Spurious correlation #2,102 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Google searches for 'who is elon musk' and the second variable is Lam Research's stock price (LRCX). The chart goes from 2005 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 Google searches for 'who is elon musk' and the second variable is Lam Research's stock price (LRCX). The chart goes from 2005 to 2023, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As more people became perplexed about the enigma known as Elon Musk, they turned to the internet for answers. This surge in searches led to an unexpected spike in internet traffic, putting a strain on servers worldwide. To meet the increased demand for data processing, companies had to purchase more semiconductor equipment, including products made by Lam Research. The quest to understand the man behind the electric car and space travel revolution inadvertently sparked a chain reaction, boosting the demand for tech hardware and ultimately contributing to the rise in Lam Research's stock price. It seems that delving into the solar-powered, flamethrower-wielding, Mars-colonizing mind of Elon Musk had a shockingly positive impact on the semiconductor industry!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a person surrounded by a futuristic data cyber art setting. The person is sitting at a sleek desk, with a holographic computer screen displaying a Google search for "who is Elon Musk." Beside the computer, there is a floating hologram of a stock market chart, specifically Lam Research's stock price graph, illuminating the scene with glowing neon colors, reflecting the data cyber art style. The person is intently looking at the screen, and the atmosphere around them is teeming with a sense of digital information and connectivity. 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: Google searches for 'who is elon musk' and Lam Research's stock price. 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: Surrealism Woodblock painting Data cyber art Woodblock painting Oragami
ChatGPT: Data cyber 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 Google searches for 'who is elon musk' and Lam Research's stock price together. Keep the description simple.
ChatGPT: A person sitting at a desk with a computer screen showing a Google search for "who is Elon Musk" and a stock market chart displaying Lam Research's stock price graph.

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: [ Data cyber art ] and depicting this scene: A person sitting at a desk with a computer screen showing a Google search for "who is Elon Musk" and a stock market chart displaying Lam Research's stock price graph. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a person surrounded by a futuristic data cyber art setting. The person is sitting at a sleek desk, with a holographic computer screen displaying a Google search for "who is Elon Musk." Beside the computer, there is a floating hologram of a stock market chart, specifically Lam Research's stock price graph, illuminating the scene with glowing neon colors, reflecting the data cyber art style. The person is intently looking at the screen, and the atmosphere around them is teeming with a sense of digital information and connectivity.

*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 who is elon musk (Google searches for 'who is elon musk') caused Lam Research's stock price (LRCX) to increase. Make it feel plausible.

AI academic paper

(Because p < 0.01)

Elon Musk: The Google Effect on Stock Prices – A Shocking Connection or Just Electric Puns?

The Journal of Electrical Finance & Humor

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 Google searches for 'who is elon musk' and Lam Research's stock price (LRCX). 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. Then you might move on to cartoons and children's shows that you watched for research.

Here is the title and abstract of the paper:
[[TITLE]]
"Elon Musk: The Google Effect on Stock Prices – A Shocking Connection or Just Electric Puns?"

[[ABSTRACT]]
In this meticulously researched study, we explore the electrifying correlation between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX). Utilizing a novel approach of mixing financial analysis with internet search trends, our research team delved into the realm of Google Trends and LSEG Analytics (Refinitiv) to unravel this enigma. The connection uncovered was nothing short of shocking, with a correlation coefficient of 0.9970861 and the statistical significance of p < 0.01 throughout the period of 2005 to 2023. Our findings not only shed light on the mysterious influence of internet curiosity on stock market behavior, but also spark some enlightening observations that are sure to electrify the scholarly debates surrounding technology, finance, and the inquisitive nature of humanity. Join us in this electrifying journey as we reveal the power of a Google search and how it may just be the spark that ignites market fluctuations.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the realm of finance, where numbers reign supreme and stock prices fluctuate like a rollercoaster ride, it's not often that we find ourselves delving into the world of internet search trends to unravel the mysteries of market behavior. Yet, here we are, embarking on a journey that not only sounds like a setup for a punchline but also promises to shed light on the influence of Google searches on stock prices.
Elon Musk, the enigmatic figure behind SpaceX, Tesla, and enough futuristic ideas to fill a sci-fi anthology, has captivated the public's curiosity like a magician pulling rabbits out of hats. While some may argue that his buccaneering spirit and eccentric tweets are the reasons for his status as a pop culture icon, others have scrutinized the connection between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX) with more vigor than a detective investigating a high-profile case.
Now, you might be wondering, "What on earth does the Google search for 'who is Elon Musk' have to do with Lam Research's stock price?" It's a valid question, and one that has intrigued us as much as it has tickled our funny bones. But fear not, dear reader, for we are about to explore this electrifying correlation with the same level of seriousness one might expect from a stand-up comedian at an academic conference.
An empirical investigation of this connection has the potential to unveil valuable insights, not only about the ever-mysterious behavior of the stock market, but also about the whimsical tendencies of internet users. In the following pages, we aim to elucidate the shocking relationship between a mere Google search and the tangled web of stock price movements. So, buckle up, put on your thinking cap, and get ready for a ride that’s sure to be more illuminating than a Tesla showroom.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In the pursuit of understanding the link between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX), our research team embarked on a quest to uncover relevant literature that could shed light on this unexpected correlation. We began with a thorough review of scholarly articles that traverse the realms of finance, technology, and search engine analytics.

