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Spurious correlation #2,804 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in corn grown in Indiana and the second variable is Rogers Communications' stock price (RCI).  The chart goes from 2003 to 2023, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

As GMO use in Indiana corn increased, it led to the creation of a new strain of corn with built-in Wi-Fi capabilities. This corn, now known as "Corny-Fi," unexpectedly disrupted the telecommunications industry by providing free internet access to nearby areas. As people flocked to corn fields for both their technologically advanced produce and convenient internet access, the demand for Rogers Communications' services skyrocketed. Additionally, the corn's ability to auto-tweet its growth progress and post on Instagram led to a viral social media sensation, further boosting the company's stock price. This unforeseen connection between agriculture and technology not only changed the way we think about farming and communication, but also gave a whole new meaning to the term "going viral!"

Model: dalle-3
Prompt: Generate an image of a vibrant Indiana cornfield, where rows of genetically modified corn stretch into the horizon. The sun shines down on the bountiful harvest, symbolizing profits for the farmers and a kernel of hope for the economy. In the background, include a stock price chart for Rogers Communications (RCI) trending upwards, signifying the surge in stock price due to investor interest. The entire scene should be depicted in a pop art style, with bold and vibrant colors, and strong graphic elements to convey the message of increased GMO use and economic significance. 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: GMO use in corn grown in Indiana and Rogers Communications' stock price (RCI). 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: A TIME magazine cover image Cyber folk Pop art A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s Scalar art
ChatGPT: Pop art

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 GMO use in corn grown in Indiana and Rogers Communications' stock price (RCI), in the following situation: The increase in GMO use in corn led to a cornucopia of profits for farmers, causing a kernel of hope in the economy. Investors went all ears and invested in Rogers Communications, driving up the stock price. It's a-maize-ing how a little corn can butter up the stock market!. Keep the description simple.
ChatGPT: In a vibrant Indiana cornfield, rows of genetically modified corn stretch into the horizon, showing the increased GMO use. The sun shines down on the bountiful harvest, symbolizing the profits for the farmers and a kernel of hope for the economy. Meanwhile, in the background, a stock price chart for Rogers Communications (RCI) is displayed, with the line trending upwards, signifying the surge in stock price due to investor interest.

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: [ Pop art ] and depicting this scene: In a vibrant Indiana cornfield, rows of genetically modified corn stretch into the horizon, showing the increased GMO use. The sun shines down on the bountiful harvest, symbolizing the profits for the farmers and a kernel of hope for the economy. Meanwhile, in the background, a stock price chart for Rogers Communications (RCI) is displayed, with the line trending upwards, signifying the surge in stock price due to investor interest. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a vibrant Indiana cornfield, where rows of genetically modified corn stretch into the horizon. The sun shines down on the bountiful harvest, symbolizing profits for the farmers and a kernel of hope for the economy. In the background, include a stock price chart for Rogers Communications (RCI) trending upwards, signifying the surge in stock price due to investor interest. The entire scene should be depicted in a pop art style, with bold and vibrant colors, and strong graphic elements to convey the message of increased GMO use and economic significance.

*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.

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 GMO use in corn grown in Indiana caused Rogers Communications' stock price (RCI) to increase.

AI academic paper

(Because p < 0.01)
Cross-Pollinating Stock Market Trends: The Corny Connection Between GMO Usage in Indiana and RCI Stock Price

The Journal of Agricultural Finance and Genetic Economics

Jan 2024

Reminder: This paper is AI-generated. Not real!
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 researcher writing an academic paper.You write titles and abstracts of goofy academic research papers.
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, and add unexpected twists in your writing.

Please draft the title and abstract of an academic research paper presenting the findings of the connection between GMO use in corn grown in Indiana and Rogers Communications' stock price (RCI). Make lots of jokes, goofy observations, and puns.

Make the title punny if you can come up with clever pun. If not, make it sound serious.

Your research team used data from USDA and LSEG Analytics (Refinitiv) to assess this nagging question. You found a correlation coefficient of 0.9258997 and p < 0.01 for 2003 to 2023.

Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)

[[TITLE]]
Lorem ipsum dolor sit

[[ABSTRACT]]
Lorem ipsum dolor sit



ChatGPT:
[[TITLE]]
"Cross-Pollinating Stock Market Trends: The Corny Connection Between GMO Usage in Indiana and RCI Stock Price"

[[ABSTRACT]]
This paper investigates the unlikely relationship between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the fluctuation of Rogers Communications Inc. (RCI) stock price. Utilizing data from the USDA and LSEG Analytics, our research team rigorously examined the correlations between these two seemingly disparate factors. Surprisingly, a robust correlation coefficient of 0.9258997 and p < 0.01 was established for the period spanning from 2003 to 2023. Our findings not only shed light on the corny impact of GMOs on stock market trends but also underscore the importance of considering unexpected linkages in financial analysis. Get ready to embrace the kernel of truth in this unconventional, kernel-based research endeavor!


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the introduction 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 introduction section of an academic research paper presenting the findings of the connection between GMO use in corn grown in Indiana and Rogers Communications' stock price (RCI). Make lots of jokes, goofy observations, and puns.

Here is the title and abstract of the paper:
[[TITLE]]
"Cross-Pollinating Stock Market Trends: The Corny Connection Between GMO Usage in Indiana and RCI Stock Price"

[[ABSTRACT]]
This paper investigates the unlikely relationship between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the fluctuation of Rogers Communications Inc. (RCI) stock price. Utilizing data from the USDA and LSEG Analytics, our research team rigorously examined the correlations between these two seemingly disparate factors. Surprisingly, a robust correlation coefficient of 0.9258997 and p < 0.01 was established for the period spanning from 2003 to 2023. Our findings not only shed light on the corny impact of GMOs on stock market trends but also underscore the importance of considering unexpected linkages in financial analysis. Get ready to embrace the kernel of truth in this unconventional, kernel-based research endeavor!

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
INTRODUCTION

As the famous saying goes, "when life gives you corn, make stock market predictions." Okay, maybe that's not exactly how the saying goes, but our research has found a surprisingly strong connection between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the stock price of Rogers Communications Inc. (RCI).

Now, you might be wondering what on earth GMO corn in the Midwest has to do with a Canadian telecommunications company's stock. Trust us, we were scratching our heads too, but the numbers don't lie (unlike some of those market analysts, are we right?). Our investigation delves into this uncharted territory, where the stalks of corn and the stocks on Wall Street intersect in a way that would make even the most seasoned financial analyst do a double take.

But before we jump into the cornfield, let's take a step back and ponder the sheer absurdity – er, we mean, the intricacies – of the financial world. Every day, investors sift through a veritable cornucopia of data (see what we did there?) in search of insights that could make or break their portfolio. Traditional factors like interest rates, GDP growth, and company earnings often take center stage, while corn – well, let's just say it usually doesn't make the cut. But as our research will reveal, sometimes the most unexpected variables can kernel – sorry, we meant "kettle" – significant market movements.

By embracing this unconventional approach, our study aims to elevate the status of GMOs from just mere corn crops to potential influencers of financial markets. We hope to demonstrate that even in the seemingly unrelated domains of agriculture and telecommunications, there may be unseen threads connecting them – threads that, when pulled, could unravel new insights for investors, researchers, and anyone else who's curious enough to ponder the wild, wonderful world of market dynamics.

So grab a figurative cob of curiosity, and let's venture into this unconventional, kernel-based research journey together. But be forewarned – you may never look at a cornfield the same way again.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 GMO use in corn grown in Indiana and Rogers Communications' stock price (RCI). Make lots of 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]]
"Cross-Pollinating Stock Market Trends: The Corny Connection Between GMO Usage in Indiana and RCI Stock Price"

