Spurious correlation #2,541 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Vernal, Utah and the second variable is América Móvil's stock price (AMX). The chart goes from 2002 to 2023, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Vernal, Utah and the second variable is América Móvil's stock price (AMX). The chart goes from 2002 to 2023, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The extra particles in the air actually formed a microscopic protective barrier around América Móvil's headquarters, shielding it from economic downturns. As a result, investors saw the company as a safe haven and drove up the stock price. Who knew smog could be the unsung hero of the stock market!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image that encapsulates a surreal Vaporwave rendition of Vernal, Utah, with a futuristic feel. Imagine a cityscape with distorted pastel buildings and palm trees, surrounded by a hazy, ethereal glow. Amidst the chaos, tourists in retro-futuristic attire, wearing face masks, are seen wandering the neon-lit streets. In the background, a colossal billboard displays the vibrant stock price chart of América Móvil (AMX), casting a luminous glow over the city as the demand for their mobile services surges. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: Air pollution in Vernal, Utah and América Móvil's stock price (AMX). Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: High-tech Data cyber art Vaporwave Cute 2D animation Hyper-realistic
ChatGPT: Vaporwave

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Air pollution in Vernal, Utah and América Móvil's stock price (AMX), in the following situation: The increase in air pollution in Vernal, Utah led to an influx of tourists wearing face masks, inadvertently promoting América Móvil's mobile services, driving up their stock price. It's a classic case of "smogvertising" and "pollution promotion.". Keep the description simple.
ChatGPT: In Vernal, Utah, the air is thick with pollution as tourists wearing face masks roam the streets. In the midst of it all, a prominent billboard displays the stock price of América Móvil (AMX), steadily rising due to the increased demand for their mobile services in response to the city's air quality concerns.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ Vaporwave ] and depicting this scene: In Vernal, Utah, the air is thick with pollution as tourists wearing face masks roam the streets. In the midst of it all, a prominent billboard displays the stock price of América Móvil (AMX), steadily rising due to the increased demand for their mobile services in response to the city's air quality concerns. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that encapsulates a surreal Vaporwave rendition of Vernal, Utah, with a futuristic feel. Imagine a cityscape with distorted pastel buildings and palm trees, surrounded by a hazy, ethereal glow. Amidst the chaos, tourists in retro-futuristic attire, wearing face masks, are seen wandering the neon-lit streets. In the background, a colossal billboard displays the vibrant stock price chart of América Móvil (AMX), casting a luminous glow over the city as the demand for their mobile services surges.

*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in Air pollution in Vernal, Utah caused América Móvil's stock price (AMX) to increase.\Make it sound serious until the end.

AI academic paper

(Because p < 0.01)

Air We Go Again: The Atmospheric Influence on Stock Prices - A Case Study of Vernal, Utah's Air Pollution and América Móvil's (AMX) Stock Price

The Journal of Environmental Economics and Stock Market Dynamics

Jan 2024

Reminder: This paper is AI-generated. Not real!

^{Show prompt used to generate this paper} Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between Air pollution in Vernal, Utah and América Móvil's stock price (AMX). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.

Make up the lorem and ipsum part, but make it sound related to the topic at hand.

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
Air We Go Again: The Atmospheric Influence on Stock Prices - A Case Study of Vernal, Utah's Air Pollution and América Móvil's (AMX) Stock Price

[[ABSTRACT]]
This research paper investigates the curious relationship between air pollution levels in Vernal, Utah, and the stock price of América Móvil (AMX). Employing a blend of data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we diligently tracked air quality metrics and stock prices from 2002 to 2023. Our findings reveal a significant correlation coefficient of 0.7949931, with a p-value of less than 0.01, indicating a robust statistical association between these seemingly unrelated variables. While the financial world may have overlooked the atmospheric impact on stock prices, our research emphasizes the air apparent effect of air pollution on market performance. This study sheds light on the air-ily overlooked environmental factors that could blow one's expectations out of the water and suggests that one should not take the air quality-stock price relationship with a grain of salt. As we endeavor to clear the air on this peculiar correlation, our paper poses the question: could the legacy of Vernal's air pollution be more far-reaching than previously thought, permeating into the financial atmosphere in unexpected ways?

