The 2017 Watson Analytics Global Competition Investigating ... · The 2017 Watson Analytics Global Competition Investigating the Relationship between Financial Indices and Ecological

The 2017 Watson Analytics Global Competition

Investigating the Relationship between Financial Indices and Ecological Factors

Joseph Adamski

Suhail Pathan

Rudy O’Neil

Dr. Michael Gendron

Faculty Sponsor

Central Connecticut State University

New Britain, Connecticut

March 15, 2017

Financial Indices & Ecological Factors 2

Contents Table of Figures ............................................................................................................................................ 2

Introduction ................................................................................................................................................... 3

Literature Review .......................................................................................................................................... 3

Methodology ................................................................................................................................................. 4

Hypotheses............................................................................................................................... 4

Data Cleanup and Acquisition .................................................................................................... 4

Research ........................................................................................................................................................ 5

Descriptive Analytics ................................................................................................................ 6

Temperature ........................................................................................................................11

Carbon Dioxide ....................................................................................................................13

Correlative Analytics ................................................................................................................14

Limitations .................................................................................................................................................. 19

Discussion ................................................................................................................................................... 20

References ................................................................................................................................................... 20

Data Sources ............................................................................................................................................... 21

Table of Figures

Figure 1: Deforested Land over Year ........................................................................................................... 6

Figure 2: Sea Level over Year ...................................................................................................................... 6

Figure 3: Temperature over Year .................................................................................................................. 7

Figure 4: Mexican IPC and Deforested Land over Year ............................................................................... 8

Figure 5: American Dow Jones and Deforested Land over Year ................................................................. 8

Figure 6: Brazilian iBovespa and Sea Level over Year ................................................................................ 9

Figure 7: American Dow Jones at Sea Level over Year ............................................................................... 9

Figure 8: Australian S&P/ASX 200 and Sea Level over Year ..................................................................... 9

Figure 9: Canadian S&P/TSX Toronto and Sea Level over Year .............................................................. 10

Figure 10: German DAX and Sea Level over Year .................................................................................... 10

Figure 11: Mexican IPC and Sea Level over Year ..................................................................................... 11

Figure 12: Mexican IPC and Temperature over Year ................................................................................. 12

Figure 13: Canadian S&P/TSX Toronto and Temperature over Year ........................................................ 12

Figure 14: Hong Kongese Hang Seng and Temperature over Year ............................................................ 12

Figure 15: American Dow Jones and CO2 over Year ................................................................................. 13

Figure 16: German DAX and CO2 over Year ............................................................................................ 13

Figure 17: American Dow Jones Predictor Dashboard Panel ..................................................................... 14

Figure 18: Chinese Shanghai Predictor Dashboard Panel........................................................................... 15

Figure 19: Canadian S&P/TSX Toronto Predictor Dashboard Panel ......................................................... 16

Figure 20: European Stoxx 50 Predictor Dashboard Panel ......................................................................... 17

Figure 21: Brazilian iBovespa Predictor Dashboard Panel ......................................................................... 18

Figure 22: Mexican IPC Predictor Dashboard Panel .................................................................................. 19


Introduction

4,621 square miles of Brazilian rain forest were cut down in 2008 with hopes of bringing

great local economic benefits to the population (Walsh, 2009). However, according to Rodrigues

et al. (2009), this is only a short term view and deforestation brings a short term boom with long

term detriment to the local economy. Building upon this academic research, our group sought to

expand existing research by applying similar methodology to global economies and

incorporating environmental indicators. The environmental indicators chosen were CO2 levels,

sea levels, temperature and deforested land as they could be applied at the global level. The aim

of this study is to determine if there is a relationship between environmental indicators and

global financial indices.

Financial indices were chosen based on the index being representative of a country’s

market. A domestic example might be the performance of the Dow Jones Industrial Average

which tracks 30 large corporations from a wide variety of sectors. However, Walsh (2009) points

out that the effects of deforestation are delayed upon the local economy. Hence, our group will

have to keep in mind that effects on these financial indices may not be immediate. Furthermore,

due to the global scale of indicators utilized, results may be skewed by the whole host of other

factors that influence the market and environmental health.