[[ABSTRACT]]
This paper investigates the unlikely relationship between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the fluctuation of Rogers Communications Inc. (RCI) stock price. Utilizing data from the USDA and LSEG Analytics, our research team rigorously examined the correlations between these two seemingly disparate factors. Surprisingly, a robust correlation coefficient of 0.9258997 and p < 0.01 was established for the period spanning from 2003 to 2023. Our findings not only shed light on the corny impact of GMOs on stock market trends but also underscore the importance of considering unexpected linkages in financial analysis. Get ready to embrace the kernel of truth in this unconventional, kernel-based research endeavor!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
As the famous saying goes, "when life gives you corn, make stock market predictions." Okay, maybe that's not exactly how the saying goes, but our research has found a surprisingly strong connection between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the stock price of Rogers Communications Inc. (RCI).
Now, you might be wondering what on earth GMO corn in the Midwest has to do with a Canadian telecommunications company's stock. Trust us, we were scratching our heads too, but the numbers don't lie (unlike some of those market analysts, are we right?). Our investigation delves into this uncharted territory, where the stalks of corn and the stocks on Wall Street intersect in a way that would make even the most seasoned financial analyst do a double take.
But before we jump into the cornfield, let's take a step back and ponder the sheer absurdity – er, we mean, the intricacies – of the financial world. Every day, investors sift through a veritable cornucopia of data (see what we did there?) in search of insights that could make or break their portfolio. Traditional factors like interest rates, GDP growth, and company earnings often take center stage, while corn – well, let's just say it usually doesn't make the cut. But as our research will reveal, sometimes the most unexpected variables can kernel – sorry, we meant "kettle" – significant market movements.
By embracing this unconventional approach, our study aims to elevate the status of GMOs from just mere corn crops to potential influencers of financial markets. We hope to demonstrate that even in the seemingly unrelated domains of agriculture and telecommunications, there may be unseen threads connecting them – threads that, when pulled, could unravel new insights for investors, researchers, and anyone else who's curious enough to ponder the wild, wonderful world of market dynamics.
So grab a figurative cob of curiosity, and let's venture into this unconventional, kernel-based research journey together. But be forewarned – you may never look at a cornfield the same way again.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The connection between genetically modified organisms (GMOs) in corn cultivation and stock market trends sounds about as strange as a cow trying to trade stocks, but the research literature surprisingly offers some insight into this unlikely relationship. Smith and Doe (2015) explored the impact of GMOs in agriculture on financial markets, and while their findings were eye-opening, they couldn't quite "corn-vince" us that there was a direct connection to specific stock prices. Jones (2018) took a different approach, focusing on the broader implications of GMO usage, but we think they may have missed the kernel – sorry, "neural" – connection to stock market dynamics.

But wait, before you start picturing corn stalks doing the hustle on Wall Street, let's take a detour into some related literature. In "The GMO Deception" by Jeffrey M. Smith, the authors delve into the controversies surrounding GMOs, but unfortunately, they don't touch upon their potential to influence stock prices. On the fictional side, "Corn y Corners: Tales from the Midwest" by Agatha Cornchristie sounds like it could offer some suspenseful twists and turns, but we doubt it contains any stock market secrets hidden among the cornfields.

Let's not forget the delightful world of board games, where titles like "Cornopoly" and "Stocks and Stalks" might lead one to believe there's some connection between corn and finance, but we suspect those games are more about having some corny fun than providing serious financial insights.

It seems that the literature falls short of directly addressing the corny connection we're investigating, but fear not – our research aims to unearth the hidden truths in the maize of financial data. Get ready for a-maize-ing discoveries ahead!


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 GMO use in corn grown in Indiana and Rogers Communications' stock price (RCI). Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from USDA and LSEG Analytics (Refinitiv) . You used data from 2003 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]]
"Cross-Pollinating Stock Market Trends: The Corny Connection Between GMO Usage in Indiana and RCI Stock Price"

[[ABSTRACT]]
This paper investigates the unlikely relationship between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the fluctuation of Rogers Communications Inc. (RCI) stock price. Utilizing data from the USDA and LSEG Analytics, our research team rigorously examined the correlations between these two seemingly disparate factors. Surprisingly, a robust correlation coefficient of 0.9258997 and p < 0.01 was established for the period spanning from 2003 to 2023. Our findings not only shed light on the corny impact of GMOs on stock market trends but also underscore the importance of considering unexpected linkages in financial analysis. Get ready to embrace the kernel of truth in this unconventional, kernel-based research endeavor!

[[INTRODUCTION]]
INTRODUCTION
As the famous saying goes, "when life gives you corn, make stock market predictions." Okay, maybe that's not exactly how the saying goes, but our research has found a surprisingly strong connection between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the stock price of Rogers Communications Inc. (RCI).
Now, you might be wondering what on earth GMO corn in the Midwest has to do with a Canadian telecommunications company's stock. Trust us, we were scratching our heads too, but the numbers don't lie (unlike some of those market analysts, are we right?). Our investigation delves into this uncharted territory, where the stalks of corn and the stocks on Wall Street intersect in a way that would make even the most seasoned financial analyst do a double take.
But before we jump into the cornfield, let's take a step back and ponder the sheer absurdity – er, we mean, the intricacies – of the financial world. Every day, investors sift through a veritable cornucopia of data (see what we did there?) in search of insights that could make or break their portfolio. Traditional factors like interest rates, GDP growth, and company earnings often take center stage, while corn – well, let's just say it usually doesn't make the cut. But as our research will reveal, sometimes the most unexpected variables can kernel – sorry, we meant "kettle" – significant market movements.
By embracing this unconventional approach, our study aims to elevate the status of GMOs from just mere corn crops to potential influencers of financial markets. We hope to demonstrate that even in the seemingly unrelated domains of agriculture and telecommunications, there may be unseen threads connecting them – threads that, when pulled, could unravel new insights for investors, researchers, and anyone else who's curious enough to ponder the wild, wonderful world of market dynamics.
So grab a figurative cob of curiosity, and let's venture into this unconventional, kernel-based research journey together. But be forewarned – you may never look at a cornfield the same way again.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To undertake this unconventional yet corny research endeavor, we employed a robust methodology that involved a kaleidoscope of data collection, statistical analysis, and a sprinkle of humor to keep things lively.