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
Light as air but heavy with implications, our research delves into the uncharted territory of the relationship between air pollution and stock prices. While the stock market may seem like a realm far removed from the atmospheric concerns of Vernal, Utah, our investigation reveals an unexpected link between the two. As we embark on this academic journey, we are reminded of the sage words of Mark Twain: "Buy land, they're not making it anymore." Perhaps now we should add, "And keep an eye on air quality while you're at it."
In recent decades, the environmental impact of air pollution has been a topic of heightened concern and debate. With initiatives such as the Clean Air Act setting the stage for stringent air quality standards, the focus on atmospheric well-being has intensified. Simultaneously, the world of finance has been a domain of intense scrutiny and speculation, with stock prices reflecting the ebb and flow of market forces. Yet, surprisingly little attention has been devoted to exploring the potential interplay between these two realms.
Our paper seeks to fill this gap by examining the intriguing case of Vernal, Utah, and the stock price of América Móvil (AMX). Not only does this study offer insights into the specific dynamics at play in this unique context, but it also contributes to a broader understanding of the often unforeseen relationships between environmental factors and financial markets.
With a twinkle in our eyes and our feet firmly grounded in statistical analysis, we endeavor to unravel the complex web of influences at work. As we navigate through the ether of air pollution and financial data, our aim is not simply to inflate the importance of this correlation, but to provide a breath of fresh air in the field of stock market research. So, let us proceed with the caution of a delicate breeze and the determination of a strong gale, as we set out to untangle the strands of air and stocks in this captivating tale.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "The Effects of Air Pollution on Financial Markets," Smith et al. explore the impact of air pollution on various stock prices, shedding light on the interconnectedness of environmental factors and financial performance. The findings underscore the significance of considering atmospheric variables in understanding market dynamics. Similarly, Doe's study, "Air Quality and Stock Price Volatility," delves into the relationship between air quality and stock price volatility, revealing nuanced patterns that hint at a deeper influence of environmental conditions on market behavior. Building on these foundations, Jones et al. in "The Invisible Hand of Smog: Unearthing the Link Between Air Pollution and Stock Market Movements" employ a multi-faceted approach to examine the subtle yet profound influence of air pollution on stock market movements, offering compelling insights into the atmospheric undercurrents shaping financial outcomes.

Moving beyond the realm of academic studies, renowned non-fiction books such as "The Air We Breathe: A Comprehensive Analysis of Atmospheric Impact on Financial Performance" by Green, as well as "Pollution and Prosperity: Unraveling the Financial Ramifications of Environmental Conditions" by Blue, provide comprehensive analyses of the intricate connections between air quality and financial prosperity. These books serve as fundamental resources in comprehending the broader implications of environmental factors on economic outcomes.

However, delving into the realm of fiction, the classic novel "Great Expectations" by Charles Dickens, though seemingly unrelated, inadvertently offers a whimsical allegory for the unsuspecting impact of air pollution on stock prices. Furthermore, the sci-fi thriller "Airborne: When Pollutants Went Public" by Novel Writer weaves a tantalizing narrative that, beneath its fictional facade, mirrors the clandestine interplay between airborne pollutants and financial markets.

Drawing inspiration from the unlikeliest of sources, even board games like "Stockopoly: Air Pollution Edition" playfully elucidate the intricacies of environmental variables in shaping stock prices. While certainly a departure from traditional academic pursuits, such unconventional influences serve to underscore the ubiquitous presence of this correlation in unexpected corners of life.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between Air pollution in Vernal, Utah and América Móvil's stock price (AMX). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and LSEG Analytics (Refinitiv) . You used data from 2002 to 2023

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Air We Go Again: The Atmospheric Influence on Stock Prices - A Case Study of Vernal, Utah's Air Pollution and América Móvil's (AMX) Stock Price

[[ABSTRACT]]
This research paper investigates the curious relationship between air pollution levels in Vernal, Utah, and the stock price of América Móvil (AMX). Employing a blend of data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we diligently tracked air quality metrics and stock prices from 2002 to 2023. Our findings reveal a significant correlation coefficient of 0.7949931, with a p-value of less than 0.01, indicating a robust statistical association between these seemingly unrelated variables. While the financial world may have overlooked the atmospheric impact on stock prices, our research emphasizes the air apparent effect of air pollution on market performance. This study sheds light on the air-ily overlooked environmental factors that could blow one's expectations out of the water and suggests that one should not take the air quality-stock price relationship with a grain of salt. As we endeavor to clear the air on this peculiar correlation, our paper poses the question: could the legacy of Vernal's air pollution be more far-reaching than previously thought, permeating into the financial atmosphere in unexpected ways?

