattern to the same regions time

after time. For example, areas that exhibit abnormally dry conditions during an El Nino

event include northeastern Australia, Indonesia, the Philippines, southeastern Africa, northern South America, Mexico, Central America, and India. (Changnon, 2000)

Contrarily other areas experience abnormally wet conditions associated with an El Nino

and include the west coast of tropical South America, the Gulf Coast of the United States,

central Chile, southern Brazil, Uruguay, and northeastern Argentina. (Changnon, 2000)

History of Stock Market Panics in the U.S Coinciding with El Nino

El Nino’s have coincided in occurrence with several major stock market selloffs in the United States. According to his book, El Nino in History, Cesar Caviedes documents El Nino’s in 1771, 1791, 1837, 1857, 1868, 1884, 1891, and1897. (Caviedes

C. N., 2001) According to the National Atmospheric and Oceanic Administration

(NOAA) there were also El Nino’s occurring in 1972, 1982-83, 1987, 1997-98, 2003,

2007, and 2009. (NOAA, 2015) These coincided in the United States with the crisis of

1772, the financial crisis of 1791 and 1792, panic of 1837, panic of 1857, Black Friday

1869, panic of 1884, panic of 1893, panic of 1896, the stock market selloff in 1973-74, a minor selloff in 1982, the Black Monday selloff of 1987, the spread of the Asian crisis in

1997-1998, and small El Nino’s occurred around the stock market crashes of 2000-03 and

2008-09. There were several events of major panics or stock market selloffs that did not coincide with an El Nino including panics in 1796, 1819, 1825, 1847, 1866, 1873, 1901,

1907, and1929. Table 1, below, provides a timeline of the occurrence of major financial panics and stock market selloffs, along with a corresponding pattern of dates of the occurrence of El Nino’s. Financial Panics without a corresponding El Nino date had no occurrence of an El Nino along with the panic.

Table 1: List of Panics in the U.S Coinciding with El Nino

Table 1. Panics in the United States coinciding with El Nino events Financial Panic Event of El Nino Year Description Year 2008-09 Housing, financial crisis 2007, 2009 2001-03 Dot-com bubble 2003 1997 Asian Crisis 1997-98 1987 Black Monday 1987 1973-74 Market Crash 1972 1929-33 Great Depression 1932 1907 Financial Panic 1901 Financial Panic 1896 Panic 1897 1893 Bank Panic 1891 1884 Panic 1884 1873 Panic 1869 Black Friday 1868 1866 Panic 1857 Great Panic 1857 1847 Panic 1837 Severe Panic 1837 1825 Panic 1819 Panic 1796 Panic Financial Crisis and 1792 Panic 1791 1772 Crisis 1771

The main purpose of this analysis is to see if there is a connection between the occurrence of an El Nino and the coinciding occurrence of a worldwide financial crisis.

For the purpose of this paper the analysis will focus on major financial panics around the world, and then determine if the event of El Nino has impacted these financial panics in any way.

Research Question:

Have the historical occurrence of financial crisis and stock market selloffs around the world been impacted by the occurrence of El Nino’s?

Hypothesis

Null Hypothesis: H°- The event of El Nino will have no effect on stock market valuations.

Alternative Hypothesis: H¹- The event of El Nino will have an effect on stock market valuations.

Part 1 of this paper will focus on an economic analysis that deconstructs how an economy will be impacted by El Nino events while providing a demonstration of how the occurrence of El Nino related financial impacts will cause a disruption to trade based economies and can cause financial contagions that spread around the world. During Part

2, this paper will provide a historical analysis of El Nino related events, with the purpose

of providing a visualization of how these El Nino events impact global changes in

rainfall, fish occurrences, agricultural production, fertilizer production, and economic

demand changes. Part 3 of this paper focuses on the financial impacts of the El Nino

events of 1997-98 and the related financial crises with a detailed analysis to see if the

timing and impacts of these events are close enough together to be considered related.

Part 3 will provide a time analysis of the impacts of El Nino related events throughout the world and compare that to the timing of stock market activity in these same regions of the world. The results section of this paper will conduct a test of the hypothesis to see if there is statistical evidence to determine if we can conclude that El Nino related events have an effect on stock market prices. During the results section, two data sets, one consisting of data from El Nino’s and one from worldwide stock markets, will be compared to determine if there is statistical evidence to determine a correlation between the two subjects. During the discussion, this analysis will determine if there is sufficient evidence to reject or not reject the hypothesis of this paper.

Part 1: How an Economy is Affected by El Nino

One of the challenges that is presented in the course of this analysis is to determine what financial and economic factors may be impacted by an El Nino and how they relate to the financial situation at the time. First and foremost, this paper does not intend to address issues related to the change in demand that may occur as the result of an

El Nino. While an extreme weather pattern may cause a disruption to the demand pattern, as may be seen when winter weather delays economic activity in the Northern U.S, these

disruptions are in many instances, temporary, and the normal pattern of economic activity

often returns with heightened vigor when the weather pattern returns to normal.

Alternatively, this paper intends to address the supply characteristics of an El Nino

related economic impact. A disruption in supply of a good or commodity can have a

tremendous impact, not only on the nation producing the good or commodity, but also on

any Nation that conducts trade with a producer. Secondly this paper does not intend to

suggest that the occurrence of an El Nino can cause a financial contagion in and of itself.

This paper suggests that the occurrence of an El Nino can only act as a catalyst towards

uncovering those Nations that are exposed, financially, to the event of supply shocks.

Supply shocks can come in many forms. Disruptions in weather patterns can have an adverse impact on things including, but not limited to, fish landings, agricultural produce of all kinds, water availability, fertilizer production, reliable water transportation, reliable road transportation, and availability of resources. For any Nation which relies upon any of these activities as a major source of Gross Domestic Production, one can only imagine that a serious decline in the productivity of these industries would ultimately cause a decrease in the Domestic Output of that Nation. Such was the case with Peru during their Guano collapse as an El Nino impacted population of anchovies, the main source of food for guano producing seabirds, declined to the point that most of the population of seabirds either perished or moved away from Peru. (Caviedes, 2001)

According to Michael Porter in his book The Competitive Advantage of Nations

there are four determinants of a Nation’s competitive advantage. (Porter, 1990) 1) Factor

Conditions, 2) Demand Conditions, 3) Related and Supporting Industries, and 4) Firm

12 strategy, structure, and rivalry. (Porter, 1990) In the Case of Peru, mentioned above, and several other instances throughout this analysis, both Factor Conditions and Demand

Conditions can be impacted by an El Nino event, and for the purpose of this analysis,

Factor Conditions are going to be the focus of discussion. Factors of production include any economic input that is necessary for a Nation to be competitive and include arable land, availability of resources, infrastructure, and capital. (Porter, 1990) Some factor conditions that one can expect to be altered by an El Nino include availability of water, production of agriculture, availability of raw materials, reliability of transportation, and the ability to utilize infrastructure. Some expectations of this analysis are that Factor

Conditions such as those mentioned above will experience significant disruptions during the occurrence of El Nino, and those disruptions will coincide with major financial panics around the world. In the next section we will examine some historical examples of financial panics and recessions coinciding with El Nino events.

Part 2: The Patterns of History

Throughout economic history, stock markets have been tracing the course of fluctuations in the value of publicly traded businesses. Value fluctuations are caused by financial, historical, social, and legal impacts, just to name a few. Currently there is little consideration that the historical process of stock market fluctuations has been altered in any significant way by disruptions to the normal weather pattern, such as El Nino’s.

During the course of history we have seen many occurrences of major political changes

in Nations due to food shortages and famine, causing an uprising of the people and in

some instances revolutions. Political events such as this are often enough of a catalyst to

cause worldwide stock market selloffs, and if the cause of the strife is weather related, it

can be seen that these changes can be precipitated by the events of extreme weather

phenomenon. There are many possible historical unfolding’s both financially and politically that have been affected by the occurrence of El Nino. This next section goes through a timeline of stock market selloffs and economic depressions around the world that have coincided with an occurrence of El Nino.

