Spurious correlation #2,828 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Global Rice Consumption and the second variable is Capital One Financial's stock price (COF). The chart goes from 2009 to 2022, 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 Global Rice Consumption and the second variable is Capital One Financial's stock price (COF). The chart goes from 2009 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As global rice consumption rises, so does the demand for chopsticks. With more people using chopsticks, there is a higher chance of accidentally dropping coins. This inadvertently boosts the profitability of Capital One, leading to an increase in their stock price as more loose change finds its way into the banking system. It's a rice-fueled prosperity that's stirring up the stock market!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a vibrant global market with stalls filled with colorful rice-based dishes, as people joyously use bamboo chopsticks to savor their meals. In the distance, bamboo forests flourish, where workers diligently harvest the versatile plant. Additionally, subtly include Capital One Financial's stock price chart showing a positive trajectory, symbolizing the anticipation of the company's potential entry into the competitive bamboo banking market. 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: Global Rice Consumption and Capital One Financial's stock price (COF). 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 photo-realistic image Low poly a Polaroid photo Cybernetic art Realistic
ChatGPT: Realistic

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 Global Rice Consumption and Capital One Financial's stock price (COF), in the following situation: Because as rice consumption increased globally, so did the demand for chopsticks, causing a surge in the profitability of bamboo forests. With an increase in bamboo-related revenue, Capital One Financial's stock price saw a corresponding boost as investors anticipated a branch out into the competitive bamboo banking market.. Keep the description simple.
ChatGPT: A bustling global market with people enjoying rice-based dishes using chopsticks made from bamboo. In the background, bamboo forests thrive, with workers industriously harvesting bamboo. Capital One Financial's stock price chart shows an upward trend, reflecting investors' anticipation of the company's potential entry into the competitive bamboo banking market.

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: [ Realistic ] and depicting this scene: A bustling global market with people enjoying rice-based dishes using chopsticks made from bamboo. In the background, bamboo forests thrive, with workers industriously harvesting bamboo. Capital One Financial's stock price chart shows an upward trend, reflecting investors' anticipation of the company's potential entry into the competitive bamboo banking market. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a vibrant global market with stalls filled with colorful rice-based dishes, as people joyously use bamboo chopsticks to savor their meals. In the distance, bamboo forests flourish, where workers diligently harvest the versatile plant. Additionally, subtly include Capital One Financial's stock price chart showing a positive trajectory, symbolizing the anticipation of the company's potential entry into the competitive bamboo banking market.

*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 Global Rice Consumption caused Capital One Financial's stock price (COF) to increase.

AI academic paper

(Because p < 0.01)

A Grainy Situation: Exploring the Link between Global Rice Consumption and the Rise and Fall of Capital One Financial's Stock Price

The International Journal of Culinary Economics and Financial Analysis

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 dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.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 Global Rice Consumption and Capital One Financial's stock price (COF). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name a few real TV shows that sound like they might be relevant to the topic that you watched as research.

Here is the title and abstract of the paper:
[[TITLE]]
A Grainy Situation: Exploring the Link between Global Rice Consumption and the Rise and Fall of Capital One Financial's Stock Price

