Advension

advension

Machine Ethics and Artificial Moral Agents

ASCENSION

One of the most important things you can do as a startup, and in life, is be smart about how you choose to invest your time and energy. Every meeting you schedule, feature you decide to build, or bug you decide to fix is ultimately a bet. You are betting that your actions will ultimately result in generating or capturing more value as a company. What impacts value? Start by thinking about your core actions that have the biggest impact on value. This shouldn't be an exhaustive list full of nuance. Generalize your work into a small number of buckets for now. You can always refine your thinking later. For me, I tend to think a lot about these 3 types of work when building product at Hacker Noon. Actions that add stability into our product and make sure the thing does what it says on the tin. Bug fixing: Actions that make the product experience more enjoyable, intuitive, and usable. Iteration also includes small functional mutations designed to better accommodate known use cases. Iteration: Actions that explore entirely new use cases or solutions to existing problems. People too often put innovation on a pedestal. It doesn't have to be this mythical thing only attainable by the unicorns. It should be a core function of every company. Innovation: Example of how actions impact value Imagine you are a pizza company in a small town. There are 3 people who have a weekly pizza budget. This is overly simplified I know, but stay with me. $50 🍕🍕🍕🍕🍕 $30 🍕🍕🍕 $20 🍕🍕 $100 Bob: Alice: Sam: Total: You've carved out a market has $100/week worth of potential value. Now imagine your pizza oven is broken. You're stuck capturing $0 out of $100 in available value. Once you , you raise the floor of value you're able to capture from $0 to a bigger number. After a few weeks, you find yourself selling $30. Maybe you're selling slices for $10 and Bob, Alice and Sam each buy a slice. fix the bug Only selling $30 of pizza is disappointing. So you devise a plan to capture more of the market by on your offering. You ask your customers what additional toppings they'd be interested in. After gathering feedback, you expand the number of toppings and charge $.50 per additional topping. iterating After a few weeks, you see sales grow from $30 to $42. People are buying slices with more toppings, and you're also finding that people are buying more slices given the added variety. In addition to raising the floor (value captured), you also find that you're raising the ceiling of potential value because your new product is so enjoyable that Sam is now budgeting $30/week instead of $20. A revenue potential of $110 per week is pretty disappointing. You could keep iterating and slowly raise your potential in small increments. Continued iteration is often a viable strategy. Alternatively, you could redraw the boundaries of your market. You come up with an called a taco pizza 🍕🌮. It's risky because you aren't really sure if people will like your new Frankenstein creation. But you try it and the trick works. Some people who previously ate tacos for lunch now eat taco pizza. Here is your new market. innovation $70 🍕🍕🍕🍕🍕🌮🌮 $30 🍕🌮🌮🌮 $30 🍕🌮🌮🌮 $40 🍕🍕🍕🌮 $30 🍕🍕🍕 $220 Bob: Jane: Joe: Alice: Sam: Total: With the latest innovation, you've effectively doubled the ceiling of available value to be captured. Now it'll take bug fixing and iteration to close the gap between the captured and potential value. Real vs Perceived Value Startups often lie to themselves and to investors when talking about value. It's a bigger story to say "This town spends $20,000 per week on food. That's our market." They might even be emboldened to tell an even bigger story about how their potential value extends to neighboring towns, cities, or even countries. Telling a bigger story is all about creating perceived value. It can be a good thing if you have the discipline to tether your story to reality. You might be wondering why. Big stories raise money and you can always fake it until you make it right? And what my premise that risk aversion killing startups? Breaking the tether When perceived value is no longer rooted in reality, teams tend to think about "the story" and their "work" as two entirely different things. Over time stories tend to grow in ambition while work tends to become increasingly conservative. I believe this happens because most people usually only take risks and innovate when they feel inspired. If the story is achievable, they will take risks and close the gap between reality and perception. Without inspiration, people tend to make safe bets. Let's get back to the pizza company to show this concept in action. Conservative investments While your pizza company might be raising truck loads of cash on the promise of going global, think about how the team is actually investing their time. The manager of your only pizza shop has identified a list of problems with the store and creates a backlog. The windows get too dirty later in the week. Make sure the daily shop cleaning routine includes the windows. The pizza oven heat seems to be off. Buy and install an oven thermometer to monitor heat. Too many customers have complained about bad service. Let's create a more friendly greeting process. The pizza dough is too chewy. Let's try using less water and a little more salt. All of the above tasks are examples of bug fixing and iterating to attract more customers and sell more pizza. But no matter how long this list grows, and how many items are checked off, this pizza shop will not magically franchise into global enterprise by just doing these tasks. Conservative stories inspire ambitious execution Now imagine a more measured approach of creating perceived value. Instead of making an absurd initial claim that the total food budget for the town is the potential value you can capture, think of something more reasonable. Maybe you think the average person could be eating pizza 3 times per week which is more like 1/7th of their food budget. It might sound like you are giving up a ton of value by admitting to yourself and your investors that you are operating in a much smaller market. But the more real that number feels, the more likely it will influence how your team chooses to invest their time. If everyone agrees that it is possible to make $3,000/week selling pizza and current revenue is $300/week, then the gap between captured and potential value is pretty huge, but achievable. Instead of making micro-adjustments to cooking time or temp, maybe it's better to invest in an ad campaign or branding. Or maybe the you decide the recipe isn't that great and you need to go back to the drawing board. How risk aversion kills startups When you raise money on a promise of a massive ceiling and your team can't see the ceiling, they'll focus on the floor. Then you start investing in more people to accelerate growth, but everyone is still looking at the floor. So you burn through your runway and have to close shop. Conclusion This story is based on experience working with over a dozen startups in my career so far. I'm definitely not singling out any individual startup here. Every team has a different set of challenges that contribute to success or failure. But I have noticed a pattern that teams generally need to take more risks. Narrowing the gap between reality and perception can be one way to inspire more risk taking. My assertion could be right, wrong or incomplete. Ping me on Twitter to continue the discussion. @duilen