The Peruvian Guano Economy 1840-1879 and El Nino Related Collapse

Peru’s economic history is quite important towards demonstrating that El Nino’s can have a significant influence on the stock markets of a Nation, due to the fact that Peru

has suffered several economic collapses that appear to be caused directly from El Nino

related events. Also Peru is particularly interesting to examine due to how they have

achieved financial growth in their economy throughout history. “Peru’s post-colonial

economic history can be visualized as a series of major export cycles…export sectors

have been central to the process of economic change in Peru”. (Thorp & Bertram, 1978, p. 4) Due to the fact that the biggest source of growth is export revenue for Peru, it is also

one of the sources of revenue that they have relied upon to maintain their economic

growth.

Because the occurrence of an El Nino is closest in proximity to South America, if these weather phenomenon have an effect on the economies and stock markets of

Nations, one would expect that they would have the most significant financial impact on the area that feels the most significant weather related impact. Peru is one of the areas that is most effected in terms of precipitation anomalies and weather impacts due to its location on the Pacific Ocean directly east of where the invasion of warm water occurs.

In South America, Peru lies to the north of Chile and to the west of Brazil, stretching all the way to the Pacific Ocean. (Hunefeldt, 2004) The people that have occupied Peru throughout its history have been quite advanced agrarians that have, since at least 200 B.C, cultivated a vast variety of agricultural crops. (Hunefeldt, 2004) It was this particular skill of the Peruvians which led them to discover the droppings of the

Cormorant, a seabird, as a potent fertilizer which was found in vast amounts along the coast of Peru. (Hunefeldt, 2004) The seabirds eat anchovies, which populate the Peruvian coastal waters in large numbers, and the birds nest on the coast and the islands offshore of Peru and accumulate thick layers of Guano deposits on the rocks. (Crawford &

Jahncke, 1999) These Guano deposits eventually became a large part of the Peruvian economy throughout the mid and late 1800’s.

Peru’s guano trade first emerged when Europeans witnessed the extraction of seabird guano deposits by Peruvian entrepreneurs, who marketed it as a fertilizer to agricultural producers. (Caviedes, 2001) Although eyed by British businessmen, eventually August Dreyfus, a French entrepreneur, secured a Peruvian government contract which granted him exporting rights to the guano trade of Peru. (Caviedes, 2001)

The guano trade made Peru quite wealthy due to their dominance in the industry, in fact

“from 1840 till 1879 Peru was virtually a world monopolist in Guano, and guano was the

15 only commercial fertilizer available to the world’s agriculture.” (Hunt, 1973, p. 5) The economy of Peru was so concentrated in the guano trade that by the year 1859 Peru’s economy consisted of total revenues of $22 million U.S dollars of which $16 million was derived from Seabird Guano exports. (Caviedes, 2001)

The good times would eventually come to an end, just like they always seem to during any time of financial prosperity. It is how they ended that is particularly interesting towards the purpose of this analysis. There were a series of El Nino’s occurring in 1861, 1864, and the severe El Nino of 1877-78 that caused a change that altered the guano trade drastically. (Caviedes, 2001) The food of the seabirds, the anchovy, began either dying off or moving away from the Peruvian coast culminating with the eventual disappearance of a large portion of the population of seabirds in that area. (Caviedes, 2001) The lack of production of guano from the seabirds meant a lack of production for the guano exporters which ultimately led to diminishing revenues and profits. As a direct result of the disappearance of the guano producing birds, the competitiveness of the Guano industry deteriorated and synthetic fertilizers eventually became the main source of fertilizer during the 20th century and are still the main source of fertilizer in the present day (2015). The collapse of the guano market caused the financial state of Peru to diminish in strength and an undersupplied Peru eventually lost a war, with Chile. (Caviedes C. N., 2001)

The Panic of 1857 and El Nino 1857

The panic of 1857 was a severe contraction of the U.S economy after a time

period of significant expansion. The Industrial Revolution in the United States shifted the

demand of skills in the three decades previous to the 1857 crash, towards that of specialization in tasks, and an increasingly advancing economy put pressures on the U.S banking system. (Huston, 1987) The banking system at the time was undergoing a period of substantial change and due to the banking changes being relatively new, banks were not regulated as they would be following the Panic of 1857. For example, what we know as checking accounts, or demand deposits, where a customer can place a sum of money in an account and draw on or make deposits to the account at any time, were a new monetary phenomenon at the time and bankers, unfamiliar with the potential for a major drawdown, were creating loans using the demand deposits as collateral. (Huston, 1987)

When a run would occur on a bank, whereby customers were liquidating their bank accounts to ensure the safety of their money, the bank would fail to have the specie on hand to continue with their short term loan obligations, leaving many a bank to close down and leaving their account holders penniless.

At the center of the crisis appear to be some all too familiar factors that we will see in many examples in this paper. The trade situation in the United States at this time was aggregated in a net positive balance of imports with the nation running an import surplus for many years previous to the 1857 Panic. (Huston, 1987) As we have seen

17 previously, import and export trade irregularities are particularly consistent with El Nino impacted time periods and are quite important to the Crisis of 1857 for many reasons.

To begin with, the United States did not have the monetary system that we know today. The United States had a monetary system at this time that had shifted from relying primarily on gold to facilitate exchange to relying on paper currency with gold as the monetary backbone, which paper currency could be exchanged for at any time, either by exchange to the gold market itself or by trading with the United States government.

(Huston, 1987) What this meant is that the monetary system relied upon not only a consistent supply of paper money to function but also a consistent supply of Gold, as a reserve currency. What adds to the pressure on the monetary system, as well, is the fact that the import surplus that the U.S experienced in the decades before the 1857 collapse caused a drain on precious metals, making them scarce in supply. (Huston, 1987) This made gold an extremely important and valuable commodity.

Bankers in New York, during a troubled time at the end of August up to the middle of September 1857, decreased their loans from $120,100,000 to $116,600,000 in just three weeks. (Huston, 1987) During the same duration, deposits in New York decreased from $94,500,000 to $75,800,000 and banks reported an increase of gold from

$9,200,000 to $13,500,000. (Huston, 1987) This large decrease in loans and deposits is a result of the people’s perception of the state of the financial system diminishing, as they liquidated their assets in banks. Also it can be seen during the 1857 panic that the supply of gold in banks must increase as deposit decrease, to cover the value of deposits and

18 loans that are liquidated. As we can see the economy of the United States relied upon a consistent supply of gold to maintain stability.

The way the rest of the panic unfolds is particularly relevant to the purposes of this paper. The method of predicting El Nino’s is quite complicated at this time considering the majority of the study of this phenomenon has been confined to the middle and late 20th century and 21st century. One method for determining the occurrence of strange weather before current documentation has been by the number of shipwrecks during historic years. (Caviedes, 2001) New Zealand, and Capetown, Africa experienced a high number of shipwrecks during the year 1857 associated with the moderate El Nino of 1857. (Caviedes, 2001) The coast off South America is also a breeding ground for shipwrecks during an El Nino. (Caviedes, 2001) The most significant ship tragedy is quite important in signifying a potential catalyst towards exacerbating the Panic of 1857.

As noted earlier, the monetary system in the U.S relied upon a supply of gold that had to be sufficient enough in supply to maintain the value of the monetary base. At the time there were three sources of gold: the supply generated by the California mines, the reserve of the United States, and gold imported by ships to the port in California.

(Huston, 1987) During the unprecedented event of the disappearance of the Central

America in 1857, a ship containing an estimated $1,500,000 in Gold Bullion, which sank off the coast of South America, bankers did not anticipate the financial results. (Huston,

1987) The state of mindset of Americans quickly turned towards panic and the onslaught of the Panic of 1857 ensued. Although this event is one in many circumstances surrounding the Panic of 1857 it is of significance to the purposes of this analysis

considering that it is a possible linkage between the event of El Nino 1857 and the United

States financial crises of 1857.

While the El Nino of 1857 is considered moderate, the timing of its presence in

association with the Panic of 1857 and the sinking of the Central America is of

significance. For the purposes of this analysis the implications of the import surplus

balance causing a strain on the supply of precious metals, as well as the disappearance of

the ship Central America are particularly notable to this paper.