Smith et al. (2018) conducted a comprehensive study on the impact of internet search trends on stock market performance, delving into the intricate relationship between online user behavior and financial markets. Their findings highlighted the potential influence of internet curiosity on stock prices, laying the groundwork for our investigation into the specific case of Elon Musk and Lam Research.

Doe and Jones (2020) explored the influence of public figures on stock market dynamics, emphasizing the role of media attention and public interest in shaping market sentiment. Their research provided valuable insights into the interconnectedness of celebrity fascination and stock price movements, setting the stage for our examination of Elon Musk's enigmatic persona and its potential impact on Lam Research's stock performance.

Venturing beyond the traditional confines of academic literature, we expanded our exploration to encompass non-fiction works that could offer unconventional perspectives on the intersection of technology, finance, and public curiosity. "Elon Musk: Tesla, SpaceX, and the Quest for a Fantastic Future" by Ashlee Vance (2015) provided valuable biographical insights into Musk's captivating influence on public imagination, inspiring us to delve deeper into the ramifications of his larger-than-life persona on stock market dynamics.

In the realm of fiction, where imagination meets reality in whimsical ways, we turned our attention to novels that, although not grounded in empirical analysis, offered imaginative interpretations of the interplay between celebrity intrigue and market fluctuations. "The Big Short" by Michael Lewis (2010) offered a satirical glimpse into the world of high-stakes finance, reminding us that sometimes truth is indeed stranger than fiction, especially when it comes to the intriguing dance of stock prices and public fascination.

To infuse our research endeavor with a dose of creativity and unorthodox inspiration, we even sought insights from unexpected sources, including cartoons and children's shows that, although seemingly unrelated to our scholarly pursuits, sparked novel perspectives on the curious confluence of technology, celebrity, and financial markets. After all, who better to offer whimsical wisdom than the beloved characters from "SpongeBob SquarePants" and "Phineas and Ferb"? Their playful take on exploration and innovation served as a lighthearted reminder that insight often comes from the most unexpected places.

In synthesizing these diverse sources, we aim to present a literature review that not only captures the breadth of existing knowledge but also infuses our scholarly investigation with a touch of unpredictability and humor. By drawing on a wide array of sources, both serious and whimsical, we endeavor to illuminate the electrifying connection between a seemingly innocuous Google search and the captivating dance of stock prices, inviting readers to join us in this exhilarating pursuit of knowledge and amusement.

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 Google searches for 'who is elon musk' and Lam Research's stock price (LRCX). Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from Google Trends and LSEG Analytics (Refinitiv) . You used data from 2005 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]]
"Elon Musk: The Google Effect on Stock Prices – A Shocking Connection or Just Electric Puns?"

[[ABSTRACT]]
In this meticulously researched study, we explore the electrifying correlation between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX). Utilizing a novel approach of mixing financial analysis with internet search trends, our research team delved into the realm of Google Trends and LSEG Analytics (Refinitiv) to unravel this enigma. The connection uncovered was nothing short of shocking, with a correlation coefficient of 0.9970861 and the statistical significance of p < 0.01 throughout the period of 2005 to 2023. Our findings not only shed light on the mysterious influence of internet curiosity on stock market behavior, but also spark some enlightening observations that are sure to electrify the scholarly debates surrounding technology, finance, and the inquisitive nature of humanity. Join us in this electrifying journey as we reveal the power of a Google search and how it may just be the spark that ignites market fluctuations.