First off, we scoured the vast expanse of the internet, trawling through virtual cornfields and financial databases alike. We primarily relied on data sourced from the United States Department of Agriculture (USDA) and the LSEG Analytics (Refinitiv) for our corn-related and stock market price data, respectively.

Our data collection spanned the years from 2003 to 2023, giving us a generous harvest (pun intended) of information to work with. We fancied ourselves as modern-day data farmers, planting the seeds of inquiry and reaping the statistical bounty of our labor.

Now, onto the crux of our statistical analysis. We jested, we mean, we tested the relationship between the prevalence of GMO usage in corn cultivation in Indiana and the stock price of Rogers Communications Inc. (RCI). To do this, we unleashed the formidable arsenal of correlation analysis, conjuring up Pearson's correlation coefficient to measure the degree of association between these two seemingly unrelated variables. And behold, what did we find? A correlation coefficient of 0.9258997, with a p-value less than 0.01, indicating a robust and statistically significant correlation. The connection between GMOs and RCI stock price was as clear as a crisp autumn day – or should we say, as clear as a stalk of corn standing resolute in the wind.

But we didn't stop there. To ensure the integrity of our findings, we further engaged in a time series analysis, constructing not a mere sandcastle but rather a towering edifice of graphs, charts, and statistical wizardry. We delved into the ebb and flow of GMO usage in Indiana cornfields and the undulating rollercoaster ride of RCI stock prices, seeking patterns and trends that might otherwise have remained hidden in the husk of conventional financial analysis.

In tandem with these quantitatively driven endeavors, we also engaged in qualitative research, conducting interviews with farmers, analysts, and the occasional financial clairvoyant (kidding) to glean insights into the real-world implications of our findings. Our qualitative exploration allowed us to peel back the layers of financial jargon and agricultural enchantment, revealing the genuine impact of GMO usage on corn growth and, amusingly enough, its surprising ripple effect on stock market dynamics.

In conclusion, our methodology spared no effort in plumbing the depths of this peculiar relationship between GMO-laden cornfields and RCI stock prices. We wielded statistical tools with finesse, channeled the wisdom of corn-growing communities, and perhaps even had a few good laughs along the way. The end result? A research endeavor that's as informative as it is, dare we say, corny.

Remember, dear readers, in the world of research, just like in a corn maze, the path may be convoluted, but the surprise at the end makes it all worthwhile. Or at least, that's what we like to tell ourselves.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the results section of an academic research paper. You speak in the past tense (you found...).
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 results section of an academic research paper presenting the findings of the connection between GMO use in corn grown in Indiana and Rogers Communications' stock price (RCI). Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from USDA and LSEG Analytics (Refinitiv) .

For the time period 2003 to 2023, you found a correlation 0.9258997, r-squared of 0.8572903, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
"Cross-Pollinating Stock Market Trends: The Corny Connection Between GMO Usage in Indiana and RCI Stock Price"

[[ABSTRACT]]
This paper investigates the unlikely relationship between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the fluctuation of Rogers Communications Inc. (RCI) stock price. Utilizing data from the USDA and LSEG Analytics, our research team rigorously examined the correlations between these two seemingly disparate factors. Surprisingly, a robust correlation coefficient of 0.9258997 and p < 0.01 was established for the period spanning from 2003 to 2023. Our findings not only shed light on the corny impact of GMOs on stock market trends but also underscore the importance of considering unexpected linkages in financial analysis. Get ready to embrace the kernel of truth in this unconventional, kernel-based research endeavor!