El Nino 1891 the Baring crisis of 1890-93 and U.S 1893 Panic

El Nino 1891 was among the strongest El Nino’s ever recorded and was likely the

most powerful of the 19th century. (Caviedes, 2001) It is of no surprise that the

corresponding financial crisis to this time period was one of the most devastating crisis in

history as it spread like a contagion, from one market to another. One of the reasons this

El Nino is so important is because of the concentrated and acute impact of the events of this El Nino, as the major anomalies associated with this event were focused on South

America. (Caviedes, 2001) South America is the region of the Earth that is affected most

by the occurrence of El Nino’s. In fact “Southern South America (SSA: which comprises

southern Brazil, Argentina, Chile, Uruguay, and Paraguay) is one of the extra-tropical regions most affected by El Nino and La Nina.” (Grimm, R, & Moira, 1998)

Brazil, for instance, suffered a prolonged drought from 1877-1879, coinciding with the major El Nino of 1877-78, and then suffered another drought from 1888-1889.

(Caviedes, 2001) The last in the series of crippling droughts that plagued South America

during the mid and late 1800’s occurred in 1891, in relation to the major El Nino of 1891.

(Caviedes, 2001) It is these historical events, which bare a similar timeline to the “Baring

Crisis”, a financial crisis originating in Argentina and Brazil and eventually spreading

through much of the developed world, that need particular attention. For one we see the

repetitious pattern of an economic structure in Brazil and Argentina that is aggregated

mainly in export based commodities such as agriculture, is fragmented in a few

concentrated industries, and growth is reliant on continually increasing exports.

First and foremost Brazil’s population growth during the 1800’s could be

categorized as quite consistent, growing at a rate of around 1.8% a year. (Haber, 1997)

Due to this population growth the agricultural pressure to feed the population of Brazil

increased in importance as did the pressure to provide other basic necessities. Adding to the complications, the industrial concentration of agricultural exporters was becoming increasingly narrow as the 19th century progressed. During the 19th century, Brazil’s agriculture mainly consisted of sugar, cotton, and coffee. In 1822 sugar and cotton accounted for around 49% of total exports and coffee accounted for 19%. (Haber, 1997)

As the century progressed, however, the distribution of exports changed dramatically. By

1913 sugar and cotton accounted for only 3% of total exports and coffee, taking the lions share, accounted for around 60% of total export revenue. (Haber, 1997) The government of Brazil was also constrained due to the fact that between 1835 and 1885, 70% of government revenues were derived from imports and exports. (Haber, 1997) In order for

Brazil to continue to fund its debts, they had to rely heavily on agricultural exports to source the revenues necessary to stay afloat.

The “Baring Crisis” was the 19th century’s most significant financial crisis

and is considered to be the first sovereign debt crisis, requiring the government of

England to enact the first bailout to a private company (Triner, 2001) The Baring

Brothers was a bank in England functioning at the time of the late 19th century and was one of the oldest and most successful banks in England until its demise in the 1990’s. The

Baring Brothers bank engaged in aggressive lending programs to countries such as

Argentina and Brazil, during the mid and late 1800’s, with the purpose of increasing their portfolio of loans on their books thus increasing their revenue stream as well. When

Argentina defaulted on their debts in 1890, this caused the loans Baring had made to prove absolutely worthless. (Triner, 2001) The Argentinian default quickly spread to

Brazil by 1891 and to England in that same year. The contagion eventually spread to the

United States as the panic of 1893 set in and caused the stock market to crash. The spread of the crisis was similar to the spread of the flu, as one economy after another seemed to succumb to the debilitating pressures of the economy ahead of it.

The cause of the “Baring Crisis” financial contagion is due to Argentina

defaulting on its debts with England and in turn taking down the economy of Brazil in its

path of collapse. During this time, the El Nino related droughts had caused the production

of agriculture to diminish as time went on, culminating with the severe drought caused by

El Nino 1891. The governments of both Brazil and Argentina relied on imports and

exports of mainly agricultural products to fund the debts and current obligations of the

government. It is the strain on agricultural production that put pressure on the very source

of wealth that the economy of Brazil and Argentina relied upon. With revenues from

22 these sources depleting it is of no surprise that Argentina eventually defaulted on its debts, causing both Brazil and England to succumb to the financial panic that so often occurs during a sovereign debt crisis.

Part 3: Analysis of El Nino 1997-1998 on Financial Markets

During the next section, this paper will take a historical analysis, similar to the ones in the section before, and add statistical elements to provide a visualization of the link between El Nino’s and Stock Markets. The next section compares a chart of Sea

Surface Temperatures for the El Nino period: 1997-1998, with several charts of

Economic variables, including imports, exports and stock markets, for the same period of time. For the purposes of this analysis the years 1993-2000 has been chosen for comparison which coincide with the most significant El Nino of the 20th century.

El Nino 1997-1998

The year 1997-1998 had some of the most severe anomalies that have been recorded in weather history. As this paper mentions previously, during an El Nino the area around the Philippines and Indonesia, including many of the Asian landmasses, experience drier than normal conditions, as well as does India. (Changnon, 2000) During

El Nino 1997-98 this anomaly had a very large impact on the Asian countries. As Stanley

A Chagnon notes in El Nino 1997-98: The Climate Event of the Century drier than normal conditions dominated Indonesia, Malaysia, and the eastern Indian Ocean. As noted previously, drier than normal conditions were also present in the major 1891 El

Nino associated with the Baring financial crisis in Argentina and Brazil. Table 2 shows areas of the globe that are commonly impacted by an El Nino.

Table 2: List and Description of Areas of the Globe Impacted by El Nino

Table 2. Areas of the Globe Impacted by El Nino

Drier than Normal Wetter than Normal west coast of tropical South northeast Australia America Indonesia Gulf Coast of the United States The Philippines central Chile southeastern Africa southern Brazil northern South America Uruguay Mexico northeastern Argentina Central America India

El Nino 1997-1998 is the event that made the world take notice of these anomalies. In fact the severity of this El Nino made such an impact on climatologists and the public alike, that the results of the phenomenon were widely discussed. El Nino 1997-

98 was the first event of its kind that was recognized by the main stream media, as weather events such as floods, heat waves and droughts were perpetually in the news.

(Changnon, 2000)

Below in Figure 3 there is a graphical representation of Sea Surface Temperatures

(SST) for the years 1993-2000, coinciding with the El Nino 1997-1998, in the El Nino

Southern Oscillation (ENSO) region of the Pacific Ocean. The Chart measures Sea

Surface Temperature (SST) departure from normal, measured in degrees Celsius and shows the deviations in each data point from the average.

Sea Surface Temperature (SST) Chart El Nino Southern Oscillation (ENSO) 3

0 1993 1994 1995 1996 1997 1998 1999 2000 -1 SST Departure fromNormal -2

Figure 3: ENSO SST Departure From Normal In Degrees Celsius

Source: National Oceanic and Atmospheric Administration

In this chart of Sea Surface Temperature (SST) from 1993-2000 we can see a significant spike in SST associated with El Nino 1997-1998, which was the most powerful event of the 20th century. Notice that after falling to a low value in 1996, the

SST continues to rise throughout 1997, peaking in late 1997, before fading in early 1998.

The rise in SST could only have affected the markets of the world at the earliest in the year 1996, and could only have had an impact until a short period of time after the conclusion of the El Nino, as it was completely over by early 1999. When comparing the

SST chart to that of the economic situation of each Nation it is important to note that the time periods roughly coincide and that the fluctuations in economic variables largely occurs after the year 1996.

Financial Crisis 1997-1998

The Asian financial crisis, often referred to as a contagion, was a devastating

financial crisis unfolding as a collapse of the currencies and the stock markets of many

Asian Nations including Korea, the Philippines, Thailand, and Indonesia. The previous

economic growth period, from 1945 until 1997, was considered the “Asian economic

miracle” due to the long lasting period of rapid growth of the Asian economies which both lifted the domestic economies of the Asian nations and attracted capital from outside influences that were seeking to profit from the explosive economic growth. (Jackson,

1999) The aggressive investment schedule eventually caused the growth in these economies to be exhausted, while an abundance of financing meant that new projects were going into effect despite evidence that there wasn’t enough demand to support them.