[[INTRODUCTION]]
INTRODUCTION
Light as air but heavy with implications, our research delves into the uncharted territory of the relationship between air pollution and stock prices. While the stock market may seem like a realm far removed from the atmospheric concerns of Vernal, Utah, our investigation reveals an unexpected link between the two. As we embark on this academic journey, we are reminded of the sage words of Mark Twain: "Buy land, they're not making it anymore." Perhaps now we should add, "And keep an eye on air quality while you're at it."
In recent decades, the environmental impact of air pollution has been a topic of heightened concern and debate. With initiatives such as the Clean Air Act setting the stage for stringent air quality standards, the focus on atmospheric well-being has intensified. Simultaneously, the world of finance has been a domain of intense scrutiny and speculation, with stock prices reflecting the ebb and flow of market forces. Yet, surprisingly little attention has been devoted to exploring the potential interplay between these two realms.
Our paper seeks to fill this gap by examining the intriguing case of Vernal, Utah, and the stock price of América Móvil (AMX). Not only does this study offer insights into the specific dynamics at play in this unique context, but it also contributes to a broader understanding of the often unforeseen relationships between environmental factors and financial markets.
With a twinkle in our eyes and our feet firmly grounded in statistical analysis, we endeavor to unravel the complex web of influences at work. As we navigate through the ether of air pollution and financial data, our aim is not simply to inflate the importance of this correlation, but to provide a breath of fresh air in the field of stock market research. So, let us proceed with the caution of a delicate breeze and the determination of a strong gale, as we set out to untangle the strands of air and stocks in this captivating tale.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

METHODOLOGY

To uncover the mysterious and unlikely connection between air pollution in Vernal, Utah, and the stock price of América Móvil (AMX), we employed a combination of traditional research methods and a sprinkle of academic whimsy. Our approach drew heavily from data harvested across the depths of the internet, primarily from the Environmental Protection Agency's comprehensive air quality metrics and LSEG Analytics (Refinitiv) robust stock price databases.

Firstly, we engaged in the mesmerizing art of data collection, weaving through digital databases with the dexterity of a seasoned treasure hunter seeking precious gems. We meticulously examined air quality parameters, including levels of particulate matter, sulfur dioxide, nitrogen dioxide, and ozone, all of which contribute to Vernal's atmospheric tapestry. Simultaneously, we delved into the labyrinthine world of stock prices, tracking the fluctuations of América Móvil's (AMX) financial fortunes with the diligence of a curious cat chasing elusive shadows.

Next, we unleashed the formidable power of statistical analysis, employing regression models to discern patterns that may elude the untrained eye. Through the manipulation of data with the finesse of a knitting enthusiast crafting an intricate pattern, we sought to reveal the hidden threads linking air pollution and stock prices. The culmination of this analytical odyssey resulted in the emergence of a correlation coefficient of 0.7949931, accompanied by a p-value that winked mischievously at us, whispering secrets of statistical significance with a sly grin.

We further delved into the realm of time-series analysis, navigating the ebbs and flows of temporal data with the tenacity of intrepid explorers charting uncharted waters. By unraveling the chronicles of air pollution and stock prices from 2002 to 2023, we endeavored to capture the essence of their intertwining narrative, akin to archeologists meticulously piecing together fragments of a forgotten civilization.

In addition, we employed a multivariate approach to account for potential confounding variables, recognizing that the entanglement of atmospheric whims and financial figures could be influenced by external factors. Just as an astute chef balances the flavors in a culinary masterpiece, we attempted to tease apart the unique contribution of Vernal's air pollution to the tantalizing recipe of América Móvil's stock price movements.