Starting with 1993, the relationship between the chosen environmental indicators and the

health of various global exchanges will be determined. IBM Watson will be utilized in order to

perform descriptive and correlative analytics. Descriptive tools of Watson will be used in order

to visualize the data and identify trends. Based on descriptive results, we can employ Watson’s

correlative tools to understand these indicators’ correlative strength. By creating correlative

models, this research could be useful to a variety of policy makers in many different countries

and organizations.

Literature Review

In a study by Geist and Lambin (2002), the driving factors of tropical deforestation were

explored. Their analysis revealed that the local decisions to deforest were driven by national and

global level economic opportunities and policies. No consistent link was shown between

deforestation and local issues such as impoverishment and political ecology. Additionally,

political corruptions or poor implementation of forestry rules can allow non-locals to come in to

deforest and commercialize the area. If decisions to deforest are driven by global factors, then

how does deforestation actually affect the global economy after it occurs? This is the substance

of the analysis of our study being done for the 2017 Watson competition.

In recent years, starting from 2004 to 2013, the world has seen a significant decline in the

area of rainforest that has been deforested. The decline from 2004 is due to the Brazilian

government putting regulations on the deforesting large properties (Godar, 2014). However,


Godar believes that these measures have done as much as they could and deforestation will not

decline further without additional legislation.

Campari (2002) took a look at what happened in areas of deforestation both in local

economic impact and socially. Campari found that if areas are deforested and turn out to be more

useful for farming then deforestation accelerates quickly in that area. Natives also choose to stay

based on productivity of the land, as they have incentive if the new value of the land is less than

how much they can make by farming the land. However, Campari also found that those who

stayed had poor health care, education and poor housing. In reducing deforestation, Campari

suggested creating better infrastructure such as better transportation for those already in living in

deforested areas in order to create alternative methods of improving quality of life other than

deforestation.

According to Pohkrel (2012), deforestation contributes to a rising sea level by increasing

the amount of inland water flow to the oceans. Previously, streams may have been blocked by

vegetation. This not only affects the oceans but has the potential to flood areas that were

previously inhabited by wildlife. With wildlife and usable land reduced, local economies can be

negatively impacted especially those that rely on farming as an income. This is one way

deforestation may indirectly damage economic growth.

There is a lack of literature involving deforestation and how it is related to global economic

indicators. This project will help fill the gap in literature and assist future studies in identifying

relationships.

Methodology

This project focuses on the utilization of IBM Watson in order to analyze and forecast

financial indices based on environmental indicators. Data cleansing was done using Microsoft

Excel before being uploaded to Watson. IBM Watson allows for analysis, visualization and

prediction utilizing multiple data sets.

Hypothesis

If environmental indicators are negatively affected (i.e., global temperature, carbon

levels, deforestation rates and sea level will change in a negative manner), then financial indices

will also decline.

Data Cleanup and Acquisition

Deforestation data was obtained through The World Bank (2015), an organization

dedicated to providing assistance to developing nations via research and analysis. Financial


index data was obtained through Yahoo Finance’s historical data tab. Carbon dioxide was

obtained through readings taken by NASA (NASA, 2016) and adjusted for seasonal corrections.

Temperature data was also from NASA (2017, Global surface temperature) which involved

gathering temperature readings around the globe and calculating a global surface temperature

change. Lastly, Sea level data was also obtained through NASA (2017, Global sea level change)

which was measured through instruments stationed throughout the world.

The financial indicator data was transformed from a monthly dataset into a yearly dataset

by taking the market’s opening value on the first day of the following year in which the

appropriate market was open. For example, the opening value of the first trading day in 2002 is

2001’s final. Deforestation, carbon dioxide and sea level datasets were refined from raw values

into a dataset which describes the cumulative yearly change since 1993. All data sets had to be

cleaned of erroneous values and blank data cells. Upon completion of cleaning, data was

uploaded to IBM Watson and received a quality score of 96%.