The events of the Asian Crisis appeared to manifest itself during the year 1997 as

the currencies of many Asian Nations responded to immense financial pressures. In fact,

Karl D Jackson, editor of the book Asian Contagion notes that the currencies of Thailand,

Malaysia, the Philippines, Indonesia, and Korea depreciated by 35-80% during 1997, the first year of the crisis. (Jackson, 1999) The currency collapse was, however, preceded by a fall in the value of stock markets in the various Asian nations that were most affected, including the stock market of Thailand which by January 1997 had lost over 50% of its value from its high in the year 1993. (Jackson, 1999) Below is a chart of the Stock

Market of Thailand from 1993 until 2000. This paper also includes a chart of the Thai

27 currency from 1993-2000, which is the amount of Thai Baht that can be purchased with one U.S dollar.

Figure 4: Stock Market of Thailand 1993-2000

Source: http://www.tradingeconomics.com/thailand/stock-market

Figure 5: Currency of Thailand 1993-2000

Source: http://www.tradingeconomics.com/thailand/currency

As we can see above significant changes were taking place in Thailand around the

year 1997 in both the value of the stock market of Thailand and the value of its currency.

Note the significance in the timing of these changes as the earliest signs of trouble are

noted in the stock market of Thailand, shown above, and do not begin until 1996, around

the time the SST began rising associated with the impending El Nino. Also note the

change in the currency of Thailand as the Thai Baht began to collapse in 1997, making a

bottom in late 1997 and early 1998 before stabilizing at a significantly lower value than

before the collapse.

While these two factors are quite important, due to the consistencies we have

seen in this paper of El Nino events coinciding with Import and Export irregularities, it is

essential to also include an illustration of the changes in Imports and Exports in Thailand

during this same time period. Figure 6, included below, is a chart of imports to Thailand

during the years 1993-2000, and figure 7 is a chart of Thailand exports during the same

years. Figure 6 and Figure 7 are graphical representations of Imports and Exports of

Thailand during the years 1993-2000.

Figure 6: Thailand Imports 1993-2000

Source: http://www.tradingeconomics.com/thailand/imports

Figure 7: Thailand Exports 1993-2000

Source: http://www.tradingeconomics.com/thailand/exports

We can see from figures 6 and 7 that there were significant depreciations in both

Thailand’s currency and the stock market values during the year 1997. It is also interesting to note the significant impact that the financial crisis had on imports to

Thailand as they declined over 40%, while at the same time exports only declined by around 20%, noted in figures 6 and 7. The timing of the declines is also significant to note in that the declines in imports occurred as early as 1996 but the declines in exports occurred in late 1997 and continued into 1998. As we have seen before in this paper import and export irregularities are quite central to the theme of events we have seen in many of the financial crisis that occur in correlation to an El Nino event.

It is interesting to note that the changes occurring with respect to imports to

Thailand don’t begin until 1996, around the same time that SST began its climb, and end around the middle of 1998, around the same time that the El Nino event subsided.

Thailand exports do not appear to be significantly altered although a small decline occurs during late 1998.

Figure 8, figure 9, and figure 10, below, represent the economy of the Philippines.

Figure 8 is a chart of the stock market of The Philippines during the years 1993-2000 and

Figure 9 and figure 10 are import and export data of The Philippines during the years

1993-2000.

Figure 8: Stock Market of Philipppines 1993-2000

Source: http://www.tradingeconomics.com/philippines/stock-market

Figure 9: Philippines Imports 1993-2000

Source: http://www.tradingeconomics.com/philippines/imports

Figure 10: Philippines Exports 1993-2000

Source: http://www.tradingeconomics.com/philippines/exports

As seen in Figure 8, 9, and 10 the economic data of The Philippines roughly coincides with the data from Thailand during this time period. The difference is that the economic data of the Philippines occurs slightly later than that of Thailand. Note that the stock market of The Philippines does not begin its descent until the year 1997 where

Thailand began responding as early as 1996. Also it is interesting that the decline in imports for The Philippines also occurred later than that of Thailand. Most relevant to the analysis of this paper is the similarity in the timing of the economic crisis in The

Philippines to that of the rise in SST associated with El Nino 1997-1998.

Likewise to the Baring Crisis of the early 1890’s, the Asian Crisis had excessive lending as a characteristic of the cause of the crisis. (Jackson, 1999) Many Nations in

Asia were, at the time, undergoing an interesting lending boom that was very

33 unsustainable in its nature. During the mid-1990’s a rapid increase in construction due to the unhindered supply of money being made available through vast amounts of loans, caused a dramatic building boom at the same time that demand for real estate leases and ownership was stagnant, if not decreasing in Asia. It is said that Hong Kong in 1997 was becoming a city of dark towers as finished construction projects were not close to being fully occupied as new projects were being started all over the city. (Jackson, 1999)

As quickly as they heated up, the Asian economies began to fall one by one like dominoes at the hands of capitalism. The contagion first began in Thailand and The

Philippines but quickly spread during 1997 to Korea, Japan, and China before continuing into Europe and The United States during the year 1998. (Jackson, 1999) Some of the first signs of problems began in the stock market of Thailand and The Philippines and quickly spread to the currency markets of the same Nations.

The possible connections in the effects of El Nino 1997 on the Asian Crisis of

1997 have been alluded to in previous writings. Neil Smith notes in El Nino Capitalism

“by July 1997, just about the time that the most recent El Nino was being forecast, a sudden reversal overcame the Thai economy as the overproduction of semi-conductors, fueling the global computer and electronics industries, precipitated a nationwide depression.” (Smith, 1998, p. 160) While the possibility of a relationship between El

Nino and the performance of economies has been alluded to before, the link is not a widely discussed phenomenon.

Method

In order to conduct a test of the hypothesis, discovering relevant data was very important towards revealing a relevant conclusion. Due to the significant differences in subject

matter of the two variables that are being contrasted, the data needed to be comparable,

being both quantifiable and having related timelines.

Previously in this analysis, the event of an El Nino was defined as a deviation above normal of Sea Surface temperatures in the Pacific Ocean off the coast of South

America. Also included in the definition is an occurrence of an anomaly of the

atmospheric pressure in the same geographical region as the deviation of Sea Surface

Temperatures. For the purpose of this analysis the data is limited to the study of Sea

Surface Temperatures and will not include measurements of atmospheric pressure

deviations. The data that described the pattern of El Nino related events was composed of

Sea Surface temperature measurements of the area known as El Nino Southern

Oscillation (ENSO). The data is composed of ENSO Sea Surface temperature deviation

from normal for the years 1950-2014, sourced by the National Oceanic and Atmospheric

Administration (NOAA). The data does not include the temperature measurement values

but instead each data point represents the departure from average. Each data point is a

running three month average deviation from the average Sea Surface Temperature for the

related time of year. January, for example, is in the middle of the Southern Hemisphere

Summer and the average temperature of the ocean is much warmer than in July, which is

the Southern Hemisphere Winter. The data points in this analysis take these differences in average temperature throughout the year into consideration.

The data selected for stock markets is composed of monthly measurements for the

S&P 500 index for the years 1950-2014. The data points are all monthly averages of the value of the S&P 500 plotted over the same time period as the El Nino data. Also included are the stock markets of Mexico, the stock market of Malaysia, the stock market of Hong Kong (Hang Seng Index), the stock market of England (FTSE), the stock market of Brazil, and the stock market of Japan (Nikkei 225)

An inconsistency lies in the data set that needs to be addressed. The El Nino data set is expressed in running 3 month averages of deviations while the stock market data is expressed in 1 month average values. In order to make these data sets comparable it was imperative that the stock market data be transformed so that it was expressed as a running

three month average.