It is important to note that, while we approached our research with the gravity of seasoned scholars, we also embraced the unforeseen plot twists and serendipitous discoveries with the glee of children unraveling a mystery. Our methodology marries rigorous academic rigor with the spirit of adventure, as we sought to enshroud the curious relationship between air quality and stock prices in objective analysis and playful exploration.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between Air pollution in Vernal, Utah and América Móvil's stock price (AMX). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
Air We Go Again: The Atmospheric Influence on Stock Prices - A Case Study of Vernal, Utah's Air Pollution and América Móvil's (AMX) Stock Price

[[ABSTRACT]]
This research paper investigates the curious relationship between air pollution levels in Vernal, Utah, and the stock price of América Móvil (AMX). Employing a blend of data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we diligently tracked air quality metrics and stock prices from 2002 to 2023. Our findings reveal a significant correlation coefficient of 0.7949931, with a p-value of less than 0.01, indicating a robust statistical association between these seemingly unrelated variables. While the financial world may have overlooked the atmospheric impact on stock prices, our research emphasizes the air apparent effect of air pollution on market performance. This study sheds light on the air-ily overlooked environmental factors that could blow one's expectations out of the water and suggests that one should not take the air quality-stock price relationship with a grain of salt. As we endeavor to clear the air on this peculiar correlation, our paper poses the question: could the legacy of Vernal's air pollution be more far-reaching than previously thought, permeating into the financial atmosphere in unexpected ways?

[[LITERATURE REVIEW]]
In "The Effects of Air Pollution on Financial Markets," Smith et al. explore the impact of air pollution on various stock prices, shedding light on the interconnectedness of environmental factors and financial performance. The findings underscore the significance of considering atmospheric variables in understanding market dynamics. Similarly, Doe's study, "Air Quality and Stock Price Volatility," delves into the relationship between air quality and stock price volatility, revealing nuanced patterns that hint at a deeper influence of environmental conditions on market behavior. Building on these foundations, Jones et al. in "The Invisible Hand of Smog: Unearthing the Link Between Air Pollution and Stock Market Movements" employ a multi-faceted approach to examine the subtle yet profound influence of air pollution on stock market movements, offering compelling insights into the atmospheric undercurrents shaping financial outcomes.
Moving beyond the realm of academic studies, renowned non-fiction books such as "The Air We Breathe: A Comprehensive Analysis of Atmospheric Impact on Financial Performance" by Green, as well as "Pollution and Prosperity: Unraveling the Financial Ramifications of Environmental Conditions" by Blue, provide comprehensive analyses of the intricate connections between air quality and financial prosperity. These books serve as fundamental resources in comprehending the broader implications of environmental factors on economic outcomes.
However, delving into the realm of fiction, the classic novel "Great Expectations" by Charles Dickens, though seemingly unrelated, inadvertently offers a whimsical allegory for the unsuspecting impact of air pollution on stock prices. Furthermore, the sci-fi thriller "Airborne: When Pollutants Went Public" by Novel Writer weaves a tantalizing narrative that, beneath its fictional facade, mirrors the clandestine interplay between airborne pollutants and financial markets.
Drawing inspiration from the unlikeliest of sources, even board games like "Stockopoly: Air Pollution Edition" playfully elucidate the intricacies of environmental variables in shaping stock prices. While certainly a departure from traditional academic pursuits, such unconventional influences serve to underscore the ubiquitous presence of this correlation in unexpected corners of life.

[[RESULTS]]
The statistical analysis of the data yielded a noteworthy correlation coefficient of 0.7949931, indicating a strong positive relationship between air pollution levels in Vernal, Utah, and the stock price of América Móvil (AMX). This finding suggests that as the air quality in Vernal deteriorated, the stock price of AMX exhibited a notable tendency to follow suit. In other words, when the air quality took a hit, so did the stock price. It's as if the stock price just couldn't catch its breath in the face of poor air quality!
Further bolstering this finding, the r-squared value of 0.6320141 indicates that approximately 63.20% of the variability in AMX stock price can be explained by changes in air pollution levels. This suggests that changes in Vernal's air quality have been a significant factor in driving changes in América Móvil's stock price. Who knew that the winds of change in the stock market could be so closely tied to the winds of Vernal?
The p-value of less than 0.01 underscores the robustness of this correlation, providing strong evidence against the null hypothesis of no relationship between air pollution and AMX stock price. It seems that the connection between air quality and stock prices is anything but thin air; it's a substantial relationship that demands attention and further exploration.
Figure 1 (to be inserted) illustrates the scatterplot depicting this striking correlation between air pollution levels in Vernal, Utah, and the stock price of América Móvil (AMX). The data points form a clear pattern, reminiscent of the way a gust of wind can swiftly alter the landscape. The graph serves as a visual testament to the tangible link between these seemingly disparate variables and invites us to ponder the atmospheric influence on financial markets in a breath of fresh insight.
In conclusion, our findings bring to light the significant association between air pollution in Vernal, Utah, and the stock price of América Móvil. This study underscores the need to not only monitor the financial forecast but also keep an eye on the atmospheric conditions, as they may hold greater sway over stock prices than previously assumed. As we unveil this tangled web of air and stocks, we are reminded of the words of Will Rogers, "Buy stocks with clean air," or something to that effect.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our findings support and extend the existing research on the influence of air pollution on stock prices, affirming the air-ily captivating correlation between these seemingly disparate domains. The substantial correlation coefficient and r-squared value obtained in our analysis resonate with the prior literature, serving as a breath of fresh air in substantiating the atmospheric impact on financial markets. It appears that the impact of air pollution on stock prices, much like the pollutants themselves, is not merely dissipating into thin air, but rather permeating into market dynamics with tangible effects.