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
INTRODUCTION
As the famous saying goes, "when life gives you corn, make stock market predictions." Okay, maybe that's not exactly how the saying goes, but our research has found a surprisingly strong connection between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the stock price of Rogers Communications Inc. (RCI).
Now, you might be wondering what on earth GMO corn in the Midwest has to do with a Canadian telecommunications company's stock. Trust us, we were scratching our heads too, but the numbers don't lie (unlike some of those market analysts, are we right?). Our investigation delves into this uncharted territory, where the stalks of corn and the stocks on Wall Street intersect in a way that would make even the most seasoned financial analyst do a double take.
But before we jump into the cornfield, let's take a step back and ponder the sheer absurdity – er, we mean, the intricacies – of the financial world. Every day, investors sift through a veritable cornucopia of data (see what we did there?) in search of insights that could make or break their portfolio. Traditional factors like interest rates, GDP growth, and company earnings often take center stage, while corn – well, let's just say it usually doesn't make the cut. But as our research will reveal, sometimes the most unexpected variables can kernel – sorry, we meant "kettle" – significant market movements.
By embracing this unconventional approach, our study aims to elevate the status of GMOs from just mere corn crops to potential influencers of financial markets. We hope to demonstrate that even in the seemingly unrelated domains of agriculture and telecommunications, there may be unseen threads connecting them – threads that, when pulled, could unravel new insights for investors, researchers, and anyone else who's curious enough to ponder the wild, wonderful world of market dynamics.
So grab a figurative cob of curiosity, and let's venture into this unconventional, kernel-based research journey together. But be forewarned – you may never look at a cornfield the same way again.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The results of our analysis revealed a striking correlation between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the stock price of Rogers Communications Inc. (RCI). Our study, spanning from 2003 to 2023, uncovered a robust correlation coefficient of 0.9258997, an r-squared of 0.8572903, and a statistically significant p-value of less than 0.01.

We present our findings in Fig. 1, which depicts a scatterplot showcasing the strong relationship between the two variables. It's like they say, "corn there, done that" – but we couldn't have predicted this surprising connection without the power of data analysis.

So what does this all mean? Well, for one, it suggests that GMO corn may truly be "stalking" the stock market in ways we never anticipated. It's a-maize-ing to think that something as seemingly unrelated as corn cultivation practices could sow trends in the financial world. But that's the cornundrum we find ourselves in – sometimes the most unexpected variables can pop up in the most unexpected places.

Our results not only highlight the influence of GMO usage in corn production on stock market dynamics but also open new doors for research into unconventional factors impacting financial markets. Who would have thought that GMOs and stock prices could be like two peas in a pod – or should we say, two kernels on a cob?

In closing, our research serves as a reminder that in the ever-evolving landscape of financial analysis, we should always keep our eyes peeled for unexpected linkages, even when they seem as unlikely as a teleporting cornstalk. After all, as our findings demonstrate, even the most unexpected variables can "stalk" the stock market scene and kernel – oops, we meant "kindle" – new avenues of exploration.

Stay tuned for more exciting developments as we continue to peel back the layers of this perplexing, yet utterly fascinating, GMO-corn-and-stock-market saga. We promise, there's plenty more corny humor where that came from!


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 GMO use in corn grown in Indiana and Rogers Communications' stock price (RCI). Make lots of 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]]
"Cross-Pollinating Stock Market Trends: The Corny Connection Between GMO Usage in Indiana and RCI Stock Price"

[[ABSTRACT]]
This paper investigates the unlikely relationship between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the fluctuation of Rogers Communications Inc. (RCI) stock price. Utilizing data from the USDA and LSEG Analytics, our research team rigorously examined the correlations between these two seemingly disparate factors. Surprisingly, a robust correlation coefficient of 0.9258997 and p < 0.01 was established for the period spanning from 2003 to 2023. Our findings not only shed light on the corny impact of GMOs on stock market trends but also underscore the importance of considering unexpected linkages in financial analysis. Get ready to embrace the kernel of truth in this unconventional, kernel-based research endeavor!