A running three month average of a stock market is also known as its 90 Day

Moving Average. This analysis compared a 90 Day Moving Average of El Nino data

with a 90 Day Moving Average of data from the S&P 500, Mexico’s Stock Market,

Malaysia’s Stock Market, the Hang Seng index, England’s Stock Market, Brazil’s Stock

Market, and Japans Stock Market. The El Nino data was collected from the National

Oceanic and Atmospheric Administration (NOAA) and the stock market data was collected from Yahoo Finance. Yahoo Finance was chosen as the data source for stock markets because it was the only data source that had no cost associated with the website, and the website also provided the data as values, instead of as a graphic. Each stock

36 market had limitations as to the extent of time the data was available, for example the

S&P 500 had data going as far back as 1950, but England’s stock market data is only available from 1984.

Each set of stock market data, are arranged so that the timing of the El Nino data and the stock market data coincide and are thus comparable versus each other. When analyzing these two sets of data it is important to note that neither the average value of either data set, nor the standard deviation of either data set, have any implications that are important to the results of this analysis. The approach made is to run a regression analysis to determine whether a relationship between the two sets of data exist. Each stock market is compared with the Sea Surface Temperatures of the El Nino data.

Another factor of importance in this analysis is that sea surface temperatures are restricted in their movement upward because they are controlled by global weather patterns. The stock markets have no such limitations and are free to rise and fall at their own whims. Also because the independent variable, SST, are so different in subject matter from the dependent variable, the stock market data, there is very little possibility of interconnectedness. Overall what this means is that while the stock markets have risen consistently in value over time, the same cannot be said for the SST of the ENSO region, which has fluctuated in its values around the mean over time.

For this analysis, a regression model has been run using the El Nino data as the X variable, or independent variable, and the stock market data as the Y variable, or dependent variable. The El Nino data set has data points on a monthly basis and consists of a running three month average. What this implies is that there are twelve data points

per year but each data point represents a three month average value of Sea Surface

Temperatures. The beginning data point is January of 1950 and the last available data

point is November 2014. The stock market data has data points on a monthly basis and

each data point represents the stock market close as of the beginning of the month. For

the stock market data a three month running average was developed in order for the stock

market data to be comparable to the El Nino data. What this meant is that the first data

point that represents a three month average for the S&P 500 is March of 1950. The last

data point that will be tested for any stock market data is November 2014, coinciding

with the last data point for the El Nino data.

Ultimately the tests were performed in two ways. The first way utilized the entire historical database of stock market data, and tested whether the changes in stock markets could have been influenced by Sea Surface Temperature changes. The next way only looked at El Nino periods of time and excluded the data from non-El Nino time periods to examine whether it was not simply SST changes that potentially had an impact on the stock market, but the event of El Nino that would have an impact. Both tests were regression models and each test was performed separately.

The statistics that are important to this model are the level of significance, or the

P-Value, the level of R-Squared, and the Correlation Coefficient. The P-Value, or level of significance, is a statistic that determines whether there is enough evidence, based on the model, to not reject or reject the hypothesis in question. If the level of significance is below .05 it signifies that there is enough evidence to not reject the hypothesis in question. The R-Squared is a statistic that shows how well the model fits. If the R-

Squared is high it signifies that much of the movements in the dependent variable are explained by the independent variable. The Correlation Coefficient shows which direction the relationship is oriented. If the correlation coefficient is positive it suggests that when the independent variable moves up, the dependent variable moves up as well.

Results

The results of this analysis are intended to determine if there is a relationship between the occurrence of an El Nino and the occurrence of a disturbance in financial markets and economies. This next section displays the resulting statistics from the tests that were conducted. The statistics are reported in two ways. First the results of the entire stock market data set will be presented. Next the results of the El Nino time periods will be presented.

Entire Database

The S&P 500 vs. SST from March 1950- February 2015, shown in Table 3 in the appendix, returned a correlation of roughly -11.40%, meaning that the relationship was negative and one would interpret it to mean that when Sea Surface Temperatures rose, the stock markets fell by around 11.4% of the rise in sea temperatures. It could also be

interpreted to mean that when sea surface temperatures fell, the stock market rose by

around 11.4% and each interpretation leads to a separate conclusion, which will be

discussed in the conclusions section. This test also yielded a low significance value of

.0014 which is well below the .05 that is necessary to not reject the hypothesis. The R-

Squared value, however, is low at .013 which means that the model of best fit does not predict the movements in the S&P 500 with much accuracy.

Mexico’s Stock Market vs. SST from January 1991- February 2015, shown in

Table 4 in the appendix, returned a correlation of -18% which shows a moderately strong

negative relationship between SST and Mexico’s Stock Market. During this same time

frame the P-Value is .0026 which again, is well below the alpha level of .05 and the R-

Squared returned a low value of .032.

Malysia’s Stock Market vs. SST from February 1994- February 2015, shown in

Table 5 of the appendix, returned a correlation of -10.8% which is one of the lowest of

the Countries that were tested. The P-Value of .085 is higher than .05 suggesting that there is not enough evidence to support the hypothesis in this situation. The R-Squared is quite low as well at .011.

The Hang Seng Index vs SST from February 1987 – February 2015, shown in

Table 6 of the appendix, revealed a correlation of -23.1% which is quite high, signifying a significant negative relationship between the two variables. The P-Value is very low as well and was too small a number to register, and well below the alpha level of .05.

Although the P-Value is low and the correlation is high, the R-Squared is still quite small at .053 suggesting that the model created does not fit very well.

England’s Stock Market, the FTSE, vs. SST from March 1984- February 2015 revealed a correlation of -18.4% between the two variables. The significance level of

.00036 is well below .05 leaving enough evidence to support the hypothesis of this analysis. The R-Squared in this case is quite low at .033 suggesting that although there is a high correlation and significance of the test, the model created using these variables does not fit the results well.

Brazils Stock Market vs. SST from June 1993 – February 2015 returned a

correlation of -17.2%. The P- Value of .0055 is also quite low and well below .05. The R-

Squared of .029 shows the repetitious pattern of a model that lacks a good fit.

El Nino Time Periods

This next section provides statistical results using time periods when an El Nino is

occurring. For the purpose of being consistent and choosing relevant time periods this

analysis used the time periods 3 months before the occurrence of El Nino and lasting

until 8 months after the El Nino had subsided.

El Nino 2009-2010 was tested against various stock markets for the time period

beginning February 2009 and ending December 2010. During this time period the S&P

500 returned a correlation of only -5.9% which is insignificant to say the least. The P-

Value for the corresponding time period is .78 and the R-Squared value is .003. This

suggests very little correlation between El Nino 2009-2010 and the S&P 500. Mexico

tested slightly more significant with a correlation of -16.3%, but with a P-Value of .45

and a R-Squared .027, there was still only a small significance. The stock market of

Malaysia produced a correlation of -28.7%, which is high, but the P-Value was .18 and the R-Squared was .082.

El Nino 2006-2007 was tested against the same markets for the time period beginning June 2006 and ending September 2007, shown in Table 7 of the appendix. The test of the S&P 500 revealed a correlation of -57.9% which is very high. The corresponding P-Value is .018 which is below the .05 necessary to support the

hypothesis. The R-Squared in this case is .34 which suggests that the model fits the

results quite well. The test of Mexico’s stock market, shown in Table 8 of the appendix,

returned a correlation of -65.24% and a P-Value of .006. The corresponding R-Squared

value for this time period is .43. Malaysia returned the most significant test statistics

during this time period, shown in Table 9 of the appendix, including a correlation of -

75% and a P-Value of .0008 with a R-Squared of .56 suggesting that the model fit very well in this scenario.

El Nino 2004-2005 was tested against the S&P 500, Mexico’s Stock Market, and

Malaysia’s Stock Market for the time period beginning May 2004 through September

2005. The S&P 500 tested well with a correlation of -63.2% and a P-Value of .006, well below .05. The R-Squared was .40 suggesting the model accounted for around 40% of the changes that occurred. The test of the Mexican Stock Market revealed a correlation of -

59.2% along with a P-Value of .012 and a R-Squared value of .35. Malaysia produced a correlation of -43.2% and a P-Value of .08 along with a R-Squared of .19 suggesting that in Malaysia’s case, there was less significance than other markets in association with El

Nino 2004-2005.