[[ABSTRACT]]
This paper delves into the uncharted territory of the relationship between global rice consumption and the fluctuation of Capital One Financial's stock price. Utilizing data from Statista and LSEG Analytics (Refinitiv), our research team meticulously examined the patterns from 2009 to 2022. With a striking correlation coefficient of 0.9540944 and p < 0.01, the findings uncover a compelling association between the two seemingly unrelated domains. The implications of our study not only extend to financial and agricultural sectors but also prompt further contemplation of the intricate interconnectedness of seemingly unrelated factors in the broader economic landscape. This warrants a more nuanced understanding of the grains that sustain us and the financial crops that bear the fruits of investment.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The surge of interest in the interconnectedness of seemingly disparate fields has led to the exploration of unique relationships that were previously overlooked or dismissed as purely coincidental. This study endeavors to shed light on the curious connection between global rice consumption and the stock price of Capital One Financial (COF). While on the surface, one might wonder what grains and financial gains have in common, our analysis aims to elucidate the potential associations that could impact investment strategies and agricultural policies alike.
The research team embarked on this endeavor with a sense of curiosity and skepticism, akin to the cautious anticipation of a rice cooker reaching its boiling point. As we delved into the voluminous datasets from Statista and LSEG Analytics (Refinitiv), we couldn't help but marvel at the parallel fluctuations in rice consumption and COF's stock price. It was as if the grains of rice and the financial grains were engaged in a subtle, synchronized dance, comparable to the artful maneuvers of a synchronized swimming team.
The striking correlation coefficient of 0.9540944 and the resoundingly significant p-value of less than 0.01 served as the proverbial breadcrumbs, leading us deeper into the labyrinth of this peculiar association. As we traversed through the intricate web of data, it became evident that this correlation was not a mere fluke or statistical quirk but rather a compelling pattern that demanded further scrutiny.
This investigation is not merely an exercise in statistical acrobatics but rather a sincere effort to unpack the underlying mechanisms that tie together global dietary habits and the performance of a financial institution. The implications of this inquiry extend beyond the realms of investment portfolios and agricultural trade, offering a glimpse into the nuanced tapestry of interdependence that governs the global economy.
As we embark on this scholarly expedition, let us adopt the watchful gaze of a paddy field farmer, attuned to the subtle rhythms of nature and finance alike, recognizing that the seeds of insight may sprout from the most unexpected soil.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

Previous research has demonstrated the complex and often unexpected interconnections between seemingly unrelated economic and agricultural phenomena. Smith et al. (2015) investigated the impact of global rice consumption on various financial indicators, noting a modest yet statistically significant correlation with stock market performance. Similarly, Doe (2018) conducted a comprehensive analysis of commodity prices and their influence on the valuation of financial assets, revealing intriguing patterns and relationships that defied conventional economic wisdom. Jones (2020) explored the psychological factors that underpin investment decisions, shedding light on the role of subconscious influences such as dietary habits in shaping investors' behavior.

Turning to non-fiction literature, "The Rice Economy of Asia" by David Dawe delves into the intricate dynamics of rice production and trade in the Asian continent, offering valuable insights into the global rice market. In a similar vein, "The Stock Market Explained" by Alvin Hall provides a comprehensive overview of the factors that drive stock prices and the often enigmatic forces that govern financial markets.

In the realm of fiction, "Rice" by Su Tong weaves a mesmerizing narrative that intertwines the lives of individuals in a rice-growing community, offering a vivid portrayal of the profound societal impact of rice cultivation. Likewise, "The Financial Lives of the Poets" by Jess Walter presents a satirical exploration of the tumultuous world of personal finance and economic uncertainty, resonating with the unpredictable fluctuations of stock prices.

On a more unconventional note, the TV series "Tales of the Grain Silo" and "Bankers in the Paddy Fields" have captured the imagination of viewers with their unconventional blend of agricultural drama and financial intrigue, providing a unique perspective on the intersection of rice cultivation and investment activities.

It is evident that a diverse array of literature and media has touched upon the themes relevant to our investigation, offering a rich tapestry of perspectives to inform our exploration of the relationship between global rice consumption and the stock price of Capital One Financial.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.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 Global Rice Consumption and Capital One Financial's stock price (COF). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Statista and LSEG Analytics (Refinitiv) . You used data from 2009 to 2022

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]]
A Grainy Situation: Exploring the Link between Global Rice Consumption and the Rise and Fall of Capital One Financial's Stock Price

[[ABSTRACT]]
This paper delves into the uncharted territory of the relationship between global rice consumption and the fluctuation of Capital One Financial's stock price. Utilizing data from Statista and LSEG Analytics (Refinitiv), our research team meticulously examined the patterns from 2009 to 2022. With a striking correlation coefficient of 0.9540944 and p < 0.01, the findings uncover a compelling association between the two seemingly unrelated domains. The implications of our study not only extend to financial and agricultural sectors but also prompt further contemplation of the intricate interconnectedness of seemingly unrelated factors in the broader economic landscape. This warrants a more nuanced understanding of the grains that sustain us and the financial crops that bear the fruits of investment.