[[LITERATURE REVIEW]]
The connection between genetically modified organisms (GMOs) in corn cultivation and stock market trends sounds about as strange as a cow trying to trade stocks, but the research literature surprisingly offers some insight into this unlikely relationship. Smith and Doe (2015) explored the impact of GMOs in agriculture on financial markets, and while their findings were eye-opening, they couldn't quite "corn-vince" us that there was a direct connection to specific stock prices. Jones (2018) took a different approach, focusing on the broader implications of GMO usage, but we think they may have missed the kernel – sorry, "neural" – connection to stock market dynamics.
But wait, before you start picturing corn stalks doing the hustle on Wall Street, let's take a detour into some related literature. In "The GMO Deception" by Jeffrey M. Smith, the authors delve into the controversies surrounding GMOs, but unfortunately, they don't touch upon their potential to influence stock prices. On the fictional side, "Corn y Corners: Tales from the Midwest" by Agatha Cornchristie sounds like it could offer some suspenseful twists and turns, but we doubt it contains any stock market secrets hidden among the cornfields.
Let's not forget the delightful world of board games, where titles like "Cornopoly" and "Stocks and Stalks" might lead one to believe there's some connection between corn and finance, but we suspect those games are more about having some corny fun than providing serious financial insights.
It seems that the literature falls short of directly addressing the corny connection we're investigating, but fear not – our research aims to unearth the hidden truths in the maize of financial data. Get ready for a-maize-ing discoveries ahead!

[[RESULTS]]
The results of our analysis revealed a striking correlation between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the stock price of Rogers Communications Inc. (RCI). Our study, spanning from 2003 to 2023, uncovered a robust correlation coefficient of 0.9258997, an r-squared of 0.8572903, and a statistically significant p-value of less than 0.01.
We present our findings in Fig. 1, which depicts a scatterplot showcasing the strong relationship between the two variables. It's like they say, "corn there, done that" – but we couldn't have predicted this surprising connection without the power of data analysis.
So what does this all mean? Well, for one, it suggests that GMO corn may truly be "stalking" the stock market in ways we never anticipated. It's a-maize-ing to think that something as seemingly unrelated as corn cultivation practices could sow trends in the financial world. But that's the cornundrum we find ourselves in – sometimes the most unexpected variables can pop up in the most unexpected places.
Our results not only highlight the influence of GMO usage in corn production on stock market dynamics but also open new doors for research into unconventional factors impacting financial markets. Who would have thought that GMOs and stock prices could be like two peas in a pod – or should we say, two kernels on a cob?
In closing, our research serves as a reminder that in the ever-evolving landscape of financial analysis, we should always keep our eyes peeled for unexpected linkages, even when they seem as unlikely as a teleporting cornstalk. After all, as our findings demonstrate, even the most unexpected variables can "stalk" the stock market scene and kernel – oops, we meant "kindle" – new avenues of exploration.
Stay tuned for more exciting developments as we continue to peel back the layers of this perplexing, yet utterly fascinating, GMO-corn-and-stock-market saga. We promise, there's plenty more corny humor where that came from!

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

Well, folks, it seems our research has unveiled a kernel of truth in the corn fields of financial analysis. As we whimsically waded through corny jokes and unexpected connections, our results corn-vincingly supported the prior literature's half-serious, half-teasing suggestions. Who would have thought that the GMO-laden cornfields of Indiana could have such a stalk-ing impact on RCI stock prices? It's like they say, "corn there, done that" – but we hadn't cornsidered the potential implications until now!

Our robust correlation coefficient and p-value that's less than 0.01 got us popping like popcorn in excitement. It's a-maize-ing to think that GMO usage in corn cultivation could husk a statistically significant relationship with stock prices. The 'ear'-resistible scatterplot in Fig. 1 showed a strong and un-"corn"-y association, leaving us kerned with questions and puns aplenty.

Jones (2018) may have missed the kernel – sorry, "neural" – connection, but our findings demonstrate that GMOs in cornfields aren't just 'cobbing' around when it comes to stock market dynamics. It's like these variables are locked in a game of "Stocks and Stalks" that board game enthusiasts have been playing all along, without even realizing it. And who would have thought "Cornopoly" was actually a stock market crash in disguise?

So, what's the takeaway from our corn-mazing discoveries? Well, first and foremost, it's a reminder that financial analysis can be as strange and unpredictable as a teleporting cornstalk. The maize of financial data frequently holds kernel - ahem, I meant, "kernels" - of truth where we least expect them. Who would have thought that GMOs and stock prices could be like two peas in a pod or, dare we say, two kernels on a cob?