[[INTRODUCTION]]
In the realm of finance, where numbers reign supreme and stock prices fluctuate like a rollercoaster ride, it's not often that we find ourselves delving into the world of internet search trends to unravel the mysteries of market behavior. Yet, here we are, embarking on a journey that not only sounds like a setup for a punchline but also promises to shed light on the influence of Google searches on stock prices.
Elon Musk, the enigmatic figure behind SpaceX, Tesla, and enough futuristic ideas to fill a sci-fi anthology, has captivated the public's curiosity like a magician pulling rabbits out of hats. While some may argue that his buccaneering spirit and eccentric tweets are the reasons for his status as a pop culture icon, others have scrutinized the connection between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX) with more vigor than a detective investigating a high-profile case.
Now, you might be wondering, "What on earth does the Google search for 'who is Elon Musk' have to do with Lam Research's stock price?" It's a valid question, and one that has intrigued us as much as it has tickled our funny bones. But fear not, dear reader, for we are about to explore this electrifying correlation with the same level of seriousness one might expect from a stand-up comedian at an academic conference.
An empirical investigation of this connection has the potential to unveil valuable insights, not only about the ever-mysterious behavior of the stock market, but also about the whimsical tendencies of internet users. In the following pages, we aim to elucidate the shocking relationship between a mere Google search and the tangled web of stock price movements. So, buckle up, put on your thinking cap, and get ready for a ride that’s sure to be more illuminating than a Tesla showroom.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Now, onto the serious – or perhaps not so serious – business of methodology. Our approach aimed to blend the rigor of financial analysis with the capricious nature of internet search trends, creating a scientific cocktail that would make even the most stoic of statisticians raise an eyebrow.

Data Collection:
Our research team scoured the vast digital landscape, venturing into the wilds of the internet to capture the elusive data we sought. Embracing the chaos of the online realm, we collected Google search trends for "who is Elon Musk" with the zealousness of caffeinated squirrels on a mission. The data from Google Trends provided us with a comprehensive view of users' curiosity about this paradigm-shifting figure, allowing us to measure the ebb and flow of interest in the man of the hour.

As for the stock price data, we turned to LSEG Analytics (Refinitiv) like treasure hunters seeking the fabled riches of empirical evidence. The stock prices of Lam Research (LRCX) served as our window into the tempest-tossed seas of market fluctuations, giving us a front-row seat to the tumultuous dance of supply and demand, speculation and analysis.

Time Frame:
To capture the full essence of this enigmatic relationship, we cast our net wide, spanning a temporal dimension from 2005 to 2023. This expansive timeline allowed us to witness the evolution of Google searches from the era when people were still asking Jeeves for answers to the present day, where Google has become the oracle of our digital age. It also encompassed the turbulence of market events, from the subprime mortgage crisis to the roaring triumphs of technological innovation, providing a comprehensive canvas for our analysis.

Data Analysis:
With data in hand and a cup of coffee nearby for sustenance, we dove into the labyrinth of statistical analysis. Our noble quest was to unearth the underlying patterns, correlations, and anomalies that might lay hidden beneath the surface of numbers and graphs. Using sophisticated statistical tools, we sought to unveil the elusive bond between the ebb and flow of Google searches and the undulating trajectory of stock prices.

Correlation Coefficient Calculation:
Utilizing the ancient arts of statistical calculation, we determined the correlation coefficient between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX). This numerical metric served as our compass, guiding us through the mists of uncertainty to reveal the strength and direction of the relationship between these seemingly disparate elements. Lo and behold, the correlation coefficient of 0.9970861 emerged like a bolt of lightning, illuminating the path forward with an intensity that sent shivers down our statistical spines.

Statistical Significance Testing:
To validate the robustness of our findings, we subjected the correlation to the crucible of statistical significance testing. With a level of significance set at p < 0.01, we sought to distinguish the signal from the noise, ensuring that the relationship we unearthed was not a mere mirage in the desert of data but a bona fide oasis of insight.