Echoing the sentiments of Smith et al., our study underscores the compelling role of atmospheric variables in shaping market performance, revealing how air quality fluctuations in Vernal, Utah have not only affected the local environment but also reverberated into the domain of financial markets. Furthermore, our results resonate with Doe's insights on stock price volatility, emphasizing the nuanced patterns that reflect the subtler influence of environmental conditions on market behavior. It's as if the financial world is finally awakening to the air-raising implications of air pollution on market volatility!

Our investigation also aligns with the multi-faceted approach adopted by Jones et al., as we, too, have unveiled the profound influence of air pollution on stock market movements, highlighting the interconnectedness of these domains in a manner that's nothing to sneeze at. The significance of environmental factors in shaping economic outcomes, as emphasized by Green and Blue, finds reinforcement in our findings, suggesting that the atmosphere's influence on financial prosperity is indeed nothing to blow off.

Additionally, leveraging insights from "Great Expectations" by Charles Dickens, our study inadvertently aligns with the whimsical allegory of the unsuspecting impact of air pollution on stock prices, providing a real-world application for this timeless classic. While the sci-fi thriller "Airborne: When Pollutants Went Public" may seem lightyears away from academic inquiry, it serves as an unexpected yet insightful parallel to the clandestine interplay between airborne pollutants and financial markets that our research has uncovered. The unlikeliest of sources, including a board game like "Stockopoly: Air Pollution Edition," further underscore the ubiquitous presence of this correlation in unexpected corners of life, hinting at a distinct possibility: perhaps understanding the interplay of air pollution and stock prices is not child's play after all.

In sum, our results not only endorse the prior body of work but also blow fresh air into the sails of this blooming field of inquiry. As we continue to unravel the complex web of air pollution and stock prices, one must bear in mind that the stock market may not be the only place where birds of a feather flock together – it seems that when it comes to air pollution and stock prices, there's more than meets the eye.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Air pollution in Vernal, Utah and América Móvil's stock price (AMX). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Air We Go Again: The Atmospheric Influence on Stock Prices - A Case Study of Vernal, Utah's Air Pollution and América Móvil's (AMX) Stock Price

[[ABSTRACT]]
This research paper investigates the curious relationship between air pollution levels in Vernal, Utah, and the stock price of América Móvil (AMX). Employing a blend of data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we diligently tracked air quality metrics and stock prices from 2002 to 2023. Our findings reveal a significant correlation coefficient of 0.7949931, with a p-value of less than 0.01, indicating a robust statistical association between these seemingly unrelated variables. While the financial world may have overlooked the atmospheric impact on stock prices, our research emphasizes the air apparent effect of air pollution on market performance. This study sheds light on the air-ily overlooked environmental factors that could blow one's expectations out of the water and suggests that one should not take the air quality-stock price relationship with a grain of salt. As we endeavor to clear the air on this peculiar correlation, our paper poses the question: could the legacy of Vernal's air pollution be more far-reaching than previously thought, permeating into the financial atmosphere in unexpected ways?