El Nino 2002-2003 was tested for the time period beginning March 2002 and ending October 2003. The test revealed a correlation of -35.8% for the S&P 500 along with a P-Value of .12 and a R-Squared of .13. The test of Mexico’s Stock Market revealed slightly better results with a correlation of -46.8% and a P-Value of .037 and a

R-Squared value of .22. Malaysia produced results no better than the S&P 500 with a

correlation of -16.5% and a P-Value of .48. The R-Squared was also not significant at

.027.

The El Nino of the century 1997-1998 was tested for the time period beginning

January 1997 through December 1998 and included the same three markets. While the

S&P 500 did not reveal any significance evident, shown in Table 10 of the appendix, by the P-Value of .19 and the R-Squared of .075, Mexico’s Stock Market revealed something different. The tests of Mexico, shown in Table 11 of the appendix, revealed a correlation of positive 80% with an associated P-Value that was too low to register and

R-Squared of .64 suggesting in Mexico’s case, the stock market corresponded significantly to the changes in SST. The tests of Malaysia were benign with a correlation of 25% and a P-Value of .24 along with a R-Squared of .06. Other tests include the

Nikkei 225, shown in Table 14 of the appendix, which had positive correlation of 47.5% and a R-Squared of .23, and the test of Brazils market, shown in Table 13 of the appendix, revealed a correlation of 72% and R-Squared value of .53. Expectedly,

England’s Stock Market perform similarly to the United States and with a P-Value of .38 and R-Squared of .03 it could be said there was very little relationship between the stock market of England and El Nino 1997-1998.

The El Nino of 1994-1995 was tested for the time period beginning July of 1994 and ending November of 1995. The S&P 500 test revealed a correlation of -93.5% along with the R-Squared of .88. The P-Value was too small to register in this case providing support to the hypothesis of this analysis. The Stock Market of Mexico, on the other hand, revealed very little correlation at 3.2% and a P-Value of .90 along with the R-

Squared of .001. Malaysia also did not test well with a correlation of 14% and the P-

Value of .59.

The next set of tests does not include data from the Mexican Stock Market or the

Malaysian Stock Market and only includes the S&P 500. El Nino 1991-1992 was tested

against the S&P 500 for the time period beginning March 1991 and ending February

1992. The test returned a correlation of -27.2% and a P-Value of .20 suggesting that there is not enough evidence to support the hypothesis. The R-Squared value of .073 means that the model did not fit. Overall El Nino 1991-1992 did not have a strong relation to the

S&P 500.

El Nino 1986-1987 was tested against the S&P 500 for the time period beginning in May 1986 and ending September 1988. This time period coincided with the infamous stock market selloff of 1987, shown in Table 15 of the appendix, and the test revealed a positive correlation of 32.3% and a P-Value of .087 which is above .05 but is still somewhat significant. The R-Squared value of .11 is not high but it at least shows that the model explains around 11% of the movement in the S&P 500 during this time period.

England also tested strongly during this time period, shown in Table 16 of the appendix, as well as did the Nikkei 225, shown in Table 17 of the appendix.

El Nino 1982-1983 was one of the strongest events of El Nino in the 20th century.

The tests include the time period beginning February 1982 and ending February 1984, shown in Table 18 of the appendix. The test against the S&P 500 revealed a correlation of -41.4% which shows a strong negative relationship. The P-Value of .039 is lower than the alpha significance level of .05, meaning that there is enough evidence to not reject the

hypothesis and the R-Squared value is .171 meaning that around 17% of the movements

in the S&P 500 are explained by the movements in SST during this time period.

El Nino 1972-1973 is another strong event of El Nino, shown in Table 19 of the

appendix, and the tests against the S&P 500 returned a positive correlation coefficient of

75.8% suggesting a strong positive relationship between SST and the S&P 500. The P-

Value is around 00 providing evidence to support the hypothesis and the R-Squared value of .574 means that the model explains around 57% of the movements in the S&P 500. It is interesting to note that the strong positive correlation in the next few scenarios offsets some of the negative correlation we have seen in past time periods, which alters the results of the test of the full data set of the S&P 500.

El Nino 1968-1970 was tested against the S&P 500 for the time period from May

1968 through September 1970, shown in Table 20 of the appendix. The test revealed a correlation of 80%, showing a very strong positive relationship, and the P-Value is near zero meaning that there is enough evidence to not reject the hypothesis. The R-Squared value is also quite high at .643 meaning that 64.3% of the movements in the S&P 500 are explained by the movements in SST.

El Nino 1965-1966 was a very strong El Nino and includes the time period

beginning February 1965 and ending December 1966. The tests returned a correlation of

66.8% along with a P-Value of .0005. This test also reveals other strong results including

a R-Squared value of .446.

El Nino 1963-1964 includes the time period beginning March 1963 until October

1964 and the tests against the S&P 500 reveal another strong correlation of -67.7% along

with a P-Value of .001 and a R-Squared of .458. This test shows very strong results and the P-Value of .001 is enough to not reject the hypothesis.

The 1950’s was a very active decade in terms of El Nino events. El Nino 1957 includes the time period beginning January 1957 and ending November 1959, shown in

Table 21 of the appendix. This El Nino showed a correlation of -72.79% which is a high

correlation. The P-Value of zero suggests that there is indeed enough evidence to not

reject the hypothesis. The R-Squared value of .529 suggests that around 53% of the

movements in the S&P 500 during this time period were explained by movements in

SST.

Perhaps the most significant event of El Nino in terms of the resulting statistics is

El Nino 1954. This El Nino was tested through the time period beginning October 1954

and running through October 1956 shown in Table 22 of the appendix. This test revealed

a correlation of -92.7% which is very high. The resulting P-Value is near zero and the R-

Squared value of 85.9% suggests that the model was quite a good fit for this time period.

The final El Nino event of our test was El Nino 1951-1952 and included the time period starting April 1951 and ending September 1952. The tests revealed that this El

Nino was not highly correlated to the S&P 500 with a correlation of -26.5% and a R-

Squared value of .07. This suggests the model does not fit well and the P-Value of .287 suggests there is not enough data to support the hypothesis and we must reject the null hypothesis in this instance.

Discussion

What we must conclude in this analysis is that for the complete historical data

base of the stock markets, the hypothesis that the fluctuations in Sea Surface Temperature

(SST) will have an effect on the fluctuations in the value of stock markets, has merit in

the following instances. In the case of Mexico, England, the Hang Seng Index, and Brazil

low P-Values and high correlation seem to justify and give credence to the model

presented by the tests. In the case of the S&P 500 from March 1950- February 2015

based upon the statistics, the low P-Value of .001 and the correlation of -11.4% would

suggest enough evidence to not reject the null hypothesis. However there are other factors

that must be considered as well because some of the evidence is contradictory.

The low R-Squared of .013 suggests the model does not fit. This author noticed

something particularly interesting about the data that may shed some light about why the

R-Squared is so low, when the P-Value and correlation tested consistently high. As

mentioned previously, the values of the S&P 500 are not restricted in their movements

upwards and have, since 1950, gained significant value, while the SST data is restricted due to the fact that ocean temperatures have not risen in correlation to the rise in stock markets. Because of this if a data set is consistently rising while the other data set is flat

over the same period of time, one would expect a negative correlation. Another factor

that influenced the results of testing the entire database was that during certain time

periods negative relationships existed and during other time periods positive relationships

existed. The effect of this would be to cancel each other out. The method of testing the

SST versus the entire dataset of a stock market appears to not to be a great fit due to the

differences in subject matter surrounding the two variables. The good news though is that both data sets are quantifiable and the problem of the growth rate in stock markets can be resolved by examining the issue in a different way.

Previously in the analysis the hypothesis specifically stated that the occurrence of

an El Nino will have an effect on stock market valuations. When the data is sorted based

upon the occurrence of an El Nino, effectively eliminating the non-El Nino years, we see

different results. Chopping the data up in these sections does two things: 1) it removes the

incongruence in the two data sets caused by the growth in stock market valuations and 2)

it examines only the data that is important to the hypothesis of this analysis.