In conclusion, our methodology blended the precision of financial analysis with the whimsy of internet search trends, forging a path through the digital wilderness to shed light on the surprising connection between an inquisitive click and the electrifying dance of stock prices. With our data collection, analysis, and statistical sorcery in hand, we ventured forth to unveil the captivating tale of Elon Musk, Google searches, and the stock market.

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 Google searches for 'who is elon musk' and Lam Research's stock price (LRCX). 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]]
"Elon Musk: The Google Effect on Stock Prices – A Shocking Connection or Just Electric Puns?"

[[ABSTRACT]]
In this meticulously researched study, we explore the electrifying correlation between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX). Utilizing a novel approach of mixing financial analysis with internet search trends, our research team delved into the realm of Google Trends and LSEG Analytics (Refinitiv) to unravel this enigma. The connection uncovered was nothing short of shocking, with a correlation coefficient of 0.9970861 and the statistical significance of p < 0.01 throughout the period of 2005 to 2023. Our findings not only shed light on the mysterious influence of internet curiosity on stock market behavior, but also spark some enlightening observations that are sure to electrify the scholarly debates surrounding technology, finance, and the inquisitive nature of humanity. Join us in this electrifying journey as we reveal the power of a Google search and how it may just be the spark that ignites market fluctuations.

[[LITERATURE REVIEW]]
In the pursuit of understanding the link between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX), our research team embarked on a quest to uncover relevant literature that could shed light on this unexpected correlation. We began with a thorough review of scholarly articles that traverse the realms of finance, technology, and search engine analytics.
Smith et al. (2018) conducted a comprehensive study on the impact of internet search trends on stock market performance, delving into the intricate relationship between online user behavior and financial markets. Their findings highlighted the potential influence of internet curiosity on stock prices, laying the groundwork for our investigation into the specific case of Elon Musk and Lam Research.
Doe and Jones (2020) explored the influence of public figures on stock market dynamics, emphasizing the role of media attention and public interest in shaping market sentiment. Their research provided valuable insights into the interconnectedness of celebrity fascination and stock price movements, setting the stage for our examination of Elon Musk's enigmatic persona and its potential impact on Lam Research's stock performance.
Venturing beyond the traditional confines of academic literature, we expanded our exploration to encompass non-fiction works that could offer unconventional perspectives on the intersection of technology, finance, and public curiosity. "Elon Musk: Tesla, SpaceX, and the Quest for a Fantastic Future" by Ashlee Vance (2015) provided valuable biographical insights into Musk's captivating influence on public imagination, inspiring us to delve deeper into the ramifications of his larger-than-life persona on stock market dynamics.
In the realm of fiction, where imagination meets reality in whimsical ways, we turned our attention to novels that, although not grounded in empirical analysis, offered imaginative interpretations of the interplay between celebrity intrigue and market fluctuations. "The Big Short" by Michael Lewis (2010) offered a satirical glimpse into the world of high-stakes finance, reminding us that sometimes truth is indeed stranger than fiction, especially when it comes to the intriguing dance of stock prices and public fascination.
To infuse our research endeavor with a dose of creativity and unorthodox inspiration, we even sought insights from unexpected sources, including cartoons and children's shows that, although seemingly unrelated to our scholarly pursuits, sparked novel perspectives on the curious confluence of technology, celebrity, and financial markets. After all, who better to offer whimsical wisdom than the beloved characters from "SpongeBob SquarePants" and "Phineas and Ferb"? Their playful take on exploration and innovation served as a lighthearted reminder that insight often comes from the most unexpected places.
In synthesizing these diverse sources, we aim to present a literature review that not only captures the breadth of existing knowledge but also infuses our scholarly investigation with a touch of unpredictability and humor. By drawing on a wide array of sources, both serious and whimsical, we endeavor to illuminate the electrifying connection between a seemingly innocuous Google search and the captivating dance of stock prices, inviting readers to join us in this exhilarating pursuit of knowledge and amusement.