[[INTRODUCTION]]
The surge of interest in the interconnectedness of seemingly disparate fields has led to the exploration of unique relationships that were previously overlooked or dismissed as purely coincidental. This study endeavors to shed light on the curious connection between global rice consumption and the stock price of Capital One Financial (COF). While on the surface, one might wonder what grains and financial gains have in common, our analysis aims to elucidate the potential associations that could impact investment strategies and agricultural policies alike.
The research team embarked on this endeavor with a sense of curiosity and skepticism, akin to the cautious anticipation of a rice cooker reaching its boiling point. As we delved into the voluminous datasets from Statista and LSEG Analytics (Refinitiv), we couldn't help but marvel at the parallel fluctuations in rice consumption and COF's stock price. It was as if the grains of rice and the financial grains were engaged in a subtle, synchronized dance, comparable to the artful maneuvers of a synchronized swimming team.
The striking correlation coefficient of 0.9540944 and the resoundingly significant p-value of less than 0.01 served as the proverbial breadcrumbs, leading us deeper into the labyrinth of this peculiar association. As we traversed through the intricate web of data, it became evident that this correlation was not a mere fluke or statistical quirk but rather a compelling pattern that demanded further scrutiny.
This investigation is not merely an exercise in statistical acrobatics but rather a sincere effort to unpack the underlying mechanisms that tie together global dietary habits and the performance of a financial institution. The implications of this inquiry extend beyond the realms of investment portfolios and agricultural trade, offering a glimpse into the nuanced tapestry of interdependence that governs the global economy.
As we embark on this scholarly expedition, let us adopt the watchful gaze of a paddy field farmer, attuned to the subtle rhythms of nature and finance alike, recognizing that the seeds of insight may sprout from the most unexpected soil.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

In this pursuit of unraveling the enigma between global rice consumption and the stock price of Capital One Financial (COF), the research team employed a multi-faceted methodology to capture the essence of this intricate relationship. The primary data sources harnessed for this quest were the expanse of Statista, which provided thorough insights into global rice consumption patterns, and LSEG Analytics (Refinitiv), which bestowed the precious stock price data of COF. The data encompassed the years 2009 to 2022, offering a panoramic view of the evolving dynamics.

To commence this investigative odyssey, the team levied the potent combination of statistical analysis and financial modeling techniques. The global rice consumption data underwent meticulous scrutiny, entailing a comprehensive exploration of regional consumption patterns, per capita utilization, and market trends. Concurrently, the stock price data of COF was subjected to rigorous time series analysis, embracing the ebb and flow of market sentiments, economic upheavals, and corporate milestones.

The correlation analysis emerged as the cornerstone of this pursuit, akin to an astute detective scrutinizing the subtle clues that bridge seemingly unrelated events. Through the employment of Pearson's correlation coefficient, the research team adeptly gauged the degree of association between global rice consumption and COF's stock price. This statistical maneuver furnished a quantitative expression of the relationship, transcending the vagaries of mere speculation. The significance of this correlation was further validated through hypothesis testing, with due regard for the presumptions and peculiarities of each dataset.

Furthermore, to tease out the nuances of this association, the research team ventured into the realm of econometric modeling, employing time series regressions and Granger causality tests. This approach sought to unravel not just the mere co-movement of variables but also the potential directional influences that could permeate between global rice consumption and COF's stock price. The model evaluation was underscored by robustness tests and sensitivity analyses, serving as sentinels against the perils of statistical flukes and spurious relationships.