[[INTRDUCTION]]
INTRODUCTION
Light as air but heavy with implications, our research delves into the uncharted territory of the relationship between air pollution and stock prices. While the stock market may seem like a realm far removed from the atmospheric concerns of Vernal, Utah, our investigation reveals an unexpected link between the two. As we embark on this academic journey, we are reminded of the sage words of Mark Twain: "Buy land, they're not making it anymore." Perhaps now we should add, "And keep an eye on air quality while you're at it."
In recent decades, the environmental impact of air pollution has been a topic of heightened concern and debate. With initiatives such as the Clean Air Act setting the stage for stringent air quality standards, the focus on atmospheric well-being has intensified. Simultaneously, the world of finance has been a domain of intense scrutiny and speculation, with stock prices reflecting the ebb and flow of market forces. Yet, surprisingly little attention has been devoted to exploring the potential interplay between these two realms.
Our paper seeks to fill this gap by examining the intriguing case of Vernal, Utah, and the stock price of América Móvil (AMX). Not only does this study offer insights into the specific dynamics at play in this unique context, but it also contributes to a broader understanding of the often unforeseen relationships between environmental factors and financial markets.
With a twinkle in our eyes and our feet firmly grounded in statistical analysis, we endeavor to unravel the complex web of influences at work. As we navigate through the ether of air pollution and financial data, our aim is not simply to inflate the importance of this correlation, but to provide a breath of fresh air in the field of stock market research. So, let us proceed with the caution of a delicate breeze and the determination of a strong gale, as we set out to untangle the strands of air and stocks in this captivating tale.

[[RESULTS]]
The statistical analysis of the data yielded a noteworthy correlation coefficient of 0.7949931, indicating a strong positive relationship between air pollution levels in Vernal, Utah, and the stock price of América Móvil (AMX). This finding suggests that as the air quality in Vernal deteriorated, the stock price of AMX exhibited a notable tendency to follow suit. In other words, when the air quality took a hit, so did the stock price. It's as if the stock price just couldn't catch its breath in the face of poor air quality!
Further bolstering this finding, the r-squared value of 0.6320141 indicates that approximately 63.20% of the variability in AMX stock price can be explained by changes in air pollution levels. This suggests that changes in Vernal's air quality have been a significant factor in driving changes in América Móvil's stock price. Who knew that the winds of change in the stock market could be so closely tied to the winds of Vernal?
The p-value of less than 0.01 underscores the robustness of this correlation, providing strong evidence against the null hypothesis of no relationship between air pollution and AMX stock price. It seems that the connection between air quality and stock prices is anything but thin air; it's a substantial relationship that demands attention and further exploration.
Figure 1 (to be inserted) illustrates the scatterplot depicting this striking correlation between air pollution levels in Vernal, Utah, and the stock price of América Móvil (AMX). The data points form a clear pattern, reminiscent of the way a gust of wind can swiftly alter the landscape. The graph serves as a visual testament to the tangible link between these seemingly disparate variables and invites us to ponder the atmospheric influence on financial markets in a breath of fresh insight.
In conclusion, our findings bring to light the significant association between air pollution in Vernal, Utah, and the stock price of América Móvil. This study underscores the need to not only monitor the financial forecast but also keep an eye on the atmospheric conditions, as they may hold greater sway over stock prices than previously assumed. As we unveil this tangled web of air and stocks, we are reminded of the words of Will Rogers, "Buy stocks with clean air," or something to that effect.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In closing, our study illuminates the tangible connection between air pollution in Vernal, Utah, and the stock price of América Móvil (AMX). The robust correlation coefficient and p-value, coupled with the substantial r-squared value, paint a clear picture of the atmospheric influence on financial markets. It seems that when it comes to stock prices, the air quality in Vernal is no mere breeze; it's a gust that can significantly impact market performance.

Our findings invite us to reevaluate the factors that sway stock prices, emphasizing the need to consider not just economic indicators, but also the air-ily overlooked environmental elements. We've blown the lid off the idea that stock prices exist in a vacuum, demonstrating that Vernal's air quality can profoundly influence AMX stock prices. It's a revelation that might leave some stock analysts gasping for air!

As we draw the curtain on this investigation, we are left with a breath of fresh insight into the surprising ways in which environmental and financial spheres intertwine. It's a reminder that, in the swirling whirlwind of market forces, the air we breathe may hold more significance than we realize. So, as we exhale the conclusions of this study, we do so with the hope that future research will continue to air out the complexities of the atmospheric impact on stock prices, even though we think this paper has already cleared the air on the subject. There's no need for more research – we've already Nitrogen-ated the results!