As our research unfolds, we invite you to join us in peeling back the layers of this perplexing yet utterly fascinating GMO-corn-and-stock-market saga. Get ready for a-maize-ing developments ahead – we promise, there's plenty more corny humor where that came from! So, stay tuned for the next chapter of this stalk-ing revelation. Remember, in the financial world, sometimes the most unexpected variables can 'stalk' the stock market scene and "kernel" new avenues of exploration.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 GMO use in corn grown in Indiana and Rogers Communications' stock price (RCI). Make lots of 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]]
"Cross-Pollinating Stock Market Trends: The Corny Connection Between GMO Usage in Indiana and RCI Stock Price"

[[ABSTRACT]]
This paper investigates the unlikely relationship between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the fluctuation of Rogers Communications Inc. (RCI) stock price. Utilizing data from the USDA and LSEG Analytics, our research team rigorously examined the correlations between these two seemingly disparate factors. Surprisingly, a robust correlation coefficient of 0.9258997 and p < 0.01 was established for the period spanning from 2003 to 2023. Our findings not only shed light on the corny impact of GMOs on stock market trends but also underscore the importance of considering unexpected linkages in financial analysis. Get ready to embrace the kernel of truth in this unconventional, kernel-based research endeavor!

[[INTRDUCTION]]
INTRODUCTION
As the famous saying goes, "when life gives you corn, make stock market predictions." Okay, maybe that's not exactly how the saying goes, but our research has found a surprisingly strong connection between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the stock price of Rogers Communications Inc. (RCI).
Now, you might be wondering what on earth GMO corn in the Midwest has to do with a Canadian telecommunications company's stock. Trust us, we were scratching our heads too, but the numbers don't lie (unlike some of those market analysts, are we right?). Our investigation delves into this uncharted territory, where the stalks of corn and the stocks on Wall Street intersect in a way that would make even the most seasoned financial analyst do a double take.
But before we jump into the cornfield, let's take a step back and ponder the sheer absurdity – er, we mean, the intricacies – of the financial world. Every day, investors sift through a veritable cornucopia of data (see what we did there?) in search of insights that could make or break their portfolio. Traditional factors like interest rates, GDP growth, and company earnings often take center stage, while corn – well, let's just say it usually doesn't make the cut. But as our research will reveal, sometimes the most unexpected variables can kernel – sorry, we meant "kettle" – significant market movements.
By embracing this unconventional approach, our study aims to elevate the status of GMOs from just mere corn crops to potential influencers of financial markets. We hope to demonstrate that even in the seemingly unrelated domains of agriculture and telecommunications, there may be unseen threads connecting them – threads that, when pulled, could unravel new insights for investors, researchers, and anyone else who's curious enough to ponder the wild, wonderful world of market dynamics.
So grab a figurative cob of curiosity, and let's venture into this unconventional, kernel-based research journey together. But be forewarned – you may never look at a cornfield the same way again.

[[RESULTS]]
The results of our analysis revealed a striking correlation between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the stock price of Rogers Communications Inc. (RCI). Our study, spanning from 2003 to 2023, uncovered a robust correlation coefficient of 0.9258997, an r-squared of 0.8572903, and a statistically significant p-value of less than 0.01.
We present our findings in Fig. 1, which depicts a scatterplot showcasing the strong relationship between the two variables. It's like they say, "corn there, done that" – but we couldn't have predicted this surprising connection without the power of data analysis.
So what does this all mean? Well, for one, it suggests that GMO corn may truly be "stalking" the stock market in ways we never anticipated. It's a-maize-ing to think that something as seemingly unrelated as corn cultivation practices could sow trends in the financial world. But that's the cornundrum we find ourselves in – sometimes the most unexpected variables can pop up in the most unexpected places.
Our results not only highlight the influence of GMO usage in corn production on stock market dynamics but also open new doors for research into unconventional factors impacting financial markets. Who would have thought that GMOs and stock prices could be like two peas in a pod – or should we say, two kernels on a cob?
In closing, our research serves as a reminder that in the ever-evolving landscape of financial analysis, we should always keep our eyes peeled for unexpected linkages, even when they seem as unlikely as a teleporting cornstalk. After all, as our findings demonstrate, even the most unexpected variables can "stalk" the stock market scene and kernel – oops, we meant "kindle" – new avenues of exploration.
Stay tuned for more exciting developments as we continue to peel back the layers of this perplexing, yet utterly fascinating, GMO-corn-and-stock-market saga. We promise, there's plenty more corny humor where that came from!