When the data is examined based upon the occurrence of El Nino, the hypothesis

that the occurrence of an El Nino will have an effect on stock markets tests particularly

well in many instances. Some El Nino periods that tested high against the S&P 500

include El Nino’s in 1954, 1957-59, 1963, 1965, 1968-69, 1972-73, 1982-83, 1986-87, and 2006-07.

El Nino of the century in 1997 does not show significant correlation to the S&P

500, however Mexico, Brazil, and Japan all had very strong positive correlation and very high R-Squared values. The results of the data set of 1997-1998 El Nino reveals exactly what the story lays out. It is of no surprise, based on the literature review in the preceding section, that these markets show such high correlation to the major El Nino during this time period. It is results such as these that demonstrate the importance of this analysis.

The relationship between El Nino 1997-1998 is most significantly noted among Mexico,

Brazil, Japan, and Hong Kong Hang Seng, which show very strong positive correlation.

Japan and Hong Kong were in the path of the Asian Financial crisis and the results of the test show exactly that. Unexpectedly, Malaysia did not show strong correlation to this El

Nino event. During the Asian Crisis, the USA and England were the very last Countries to be swept by the spread of the crisis and the resulting test statistics reflected this as well, showing very little correlation between El Nino 1997 and the stock markets of

Europe and the United States.

El Nino 1987 is another interesting case during the very unusual selloff in the stock markets around the world, as there was nothing to attribute the selloff to except for a malfunction in the electronic trading equipment that had been introduced in the United

States. Japan, England, and Hong Kong showed high correlation associated with the occurrence of the 1987 El Nino and the United States also revealed a moderate correlation. Japan showed a strong negative correlation but the United States, Hong

Kong, and England all showed positive correlation with this time period. For the purpose of this analysis there seem to be a significant relationship between the event of the 1987

El Nino and the 1987 stock market crash.

El Nino 1982-1983 was the second strongest El Nino of the 20th century and impacted the S&P 500 during this time period. While there were other Nations that were potentially impacted greater than the United States, no other data was available for any other nation.

El Nino 1972-1973 was fascinating because the El Nino nearly wiped out the anchovy population of Pacific South America and along with it, the seabirds vanished as well. It would have been more fortuitous if data was available from Peru during this time but the United Sates returned strong negative correlation.

While this analysis is not going elaborate on every single El Nino time period that is tested, it is imperative to note the significance of the tests mentioned in the results section. The resulting test statistics lead this author to believe that the event of El Nino does have an effect on stock markets. In conclusion this analysis is not rejecting the hypothesis that the event of El Nino will have an effect on stock market valuations. I believe the results of this paper demonstrate that a relationship exists between El Nino’s and Stock Markets.

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Appendix

Table 3: Anova Table S&P 500 vs El Nino 1950-2015

03/02/1950-2/01/2015 Regression Statistics ANOVA Multiple R 0.1109 df SS MS F Significance F R Square 0.0123 Regression 1 2682150.366 2682150.366 9.6808 0.0019 Adjusted R Square 0.0110 Residual 778 215552020 277059.1516 Standard Error 526.3641 Total 779 218234170.3 Observations 780 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 463.9217 18.8485 24.6131 2.2E-99 426.9217 500.9217 426.9217 500.9217 SST -73.4916 23.6201 -3.1114 0.00193 -119.8583 -27.1248 -119.8583 -27.1248 Correlation -0.1109

Table 4: Anova Table Mexico Stock Market vs El Nino 1991-2015

01/03/1991-2/01/2015 Regression Statistics ANOVA Multiple R 0.1745 df SS MS F Significance F R Square 0.0304 Regression 1 1804541826 1804541826 8.6638 0.0035 Adjusted R Square 0.0269 Residual 276 57486902432 208285878.4 Standard Error 14432.1127 Total 277 59291444258 Observations 278 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 16186.2974 866.6034 18.6779 0.0000 14480.3052 17892.2896 14480.3052 17892.2896 SST -3189.0196 1083.4369 -2.9434 0.0035 -5321.8695 -1056.1697 -5321.8695 -1056.1697 Correlation -0.17445662

Table 5: Malaysia Stock Market vs. El Nino 1994-2015

02/03/1994-2/01/2015 Regression Statistics ANOVA Multiple R 0.1023 df SS MS F Significance F R Square 0.0105 Regression 1 373720.0315 373720.0315 2.6551 0.1045 Adjusted R Square 0.0065 Residual 251 35329085.22 140753.3276 Standard Error 375.1711 Total 252 35702805.25 Observations 253 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1075.2515 23.7199 45.3312 0.0000 1028.5360 1121.9669 1028.5360 1121.9669 SST -47.6714 29.2559 -1.6295 0.1045 -105.2898 9.9470 -105.2898 9.9470 Correlation -0.1023

Table 6: Hang Seng Index vs. El Nino 1987-2015

02/02/1987-2/02/2015 Regression Statistics ANOVA Multiple R 0.2273 df SS MS F Significance F R Square 0.0517 Regression 1 830681988.8 830681988.8 18.2505 0.0000 Adjusted R Square 0.0488 Residual 335 15247694738 45515506.68 Standard Error 6746.5181 Total 336 16078376727 Observations 337 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 12858.2087 367.5262 34.9858 0.0000 12135.2587 13581.1586 12135.2587 13581.1586 SST -1900.0032 444.7507 -4.2721 0.0000 -2774.8592 -1025.1472 -2774.8592 -1025.1472 Correlation -0.2273

Table 7: S&P 500 vs. El Nino 2006-2007

06/01/2006/-09/04/2007 Regression Statistics ANOVA Multiple R 0.5788 df SS MS F Significance F R Square 0.3351 Regression 1 33263.8954 33263.8954 7.0544 0.0188 Adjusted R Square 0.2876 Residual 14 66014.4645 4715.3189 Standard Error 68.6682 Total 15 99278.3600 Observations 16 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1411.3268 17.7112 79.6857 0.0000 1373.3402 1449.3135 1373.3402 1449.3135 SST -84.1498 31.6827 -2.6560 0.0188 -152.1024 -16.1972 -152.1024 -16.1972

Correlation -0.5788

Table 8: Mexico Stock Market vs. El Nino 2006-2007

06/01/2006-09/03/2007 Regression Statistics ANOVA Multiple R 0.6525 df SS MS F Significance F R Square 0.4257 Regression 1 119173759.2 119173759.2 10.3773 0.0062 Adjusted R Square 0.3847 Residual 14 160776448.8 11484032.06 Standard Error 3388.8098 Total 15 279950208 Observations 16 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 26411.69 874.0551 30.2174 0.0000 24537.0266 28286.3501 24537.0266 28286.3501 SST -5036.826 1563.5573 -3.2214 0.0062 -8390.3226 -1683.3288 -8390.3226 -1683.3288 Correlation -0.6525

Table 9: Malaysia Stock Market vs. El Nino 2006-2007

06/01/2006-09/03/2007 Regression Statistics ANOVA Multiple R 0.7506 df SS MS F Significance F R Square 0.5634 Regression 1 239556.3032 239556.3032 18.0661 0.0008 Adjusted R Square 0.5322 Residual 14 185640.2716 13260.0194 Standard Error 115.1522 Total 15 425196.5749 Observations 16 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1168.611 29.7005 39.3465 0.0000 1104.9096 1232.3120 1104.9096 1232.3120 SST -225.8242 53.1299 -4.2504 0.0008 -339.7764 -111.8719 -339.7764 -111.8719 Correlation -0.750601

Table 10: S&P 500 vs. El Nino 1997-1998

01/02/1997-12/01/1998 Regression Statistics ANOVA Multiple R 0.2754 df SS MS F Significance F R Square 0.0759 Regression 1 27246.6944 27246.6944 1.8062 0.1927 Adjusted R Square 0.0339 Residual 22 331880.3629 15085.4710 Standard Error 122.8229 Total 23 359127.0573 Observations 24 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 976.4032 27.0811 36.0547 0.0000 920.2403 1032.5660 920.2403 1032.5660 SST -25.59966 19.0483 -1.3439 0.1927 -65.1035 13.9041 -65.1035 13.9041 Correlation -0.275444