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited Air We Go Again: The Atmospheric Influence on Stock Prices - A Case Study of Vernal, Utah's Air Pollution and América Móvil's (AMX) Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Environmental Economics and Stock Market Dynamics

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Air We Go Again: The Atmospheric Influence on Stock Prices - A Case Study of Vernal, Utah's Air Pollution and América Móvil's (AMX) Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Environmental Economics and Stock Market Analysis (CEESMA)

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Air We Go Again: The Atmospheric Influence on Stock Prices - A Case Study of Vernal, Utah's Air Pollution and América Móvil's (AMX) Stock Price with an abstract of This research paper investigates the curious relationship between air pollution levels in Vernal, Utah, and the stock price of América Móvil (AMX). Employing a blend of data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we diligently tracked air quality metrics and stock prices from 2002 to 2023. Our findings reveal a significant correlation coefficient of 0.7949931, with a p-value of less than 0.01, indicating a robust statistical association between these seemingly unrelated variables. While the financial world may have overlooked the atmospheric impact on stock prices, our research emphasizes the air apparent effect of air pollution on market performance. This study sheds light on the air-ily overlooked environmental factors that could blow one's expectations out of the water and suggests that one should not take the air quality-stock price relationship with a grain of salt. As we endeavor to clear the air on this peculiar correlation, our paper poses the question: could the legacy of Vernal's air pollution be more far-reaching than previously thought, permeating into the financial atmosphere in unexpected ways?

ChatGPT:

Air pollution, stock prices, América Móvil, Vernal Utah, correlation coefficient, Environmental Protection Agency, LSEG Analytics, Refinitiv, air quality metrics, market performance, financial atmosphere, statistical association

*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.

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Data details

Air pollution in Vernal, Utah
Detailed data title: Percentage of days with moderate or worse air quality in Vernal, UT
Source: Environmental Protection Agency
See what else correlates with Air pollution in Vernal, Utah

América Móvil's stock price (AMX)
Detailed data title: Opening price of América Móvil (AMX) on the first trading day of the year
Source: LSEG Analytics (Refinitiv)
Additional Info: ^{See full details} Via Microsoft Excel Stockhistory function

See what else correlates with América Móvil's stock price (AMX)

Correlation r = 0.7949931 (Pearson correlation coefficient)
Correlation is a measure of how much the variables move together. If it is 0.99, when one goes up the other goes up. If it is 0.02, the connection is very weak or non-existent. If it is -0.99, then when one goes up the other goes down. If it is 1.00, you probably messed up your correlation function.

r² = 0.6320141 (Coefficient of determination)
This means 63.2% of the change in the one variable (i.e., América Móvil's stock price (AMX)) is predictable based on the change in the other (i.e., Air pollution in Vernal, Utah) over the 22 years from 2002 through 2023.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 9.8E-6.^Show 0.0000098417618329043810000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.79 in 0.00098% of random cases. Said differently, if you correlated 101,608 random variables^Note You don't actually need 101 thousand variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 21 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 21 because we have two variables measured over a period of 22 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.56, 0.91 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
Air pollution in Vernal, Utah (Bad air quality days)	0	0	0	0	0	41.1043	54.2169	30.315	54.7945	46.3014	52.459	63.2877	35.6164	31.7808	31.9672	43.0137	48.2192	38.9041	30.3279	39.1781	29.0411	52.7473
América Móvil's stock price (AMX) (Stock price)	3.16	2.36	4.47	8.86	14.48	22.55	30.15	15.39	23.42	28.24	22.6	23.09	22.79	21.65	13.75	12.69	17.3	14.26	16.07	14.67	21.08	18.12

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{Show the steps to do this.} Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."

# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([0,0,0,0,0,41.1043,54.2169,30.315,54.7945,46.3014,52.459,63.2877,35.6164,31.7808,31.9672,43.0137,48.2192,38.9041,30.3279,39.1781,29.0411,52.7473,])
array_2 = np.array([3.16,2.36,4.47,8.86,14.48,22.55,30.15,15.39,23.42,28.24,22.6,23.09,22.79,21.65,13.75,12.69,17.3,14.26,16.07,14.67,21.08,18.12,])
array_1_name = "Air pollution in Vernal, Utah"
array_2_name = "América Móvil's stock price (AMX)"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. ^Note Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

Full license details.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

High resolution line chart ^Note The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.

If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only Air pollution in Vernal, Utah
Line chart for only América Móvil's stock price (AMX)
AI-generated correlation image
The spurious research paper: Air We Go Again: The Atmospheric Influence on Stock Prices - A Case Study of Vernal, Utah's Air Pollution and América Móvil's (AMX) Stock Price

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Correlation ID: 2541 · Black Variable ID: 21390 · Red Variable ID: 1712


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