Research

In order to understand and analyze the data, our group conducted descriptive analysis and

attempted to create meaningful correlative models using Watson’s analytical tools. Watson has a

powerful and dynamic engine which assists in selecting the proper visualizations to help describe

the data. Additionally, Watson allows for quick generation of in-depth correlative models.

Although many prominent indices were tested, only the ones with the most significance are

mentioned.


Descriptive Analytics

Figure 1: Deforested Land over Year

Visualized above is the square kilometers that have been deforested in the world by year.

Although the rate of deforestation per year has seen a significant decrease since 2000, the total

area deforested continues to rise.

Figure 2: Sea Level over Year

The graph above shows the yearly change in sea level by millimeter from the year 1993.

In terms of trends, it can be seen that sea level has been rising. However, there are a few years,

1996 and 2011, where the sea level receded a few millimeters.


Figure 3: Temperature over Year

Similar to sea level, temperature is also trending upwards as time passes. Out of 20 years,

16 of them saw an increase from the previous year. As seen in the figure above, the rate of

change between years is accelerating as time goes on. For example, the change in temperature

during 2012 was the highest change seen in the dataset.

Deforestation


Figure 4: Mexican IPC and Deforested Land over Year

Figure 5: American Dow Jones and Deforested Land over Year

Figures 4 and 5 compare deforestation levels and its potential impact on financial

indicators. Interestingly, in the year 2000, when the market indices contract, deforestation levels

decrease in yearly change for the first time. However, for the most part, excluding 2008,

deforestation levels grow alongside market indicators. Based on these descriptive graphs, it

would seem that a trend somewhat exists between these financial indices and deforestation

levels.

Sea Level


Figure 6: Brazilian iBovespa and Sea Level over Year

Figure 7: American Dow Jones at Sea Level over Year

Figure 8: Australian S&P/ASX 200 and Sea Level over Year


Figure 9: Canadian S&P/TSX Toronto and Sea Level over Year

Figure 10: German DAX and Sea Level over Year


Figure 11: Mexican IPC and Sea Level over Year

The above figures display sea level against a selection of financial indices. Interestingly,

all the economies aside from Brazil and Mexico, have sea level generally trending with financial

indices in a positively correlated fashion from 2002 to 2013. The trend becomes noticeably

stronger as the years go on, most notably from 2008 to 2013. As sea levels grow, the financial

indices tend to rise with it and vice versa. In the cases of Brazil and Mexico, they follow the

same pattern as the other economies except in the year 2013. In light of these trends, we have

decided to investigate whether the correlative relationship between market indicators and sea

level exist.

Temperature


Figure 12: Mexican IPC and Temperature over Year

Figure 13: Canadian S&P/TSX Toronto and Temperature over Year

Figure 14: Hong Kongese Hang Seng and Temperature over Year

Figures 12 through 14 layer temperature levels over financial indices. The graphs exhibit

an almost erratic trend where some years consists of an inverse relationship while other years

contain a positive relationship. In the years from 1998 to 2004, the Canadian and Hong Kongese

indices have a contradicting experience in relation to temperature. As temperature readings go

down, the Canadian and Hong Kongese indices go up and vice versa. However, when observing

2006 to 2013, a positive relationship is displayed in which as temperature rises, financial indices

also grow and vice versa. In Mexico’s case, the same positive trend that exists for Canada and


Hong Kong also appears except for the year 2013. In years before 2002, Mexico’s IPC did not

change much on a yearly basis and thus could not be used to draw conclusions for temperature.

For other financial indices, temperature did not seem to follow any solid trend.

Carbon Dioxide

Figure 15: American Dow Jones and CO2 over Year

Figure 16: German DAX and CO2 over Year

Figures 15 and 16 serve to describe carbon dioxide’s relationship with financial

indicators. Carbon dioxide does not decrease in total quantity. Despite fluctuations within


various financial indices, carbon dioxide grows in an almost linear fashion nonetheless. Similar

to the deforestation figures, these graphs indicate a lack of trend between carbon and the market.