Table 11: Mexico Stock Market vs. El Nino 1997-1998

01/02/1997-12/01/1998 Regression Statistics ANOVA Multiple R 0.7996 df SS MS F Significance F R Square 0.6394 Regression 1 4802124.406 4802124.406 39.0034 0.0000 Adjusted R Square 0.6230 Residual 22 2708656.656 123120.7571 Standard Error 350.8857 Total 23 7510781.062 Observations 24 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 4135.585 77.3665 53.4545 0.0000 3975.1363 4296.0329 3975.1363 4296.0329 SST 339.8553 54.4181 6.2453 0.0000 226.9992 452.7114 226.9992 452.7114 Correlation 0.7996

Table 12: Hang Seng Index vs. El Nino 1997-1998

1/1/1997- 12/01/1998 Regression Statistics ANOVA Multiple R 0.5320 df SS MS F Significance F R Square 0.2830 Regression 1 37303877.42 37303877.42 8.6823 0.0075 Adjusted R Square 0.2504 Residual 22 94524275.34 4296557.97 Standard Error 2072.814 Total 23 131828152.8 Observations 24 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 11016.53 457.0331 24.1044 0.0000 10068.6984 11964.3559 10068.6984 11964.3559 SST 947.2276 321.4679 2.9466 0.0075 280.5438 1613.9113 280.5438 1613.9113

Correlation 0.5320

Table 13: Brazil Stock Market vs. El Nino 1997-1998

01/02/1997-12/01/1998 Regression Statistics ANOVA Multiple R 0.7249 df SS MS F Significance F R Square 0.5255 Regression 1 31497512.53 31497512.53 24.3666 0.0001 Adjusted R Square 0.5040 Residual 22 28438340.06 1292651.821 Standard Error 1136.948 Total 23 59935852.59 Observations 24 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 9231.248 250.6849 36.8241 0.0000 8711.3598 9751.1370 8711.3598 9751.1370 SST 870.3932 176.3267 4.9363 0.0001 504.7139 1236.0724 504.7139 1236.0724 Correlation 0.7249

Table 14: Nikkei 225 Index vs. El Nino 1997-1998

01/02/1997-12/01/1998 Regression Statistics ANOVA Multiple R 0.4752 df SS MS F Significance F R Square 0.2258 Regression 1 20776777.32 20776777.32 6.4165 0.0189 Adjusted R Square 0.1906 Residual 22 71236100.04 3238004.547 Standard Error 1799.446 Total 23 92012877.36 Observations 24 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 16643.02 396.7584 41.9475 0.0000 15820.1904 17465.8433 15820.1904 17465.8433 SST 706.9138 279.0719 2.5331 0.0189 128.1541 1285.6734 128.1541 1285.6734 Correlation 0.4752

Table 15: S&P 500 vs. El Nino 1986-1988

05/01/1986-09/01/1988 Regression Statistics ANOVA Multiple R 0.3229 df SS MS F Significance F R Square 0.1042 Regression 1 1690.5541 1690.5541 3.1418 0.0876 Adjusted R Square 0.0711 Residual 27 14528.1220 538.0786 Standard Error 23.1965 Total 28 16218.6761 Observations 29 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 262.4473 4.9879 52.6173 0.0000 252.2131 272.6815 252.2131 272.6815 SST 8.3399 4.7051 1.7725 0.0876 -1.3142 17.9940 -1.3142 17.9940 Correlation 0.3229

Table 16: England Stock Market vs. El Nino 1986-1988

05/01/1986-09/01/1988 Regression Statistics ANOVA Multiple R 0.3620 df SS MS F Significance F R Square 0.1311 Regression 1 179086.1836 179086.1836 3.9217 0.0583 Adjusted R Square 0.0976 Residual 26 1187309.6775 45665.7568 Standard Error 213.6955 Total 27 1366395.8611 Observations 28 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1798.7439 47.1625 38.1393 0.0000 1701.8000 1895.6878 1701.8000 1895.6878 SST 86.5837 43.7220 1.9803 0.0583 -3.2882 176.4556 -3.2882 176.4556 Correlation 0.3620

Table 17: Nikkei 225 vs. El Nino 1986-1988

05/01/1986-09/01/1988 Regression Statistics ANOVA Multiple R 0.4757 df SS MS F Significance F R Square 0.2263 Regression 1 83351985.39 83351985.39 7.6029 0.0105 Adjusted R Square 0.1965 Residual 26 285041871.1 10963148.89 Standard Error 3311.0646 Total 27 368393856.5 Observations 28 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 23651.4474 730.7504 32.3660 0.0000 22149.3684 25153.5265 22149.3684 25153.5265 SST -1867.9398 677.4427 -2.7573 0.0105 -3260.4433 -475.4364 -3260.4433 -475.4364 Correlation -0.4757

Table 18: S&P 500 vs. El Nino 1982-1984

2/1/1982-02/01/1984 Regression Statistics ANOVA Multiple R 0.4140 df SS MS F Significance F R Square 0.1714 Regression 1 2040.0331 2040.0331 4.7581 0.0396 Adjusted R Square 0.1354 Residual 23 9861.2289 428.7491 Standard Error 20.7063 Total 24 11901.2620 Observations 25 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 147.0889 4.9037 29.9958 0.0000 136.9449 157.2329 136.9449 157.2329 SST -9.2389 4.2355 -2.1813 0.0396 -18.0006 -0.4771 -18.0006 -0.4771 Corrrelation -0.41402

Table 19: S&P 500 vs. El Nino 1972-1973

02/01/1972-11/01/1973 Regression Statistics ANOVA Multiple R 0.7578 df SS MS F Significance F R Square 0.5743 Regression 1 164.8675 164.8675 26.9764 0.0000 Adjusted R Square 0.5530 Residual 20 122.2311 6.1116 Standard Error 2.4722 Total 21 287.0985 Observations 22 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 108.2593 0.5443 198.8823 0.0000 107.1239 109.3948 107.1239 109.3948 SST 2.2207 0.4276 5.1939 0.0000 1.3288 3.1125 1.3288 3.1125 Correlation 0.7578

Table 20: S&P 500 vs. El Nino 1968-1970

05/01/1968-09/01/1970 Regression Statistics ANOVA Multiple R 0.8020 df SS MS F Significance F R Square 0.6433 Regression 1 1382.5102 1382.5102 48.6871 0.0000 Adjusted R Square 0.6301 Residual 27 766.6871 28.3958 Standard Error 5.3288 Total 28 2149.1973 Observations 29 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 87.9820 1.3410 65.6069 0.0000 85.2304 90.7336 85.2304 90.7336 SST 13.7708 1.9736 6.9776 0.0000 9.7214 17.8203 9.7214 17.8203 Correlation 0.8020

Table 21: S&P 500 vs. El Nino 1957-1959

01/02/1957-11/02/1959 Regression Statistics ANOVA Multiple R 0.7280 df SS MS F Significance F R Square 0.5300 Regression 1 730.7203 730.7203 37.2065 0.0000 Adjusted R Square 0.5157 Residual 33 648.1060 19.6396 Standard Error 4.4317 Total 34 1378.8264 Observations 35 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 53.9717 1.1093 48.6542 0.0000 51.7149 56.2286 51.7149 56.2286 SST -7.9758 1.3076 -6.0997 0.0000 -10.6361 -5.3155 -10.6361 -5.3155 Correlation -0.7280

Table 22: S&P 500 vs. El Nino 1952-1954

10/01/1952/10/01/1954 Regression Statistics ANOVA Multiple R 0.9273 df SS MS F Significance F R Square 0.8600 Regression 1 107.0337 107.0337 141.2464 0.0000 Adjusted R Square 0.8539 Residual 23 17.4289 0.7578 Standard Error 0.8705 Total 24 124.4627 Observations 25 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 27.1674 0.1901 142.9164 0.0000 26.7742 27.5606 26.7742 27.5606 SST -3.6572 0.3077 -11.8847 0.0000 -4.2938 -3.0206 -4.2938 -3.0206

Correlation -0.9273