In essence, the methodology adopted for this pursuit reflected the equipoise of analytical acumen and mathematical vigor, akin to a balletic performance where each step is imbued with purpose and precision. The aim was not merely to unearth a superficial correlation but rather to delve into the undercurrents that bind together the dietary predilections of nations and the fortunes of financial enterprises. The intricacies of this methodology lend credence to the veritable tapestry of this research endeavor, where empirical observations converge with theoretical musings to illuminate the hitherto obscure terrain of global rice consumption and financial vicissitudes.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.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 Global Rice Consumption and Capital One Financial's stock price (COF). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
A Grainy Situation: Exploring the Link between Global Rice Consumption and the Rise and Fall of Capital One Financial's Stock Price

[[ABSTRACT]]
This paper delves into the uncharted territory of the relationship between global rice consumption and the fluctuation of Capital One Financial's stock price. Utilizing data from Statista and LSEG Analytics (Refinitiv), our research team meticulously examined the patterns from 2009 to 2022. With a striking correlation coefficient of 0.9540944 and p < 0.01, the findings uncover a compelling association between the two seemingly unrelated domains. The implications of our study not only extend to financial and agricultural sectors but also prompt further contemplation of the intricate interconnectedness of seemingly unrelated factors in the broader economic landscape. This warrants a more nuanced understanding of the grains that sustain us and the financial crops that bear the fruits of investment.

[[LITERATURE REVIEW]]
Previous research has demonstrated the complex and often unexpected interconnections between seemingly unrelated economic and agricultural phenomena. Smith et al. (2015) investigated the impact of global rice consumption on various financial indicators, noting a modest yet statistically significant correlation with stock market performance. Similarly, Doe (2018) conducted a comprehensive analysis of commodity prices and their influence on the valuation of financial assets, revealing intriguing patterns and relationships that defied conventional economic wisdom. Jones (2020) explored the psychological factors that underpin investment decisions, shedding light on the role of subconscious influences such as dietary habits in shaping investors' behavior.
Turning to non-fiction literature, "The Rice Economy of Asia" by David Dawe delves into the intricate dynamics of rice production and trade in the Asian continent, offering valuable insights into the global rice market. In a similar vein, "The Stock Market Explained" by Alvin Hall provides a comprehensive overview of the factors that drive stock prices and the often enigmatic forces that govern financial markets.
In the realm of fiction, "Rice" by Su Tong weaves a mesmerizing narrative that intertwines the lives of individuals in a rice-growing community, offering a vivid portrayal of the profound societal impact of rice cultivation. Likewise, "The Financial Lives of the Poets" by Jess Walter presents a satirical exploration of the tumultuous world of personal finance and economic uncertainty, resonating with the unpredictable fluctuations of stock prices.
On a more unconventional note, the TV series "Tales of the Grain Silo" and "Bankers in the Paddy Fields" have captured the imagination of viewers with their unconventional blend of agricultural drama and financial intrigue, providing a unique perspective on the intersection of rice cultivation and investment activities.
It is evident that a diverse array of literature and media has touched upon the themes relevant to our investigation, offering a rich tapestry of perspectives to inform our exploration of the relationship between global rice consumption and the stock price of Capital One Financial.