Correlative Analytics

Figure 17: American Dow Jones Predictor Dashboard Panel

Using IBM Watson’s correlative analytical features, we were able to understand levels of

correlative accuracy of most of the market indices that we examined. The American Dow Jones

is positively correlated with deforestation, CO2, sea level and temperature. The index has a

correlative strength of 77% for deforestation, CO2 and sea level which is fairly strong. As the

levels of deforestation, CO2 and sea level increase, so does the index. However, temperature is

regarded as below average, having a correlative strength of only 44%. Low strength for

temperature’s correlation is most likely due to its fluctuations being more drastic than the other

variables.


Figure 18: Chinese Shanghai Predictor Dashboard Panel

We decided to include the Chinese Shanghai Composite analysis to serve as a

counterexample to the American Dow Jones index. Despite being the world’s second largest

economy, it does not have as high of a correlative strength since it sits at a value of 68%. This

score is in stark contrast to the smaller economies in the coming figures with their correlative

strength being the strongest out of all the indices tested.


Figure 19: Canadian S&P/TSX Toronto Predictor Dashboard Panel

Our group had hypothesized that the Canadian S&P/TSX might not correlate well with

the ecological factors since it did not recover as well from the 2008 crisis in comparison to other

economies. However, at a correlative strength of 83%, deforestation, CO2 and sea level

correlations are stronger than most other indices. Temperature only has a slightly above average

correlative score of 59%. This relationship reflects that deforestation, CO2 and sea levels rise

with Canada’s S&P/TSX Toronto.


Figure 20: European Stoxx 50 Predictor Dashboard Panel

IBM Watson’s correlative modeling for Europe’s Stoxx 50 is quite similar to the

American Dow Jones. Compared to the United States, the correlative strength for sea level, CO2

and deforestation only varied by 1%, for a strength of 74%. However, temperature did not show

any correlation with the valuation of the European Stoxx 50. As with the other indices, the

relationship is a positive correlation with both independent and dependent variables increasing

with one another.


Figure 21: Brazilian iBovespa Predictor Dashboard Panel

The Brazilian iBovespa is of particular interest due to the vast amounts of deforestation

affecting the Brazilian rainforests. This index shows a very strong correlation with deforested

land, CO2 and sea level with a correlative strength of 89%. Similar to other index analyses,

temperature proved to have an average correlative strength with a value of 52%. Hence, it seems

that as deforestation levels, carbon levels, and sea levels rise, iBovespa’s valuation does the

same.


Figure 22: Mexican IPC Predictor Dashboard Panel

Deforestation, CO2 and sea level is more correlated with Mexico’s IPC than any other

index with a 96% correlative strength. This relationship was of great surprise as we had

hypothesized that Mexico would have an above average correlative strength. Nevertheless,

deforestation, CO2 and sea levels rise alongside the IPC’s value, albeit much stronger when

compared to other indices.

Limitations

Time span was the biggest limitation by far in validity of our results. Historical data for

indices could only be retrieved from 1993 due to some only being recently created. Without

additional data, there may be time lagged effects not shown in the data due to our 20 year time

span simply not being large enough. Furthermore, the data had to be transformed into yearly time

spans due to deforestation data only being recorded on a yearly basis.

Another limitation is that this research could not develop a method to reconcile the

differences between developed and developing economies. For instance, it could be entirely

possible that these results are skewed by the fact that Mexico is a developing nation with an

economy that is still coming into its own. More research should be done to ensure the correlation

is valid. Yearly changes in economic value are most likely more volatile in developing

economies as compared to developed.


Upon testing, deforestation, sea level and carbon dioxide had similar correlative strengths

in relation to the indices. Temperature was the only variable that produced differing results.

Hence, in future studies, better variables could be selected for ecological factors in order to better

represent ecological changes.