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, the compelling correlation between GMO usage in Indiana corn cultivation and Rogers Communications Inc. (RCI) stock price has certainly left us corn-fused, to say the least. Who would have thought that the kernel of truth in financial markets could be found in a cornfield? Our findings have undoubtedly popped the lid on a-maize-ing new possibilities in the world of stock market analysis.

The significance of this study cannot be cob-verlooked. It acts as a stern reminder to always husk for unexpected connections in financial datasets. It's corny, but hey, that's the stalk market for you! As we bid adieu to this uncharted territory of GMO-corn-and-stock-market correlation, it's apparent that further research in this area might seem just as out of place as finding a kernel in a haystack. Therefore, in the spirit of full disclosure, we assert that no more research is needed in this area. But hey, if you do decide to hop on the GMO-stock correlation bandwagon, just remember – stalk responsibly!

And remember, we can learn a lot from corn. After all, it's an ear-resistible subject!


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 Cross-Pollinating Stock Market Trends: The Corny Connection Between GMO Usage in Indiana and RCI Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Agricultural Finance and Genetic Economics


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 Cross-Pollinating Stock Market Trends: The Corny Connection Between GMO Usage in Indiana and RCI Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Agricultural Economics and Financial Dynamics


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 Cross-Pollinating Stock Market Trends: The Corny Connection Between GMO Usage in Indiana and RCI Stock Price with an abstract of This paper investigates the unlikely relationship between the usage of genetically modified organisms (GMOs) in corn cultivation in Indiana and the fluctuation of Rogers Communications Inc. (RCI) stock price. Utilizing data from the USDA and LSEG Analytics, our research team rigorously examined the correlations between these two seemingly disparate factors. Surprisingly, a robust correlation coefficient of 0.9258997 and p < 0.01 was established for the period spanning from 2003 to 2023. Our findings not only shed light on the corny impact of GMOs on stock market trends but also underscore the importance of considering unexpected linkages in financial analysis. Get ready to embrace the kernel of truth in this unconventional, kernel-based research endeavor!

ChatGPT:

GMO usage in corn, Indiana, RCI stock price, genetically modified organisms, LSEG Analytics, USDA data, stock market trends, correlation coefficient, financial analysis, corn cultivation, Rogers Communications Inc., unexpected linkages, kernel-based research

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



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Data details

GMO use in corn grown in Indiana
Detailed data title: Percent of all corn planted in Indiana genetically modified with both Bt and HT
Source: USDA
See what else correlates with GMO use in corn grown in Indiana

Rogers Communications' stock price (RCI)
Detailed data title: Opening price of Rogers Communications (RCI) on the first trading day of the year
Source: LSEG Analytics (Refinitiv)
Additional Info: Via Microsoft Excel Stockhistory function

See what else correlates with Rogers Communications' stock price (RCI)

Correlation r = 0.9258997 (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 = 0.8572903 (Coefficient of determination)
This means 85.7% of the change in the one variable (i.e., Rogers Communications' stock price (RCI)) is predictable based on the change in the other (i.e., GMO use in corn grown in Indiana) over the 21 years from 2003 through 2023.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.8E-9. 0.0000000017956120459941368000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. 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 0.93 in 1.8E-7% of random cases. Said differently, if you correlated 556,913,172 random variables You don't actually need 556 million 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 20 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 20 because we have two variables measured over a period of 21 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.

[ 0.82, 0.97 ] 95% correlation confidence interval (using the Fisher z-transformation)
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: 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.
200320042005200620072008200920102011201220132014201520162017201820192020202120222023
GMO use in corn grown in Indiana (GMO corn %)124123055555656607378767575777674787978
Rogers Communications' stock price (RCI) (Stock price)4.78.3813.0821.2629.845.530.1531.3734.843946.2445.2538.713438.6650.8551.0449.8446.6747.6646.72




Why this works

  1. 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.
  2. Lack of causal connection: There is probably 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.
  3. 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 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. 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([1,2,4,12,30,55,55,56,56,60,73,78,76,75,75,77,76,74,78,79,78,])
array_2 = np.array([4.7,8.38,13.08,21.26,29.8,45.5,30.15,31.37,34.84,39,46.24,45.25,38.71,34,38.66,50.85,51.04,49.84,46.67,47.66,46.72,])
array_1_name = "GMO use in corn grown in Indiana"
array_2_name = "Rogers Communications' stock price (RCI)"

# 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)



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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."

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Correlation ID: 2804 · Black Variable ID: 740 · Red Variable ID: 1904
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