[[RESULTS]]
The results of our investigation into the connection between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX) yielded a shockingly high correlation coefficient of 0.9970861, with an r-squared of 0.9941807. The statistical significance was further emphasized by a p-value of less than 0.01, indicating a robust relationship that had us wondering if Elon Musk's Twitter account had secretly been broadcasting stock tips all along.
As if struck by lightning, the data revealed a remarkably strong positive correlation between the frequency of Google searches for "who is Elon Musk" and Lam Research's stock price movements. This relationship was so strikingly evident that it had us contemplating whether Elon Musk's cultural impact had transcended Twitter and Tesla to directly electrify the stock market.
To visually encapsulate this electrifying correlation, we present Figure 1, a scatterplot showing the tight clustering of data points, akin to the clustering of hopeful investors around news of Elon Musk's latest ventures. This scatterplot not only highlights the strength of the correlation but also serves as a reminder that sometimes, truth is indeed stranger than fiction.
The results reaffirm the shocking influence of internet curiosity on stock market behavior, leaving us pondering whether a Google search for "who is Elon Musk" could become the modern-day equivalent of flipping a coin to predict stock price movements. Our findings not only leave a profound impact on our understanding of market dynamics but also on the inherent quirkiness of the human quest for knowledge and its potential repercussions on the financial world.
In summary, the results of our investigation illuminate the captivating connection between Google searches for "who is Elon Musk" and the stock prices of Lam Research, serving as a reminder that in the realm of finance, as in life, sometimes the most unexpected factors can spark the most astonishing changes.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

Ah, the time has come to engage in a scholarly banter that may electrify the very essence of academic discourse – the discussion of our findings. The hair-raising correlation we uncovered between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX) is indeed a shocking revelation, one that may prompt us to reconsider the pun-derful nature of market influences.

Returning to the whimsical literature review, our findings unquestionably echo the insights of Smith et al. (2018). It's as if our study has turned their theoretical musings into a flesh-and-blood, or should I say, a "stock-and-searches" reality. Paralleling this notion, Doe and Jones (2020) would find our results quite electrifying, validating their emphasis on public figures' impact on market dynamics. Moreover, the imaginative perspectives we gained from Ashlee Vance (2015) and Michael Lewis (2010) now seem eerily prescient, evoking the notion that truth may indeed be stranger than fiction, especially when it involves a search engine's role in stock market furore.

In a twist that might have the stock market pundits scratching their heads, our findings support the notion that a simple "who is Elon Musk" query could hold the key to predicting Lam Research's stock performance with uncanny accuracy. It's almost as if Google searches have become the modern equivalent of reading tea leaves or consulting a fortune-teller – a notion that even the most astute of financial analysts might find shocking.

Figure 1's scatterplot, akin to a lightning bolt of insight, encapsulates the captivating relationship we uncovered – a force of nature that may leave investors thunderstruck. Our results provide compelling evidence of the profound influence of internet curiosity on stock market behavior, emphasizing the power of a Google search to jolt market dynamics.

In delving into this electrifying correlation, we have journeyed into the realm of unexpected connections, where the whimsical meets the scholarly in a delightful dance of discovery. Our investigation serves as a lighthearted reminder that in the world of finance, as in life, sometimes the most unconventional factors can spark the most astonishing changes.

And with that, we leave you with a figurative lightning bolt of knowledge, illuminating the curious connection between a seemingly innocuous Google search and the captivating dance of stock prices, serving as a reminder that in the scholarly pursuit of knowledge, a touch of whimsy can electrify the mind.

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 Google searches for 'who is elon musk' and Lam Research's stock price (LRCX). 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]]
"Elon Musk: The Google Effect on Stock Prices – A Shocking Connection or Just Electric Puns?"

[[ABSTRACT]]
In this meticulously researched study, we explore the electrifying correlation between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX). Utilizing a novel approach of mixing financial analysis with internet search trends, our research team delved into the realm of Google Trends and LSEG Analytics (Refinitiv) to unravel this enigma. The connection uncovered was nothing short of shocking, with a correlation coefficient of 0.9970861 and the statistical significance of p < 0.01 throughout the period of 2005 to 2023. Our findings not only shed light on the mysterious influence of internet curiosity on stock market behavior, but also spark some enlightening observations that are sure to electrify the scholarly debates surrounding technology, finance, and the inquisitive nature of humanity. Join us in this electrifying journey as we reveal the power of a Google search and how it may just be the spark that ignites market fluctuations.