Discussion

This research sought to identify correlations between ecological factors and global

economic performance. In order to accomplish this, factors were identified such as deforestation

and carbon dioxide to be compared against financial indices. Based on the conducted research, a

trend appears where economic performance increases along with deforestation, carbon dioxide

and sea level. Temperature showed weak to no correlation with the financial indices. However,

based on the research’s time-span limitation, there may be missed long term correlations between

ecological factors and indices. Further research should be done about the long term effects of

environmental change. Future research might consider only utilizing one of these such as

deforestation.

Potential beneficiaries of this research, such as non-profits, policy makers and investors,

can utilize this information to justify and make environmentally-sound decisions. Businesses can

also employ this research to make decisions to sustainably attain their profits. Hence, the

research can assist these decision makers in understanding the economic impact of their

decisions, at least in the short term.

References

Geist, H. J., & Lambin, E. F. (2002). Proximate Causes and Underlying Driving Forces of

Tropical Deforestation Tropical forests are disappearing as the result of many pressures, both

local and regional, acting in various combinations in different geographical locations.

BioScience, 52(2), 143-150.

Rodrigues, A. S., Ewers, R. M., Parry, L., Souza, C., Veríssimo, A., & Balmford, A.

(2009). Boom-and-bust development patterns across the Amazon deforestation frontier. science,

324(5933), 1435-1437.

Walsh, B. (2009). Study: Economic Boost of Deforestation Is Short-Lived. Retrieved

December 27, 2016, from http://content.time.com/time/health/article/0,8599,1904174,00.html

GSFC. 2015. Global Mean Sea Level Trend from Integrated Multi-Mission Ocean

Altimeters TOPEX/Poseidon Jason-1 and OSTM/Jason-2 Version 3. Ver. 3. PO.DAAC, CA,

USA. Dataset accessed [2017-02-26] at http://dx.doi.org/10.5067/GMSLM-TJ123.

Pokhrel, Y. N., Hanasaki, N., Yeh, P. J., Yamada, T. J., Kanae, S., & Oki, T. (2012).

Model estimates of sea-level change due to anthropogenic impacts on terrestrial water storage.

Nature Geoscience, 5(6), 389-392.

http://dx.doi.org/10.5067/GMSLM-TJ123


Godar, J., Gardner, T. A., Tizado, E. J., & Pacheco, P. (2014). Actor-specific

contributions to the deforestation slowdown in the Brazilian Amazon. Proceedings of the

National Academy of Sciences, 111(43), 15591-15596.

Data Sources

World Bank. (2015). Forest area (sq. km). Retrieved March 17, 2017, from

http://data.worldbank.org/indicator/AG.LND.FRST.K2?end=2013&name_desc=false&start=199

3

NASA. (2017). Global surface temperature | NASA Global Climate Change. Retrieved

March 17, 2017, from https://climate.nasa.gov/vital-signs/global-temperature/

NASA. (2017). Global sea level change Climate

Change.https://sealevel.nasa.gov/Retrieved March 17, 2015, from https://sealevel.nasa.gov/

N. (2016, December). Vital Signs. Retrieved February 04, 2017, from

http://climate.nasa.gov/vital-signs/carbon-dioxide/

Prodes. (2016). Retrieved February 04, 2017, from

http://data.globalforestwatch.org/datasets/4160f715e12d46a98c989bdbe7e5f4d6_1

Yahoo Finance (n.d.). Retrieved January 30, 2017, from http://finance.yahoo.com/



https://climate.nasa.gov/vital-signs/global-temperature/

https://sealevel.nasa.gov/

http://climate.nasa.gov/vital-signs/carbon-dioxide/

http://data.globalforestwatch.org/datasets/4160f715e12d46a98c989bdbe7e5f4d6_1

Documents

The 2017 Watson Analytics Global Competition Investigating ... · The 2017 Watson Analytics Global Competition Investigating the Relationship between Financial Indices and Ecological