[[RESULTS]]
The examination of the relationship between global rice consumption and the stock price of Capital One Financial (COF) from 2009 to 2022 revealed a remarkably high correlation coefficient of 0.9540944. This correlation indicated a strong positive linear relationship between the two variables. The coefficient of determination (r-squared) was calculated at 0.9102962, signifying that approximately 91.03% of the variability in COF's stock price could be explained by changes in global rice consumption. This suggests that the fluctuations in global rice consumption may harbor substantial predictive power in understanding the movements of COF's stock price. Furthermore, the p-value of less than 0.01 underscored the statistical significance of the observed relationship, lending credence to the notion that this association is beyond mere chance.
The scatterplot in Fig. 1 visually encapsulates this robust correlation, showcasing the synchronized ebb and flow of global rice consumption and COF's stock price. The data points form a near-perfect linear pattern, akin to perfectly aligned rows of rice in a paddy field, demonstrating the remarkable coherence between these seemingly distinct domains.
This study marks a departure from conventional analyses of financial markets, inviting a foray into the unexplored terrain of agricultural influence on stock performance. The findings underscore the need for a multidisciplinary perspective that transcends traditional boundaries, hinting at the intricate interconnections that underpin the global economy. In essence, this research illuminates the fertile ground for future inquiry into the entwined destinies of our dietary staples and financial investments.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The compelling correlation coefficient and high level of statistical significance observed in our study affirm the surprisingly strong association between global rice consumption and the stock price of Capital One Financial (COF). These findings echo the modest yet statistically significant correlations identified in previous research by Smith et al. (2015) and Doe (2018), aligning with their conclusion that there may be underlying links between agricultural consumption patterns and financial indicators. While this association may at first seem as improbable as a rice paddy in a financial district, our results lend weight to the notion that the seemingly disparate worlds of rice cultivation and stock trading are indeed entwined.

Drawing wisdom from the non-fiction literature on the rice economy by David Dawe, we can appreciate the intricate dynamics of global rice markets, which have historically played a pivotal role in shaping economic landscapes. The broader implications of our study align with the themes depicted in "Rice" by Su Tong, as it vividly portrays the societal impact of rice cultivation – a resonance with the unexpected impact of global rice consumption on the valuation of financial assets. Moreover, "The Financial Lives of the Poets" by Jess Walter, despite its satirical nature, offers a poignant illustration of the volatile nature of financial markets and the interconnectedness of personal finance and broader economic forces. These literary parallels underscore the significance of our findings and echo the notion that the "rice factor" resonates far beyond the agricultural domain.

It is noteworthy that our study, despite its lighthearted literary associations, has uncovered an unexpectedly substantial predictive power of global rice consumption in understanding the movements of COF's stock price. The robustness of this relationship, as represented by the near-perfect linear pattern akin to the aligned rows of rice in a paddy field, is not to be taken lightly. While the humor in the associations drawn may seem as unexpected as a comic strip about agricultural commodities, the statistical strength of our findings demands a serious contemplation of the interplay between global dietary habits and financial market dynamics.

In essence, our study decrypts the cryptic language of rice and stock prices, unveiling the fertile ground for a more nuanced understanding of the intricate interconnectedness of seemingly unrelated factors in the broader economic landscape. It beckons the academic community to cultivate a multifaceted perspective that transcends traditional boundaries, hinting at the surprising symbiosis between the grains that sustain us and the financial crops that bear the fruits of investment. Thus, the seeds sown in this study herald a bountiful harvest of future inquiries into the unexplored terrain of agricultural influence on stock performance, promising to yield a rich tapestry of insights that will nourish our understanding of the interconnected destinies of dietary staples and financial investments.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.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 Global Rice Consumption and Capital One Financial's stock price (COF). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
A Grainy Situation: Exploring the Link between Global Rice Consumption and the Rise and Fall of Capital One Financial's Stock Price

[[ABSTRACT]]
This paper delves into the uncharted territory of the relationship between global rice consumption and the fluctuation of Capital One Financial's stock price. Utilizing data from Statista and LSEG Analytics (Refinitiv), our research team meticulously examined the patterns from 2009 to 2022. With a striking correlation coefficient of 0.9540944 and p < 0.01, the findings uncover a compelling association between the two seemingly unrelated domains. The implications of our study not only extend to financial and agricultural sectors but also prompt further contemplation of the intricate interconnectedness of seemingly unrelated factors in the broader economic landscape. This warrants a more nuanced understanding of the grains that sustain us and the financial crops that bear the fruits of investment.

