Spurious correlation #3,598 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Annual US household spending on processed fruits and the second variable is Sony Group's stock price (SONY). The chart goes from 2002 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 Annual US household spending on processed fruits and the second variable is Sony Group's stock price (SONY). The chart goes from 2002 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As household spending on processed fruits increased, more people started using blenders to make fruit smoothies. This led to a surge in demand for high-quality blenders, and Sony, known for its innovative technology, secretly entered the blender market with their state-of-the-art SmoothieSonic™ blenders. The unexpected success of these blenders boosted investor confidence in Sony's diverse product capabilities, leading to an increase in their stock price. Remember, a rising stock price isn't the only thing that's smoothie with Sony!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a futuristic grocery store where a family is happily gathering bags of neon-colored processed fruits into their cart. The fruits are displayed in an eye-catching holographic manner, giving the scene a futuristic and high-tech feel. In the next image, depict the family at home, their living room adorned with sleek, modern furniture. They are seen enjoying their snacks while sitting in front of a transparent, futuristic TV screen displaying the Sony Group's stock price (SONY) rising dynamically. The futuristic ambiance of the scene should evoke a sense of technological advancement and modernity. 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: Annual US household spending on processed fruits and Sony Group's stock price (SONY). 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: Symbolism Realistic An image that looks like it is a scene from A Disney Movie Futuristic An image that looks like it is a scene from A Disney Movie
ChatGPT: Futuristic

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 Annual US household spending on processed fruits and Sony Group's stock price (SONY), in the following situation: Because as people spent more on processed fruits, they also needed more ways to entertain themselves while waiting for their digestive systems to process said fruits. So, they turned to Sony's products for some quality entertainment, boosting the demand for Sony products and consequently their stock price.. Keep the description simple.
ChatGPT: A family is seen at a grocery store, loading up their shopping cart with bags of processed fruits. They are smiling and chatting as they pick out their favorite fruits. In the next scene, the family is back at home enjoying their fruits while watching a Sony TV that's displaying the Sony Group's stock price (SONY) on the screen. The stock price is shown to be increasing alongside their enjoyment.

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: [ Futuristic ] and depicting this scene: A family is seen at a grocery store, loading up their shopping cart with bags of processed fruits. They are smiling and chatting as they pick out their favorite fruits. In the next scene, the family is back at home enjoying their fruits while watching a Sony TV that's displaying the Sony Group's stock price (SONY) on the screen. The stock price is shown to be increasing alongside their enjoyment. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a futuristic grocery store where a family is happily gathering bags of neon-colored processed fruits into their cart. The fruits are displayed in an eye-catching holographic manner, giving the scene a futuristic and high-tech feel. In the next image, depict the family at home, their living room adorned with sleek, modern furniture. They are seen enjoying their snacks while sitting in front of a transparent, futuristic TV screen displaying the Sony Group's stock price (SONY) rising dynamically. The futuristic ambiance of the scene should evoke a sense of technological advancement and modernity.

*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 Annual US household spending on processed fruits caused Sony Group's stock price (SONY) to increase.

AI academic paper

(Because p < 0.01)

The Big Apple's Influence: How US Household Spending on Processed Fruits Can A-peel to Sony's Stock Price

The Journal of Financial Fruit Studies

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 Annual US household spending on processed fruits and Sony Group's stock price (SONY). 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 some cartoons and childrens' shows that you watched that are related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
The Big Apple's Influence: How US Household Spending on Processed Fruits Can A-peel to Sony's Stock Price

[[ABSTRACT]]
This paper examines the correlation between annual US household spending on processed fruits and the stock price of Sony Group (SONY) from 2002 to 2022. By utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), a statistically significant correlation coefficient of 0.8581693 and p < 0.01 was identified. The findings suggest a robust connection, implying that the financial seeds of Sony's success may be partially planted in the processed fruits purchased by households across the United States. Furthermore, the results hint at the potential profit potential in the fruit market, offering a fruitful opportunity for investors to diversify their portfolios. Overall, this research adds a unique flavor to the understanding of economic indicators and encourages further exploration into the unexpected relationships that may exist between consumer spending habits and stock performance in the market.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Understanding the intricate web of factors that influence stock prices is a pursuit as old as markets themselves. From traditional financial indicators to the oft-overlooked consumer spending habits, researchers constantly seek to unearth the hidden connections that sway the whims of the market. And, as the saying goes, "an apple a day keeps the doctor away," but can it also keep the stock market bears at bay?
For many years, scholars and analysts have delved into the depths of consumer behavior, seeking to discern the underlying currents that drive economic movements. Our research seeks to contribute to this endeavor by exploring the connection between a seemingly innocuous consumer expense – household spending on processed fruits – and the stock price of Sony Group (SONY). We aim to peel back the layers of market influence and reveal the potential impact of fruit spending on a tech and entertainment giant.
The sheer variety of factors that may influence stock prices is vast, ranging from macroeconomic indicators to company-specific events. However, the potential impact of household consumption patterns on stock performance is a particularly intriguing area of study. In the case of processed fruits, the market dynamics reveal a juicy intersection between consumer preferences and corporate performance.
As we delve into this uncharted territory, we aim to demonstrate the potential significance of household consumption patterns, even those as seemingly trivial as processed fruit expenditures, on the stock price movements of a major international corporation. The findings from this study may not only shed light on the curious relationship between fruit consumption and stock prices, but also hold implications for investors looking to diversify their portfolios with a "fruitful" twist.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The existing body of literature offers multifaceted insights into the relationship between consumer behavior and stock price movements, providing a solid foundation for the present study's investigation into the connection between annual US household spending on processed fruits and the stock price of Sony Group (SONY).

In "Smith et al.'s Analysis of Consumer Expenditure Patterns," the authors find that consumer spending habits have a profound impact on market dynamics. While the study primarily focuses on broader categories of consumer expenditures, it underscores the intricate interplay between household consumption patterns and economic indicators. Similarly, Doe's research on "Consumer Preferences and Market Volatility" highlights the nuanced nature of consumer behavior and its potential influence on stock market trends, cultivating a fertile ground for exploring the impact of specific consumer purchases on corporate performance.

Jones et al., in "Economic Drivers of Stock Price Movements," delve into the intricate web of factors that shape stock price movements. While the study primarily elucidates macroeconomic indicators and firm-specific events, it hints at the possibility of unearthing unexpected determinants of stock performance. These studies lay the groundwork for delving into the seemingly unrelated realm of processed fruit expenditures and their potential impact on a tech and entertainment conglomerate's stock price.

Moreover, books such as "The Omnivore's Dilemma" by Michael Pollan and "Eating Animals" by Jonathan Safran Foer shed light on consumer choices related to food consumption, offering a broader context for understanding the significance of processed fruit spending in the context of household budgets and market dynamics. These literary works underscore the complexity of consumer behavior and the interconnectedness of consumer choices with larger societal and economic trends.

In a similar vein, fictional works such as "The Apple Orchard" by Susan Wiggs and "Fruit of the Drunken Tree" by Ingrid Rojas Contreras bring a creative dimension to the exploration of fruits and their symbolic resonance, providing an imaginative backdrop for contemplating the potential influence of processed fruit expenditures on stock prices.

Drawing inspiration from animated series and children's shows, classics such as "The Magic School Bus" and "The Berenstain Bears" subtly introduce young audiences to concepts of economics and consumer behavior, fostering an early appreciation for the intricate relationship between consumer choices and market dynamics. These seemingly unrelated sources offer unique perspectives that, when combined with empirical research, enrich the understanding of the interplay between consumer spending on processed fruits and stock price movements.

Overall, the existing literature provides a springboard for delving into the unexpected and even whimsical connections that may exist between consumer spending habits and stock performance, offering a refreshing perspective on the often-serious world of economic indicators and market influences.

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 Annual US household spending on processed fruits and Sony Group's stock price (SONY). 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]]
The Big Apple's Influence: How US Household Spending on Processed Fruits Can A-peel to Sony's Stock Price

[[ABSTRACT]]
This paper examines the correlation between annual US household spending on processed fruits and the stock price of Sony Group (SONY) from 2002 to 2022. By utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), a statistically significant correlation coefficient of 0.8581693 and p < 0.01 was identified. The findings suggest a robust connection, implying that the financial seeds of Sony's success may be partially planted in the processed fruits purchased by households across the United States. Furthermore, the results hint at the potential profit potential in the fruit market, offering a fruitful opportunity for investors to diversify their portfolios. Overall, this research adds a unique flavor to the understanding of economic indicators and encourages further exploration into the unexpected relationships that may exist between consumer spending habits and stock performance in the market.

[[LITERATURE REVIEW]]
The existing body of literature offers multifaceted insights into the relationship between consumer behavior and stock price movements, providing a solid foundation for the present study's investigation into the connection between annual US household spending on processed fruits and the stock price of Sony Group (SONY).
In "Smith et al.'s Analysis of Consumer Expenditure Patterns," the authors find that consumer spending habits have a profound impact on market dynamics. While the study primarily focuses on broader categories of consumer expenditures, it underscores the intricate interplay between household consumption patterns and economic indicators. Similarly, Doe's research on "Consumer Preferences and Market Volatility" highlights the nuanced nature of consumer behavior and its potential influence on stock market trends, cultivating a fertile ground for exploring the impact of specific consumer purchases on corporate performance.
Jones et al., in "Economic Drivers of Stock Price Movements," delve into the intricate web of factors that shape stock price movements. While the study primarily elucidates macroeconomic indicators and firm-specific events, it hints at the possibility of unearthing unexpected determinants of stock performance. These studies lay the groundwork for delving into the seemingly unrelated realm of processed fruit expenditures and their potential impact on a tech and entertainment conglomerate's stock price.
Moreover, books such as "The Omnivore's Dilemma" by Michael Pollan and "Eating Animals" by Jonathan Safran Foer shed light on consumer choices related to food consumption, offering a broader context for understanding the significance of processed fruit spending in the context of household budgets and market dynamics. These literary works underscore the complexity of consumer behavior and the interconnectedness of consumer choices with larger societal and economic trends.
In a similar vein, fictional works such as "The Apple Orchard" by Susan Wiggs and "Fruit of the Drunken Tree" by Ingrid Rojas Contreras bring a creative dimension to the exploration of fruits and their symbolic resonance, providing an imaginative backdrop for contemplating the potential influence of processed fruit expenditures on stock prices.
Drawing inspiration from animated series and children's shows, classics such as "The Magic School Bus" and "The Berenstain Bears" subtly introduce young audiences to concepts of economics and consumer behavior, fostering an early appreciation for the intricate relationship between consumer choices and market dynamics. These seemingly unrelated sources offer unique perspectives that, when combined with empirical research, enrich the understanding of the interplay between consumer spending on processed fruits and stock price movements.
Overall, the existing literature provides a springboard for delving into the unexpected and even whimsical connections that may exist between consumer spending habits and stock performance, offering a refreshing perspective on the often-serious world of economic indicators and market influences.

[[RESULTS]]
The investigation into the relationship between annual US household spending on processed fruits and Sony Group's stock price (SONY) from 2002 to 2022 yielded a correlation coefficient of 0.8581693, with an r-squared value of 0.7364546 and a p-value less than 0.01, signifying a strong and statistically significant association between the two variables. In other words, the data suggest that as American households sank their teeth into processed fruits, Sony's stock price seemed to take a bite out of the market as well.
The Figure 1 scatterplot visually encapsulates this correlation, showcasing the robust connection between US processed fruit spending and Sony's stock price. The plot presents a compelling visual representation, illustrating the fruitful impact of processed fruit spending on the financial vine of Sony's stock price.
The notable correlation coefficient reflects a substantial link between these seemingly disparate economic elements, implying that the financial roots of Sony's success may extend beyond the tech and entertainment sphere and into the realm of fruity finance. This unanticipated connection invites a reevaluation of the traditional indicators that influence stock performance, offering a fresh perspective on the interplay between consumer habits and market movements.
These findings hint at a potentially fruitful avenue for investors to diversify their portfolios, demonstrating the unexpected influence of processed fruit consumption on the stock performance of a major international corporation. The implications of this connection could provide investors with a juicy opportunity to expand their market portfolios with a delectable twist.
In conclusion, this research unearths a compelling association between US household spending on processed fruits and Sony Group's stock price, offering a unique insight into the interplay of consumer habits and stock performance. These findings challenge conventional economic logic and pique curiosity about the hidden influences shaping the market, urging researchers and investors alike to savor the intricacies of consumer spending patterns and their impact on stock dynamics.
Overall, our study enriches the understanding of economic indicators and encourages further exploration into the unexpected relationships that may exist between consumer spending habits and stock performance, urging analysts to peel back the layers of market influence and explore the potential impact of consumer behavior on stock market movements.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The robust correlation between annual US household spending on processed fruits and Sony Group's stock price (SONY) unearthed in our investigation reinforces the notion that seemingly disparate economic factors may intermingle in unexpected ways, adding a unique twist to the traditional understanding of market dynamics. Our findings align with extant research, echoing the comprehensive analysis of consumer expenditure patterns by Smith et al. as well as the nuanced exploration of consumer preferences and market volatility by Doe. These studies foreshadowed the possibility of unearthing unexpected determinants of stock performance, a proposition that has now borne fruit, so to speak, in our own research.

Furthermore, our results echo the sentiments conveyed in literary works such as "The Apple Orchard" by Susan Wiggs and "Fruit of the Drunken Tree" by Ingrid Rojas Contreras, which poetically elevate the significance of fruit-related symbolism and its potential impact on larger socioeconomic trends. The metaphorical resonance of these literary works resonates with the tangible connection between processed fruit spending and Sony's stock price, underscoring the unanticipated influence of consumer behavior on market movements.

It is noteworthy that our investigation supports the credibility of seemingly whimsical sources, such as animated series and children's shows, in shedding light on economic concepts. These sources, while often dismissed as lighthearted entertainment, unwittingly introduce young audiences to the complexities of consumer behavior and market dynamics, forging a hidden link between seemingly unrelated domains. Parallel to this unforeseen connection, our findings invite a reevaluation of the conventional indicators that dictate stock performance, urging analysts to embrace a more nuanced and eclectic approach to understanding market influences.

In essence, our research serves as a reminder of the multifaceted nature of economic interactions, demonstrating that the financial seeds of Sony's success may not only be sown in the realm of technology and entertainment but also in the aisles of grocery stores where processed fruits await savvy consumers. As such, this investigation accentuates the need for a broader recognition of the interplay between consumer habits and stock movements, offering a refreshing perspective on the often-serious world of economic indicators and market influences.

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 Annual US household spending on processed fruits and Sony Group's stock price (SONY). 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]]
The Big Apple's Influence: How US Household Spending on Processed Fruits Can A-peel to Sony's Stock Price

[[ABSTRACT]]
This paper examines the correlation between annual US household spending on processed fruits and the stock price of Sony Group (SONY) from 2002 to 2022. By utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), a statistically significant correlation coefficient of 0.8581693 and p < 0.01 was identified. The findings suggest a robust connection, implying that the financial seeds of Sony's success may be partially planted in the processed fruits purchased by households across the United States. Furthermore, the results hint at the potential profit potential in the fruit market, offering a fruitful opportunity for investors to diversify their portfolios. Overall, this research adds a unique flavor to the understanding of economic indicators and encourages further exploration into the unexpected relationships that may exist between consumer spending habits and stock performance in the market.

[[INTRDUCTION]]
Understanding the intricate web of factors that influence stock prices is a pursuit as old as markets themselves. From traditional financial indicators to the oft-overlooked consumer spending habits, researchers constantly seek to unearth the hidden connections that sway the whims of the market. And, as the saying goes, "an apple a day keeps the doctor away," but can it also keep the stock market bears at bay?
For many years, scholars and analysts have delved into the depths of consumer behavior, seeking to discern the underlying currents that drive economic movements. Our research seeks to contribute to this endeavor by exploring the connection between a seemingly innocuous consumer expense – household spending on processed fruits – and the stock price of Sony Group (SONY). We aim to peel back the layers of market influence and reveal the potential impact of fruit spending on a tech and entertainment giant.
The sheer variety of factors that may influence stock prices is vast, ranging from macroeconomic indicators to company-specific events. However, the potential impact of household consumption patterns on stock performance is a particularly intriguing area of study. In the case of processed fruits, the market dynamics reveal a juicy intersection between consumer preferences and corporate performance.
As we delve into this uncharted territory, we aim to demonstrate the potential significance of household consumption patterns, even those as seemingly trivial as processed fruit expenditures, on the stock price movements of a major international corporation. The findings from this study may not only shed light on the curious relationship between fruit consumption and stock prices, but also hold implications for investors looking to diversify their portfolios with a "fruitful" twist.

[[RESULTS]]
The investigation into the relationship between annual US household spending on processed fruits and Sony Group's stock price (SONY) from 2002 to 2022 yielded a correlation coefficient of 0.8581693, with an r-squared value of 0.7364546 and a p-value less than 0.01, signifying a strong and statistically significant association between the two variables. In other words, the data suggest that as American households sank their teeth into processed fruits, Sony's stock price seemed to take a bite out of the market as well.
The Figure 1 scatterplot visually encapsulates this correlation, showcasing the robust connection between US processed fruit spending and Sony's stock price. The plot presents a compelling visual representation, illustrating the fruitful impact of processed fruit spending on the financial vine of Sony's stock price.
The notable correlation coefficient reflects a substantial link between these seemingly disparate economic elements, implying that the financial roots of Sony's success may extend beyond the tech and entertainment sphere and into the realm of fruity finance. This unanticipated connection invites a reevaluation of the traditional indicators that influence stock performance, offering a fresh perspective on the interplay between consumer habits and market movements.
These findings hint at a potentially fruitful avenue for investors to diversify their portfolios, demonstrating the unexpected influence of processed fruit consumption on the stock performance of a major international corporation. The implications of this connection could provide investors with a juicy opportunity to expand their market portfolios with a delectable twist.
In conclusion, this research unearths a compelling association between US household spending on processed fruits and Sony Group's stock price, offering a unique insight into the interplay of consumer habits and stock performance. These findings challenge conventional economic logic and pique curiosity about the hidden influences shaping the market, urging researchers and investors alike to savor the intricacies of consumer spending patterns and their impact on stock dynamics.
Overall, our study enriches the understanding of economic indicators and encourages further exploration into the unexpected relationships that may exist between consumer spending habits and stock performance, urging analysts to peel back the layers of market influence and explore the potential impact of consumer behavior on stock market movements.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

The findings of this study provide compelling evidence of a robust and statistically significant correlation between annual US household spending on processed fruits and Sony Group's stock price (SONY). The correlation coefficient of 0.8581693, with a corresponding r-squared value of 0.7364546 and a p-value of less than 0.01, underscores the firm connection between these seemingly disparate economic elements. It appears that as American households indulged in processed fruits, Sony's stock price reaped the benefits, demonstrating a symbiotic relationship that invites a closer examination of the multifaceted influences on stock performance.

The visual representation in Figure 1 vividly encapsulates this unexpected correlation, highlighting the undeniable impact of processed fruit spending on the financial trajectory of Sony's stock price. This persuasive illustration serves as a ripe reminder of the unanticipated connections that can emerge in the economic landscape, shedding light on the intriguing interplay between consumer habits and market movements.

The implications of this study extend beyond the financial realm, offering a fresh perspective on the quintessentially American pursuit of "apple pie" economics. Indeed, as households incorporate processed fruits into their consumption patterns, the market dynamics reveal a juicy intersection between consumer preferences and corporate performance, challenging traditional economic logic and prompting a reexamination of the conventional indicators that influence stock performance.

While our findings provide a fruitful opportunity for investors to diversify their portfolios with a delectable twist, it is important to temper these insights with a dash of caution. Market dynamics are complex and multifaceted, and while our study illuminates an unexpected connection, it is essential to consider the broader context in which stock prices fluctuate.

In conclusion, this research offers a unique flavor to the understanding of economic indicators, highlighting the potential impact of consumer spending habits on stock dynamics. The serendipitous discovery of this correlation urges researchers and investors alike to savor the intricacies of consumer behavior and their influence on market movements. With these insights in mind, further exploration into the unexpected relationships that may exist between consumer spending habits and stock performance appears to be fruitful, although it may also lead to a few sour grapes along the way.

It is our contention that no further research is needed in this area. The findings of this study are as juicy as they come, and it is unlikely that additional research will yield a more fruitful connection between processed fruit consumption and stock prices.

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 The Big Apple's Influence: How US Household Spending on Processed Fruits Can A-peel to Sony's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Financial Fruit Studies

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 The Big Apple's Influence: How US Household Spending on Processed Fruits Can A-peel to Sony's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Culinary Economics and Financial Analysis

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 The Big Apple's Influence: How US Household Spending on Processed Fruits Can A-peel to Sony's Stock Price with an abstract of This paper examines the correlation between annual US household spending on processed fruits and the stock price of Sony Group (SONY) from 2002 to 2022. By utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), a statistically significant correlation coefficient of 0.8581693 and p < 0.01 was identified. The findings suggest a robust connection, implying that the financial seeds of Sony's success may be partially planted in the processed fruits purchased by households across the United States. Furthermore, the results hint at the potential profit potential in the fruit market, offering a fruitful opportunity for investors to diversify their portfolios. Overall, this research adds a unique flavor to the understanding of economic indicators and encourages further exploration into the unexpected relationships that may exist between consumer spending habits and stock performance in the market.

ChatGPT:

US household spending, processed fruits, Sony stock price, correlation, Bureau of Labor Statistics, LSEG Analytics, Refinitiv, correlation coefficient, financial seeds, consumer spending habits, stock performance, economic indicators, investment opportunity, stock market, diversify portfolios

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

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Data details

Annual US household spending on processed fruits
Detailed data title: Average annual household spend on processed fruits
Source: Bureau of Labor Statistics
See what else correlates with Annual US household spending on processed fruits

Sony Group's stock price (SONY)
Detailed data title: Opening price of Sony Group (SONY) 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 Sony Group's stock price (SONY)

Correlation r = 0.8581693 (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 = 0.7364546 (Coefficient of determination)
This means 73.6% of the change in the one variable (i.e., Sony Group's stock price (SONY)) is predictable based on the change in the other (i.e., Annual US household spending on processed fruits) over the 21 years from 2002 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 6.5E-7.^Show 0.0000006520322147567350000000
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 0.86 in 6.5E-5% of random cases. Said differently, if you correlated 1,533,667 random variables^Note You don't actually need 1 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 20 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 20 because we have two variables measured over a period of 21 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.68, 0.94 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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

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

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

	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Annual US household spending on processed fruits (Household spend)	116	108	110	106	109	112	116	118	113	116	114	115	109	108	109	112	114	112	125	133	144
Sony Group's stock price (SONY) (Stock price)	45.35	41.42	34.89	38.96	41.25	42.9	54.3	21.8	29.52	36.17	18.28	11.46	17.33	20.47	24.45	28.1	45.44	47.57	68.3	101.5	126.4

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([116,108,110,106,109,112,116,118,113,116,114,115,109,108,109,112,114,112,125,133,144,])
array_2 = np.array([45.35,41.42,34.89,38.96,41.25,42.9,54.3,21.8,29.52,36.17,18.28,11.46,17.33,20.47,24.45,28.1,45.44,47.57,68.3,101.5,126.4,])
array_1_name = "Annual US household spending on processed fruits"
array_2_name = "Sony Group's stock price (SONY)"

# 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 Annual US household spending on processed fruits
Line chart for only Sony Group's stock price (SONY)
AI-generated correlation image
The spurious research paper: The Big Apple's Influence: How US Household Spending on Processed Fruits Can A-peel to Sony's Stock Price

View another random correlation

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Correlation ID: 3598 · Black Variable ID: 19899 · Red Variable ID: 1633


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