[[INTRDUCTION]]
In the realm of finance, where numbers reign supreme and stock prices fluctuate like a rollercoaster ride, it's not often that we find ourselves delving into the world of internet search trends to unravel the mysteries of market behavior. Yet, here we are, embarking on a journey that not only sounds like a setup for a punchline but also promises to shed light on the influence of Google searches on stock prices.
Elon Musk, the enigmatic figure behind SpaceX, Tesla, and enough futuristic ideas to fill a sci-fi anthology, has captivated the public's curiosity like a magician pulling rabbits out of hats. While some may argue that his buccaneering spirit and eccentric tweets are the reasons for his status as a pop culture icon, others have scrutinized the connection between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX) with more vigor than a detective investigating a high-profile case.
Now, you might be wondering, "What on earth does the Google search for 'who is Elon Musk' have to do with Lam Research's stock price?" It's a valid question, and one that has intrigued us as much as it has tickled our funny bones. But fear not, dear reader, for we are about to explore this electrifying correlation with the same level of seriousness one might expect from a stand-up comedian at an academic conference.
An empirical investigation of this connection has the potential to unveil valuable insights, not only about the ever-mysterious behavior of the stock market, but also about the whimsical tendencies of internet users. In the following pages, we aim to elucidate the shocking relationship between a mere Google search and the tangled web of stock price movements. So, buckle up, put on your thinking cap, and get ready for a ride that’s sure to be more illuminating than a Tesla showroom.

[[RESULTS]]
The results of our investigation into the connection between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX) yielded a shockingly high correlation coefficient of 0.9970861, with an r-squared of 0.9941807. The statistical significance was further emphasized by a p-value of less than 0.01, indicating a robust relationship that had us wondering if Elon Musk's Twitter account had secretly been broadcasting stock tips all along.
As if struck by lightning, the data revealed a remarkably strong positive correlation between the frequency of Google searches for "who is Elon Musk" and Lam Research's stock price movements. This relationship was so strikingly evident that it had us contemplating whether Elon Musk's cultural impact had transcended Twitter and Tesla to directly electrify the stock market.
To visually encapsulate this electrifying correlation, we present Figure 1, a scatterplot showing the tight clustering of data points, akin to the clustering of hopeful investors around news of Elon Musk's latest ventures. This scatterplot not only highlights the strength of the correlation but also serves as a reminder that sometimes, truth is indeed stranger than fiction.
The results reaffirm the shocking influence of internet curiosity on stock market behavior, leaving us pondering whether a Google search for "who is Elon Musk" could become the modern-day equivalent of flipping a coin to predict stock price movements. Our findings not only leave a profound impact on our understanding of market dynamics but also on the inherent quirkiness of the human quest for knowledge and its potential repercussions on the financial world.
In summary, the results of our investigation illuminate the captivating connection between Google searches for "who is Elon Musk" and the stock prices of Lam Research, serving as a reminder that in the realm of finance, as in life, sometimes the most unexpected factors can spark the most astonishing changes.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our foray into the whimsical world of Google searches for "who is Elon Musk" has left us both enlightened and electrified. Our findings reveal a shockingly high correlation between these searches and the stock prices of Lam Research (LRCX), with a statistical significance that knocked our socks off. The strength of this connection is so astounding that we couldn't help but wonder if Elon Musk's aura is literally charging up the stock market like some kind of financial Pikachu.

Our investigation illuminates the enigmatic influence of internet curiosity on market behavior, highlighting the potential power of a simple Google search to send shockwaves through the stock exchange. With a correlation coefficient this high, we're starting to suspect that Elon Musk's influence extends beyond electric cars and into the very currents of the market.

As we wrap up this electrifying journey, it's clear that the connection between googling Elon Musk and stock prices is no mere coincidence. This correlation has the potential to spark new debates and challenges in both the finance and technology spheres. It’s as if the ghost of Nikola Tesla himself is whispering stock advice to those who seek knowledge of Elon Musk’s endeavors.

But as we close the book on this chapter of research, it's safe to say that no more investigations are needed in this area. We've shocked ourselves with the strength of this link, and any further exploration might just cause a short circuit in the academic community. It's time to unplug and acknowledge the electrifying results we've uncovered – for now, at least.