Risk Aversion Kills Startups

I wear two hats: a fintech entrepreneur and an angel investor. Also, I’ve spent the last 15 years investing and trading in various types of securities in public markets and worked in £bn+ public to private transactions. Over the past 18 months we at have raised £400K in two rounds. During that process I’ve met with at least 50+ investors, from angel investors, VCs, and family offices. Our company has won several awards and gotten some positive press, so there’s been a lot of interest in us. But even with all that, fundraising has been a real b*tch. CityFALCON There’s a lot said about the conservative nature of investors in the UK and Europe compared to our friends in Silicon Valley or New York — and it’s not all unfounded. But I’ve been on both sides of the fence, so I know it’s not all the fault of timid investors. We entrepreneurs are doing several things wrong. There is ‘no risk of losing out’ in the UK and Europe When does an investor actually sign the cheque to invest in a company, be it public or private? Most times it’s when they have a fear of losing out on an opportunity. For example, Oil is below $30 and everyone knows there is value there, because of the cost of production and the long term demand-supply dynamics. But how many of the investors and traders are buying Oil? Everyone wants to be part of the bounce back, but are waiting till the time they see some positive momentum. There have been very few exits compared to the number of start-ups raising funds In the UK, you don’t hear stories about people investing £10K in a start-up and then exiting for £100,000 — seemingly a regular occurrence in the Valley. It’s tempting to blame entrepreneurs for this, but that’s not the whole story. The key reason for this is we don’t have the start-up ecosystem of mentors, investors and employees who have built or worked in start-ups. In the UK we lack the experience and support of people who have gone through the different phases of and had their own successes and failures. We do have some ‘smart’ investors here, and we’re lucky for it, but most of the money in the UK is ‘dumb’ money. growth As it stands, a rational investor would stay away from start-ups. Most of the time, investing in is mostly for the tax benefits (SEIS, EIS, etc.). startups Forget exits, some entrepreneurs do not even update their investors on a regular basis One of the most frustrating things angel investors face in the UK is not getting updates from a company they have invested in. Imagine the unease these investors would feel about putting money into more start-ups. And ask yourself how likely someone is to give follow up investment after receiving no updates. At , I get the team to provide me with all metrics so that we send an update within 5 days of close of each month. It’s vitally important, and not that difficult! CityFALCON It’s become an investors’ market at an early stage There’s a popular misconception built by the media that the UK has an abundance of money, just waiting for hungry entrepreneurs to come and grab it all. This has led to entrepreneurs from all over Europe and Asia looking to raise capital in the UK. While there is money in the UK, . The traditionally risk averse investors in the UK, coupled with the flood of some of the best entrepreneurs from all over the world has given investors more choice, and made it more difficult for many start-ups to get off the ground. a lot of it isn’t for you Hustling is fine but spamming investors to raise funds is not cool Every two or three days, I get an email that says something like, “We are closing our EIS round, expected ROI of x%….”. Most of the angel investment works on references, and I’m not sure how many entrepreneurs get funding using this approach. It’s like bombarding your CV to 200 companies when you’re looking for a job. There is a lot we entrepreneurs can learn about building relationships from business development executives. The successful ones don’t start selling from my their first sentence, they first work on building a rapport, a connection with the potential customer. It’s not abundance but scarcity of assets that drives up its value. Let the investors chase opportunities instead of us chasing them. Be ready to walk away Some potential investors will take up too much of your time. They will request tons of documents and information without any real intention of investing in the company. I’ve been guilty of this too. Most of the investment we raised came with less than three meetings with the potential investor. We need to go back to the basics of negotiating. Late-stage and “successful” entrepreneurs are giving too much away Several in-demand start-ups are undervalued Take a company like Crowdcube. Their fundraise was subscribed in 15 minutes. In pure finance terms, they left too much on the table. They could have raised money at a much higher valuation. This has been the case with Seedrs, and other popular brands that have raised funds and have been oversubscribed. I’m hoping that crowdfunding sites in the future will start thinking about the basic principles of demand and supply, and have a book-building process on the site. Late-stage investors want upside of common equity with downside protection of debt — real expensive capital One of the biggest shocks for me after entering the entrepreneurial world was the term sheets that entrepreneurs are forced to sign. In the public markets when you’re buying a share in a company, you get equity in the company at the same terms as other investors. But in the start-up world, late stage investors who face less risk than early-stage investors, employees, and entrepreneurs, demand preferential liquidations, warrants, etc. The sad part of it all is that this has become accepted and most people think that it’s fair! Unfortunately, most of us will have to sign these term sheets, because they have become the standard, but do remember that you are giving too much away. The graph below shows the risk-return profile of debt and equity in public markets. READ MORE

Why we entrepreneurs are responsible for the risk aversion of investors in the UK and Europe

The US elections are over and Donald Trump has been elected to the highest public office in the US. Political pundits will spend the next few weeks dissecting what went wrong and what went right, but I want to take a moment to look at some of the Ts. T for Trump. T for Tesla. And T for technology. And we'll add in a B for good measure (more on that in just a bit). Most polls showed a close race, but betting markets overwhelmingly predicted that Trump would win. And win he did. Not only did Trump win the popular vote, he won significantly more electoral votes than his competitor, Kamala Harris. Per Reuters, Trump won 312 electoral votes against Harris' 226 electoral votes: I personally thought Harris had a chance when she announced her candidacy, but felt the scales tipping in Trump's favor as the elections neared. Betting markets had already predicted a Trump presidency, while HackerNoon readers also pegged the elections in the former US president's favor all the way in September: Although, HackerNoon readers would now rather escape Trump's presidency for the next four years, a sentiment shared by despondent Americans who are also increasingly searching moving abroad on Google, with the top destinations being Canada, New Zealand, and Australia. But back to the Ts. Trump's win has seem to done some wonderful things for the president-elect and those closest to him, starting with Trump's own social media company Trump Media & Technology Group. The election win blasted the company's stock price to the moon, giving it a valuation of over $7 billion, despite the fact that it's not profitable. John Oliver has a great segment on the disconnect between Trump Media & Technology Group's valuation and underlying fundamentals on Last Week Tonight, which I highly recommend you check out, but for our purposes, the company's stock remains elevated even right now because of its association with the president-elect. Which brings us to the second T, and by far the best return on investment on donating to a presidential candidate's campaign. Elon Musk and his electric car maker Tesla. Musk's close relationship to Trump did not go unnoticed on Wall Street, and Tesla's stock price soared as soon as it became clear Trump was going to win the White House. Investors are betting that Musk's close ties with Trump will greatly benefit his companies, so much so that Tesla gained $230 billion in valuation over the course of this past week with no signs of stopping its rally, at least, not at the time of this writing. Not only has the ascent propelled Tesla's valuation to over $1 trillion (not for the first time, however), it has also given it an oversized lead against the next automaker in line, Toyota, which is valued roughly five times less than the electric vehicle maker. Before the elections, I wrote of a very real reason why Musk was better off backing Trump, and that scenario appears to be playing out in real time. GET PUBLISHED ON HACKERNOON.
Join a global network of 45,000+ published devs, builders,  founders, makers, VCs, hodlrs, and hackers. Start submitting your tech  stories and tutorials to get published FREE on HackerNoon — no pop-ups,  no paywalls. Which brings us to the third T: Technology. Wall Street has been abuzz with trading activity ever since Trump won the elections, but Nasdaq, which is heavily skewed towards tech companies, reached an all time high this past week following the win. If I had to harbor a guess as to why that may be, it could be because Trump could roll back regulations and rules for businesses, in contrast to the hard line the Biden administration took on Big Tech. And then, the B: Bitcoin. Crypto investors must be going nuts as the world's most popular cryptocurrency inches closer and closer to the $100k mark. Trump's win propelled bitcoin to an an all time high this past week, but at the time of writing, it was on track to hit an even new peak, having crossed $84k. That's $16k short of the $100k mark! Crypto investors are perhaps excited about the idea of a crypto ally entering the White House, and when taken with Trump's aversion to SEC Chairman Gary Gensler, are expecting mainstream adoption of bitcoin. Whether that will happen or not is anyone's guess, but that's not stopping investors from jumping in on the gains while they can. Now, before you go on celebrating Trump's win and making some investments of your own, I do want to point out that the gains are tied specifically to Trump and may be divorced from reality. If history is any indicator, the Trump euphoria will die down and the hype surrounding these assets will fade. All that will be left will be a scrutiny of Trump and his time in the White House, and if Trump can bring stocks up, he can bring them down. The last thing you want is to be caught in a course correction ;-) Anyway, that was your brief recap of last week. Tesla ranked #15 on HackerNoon's Tech Company Rankings. In Other News.. 📰 MicroStrategy Buys Another 27,200 BTC for $2B; Bitcoin Profits Sit at $11B — via CoinDesk
Intuitive Machines CEO: ‘We now have the platform for a lunar economy’ — via TechCrunch
Google and Meta are blocking political ads to combat misinformation. Some experts say it’s too late — via CNN
OpenAI and others seek new path to smarter AI as current methods hit limitations — via Reuters
Anthropic, Palantir, Amazon team up on defense AI — via Axios
FTX sues crypto exchange Binance and its former CEO Zhao for $1.8 billion — via CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. The US elections are over and Donald Trump has been elected to the highest public office in the US. Political pundits will spend the next few weeks dissecting what went wrong and what went right, but I want to take a moment to look at some of the Ts. T for Trump. T for Tesla . And T for technology. And we'll add in a B for good measure (more on that in just a bit). Tesla Tesla Most polls showed a close race, but betting markets overwhelmingly predicted that Trump would win. And win he did. Not only did Trump win the popular vote, he won significantly more electoral votes than his competitor, Kamala Harris. Per Reuters , Trump won 312 electoral votes against Harris' 226 electoral votes: Reuters Reuters I personally thought Harris had a chance when she announced her candidacy, but felt the scales tipping in Trump's favor as the elections neared. Betting markets had already predicted a Trump presidency, while HackerNoon readers also pegged the elections in the former US president's favor all the way in September: already already former US president's favor former US president's favor Although, HackerNoon readers would now rather escape Trump's presidency for the next four years, a sentiment shared by despondent Americans who are also increasingly searching moving abroad on Google, with the top destinations being Canada, New Zealand, and Australia. increasingly searching increasingly searching But back to the Ts. But back to the Ts. Trump's win has seem to done some wonderful things for the president-elect and those closest to him, starting with Trump's own social media company Trump Media & Technology Group. The election win blasted the company's stock price to the moon , giving it a valuation of over $7 billion, despite the fact that it's not profitable. to the moon to the moon John Oliver has a great segment on the disconnect between Trump Media & Technology Group's valuation and underlying fundamentals on Last Week Tonight, which I highly recommend you check out , but for our purposes, the company's stock remains elevated even right now because of its association with the president-elect. check out check out Which brings us to the second T , and by far the best return on investment on donating to a presidential candidate's campaign. Elon Musk and his electric car maker Tesla. Which brings us to the second T Musk's close relationship to Trump did not go unnoticed on Wall Street, and Tesla's stock price soared as soon as it became clear Trump was going to win the White House. Investors are betting that Musk's close ties with Trump will greatly benefit his companies, so much so that Tesla gained $230 billion in valuation over the course of this past week with no signs of stopping its rally , at least, not at the time of this writing. gained gained no signs of stopping its rally no signs of stopping its rally Not only has the ascent propelled Tesla's valuation to over $1 trillion (not for the first time, however), it has also given it an oversized lead against the next automaker in line, Toyota, which is valued roughly five times less than the electric vehicle maker. Before the elections, I wrote of a very real reason why Musk was better off backing Trump, and that scenario appears to be playing out in real time. wrote wrote GET PUBLISHED ON HACKERNOON. Join a global network of 45,000+ published devs, builders,  founders, makers, VCs, hodlrs, and hackers. Start submitting your tech  stories and tutorials to get published FREE on HackerNoon — no pop-ups,  no paywalls. GET PUBLISHED ON HACKERNOON. GET PUBLISHED ON HACKERNOON . GET PUBLISHED ON HACKERNOON Join a global network of 45,000+ published devs, builders,  founders, makers, VCs, hodlrs, and hackers. Start submitting your tech  stories and tutorials to get published FREE on HackerNoon — no pop-ups,  no paywalls. Join a global network of 45,000+ published devs, builders,  founders, makers, VCs, hodlrs, and hackers. Start submitting your tech  stories and tutorials to get published FREE on HackerNoon — no pop-ups,  no paywalls. Which brings us to the third T: Technology. Wall Street has been abuzz with trading activity ever since Trump won the elections, but Nasdaq, which is heavily skewed towards tech companies, reached an all time high this past week following the win. Which brings us to the third T: Technology. abuzz with trading activity abuzz with trading activity all time high all time high If I had to harbor a guess as to why that may be, it could be because Trump could roll back regulations and rules for businesses, in contrast to the hard line the Biden administration took on Big Tech. And then, the B: Bitcoin. And then, the B: Bitcoin. Crypto investors must be going nuts as the world's most popular cryptocurrency inches closer and closer to the $100k mark. Trump's win propelled bitcoin to an an all time high this past week, but at the time of writing, it was on track to hit an even new peak , having crossed $84k. That's $16k short of the $100k mark! new peak new peak Crypto investors are perhaps excited about the idea of a crypto ally entering the White House, and when taken with Trump's aversion to SEC Chairman Gary Gensler, are expecting mainstream adoption of bitcoin. Whether that will happen or not is anyone's guess, but that's not stopping investors from jumping in on the gains while they can. Now, before you go on celebrating Trump's win and making some investments of your own, I do want to point out that the gains are tied specifically to Trump and may be divorced from reality. If history is any indicator, the Trump euphoria will die down and the hype surrounding these assets will fade. All that will be left will be a scrutiny of Trump and his time in the White House, and if Trump can bring stocks up, he can bring them down. The last thing you want is to be caught in a course correction ;-) Anyway, that was your brief recap of last week. Tesla ranked #15 on HackerNoon's Tech Company Rankings. In Other News.. 📰 In Other News.. 📰 MicroStrategy Buys Another 27,200 BTC for $2B; Bitcoin Profits Sit at $11B — via CoinDesk Intuitive Machines CEO: ‘We now have the platform for a lunar economy’ — via TechCrunch Google and Meta are blocking political ads to combat misinformation. Some experts say it’s too late — via CNN OpenAI and others seek new path to smarter AI as current methods hit limitations — via Reuters Anthropic, Palantir, Amazon team up on defense AI — via Axios FTX sues crypto exchange Binance and its former CEO Zhao for $1.8 billion — via CNBC MicroStrategy Buys Another 27,200 BTC for $2B; Bitcoin Profits Sit at $11B — via CoinDesk CoinDesk CoinDesk Intuitive Machines CEO: ‘We now have the platform for a lunar economy’ — via TechCrunch TechCrunch TechCrunch Google and Meta are blocking political ads to combat misinformation. Some experts say it’s too late — via CNN CNN CNN OpenAI and others seek new path to smarter AI as current methods hit limitations — via Reuters Reuters Reuters Anthropic, Palantir, Amazon team up on defense AI — via Axios Axios Axios FTX sues crypto exchange Binance and its former CEO Zhao for $1.8 billion — via CNBC CNBC CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan , Editor, Business Tech @ HackerNoon Sheharyar Khan Sheharyar Khan *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here . here here

The Big T

বিগ টি

Das große T

La gran T

Le grand T

द बिग टी

ビッグT

빅 티

O Grande T

Большая буква Т

Büyük T

Chữ T lớn

大T

Table of Links Abstract


Introduction
Background
Reinforcement Learning
Similar Work


Methodology
DRLAgent Design


Training Procedures


Testing Procedures


Results


SABR Experiments


Conclusions


Appendix A


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Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. “Continuous Control with Deep Reinforcement Learning.” arXiv arXiv:1509.02971. https://doi.org/10.48550/arXiv.1509.02971. Lin, Long-Ji. 1992. “Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching.” Machine Learning 8 (3): 293–321. https://doi.org/10.1007/BF00992699. Liu, Peng. 2023. “A Review on Derivative Hedging Using Reinforcement Learning.” The Journal of Financial Data Science. https://doi.org/10.3905/jfds.2023.1.124. Longstaff, Francis A., and Eduardo S. Schwartz. 2001. “Valuing American Options by Simulation: A Simple Least-Squares Approach.” UCLA: Finance. https://escholarship.org/uc/item/43n1k4jb. Mikkilä, Oskari, and Juho Kanniainen. 2023. “Empirical Deep Hedging.” Quantitative Finance 23 (1): 111–22. https://doi.org/10.1080/14697688.2022.2136037. Mnih, V., K. Kavukcuoglu, David Silver, A. Graves, I. Antonoglou, Daan Wierstra, and M. Riedmiller. 2013. “Playing Atari with Deep Reinforcement Learning.” arXiv arXiv:1312.5602. https://doi.org/10.48550/arXiv.1312.5602. Murray, Phillip, B. Wood, H. Buehler, Magnus Wiese, and Mikko Pakkanen. 2022. “Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions.” In ICAIF ’22: Proceedings of the Third ACM International Conference on AI in Finance, 361–68. Association for Computing Machinery. https://doi.org/10.1145/3533271.3561731. Pham, Uyen, Quoc Luu, and Hien Tran. 2021. “Multi-Agent Reinforcement Learning Approach for Hedging Portfolio Problem.” Soft Computing 25 (12): 7877–85. https://doi.org/10.1007/s00500-021-05801-6. Pickard, Reilly, and Yuri Lawryshyn. 2023. “Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review.” Mathematics 11 (24). https://doi.org/10.3390/math11244943. Silver, David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, et al. 2016. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature 529 (7587): 484–89. https://doi.org/10.1038/nature16961. Smith, Leslie N. 2018. “A Disciplined Approach to Neural Network Hyper-Parameters: Part 1 -- Learning Rate, Batch Size, Momentum, and Weight Decay.” https://doi.org/10.48550/arXiv.1803.09820. Sutton, Richard S., and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. A Bradford Book. Watkins, Christopher John Cornish Hellaby. 1989. “Learning from Delayed Rewards.” King’s College. https://www.cs.rhul.ac.uk/~chrisw/new_thesis.pdf. Xiao, B., W. Yao, and X. Zhou. 2021. “Optimal Option Hedging with Policy Gradient.” In 2021 International Conference on Data Mining Workshops (ICDMW), 1112–19. IEEE. https://doi.org/10.1109/ICDMW53433.2021.00145. Xu, Wei, and Bing Dai. 2022. “Delta-Gamma–Like Hedging with Transaction Cost under Reinforcement Learning Technique.” The Journal of Derivatives 29 (5): 60–82. https://doi.org/10.3905/jod.2022.1.156. Zheng, C., J. He, and C. Yang. 2023. “Option Dynamic Hedging Using Reinforcement Learning.” arXiv arXiv:2306.10743. Authors:
(1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada (reilly.pickard@mail.utoronto.ca);
(2) Finn Wredenhagen, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;
(3) Julio DeJesus, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;
(4) Mario Schlener, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;
(5) Yuri Lawryshyn, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. Table of Links Abstract Introduction
Background
Reinforcement Learning
Similar Work Methodology
DRLAgent Design Training Procedures Testing Procedures Results SABR Experiments Conclusions Appendix A References Abstract Abstract Abstract Introduction Background Reinforcement Learning Similar Work Introduction Background Background Reinforcement Learning Reinforcement Learning Similar Work Similar Work Methodology DRLAgent Design Methodology Methodology DRLAgent Design DRLAgent Design Training Procedures Training Procedures Training Procedures Testing Procedures Testing Procedures Testing Procedures Results Results Results SABR Experiments SABR Experiments SABR Experiments Conclusions Conclusions Conclusions Appendix A Appendix A Appendix A References References References REFERENCES Assa, H., C. Kenyon, and H. Zhang. 2021. “Assessing Reinforcement Delta Hedging.” SSRN SSRN 3918375. http://dx.doi.org/10.2139/ssrn.3918375. Black, F., and M. Scholes. 1973. “The Pricing of Options and Corporate Liabilities” 81 (3): 637– 54. https://doi.org/10.1086/260062. Cao, Jay, Jacky Chen, Soroush Farghadani, John Hull, Zissis Poulos, Zeyu Wang, and Jun Yuan. 2023. “Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning.” Frontiers in Artificial Intelligence 6. https://www.frontiersin.org/articles/10.3389/frai.2023.1129370. Cao, Jay, Jacky Chen, John Hull, and Zissis Poulos. 2021. “Deep Hedging of Derivatives Using Reinforcement Learning.” The Journal of Financial Data Science 3 (1): 10–27. https://doi.org/10.3905/jfds.2020.1.052. Chebyshev, P. 1864. “Sur l’interpolation.” Zapiski Akademii Nauk 1 (4): 539–60. Du, Jiayi, Muyang Jin, Petter N. Kolm, Gordon Ritter, Yixuan Wang, and Bofei Zhang. 2020. “Deep Reinforcement Learning for Option Replication and Hedging.” The Journal of Financial Data Science 2 (4): 44–57. https://doi.org/10.3905/jfds.2020.1.045. Fathi, A., and B. Hientzsch. 2023. “A Comparison of Reinforcement Learning and Deep Trajectory Based Stochastic Control Agents for Stepwise Mean-Variance Hedging.” arXiv arXiv:2302.07996. Florescu, Ionuţ, and Cristian Gabriel Pãsãricã. 2009. “A Study about the Existence of the Leverage Effect in Stochastic Volatility Models.” Physica A: Statistical Mechanics and Its Applications 388 (4): 419–32. Giurca, B., and S. Borovkova. 2021. “Delta Hedging of Derivatives Using Deep Reinforcement Learning.” SSRN SSRN 3847272. https://ssrn.com/abstract=3847272. Glau, Kathrin, Micro Mahlstedt, and Christian Potz. 2018. “A New Approach for American Option Pricing: The Dynamic Chebyshev Method.” arXiv. https://doi.org/10.48550/arXiv.1806.05579. Hagan, Patrick, Deep Kumar, Andrew Lesniewski, and Diana Woodward. 2002. “Managing Smile Risk.” Wilmott Magazine 1 (January): 84–108. Halperin, Igor. 2017. “QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds.” The Journal of Derivatives 28 (1): 99–122. https://doi.org/10.3905/jod.2020.1.108. Heston, Steven L. 1993. “A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options.” The Review of Financial Studies 6 (2): 327–43. Hull, John, 1946-. 2012. Options, Futures, and Other Derivatives. Eighth edition. Boston : Prentice Hall, [2012] ©2012. https://search.library.wisc.edu/catalog/9910112878402121. Kolm, Petter N., and Gordon Ritter. 2019. “Dynamic Replication and Hedging: A Reinforcement Learning Approach.” The Journal of Financial Data Science 1 (1): 159–71. https://doi.org/10.3905/jfds.2019.1.1.159. Lillicrap, Timothy, Johnathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. “Continuous Control with Deep Reinforcement Learning.” arXiv arXiv:1509.02971. https://doi.org/10.48550/arXiv.1509.02971. Lin, Long-Ji. 1992. “Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching.” Machine Learning 8 (3): 293–321. https://doi.org/10.1007/BF00992699. Liu, Peng. 2023. “A Review on Derivative Hedging Using Reinforcement Learning.” The Journal of Financial Data Science. https://doi.org/10.3905/jfds.2023.1.124. Longstaff, Francis A., and Eduardo S. Schwartz. 2001. “Valuing American Options by Simulation: A Simple Least-Squares Approach.” UCLA: Finance. https://escholarship.org/uc/item/43n1k4jb. Mikkilä, Oskari, and Juho Kanniainen. 2023. “Empirical Deep Hedging.” Quantitative Finance 23 (1): 111–22. https://doi.org/10.1080/14697688.2022.2136037. Mnih, V., K. Kavukcuoglu, David Silver, A. Graves, I. Antonoglou, Daan Wierstra, and M. Riedmiller. 2013. “Playing Atari with Deep Reinforcement Learning.” arXiv arXiv:1312.5602. https://doi.org/10.48550/arXiv.1312.5602. Murray, Phillip, B. Wood, H. Buehler, Magnus Wiese, and Mikko Pakkanen. 2022. “Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions.” In ICAIF ’22: Proceedings of the Third ACM International Conference on AI in Finance, 361–68. Association for Computing Machinery. https://doi.org/10.1145/3533271.3561731. Pham, Uyen, Quoc Luu, and Hien Tran. 2021. “Multi-Agent Reinforcement Learning Approach for Hedging Portfolio Problem.” Soft Computing 25 (12): 7877–85. https://doi.org/10.1007/s00500-021-05801-6. Pickard, Reilly, and Yuri Lawryshyn. 2023. “Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review.” Mathematics 11 (24). https://doi.org/10.3390/math11244943. Silver, David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, et al. 2016. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature 529 (7587): 484–89. https://doi.org/10.1038/nature16961. Smith, Leslie N. 2018. “A Disciplined Approach to Neural Network Hyper-Parameters: Part 1 -- Learning Rate, Batch Size, Momentum, and Weight Decay.” https://doi.org/10.48550/arXiv.1803.09820. Sutton, Richard S., and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. A Bradford Book. Watkins, Christopher John Cornish Hellaby. 1989. “Learning from Delayed Rewards.” King’s College. https://www.cs.rhul.ac.uk/~chrisw/new_thesis.pdf. Xiao, B., W. Yao, and X. Zhou. 2021. “Optimal Option Hedging with Policy Gradient.” In 2021 International Conference on Data Mining Workshops (ICDMW), 1112–19. IEEE. https://doi.org/10.1109/ICDMW53433.2021.00145. Xu, Wei, and Bing Dai. 2022. “Delta-Gamma–Like Hedging with Transaction Cost under Reinforcement Learning Technique.” The Journal of Derivatives 29 (5): 60–82. https://doi.org/10.3905/jod.2022.1.156. Zheng, C., J. He, and C. Yang. 2023. “Option Dynamic Hedging Using Reinforcement Learning.” arXiv arXiv:2306.10743. Authors: (1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada (reilly.pickard@mail.utoronto.ca); (2) Finn Wredenhagen, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (3) Julio DeJesus, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (4) Mario Schlener, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (5) Yuri Lawryshyn, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada. Authors: Authors: (1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada ( reilly.pickard@mail.utoronto.ca ); reilly.pickard@mail.utoronto.ca (2) Finn Wredenhagen, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (3) Julio DeJesus, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (4) Mario Schlener, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (5) Yuri Lawryshyn, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. available on arxiv available on arxiv