[[INTRDUCTION]]
The surge of interest in the interconnectedness of seemingly disparate fields has led to the exploration of unique relationships that were previously overlooked or dismissed as purely coincidental. This study endeavors to shed light on the curious connection between global rice consumption and the stock price of Capital One Financial (COF). While on the surface, one might wonder what grains and financial gains have in common, our analysis aims to elucidate the potential associations that could impact investment strategies and agricultural policies alike.
The research team embarked on this endeavor with a sense of curiosity and skepticism, akin to the cautious anticipation of a rice cooker reaching its boiling point. As we delved into the voluminous datasets from Statista and LSEG Analytics (Refinitiv), we couldn't help but marvel at the parallel fluctuations in rice consumption and COF's stock price. It was as if the grains of rice and the financial grains were engaged in a subtle, synchronized dance, comparable to the artful maneuvers of a synchronized swimming team.
The striking correlation coefficient of 0.9540944 and the resoundingly significant p-value of less than 0.01 served as the proverbial breadcrumbs, leading us deeper into the labyrinth of this peculiar association. As we traversed through the intricate web of data, it became evident that this correlation was not a mere fluke or statistical quirk but rather a compelling pattern that demanded further scrutiny.
This investigation is not merely an exercise in statistical acrobatics but rather a sincere effort to unpack the underlying mechanisms that tie together global dietary habits and the performance of a financial institution. The implications of this inquiry extend beyond the realms of investment portfolios and agricultural trade, offering a glimpse into the nuanced tapestry of interdependence that governs the global economy.
As we embark on this scholarly expedition, let us adopt the watchful gaze of a paddy field farmer, attuned to the subtle rhythms of nature and finance alike, recognizing that the seeds of insight may sprout from the most unexpected soil.

[[RESULTS]]
The examination of the relationship between global rice consumption and the stock price of Capital One Financial (COF) from 2009 to 2022 revealed a remarkably high correlation coefficient of 0.9540944. This correlation indicated a strong positive linear relationship between the two variables. The coefficient of determination (r-squared) was calculated at 0.9102962, signifying that approximately 91.03% of the variability in COF's stock price could be explained by changes in global rice consumption. This suggests that the fluctuations in global rice consumption may harbor substantial predictive power in understanding the movements of COF's stock price. Furthermore, the p-value of less than 0.01 underscored the statistical significance of the observed relationship, lending credence to the notion that this association is beyond mere chance.
The scatterplot in Fig. 1 visually encapsulates this robust correlation, showcasing the synchronized ebb and flow of global rice consumption and COF's stock price. The data points form a near-perfect linear pattern, akin to perfectly aligned rows of rice in a paddy field, demonstrating the remarkable coherence between these seemingly distinct domains.
This study marks a departure from conventional analyses of financial markets, inviting a foray into the unexplored terrain of agricultural influence on stock performance. The findings underscore the need for a multidisciplinary perspective that transcends traditional boundaries, hinting at the intricate interconnections that underpin the global economy. In essence, this research illuminates the fertile ground for future inquiry into the entwined destinies of our dietary staples and financial investments.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, the findings of this study illuminate a compelling association between global rice consumption and the stock price of Capital One Financial (COF), akin to the harmonious symphony of a well-orchestrated meal and financial prosperity. The striking correlation coefficient and the resoundingly significant p-value serve as a potent reminder that the ebbs and flows of global dietary habits hold sway over the undulating tides of financial markets. This correlation, like a stealthy ninja, has managed to evade the watchful eyes of conventional analyses, emerging as a potent force that demands acknowledgment and further exploration.

The near-perfect linear pattern observed in the scatterplot, akin to a meticulously arranged sushi platter, encapsulates the synchronous undulations of rice consumption and COF's stock price. This visual manifestation of the robust correlation serves as a poignant reminder that beneath the veneer of apparent disparity, lies an intricate network of interconnectedness that merits closer examination, much like the subtle flavors that intertwine in a culinary masterpiece.

These findings prompt a reevaluation of investment strategies and agricultural policies, underscoring the need for a more holistic approach that transcends the siloed perspectives of traditional analyses, much like the synergy of flavors in a well-prepared risotto. This study not only broadens our understanding of the complex interplay between dietary habits and financial performance but also serves as a reminder that the roots of economic forces delve deeper than we might initially perceive.

Therefore, it is with a hearty serving of whimsy and a dash of statistical rigor that we assert that no further research in this area is needed - this study has provided ample food for thought, and it's time to let these findings simmer on the back burner of academic inquiry.

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 A Grainy Situation: Exploring the Link between Global Rice Consumption and the Rise and Fall of Capital One Financial's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The International Journal of Culinary Economics and Financial Analysis

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 A Grainy Situation: Exploring the Link between Global Rice Consumption and the Rise and Fall of Capital One Financial's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Association for Global Agrarian Studies and Financial Analysis (AGASFA)

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 A Grainy Situation: Exploring the Link between Global Rice Consumption and the Rise and Fall of Capital One Financial's Stock Price with an abstract of This paper delves into the uncharted territory of the relationship between global rice consumption and the fluctuation of Capital One Financial's stock price. Utilizing data from Statista and LSEG Analytics (Refinitiv), our research team meticulously examined the patterns from 2009 to 2022. With a striking correlation coefficient of 0.9540944 and p < 0.01, the findings uncover a compelling association between the two seemingly unrelated domains. The implications of our study not only extend to financial and agricultural sectors but also prompt further contemplation of the intricate interconnectedness of seemingly unrelated factors in the broader economic landscape. This warrants a more nuanced understanding of the grains that sustain us and the financial crops that bear the fruits of investment.

ChatGPT:

global rice consumption, rice consumption trends, capital one financial stock price, stock price fluctuation, stock market analysis, stock price correlation, correlation coefficient, financial market trends, agricultural sector, economic interconnectedness, global rice market, financial data analysis, rice consumption impact on stock market

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

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Data details

Global Rice Consumption
Source: Statista
See what else correlates with Global Rice Consumption

Capital One Financial's stock price (COF)
Detailed data title: Opening price of Capital One Financial (COF) 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 Capital One Financial's stock price (COF)

Correlation r = 0.9540944 (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 = 0.9102962 (Coefficient of determination)
This means 91% of the change in the one variable (i.e., Capital One Financial's stock price (COF)) is predictable based on the change in the other (i.e., Global Rice Consumption) over the 14 years from 2009 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.2E-7.^Show 0.0000001223412375904207800000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

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

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.95 in 1.2E-5% of random cases. Said differently, if you correlated 8,173,859 random variables^Note You don't actually need 8 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 13 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 13 because we have two variables measured over a period of 14 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.

[ 0.86, 0.99 ] 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.

	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Global Rice Consumption (Thousand Metric Tons)	437179	438338	445084	460825	462623	473878	474845	468882	483817	481678	485064	493953	503437	519895
Capital One Financial's stock price (COF) (Stock price)	31.8	38.66	43.01	43.39	59.53	76.61	82.79	70.77	88.55	100.08	74.49	103.41	99.73	146.08

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.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

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([437179,438338,445084,460825,462623,473878,474845,468882,483817,481678,485064,493953,503437,519895,])
array_2 = np.array([31.8,38.66,43.01,43.39,59.53,76.61,82.79,70.77,88.55,100.08,74.49,103.41,99.73,146.08,])
array_1_name = "Global Rice Consumption"
array_2_name = "Capital One Financial's stock price (COF)"

# 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 Global Rice Consumption
Line chart for only Capital One Financial's stock price (COF)
AI-generated correlation image
The spurious research paper: A Grainy Situation: Exploring the Link between Global Rice Consumption and the Rise and Fall of Capital One Financial's Stock Price

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Correlation ID: 2828 · Black Variable ID: 437 · Red Variable ID: 1785


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