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 Elon Musk: The Google Effect on Stock Prices – A Shocking Connection or Just Electric Puns?. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Electrical Finance & Humor

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 Elon Musk: The Google Effect on Stock Prices – A Shocking Connection or Just Electric Puns?. Respond only with the name of a fictional organization.

ChatGPT:

National Institute for Tech Humor Studies

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 Elon Musk: The Google Effect on Stock Prices – A Shocking Connection or Just Electric Puns? with an abstract of In this meticulously researched study, we explore the electrifying correlation between Google searches for "who is Elon Musk" and the stock prices of Lam Research (LRCX). Utilizing a novel approach of mixing financial analysis with internet search trends, our research team delved into the realm of Google Trends and LSEG Analytics (Refinitiv) to unravel this enigma. The connection uncovered was nothing short of shocking, with a correlation coefficient of 0.9970861 and the statistical significance of p < 0.01 throughout the period of 2005 to 2023. Our findings not only shed light on the mysterious influence of internet curiosity on stock market behavior, but also spark some enlightening observations that are sure to electrify the scholarly debates surrounding technology, finance, and the inquisitive nature of humanity. Join us in this electrifying journey as we reveal the power of a Google search and how it may just be the spark that ignites market fluctuations.

ChatGPT:

Elon Musk, Google searches, stock prices, Lam Research, LRCX, correlation, Google Trends, LSEG Analytics, Refinitiv, financial analysis, internet search trends, correlation coefficient, statistical significance, market behavior, technology, finance, humanity, market fluctuations, scholarly debates, stock market influence, internet curiosity, market fluctuations, Google search impact

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

Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error

Data details

Google searches for 'who is elon musk'
Detailed data title: Relative volume of Google searches for 'who is elon musk' (Worldwide, without quotes)
Source: Google Trends
Additional Info: ^{See full details} Relative search volume (not absolute numbers)

See what else correlates with Google searches for 'who is elon musk'

Lam Research's stock price (LRCX)
Detailed data title: Opening price of Lam Research (LRCX) 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 Lam Research's stock price (LRCX)

Correlation r = 0.9970861 (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 = 0.9941807 (Coefficient of determination)
This means 99.4% of the change in the one variable (i.e., Lam Research's stock price (LRCX)) is predictable based on the change in the other (i.e., Google searches for 'who is elon musk') over the 19 years from 2005 through 2023.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.9E-20.^Show 0.0000000000000000000191805125
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 1 in 1.9E-18% of random cases. Said differently, if you correlated 52,136,250,269,642,162,176 random variables^Note You don't actually need 52 quintillion 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 18 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 18 because we have two variables measured over a period of 19 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.

[ 0.99, 1 ] 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.

	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
Google searches for 'who is elon musk' (Rel. search volume)	0.75	0.0833333	0.0833333	0.0909091	0.111111	0	0	0	0.75	1	1.58333	1.66667	3.66667	8.41667	6.08333	15.4167	26.3333	43	26.8889
Lam Research's stock price (LRCX) (Stock price)	28.7	36.09	50.95	42.96	21.47	39.7	51.69	37.72	37.02	54.04	79.87	77.77	106.68	185.15	133.46	295.78	478.15	721.13	429.72

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([0.75,0.0833333,0.0833333,0.0909091,0.111111,0,0,0,0.75,1,1.58333,1.66667,3.66667,8.41667,6.08333,15.4167,26.3333,43,26.8889,])
array_2 = np.array([28.7,36.09,50.95,42.96,21.47,39.7,51.69,37.72,37.02,54.04,79.87,77.77,106.68,185.15,133.46,295.78,478.15,721.13,429.72,])
array_1_name = "Google searches for 'who is elon musk'"
array_2_name = "Lam Research's stock price (LRCX)"

# 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 Google searches for 'who is elon musk'
Line chart for only Lam Research's stock price (LRCX)
AI-generated correlation image
The spurious research paper: Elon Musk: The Google Effect on Stock Prices – A Shocking Connection or Just Electric Puns?

View another random correlation

How fun was this correlation?

Kudos for rating!

Correlation ID: 2102 · Black Variable ID: 1487 · Red Variable ID: 1652


Problem variable:
Issue:
Additional details: Optional
Confirm you are a human: