Spurious correlation #3,530 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Solar power generated in Lebanon and the second variable is Boston Scientific's stock price (BSX). The chart goes from 2010 to 2021, 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 Solar power generated in Lebanon and the second variable is Boston Scientific's stock price (BSX). The chart goes from 2010 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The extra surge of solar energy somehow imbued the Boston Scientific facilities with an unexpected productivity boost, leading to cost savings and improved efficiency. As the solar power in Lebanon intensified, so did the company's ability to create innovative medical devices, ultimately shocking the stock market with their stellar performance. It's a shining example of how even a sunny disposition from afar can positively charge a corporation's bottom line!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image that showcases a mesmerizing parallax art scene set in Lebanon, where the radiant sun illuminates the landscape and solar panels are actively generating energy. Portray the joyous atmosphere as people bask in the uplifting sunlight, emanating positive vibes with beaming smiles. Simultaneously, illustrate the upward movement of the Boston Scientific (BSX) stock price on a graph or ticker symbol, symbolizing the company's surging sales and optimistic prospects attributed to the newfound abundance of solar power. 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: Solar power generated in Lebanon and Boston Scientific's stock price (BSX). 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: Sci-fi fantacy Scalar art Sci-fi fantacy Steampunk Parallax art
ChatGPT: Parallax art

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Solar power generated in Lebanon and Boston Scientific's stock price (BSX), in the following situation: Because the increase in solar power generated in Lebanon led to a surge in sunny dispositions, causing people to feel more positive about life, which made them more likely to buy medical devices. And who makes medical devices? Boston Scientific! So, when the sun shines in Lebanon, BSX stock rises! Sunny side up, baby! 🌞. Keep the description simple.
ChatGPT: In Lebanon, the sun is shining brightly and solar panels are generating a lot of energy. People are smiling and enjoying the positive vibes brought about by the sunny weather. Meanwhile, the stock price of Boston Scientific (BSX) is depicted as rising on a graph or ticker symbol, symbolizing the boost in sales and positive outlook due to the increase in solar power.

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: [ Parallax art ] and depicting this scene: In Lebanon, the sun is shining brightly and solar panels are generating a lot of energy. People are smiling and enjoying the positive vibes brought about by the sunny weather. Meanwhile, the stock price of Boston Scientific (BSX) is depicted as rising on a graph or ticker symbol, symbolizing the boost in sales and positive outlook due to the increase in solar power. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that showcases a mesmerizing parallax art scene set in Lebanon, where the radiant sun illuminates the landscape and solar panels are actively generating energy. Portray the joyous atmosphere as people bask in the uplifting sunlight, emanating positive vibes with beaming smiles. Simultaneously, illustrate the upward movement of the Boston Scientific (BSX) stock price on a graph or ticker symbol, symbolizing the company's surging sales and optimistic prospects attributed to the newfound abundance of solar power.

*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 Solar power generated in Lebanon caused Boston Scientific's stock price (BSX) to increase.

AI academic paper

(Because p < 0.01)

Shining Stocks: Solar Power's Surprising Influence on Boston Scientific's Stock Price

Journal of Renewable Energy Economics and Finance

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 Solar power generated in Lebanon and Boston Scientific's stock price (BSX). You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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 cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
Shining Stocks: Solar Power's Surprising Influence on Boston Scientific's Stock Price

[[ABSTRACT]]
This study investigates the curious relationship between solar power output in Lebanon and the stock price of Boston Scientific Corporation (BSX). Utilizing data from the Energy Information Administration and LSEG Analytics (Refinitiv), our research team examined the period spanning from 2010 to 2021. The statistical analysis revealed a remarkably high correlation coefficient of 0.9700195 and a p-value less than 0.01, indicating a strong and significant association between these seemingly unrelated variables.

In exploring this unexpected link, we posit the hypothesis that the sun's energy not only powers solar panels but also exerts an unseen force on the financial markets. This finding sheds light on the intricate interplay of environmental factors and stock prices, illuminating a previously overlooked dimension of market dynamics. It seems that the sun is not content with merely providing renewable energy; it also enjoys shining a spotlight on the stock market.

Furthermore, our study offers a new perspective on investment strategies, highlighting the potential impact of geographically distant environmental phenomena on stock performance. As Mark Twain once said, "Buy land, they're not making it anymore." Perhaps he should have added, "And keep an eye on solar power too."

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Solar power has increasingly gained attention as a sustainable and renewable energy source in many parts of the world. Its ability to harness the sun's abundant energy offers promise for reducing reliance on traditional fossil fuels and mitigating the effects of climate change. In addition to its environmental benefits, solar power has also been associated with financial incentives, such as tax credits and cost savings. Perhaps the sun truly is the ultimate "bright" spot in our quest for clean energy.
On the other end of the spectrum, the stock market has long been a focal point for investors and analysts seeking to understand the intricacies of asset valuation and market dynamics. The fluctuation of stock prices has been subject to extensive study, with factors ranging from company performance to macroeconomic indicators considered as potential drivers. Amidst this complexity, one might not expect to find a shining link between the rays of the sun and the fluctuation of a particular stock.
However, as they say, "Where there's a will, there's a ray." Our study delves into the unexpected connection between solar power generation in Lebanon and the stock price of Boston Scientific Corporation (BSX). While these two entities may appear as distant as the Earth is from the sun, our analysis has illuminated an astonishing correlation between them. It seems that the sun's influence extends beyond environmental sustainability and casts its glow on the financial sphere as well.
This unexpected alliance prompts the question: could the sun truly be a celestial influencer on stock markets? It seems that the power of the sun is not limited to solar panels alone; it may hold sway over the movements of stock prices thousands of miles away. This finding not only sheds light on the unexplored dynamics of market behavior but also adds a sunny perspective to the field of financial research. After all, who would have thought that the sun could have such a "solarizing" effect on stock prices?

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

Various scholarly studies have explored the impact of environmental and social factors on financial markets. Smith et al. (2018) found a significant relationship between renewable energy usage and stock prices, while Doe and Jones (2015) identified correlations between meteorological phenomena and investment behavior. The literature has provided valuable insights into the complex interplay between seemingly disparate elements, offering a nuanced understanding of market dynamics.

Turning now to the world of non-fiction literature, "The Sixth Extinction" by Elizabeth Kolbert presents a compelling narrative on environmental changes and their repercussions on human society. Similarly, "The Big Short" by Michael Lewis offers a gripping account of financial markets and the unexpected factors that can influence their trajectory.

On a more imaginative note, "The Sun Also Rises" by Ernest Hemingway ventures into the realm of literary fiction but nevertheless evokes the theme of the sun's influence, albeit in a metaphorical sense. Furthermore, "Solar" by Ian McEwan provides a satirical exploration of renewable energy and human ambition, offering a fictional account of the intersecting paths of technology and finance.

Amidst the academic and literary discourse, social media platforms have also become a conduit for discussions pertinent to our investigation. A recent tweet by @SolarStockTrader humorously mused, "Who knew that solar energy could also power up stock prices? #IlluminatingMarkets." Additionally, the Reddit thread r/FinancialSunrise sparked lively debate regarding the unexpected connection between sunlight and stock performance, with users sharing anecdotes and insights related to our research topic.

In synthesizing these diverse sources, the authors find themselves reminded of a classic dad joke: "Did you hear about the guy who invented solar-powered stock trading? He made a fortune – it was a 'daylight robbery'!" Such jests may serve to lighten the scholarly discourse, but they also underscore the intriguing and unexpected nature of our research endeavor.

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 Solar power generated in Lebanon and Boston Scientific's stock price (BSX). You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
Shining Stocks: Solar Power's Surprising Influence on Boston Scientific's Stock Price

[[ABSTRACT]]
This study investigates the curious relationship between solar power output in Lebanon and the stock price of Boston Scientific Corporation (BSX). Utilizing data from the Energy Information Administration and LSEG Analytics (Refinitiv), our research team examined the period spanning from 2010 to 2021. The statistical analysis revealed a remarkably high correlation coefficient of 0.9700195 and a p-value less than 0.01, indicating a strong and significant association between these seemingly unrelated variables.

In exploring this unexpected link, we posit the hypothesis that the sun's energy not only powers solar panels but also exerts an unseen force on the financial markets. This finding sheds light on the intricate interplay of environmental factors and stock prices, illuminating a previously overlooked dimension of market dynamics. It seems that the sun is not content with merely providing renewable energy; it also enjoys shining a spotlight on the stock market.

Furthermore, our study offers a new perspective on investment strategies, highlighting the potential impact of geographically distant environmental phenomena on stock performance. As Mark Twain once said, "Buy land, they're not making it anymore." Perhaps he should have added, "And keep an eye on solar power too."

[[LITERATURE REVIEW]]
Various scholarly studies have explored the impact of environmental and social factors on financial markets. Smith et al. (2018) found a significant relationship between renewable energy usage and stock prices, while Doe and Jones (2015) identified correlations between meteorological phenomena and investment behavior. The literature has provided valuable insights into the complex interplay between seemingly disparate elements, offering a nuanced understanding of market dynamics.
Turning now to the world of non-fiction literature, "The Sixth Extinction" by Elizabeth Kolbert presents a compelling narrative on environmental changes and their repercussions on human society. Similarly, "The Big Short" by Michael Lewis offers a gripping account of financial markets and the unexpected factors that can influence their trajectory.
On a more imaginative note, "The Sun Also Rises" by Ernest Hemingway ventures into the realm of literary fiction but nevertheless evokes the theme of the sun's influence, albeit in a metaphorical sense. Furthermore, "Solar" by Ian McEwan provides a satirical exploration of renewable energy and human ambition, offering a fictional account of the intersecting paths of technology and finance.
Amidst the academic and literary discourse, social media platforms have also become a conduit for discussions pertinent to our investigation. A recent tweet by @SolarStockTrader humorously mused, "Who knew that solar energy could also power up stock prices? #IlluminatingMarkets." Additionally, the Reddit thread r/FinancialSunrise sparked lively debate regarding the unexpected connection between sunlight and stock performance, with users sharing anecdotes and insights related to our research topic.
In synthesizing these diverse sources, the authors find themselves reminded of a classic dad joke: "Did you hear about the guy who invented solar-powered stock trading? He made a fortune – it was a 'daylight robbery'!" Such jests may serve to lighten the scholarly discourse, but they also underscore the intriguing and unexpected nature of our research endeavor.

[[RESULTS]]
The statistical analysis of the relationship between solar power output in Lebanon and the stock price of Boston Scientific Corporation (BSX) revealed a remarkably high correlation coefficient of 0.9700195. This indicates a strong positive linear relationship between the variables, suggesting that as solar power generation in Lebanon increases, the stock price of Boston Scientific also tends to rise. It appears that the sun's influence extends far beyond its ability to power renewable energy sources; it seems to have a brightening effect on stock prices as well.
The r-squared value of 0.9409379 further emphasizes the strength of this association, indicating that approximately 94.1% of the variability in Boston Scientific's stock price can be explained by changes in solar power output in Lebanon. This suggests that the sun's energy output indeed exerts a substantial influence on the stock performance of Boston Scientific, much like how it illuminates the earth during the day. It's as if the sun is providing a guiding light for investors, shining a path toward potential stock price movements.
Additionally, the p-value of less than 0.01 demonstrates a high level of statistical significance, indicating that the observed correlation is unlikely to have occurred by random chance. This robust statistical evidence further supports the notion that there is a meaningful relationship between solar power generation in Lebanon and the stock price of Boston Scientific. It seems that the sun's impact on stock prices is not merely a "light" matter, but rather a significant factor worthy of attention in financial analyses.
The findings are visually represented in Figure 1, which displays a scatterplot illustrating the strong correlation between solar power output in Lebanon and the stock price of Boston Scientific. As the solar power generation increases, there is a noticeable tendency for the stock price of Boston Scientific to rise as well, reminiscent of how the sun's rays gradually light up the sky at dawn. It's as if the sun is whispering to investors, "Let there be enlightened stock decisions."
In conclusion, the results of this study provide compelling evidence of a surprising connection between solar power output in Lebanon and the stock price of Boston Scientific. This unexpected finding raises intriguing questions about the broader impact of environmental factors on financial markets and offers a sunny perspective on the interplay between seemingly unrelated variables. It seems that the sun's influence on stock prices cannot be "eclipsed," and its role in shaping market dynamics merits further investigation.
As the famous astronomer Carl Sagan once said, "Somewhere, something incredible is waiting to be known." It appears that in the case of solar power and stock prices, that something incredible may just be the radiant influence of the sun itself.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this study align with prior research that has examined the influence of environmental factors on financial markets. The significant relationship between solar power generation in Lebanon and the stock price of Boston Scientific Corporation (BSX) supports the conclusions drawn by Smith et al. (2018) regarding the impact of renewable energy usage on stock prices. Furthermore, the robust statistical evidence of a strong positive linear relationship echoes the insights provided by Doe and Jones (2015) regarding the correlations between meteorological phenomena and investment behavior. These results lend credence to the notion that environmental forces can exert a tangible influence on stock performance, shedding light on the intricate interplay of seemingly disparate elements in the financial markets.

The high correlation coefficient and r-squared value obtained in this study underline the substantial explanatory power of solar power generation in Lebanon on Boston Scientific's stock price. This robust statistical association echoes the strength of the relationships identified in prior research, emphasizing the significant impact of environmental factors on stock market dynamics. It appears that, much like the inexorable force of gravity in Newton's physical laws, the influence of solar power on stock prices exerts a noticeable pull on the trajectory of financial markets.

The p-value of less than 0.01 further reinforces the credibility of the observed relationship, aligning with the statistically significant findings reported in previous studies. This statistical significance underscores the reliability and validity of the observed association, emphasizing the substantive impact of solar power generation in Lebanon on the stock price of Boston Scientific. It seems that the sun's radiant influence on stock prices is not a mere flash in the pan but rather a enduring force that merits attention in financial analyses.

The unexpected connection between solar power output in Lebanon and the stock price of Boston Scientific Corporation opens up new avenues of inquiry into the broader role of environmental phenomena in shaping market dynamics. These findings may prompt further explorations into the interplay between geographically distant environmental factors and stock performance, shedding light on previously unexplored dimensions of market influences. It appears that the sun's impact on stock prices is not merely a "light" matter, but rather a significant factor worthy of consideration in investment strategies and financial decision-making.

The quirky  literary references in the literature review, including the pun-infused humor of the dad joke, underscore the intriguing and unexpected nature of this research endeavor. While the presented findings align with conventional statistical analyses and research methodologies, the charming whimsicality that permeates scholarly discourse, much like the unexpected influence of solar power on stock prices, serves to illuminate the eclectic and multifaceted nature of the academic pursuit.

As Socrates purportedly remarked, "An unexamined life is not worth living." In a similar vein, an unexplored statistical relationship between solar power generation and stock prices may hold untold insights for market participants and analysts alike. This study, much like the sun itself, has cast a revealing light on the heretofore shadowy realm of financial market influences, paving the way for further research and analysis in this captivating field.

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 Solar power generated in Lebanon and Boston Scientific's stock price (BSX). You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
Shining Stocks: Solar Power's Surprising Influence on Boston Scientific's Stock Price

[[ABSTRACT]]
This study investigates the curious relationship between solar power output in Lebanon and the stock price of Boston Scientific Corporation (BSX). Utilizing data from the Energy Information Administration and LSEG Analytics (Refinitiv), our research team examined the period spanning from 2010 to 2021. The statistical analysis revealed a remarkably high correlation coefficient of 0.9700195 and a p-value less than 0.01, indicating a strong and significant association between these seemingly unrelated variables.

In exploring this unexpected link, we posit the hypothesis that the sun's energy not only powers solar panels but also exerts an unseen force on the financial markets. This finding sheds light on the intricate interplay of environmental factors and stock prices, illuminating a previously overlooked dimension of market dynamics. It seems that the sun is not content with merely providing renewable energy; it also enjoys shining a spotlight on the stock market.

Furthermore, our study offers a new perspective on investment strategies, highlighting the potential impact of geographically distant environmental phenomena on stock performance. As Mark Twain once said, "Buy land, they're not making it anymore." Perhaps he should have added, "And keep an eye on solar power too."

[[INTRDUCTION]]
Solar power has increasingly gained attention as a sustainable and renewable energy source in many parts of the world. Its ability to harness the sun's abundant energy offers promise for reducing reliance on traditional fossil fuels and mitigating the effects of climate change. In addition to its environmental benefits, solar power has also been associated with financial incentives, such as tax credits and cost savings. Perhaps the sun truly is the ultimate "bright" spot in our quest for clean energy.
On the other end of the spectrum, the stock market has long been a focal point for investors and analysts seeking to understand the intricacies of asset valuation and market dynamics. The fluctuation of stock prices has been subject to extensive study, with factors ranging from company performance to macroeconomic indicators considered as potential drivers. Amidst this complexity, one might not expect to find a shining link between the rays of the sun and the fluctuation of a particular stock.
However, as they say, "Where there's a will, there's a ray." Our study delves into the unexpected connection between solar power generation in Lebanon and the stock price of Boston Scientific Corporation (BSX). While these two entities may appear as distant as the Earth is from the sun, our analysis has illuminated an astonishing correlation between them. It seems that the sun's influence extends beyond environmental sustainability and casts its glow on the financial sphere as well.
This unexpected alliance prompts the question: could the sun truly be a celestial influencer on stock markets? It seems that the power of the sun is not limited to solar panels alone; it may hold sway over the movements of stock prices thousands of miles away. This finding not only sheds light on the unexplored dynamics of market behavior but also adds a sunny perspective to the field of financial research. After all, who would have thought that the sun could have such a "solarizing" effect on stock prices?

[[RESULTS]]
The statistical analysis of the relationship between solar power output in Lebanon and the stock price of Boston Scientific Corporation (BSX) revealed a remarkably high correlation coefficient of 0.9700195. This indicates a strong positive linear relationship between the variables, suggesting that as solar power generation in Lebanon increases, the stock price of Boston Scientific also tends to rise. It appears that the sun's influence extends far beyond its ability to power renewable energy sources; it seems to have a brightening effect on stock prices as well.
The r-squared value of 0.9409379 further emphasizes the strength of this association, indicating that approximately 94.1% of the variability in Boston Scientific's stock price can be explained by changes in solar power output in Lebanon. This suggests that the sun's energy output indeed exerts a substantial influence on the stock performance of Boston Scientific, much like how it illuminates the earth during the day. It's as if the sun is providing a guiding light for investors, shining a path toward potential stock price movements.
Additionally, the p-value of less than 0.01 demonstrates a high level of statistical significance, indicating that the observed correlation is unlikely to have occurred by random chance. This robust statistical evidence further supports the notion that there is a meaningful relationship between solar power generation in Lebanon and the stock price of Boston Scientific. It seems that the sun's impact on stock prices is not merely a "light" matter, but rather a significant factor worthy of attention in financial analyses.
The findings are visually represented in Figure 1, which displays a scatterplot illustrating the strong correlation between solar power output in Lebanon and the stock price of Boston Scientific. As the solar power generation increases, there is a noticeable tendency for the stock price of Boston Scientific to rise as well, reminiscent of how the sun's rays gradually light up the sky at dawn. It's as if the sun is whispering to investors, "Let there be enlightened stock decisions."
In conclusion, the results of this study provide compelling evidence of a surprising connection between solar power output in Lebanon and the stock price of Boston Scientific. This unexpected finding raises intriguing questions about the broader impact of environmental factors on financial markets and offers a sunny perspective on the interplay between seemingly unrelated variables. It seems that the sun's influence on stock prices cannot be "eclipsed," and its role in shaping market dynamics merits further investigation.
As the famous astronomer Carl Sagan once said, "Somewhere, something incredible is waiting to be known." It appears that in the case of solar power and stock prices, that something incredible may just be the radiant influence of the sun itself.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, it is abundantly clear that the connection between solar power output in Lebanon and the stock price of Boston Scientific Corporation (BSX) is far from being a mere flight of fancy. The statistical analysis has unveiled a remarkably high correlation coefficient and a p-value less than 0.01, underscoring the substantive and statistically significant association between these seemingly disparate variables. It seems that the sun's reach extends far beyond the embrace of our solar panels and stretches into the domain of stock market dynamics.

As we bask in the glow of these intriguing findings, it is worth noting that this unexpected association between solar power generation and stock prices invites further inquiry into the larger celestial influences on financial markets. However, it is essential to tread carefully in the pursuit of such knowledge. After all, as they say, "Investing in solar power may brighten your portfolio, but don't let it overshadow your other investments!"

Our study has shed a revealing light on the interplay between environmental phenomena and stock performance, prompting us to contemplate whether the sun may be quietly steering the market's movements from its celestial perch. This finding not only enriches our understanding of market dynamics but also serves as a shining example of the potential impact of geographically distant environmental factors on stock prices. In the words of Galileo, "The sun, with all those planets revolving around it and dependent on it, can still ripen a bunch of grapes as if it had nothing else in the universe to do." Little did we know that the sun might also be ripening stock prices across borders and industries.

In this regard, our study heeds the call to recognize the sun's role as not just a source of renewable energy but as a celestial influencer on stock markets as well. The statistical evidence leaves little room to doubt the significance of this relationship, establishing it as a shining example of the uncharted territory that awaits exploration in financial research. One might say that the sun's influence on stock prices isn't just a bright idea; it's a "powerful" force that demands attention.

Therefore, in the spirit of scientific rigor and a good sense of humor, it is safe to say that no further research is needed to establish the radiant connection between solar power output in Lebanon and the stock price of Boston Scientific Corporation. This unexpected finding provides a sunny perspective on the intersection of environmental factors and financial markets, leaving us to ponder the considerable impact of the sun's energy on the guiding light of stock prices.

In the wise words of the great astronomer Johannes Kepler, "The sun, with all those planets revolving around it and dependent on it, can still take the time to brighten our portfolios." With that, it is time for this research to transition to other uncharted domains, leaving this radiant connection to illuminate the path for future financial analyses.

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 Shining Stocks: Solar Power's Surprising Influence on Boston Scientific's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Renewable Energy Economics and Finance

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 Shining Stocks: Solar Power's Surprising Influence on Boston Scientific's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Solar Research Consortium

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 Shining Stocks: Solar Power's Surprising Influence on Boston Scientific's Stock Price with an abstract of This study investigates the curious relationship between solar power output in Lebanon and the stock price of Boston Scientific Corporation (BSX). Utilizing data from the Energy Information Administration and LSEG Analytics (Refinitiv), our research team examined the period spanning from 2010 to 2021. The statistical analysis revealed a remarkably high correlation coefficient of 0.9700195 and a p-value less than 0.01, indicating a strong and significant association between these seemingly unrelated variables.
In exploring this unexpected link, we posit the hypothesis that the sun's energy not only powers solar panels but also exerts an unseen force on the financial markets. This finding sheds light on the intricate interplay of environmental factors and stock prices, illuminating a previously overlooked dimension of market dynamics. It seems that the sun is not content with merely providing renewable energy; it also enjoys shining a spotlight on the stock market.
Furthermore, our study offers a new perspective on investment strategies, highlighting the potential impact of geographically distant environmental phenomena on stock performance. As Mark Twain once said, "Buy land, they're not making it anymore." Perhaps he should have added, "And keep an eye on solar power too.

ChatGPT:

solar power output, Boston Scientific stock price, Lebanon solar energy, stock market correlation, renewable energy impact on stock market, environmental factors in investment strategies, solar power influence on stock performance, energy information administration data, LSEG Analytics, Refinitiv data, market dynamics, financial markets and solar power, stock price correlation, solar power investment strategies

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

Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error

Data details

Solar power generated in Lebanon
Detailed data title: Total solar power generated in Lebanon in billion kWh
Source: Energy Information Administration
See what else correlates with Solar power generated in Lebanon

Boston Scientific's stock price (BSX)
Detailed data title: Opening price of Boston Scientific (BSX) 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 Boston Scientific's stock price (BSX)

Correlation r = 0.9700195 (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 = 0.9409379 (Coefficient of determination)
This means 94.1% of the change in the one variable (i.e., Boston Scientific's stock price (BSX)) is predictable based on the change in the other (i.e., Solar power generated in Lebanon) over the 12 years from 2010 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.8E-7.^Show 0.0000001813912780444183200000
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 0.97 in 1.8E-5% of random cases. Said differently, if you correlated 5,512,944 random variables^Note You don't actually need 5 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 11 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 11 because we have two variables measured over a period of 12 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.

[ 0.89, 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.

	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Solar power generated in Lebanon (Billion kWh)	0.001	0.001	0.001	0.003	0.007	0.017	0.037	0.056	0.086	0.112	0.117	0.12
Boston Scientific's stock price (BSX) (Stock price)	8.86	7.57	5.42	5.86	12.02	13.29	18.16	21.72	24.94	34.65	45.5	36.08

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.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([0.001,0.001,0.001,0.003,0.007,0.017,0.037,0.056,0.086,0.112,0.117,0.12,])
array_2 = np.array([8.86,7.57,5.42,5.86,12.02,13.29,18.16,21.72,24.94,34.65,45.5,36.08,])
array_1_name = "Solar power generated in Lebanon"
array_2_name = "Boston Scientific's stock price (BSX)"

# 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 Solar power generated in Lebanon
Line chart for only Boston Scientific's stock price (BSX)
AI-generated correlation image
The spurious research paper: Shining Stocks: Solar Power's Surprising Influence on Boston Scientific's Stock Price

View another random correlation

How fun was this correlation?

Your rating is pure awesomeness!

Correlation ID: 3530 · Black Variable ID: 23787 · Red Variable ID: 1662


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