Hedging American Put Options with Deep Reinforcement Learning: References

Sounds (“Music”) and Images (“Art”) have been somewhat conquered by GenAI; it’s never been easier to create slop that’s “close enough” to the real thing. Creatives have taken notice, with 73% of them now believing that AI will significantly impact their industry. Fashion has up until now been somewhat disconnected from these conversations. Sure, it’s a creative sector, but it also has glamor… and a certain exclusivity that makes AI’s infinite abundance seem gauche in comparison. But times are changing, and even the Milanese are now asking if machines could craft the kind of cultural cachet luxury designers have spent lifetimes building.’ Three researchers — Page Moreau from the University of Wisconsin-Madison, Emanuela Prandelli from Bocconi University, and Martin Schreier from WU Vienna — recently added complexity to this debate with a research paper titled, “Generative Artificial Intelligence and Design Co-Creation in Luxury New Product Development: The Power of Discarded Ideas.” Their research explores whether generative AI can compete with human designers when it comes to luxury fashion… and how consumers may react to machine-made chic. Can AI Design Fashion Luxury? The researchers teamed up with AwayToMars, Missoni, and IBM Watson to create and sell t-shirts designed either by human designers or by AI. The AI, IBM Watson, was trained on designs previously discarded by AWTM, using machine learning to generate new design ideas based on recurring features and patterns. They conducted a controlled experiment with over 1,000 participants and analyzed real sales data of the t-shirts over 20 weeks. The results? Consumers preferred AI-designed t-shirts — but only if they didn’t know they were designed by AI. When the source was revealed, consumer enthusiasm waned. Sales data showed a whopping 127% increase for AI-designed shirts compared to human-designed ones. Turns out, when fashion is sold without disclaimers, AI might just have a better eye for the cutting edge. What Does This Mean for the Fashion Industry? AI-designed products can outsell human creations. In fact, AI-generated t-shirts outsold their human-designed counterparts by more than twofold in the study. This means that any sense of superiority capitalistically-inclined designers may have needed to be adjusted to a new reality. Consumer perception of AI is still a barrier. When consumers were informed that designs were AI-generated, their willingness to pay for the garments decreased (“Knowing that a luxury fashion item was designed by AI reduced consumers’ responses towards it.”) This reflects an ongoing distrust of AI’s creative capability. Some may be tempted to hide the use of AI in the face of these results, but consumers also value transparency, and laws are being proposed to improve that transparency. This no doubt will be the toughest nut to crack in the near future. AI can enhance brand identity. The AI-designed shirts more effectively embodied Missoni’s signature zigzag patterns and brand codes compared to the human designs. This can be well-received by customers and can reinforce a brand identity if used properly. “Trash” is more useful than we think. The AI drew inspiration from designs that had been previously discarded — transforming “waste” into wearable art. This highlights how AI’s capacity to learn from mass data can unlock the value of overlooked ideas. This is without a doubt my favorite part of the study. How Do We Move Forward? This is a lot to take in; enough to give fashion executives some vertigo. While they get their bearings, there are a few things the fashion community can push for. We need to implement mandatory AI disclosure in product descriptions. Transparency is crucial. Consumers should know if an item was designed by AI, even if it impacts their perception negatively. If 44% of participants disliked AI-designed fashion when informed, brands need to confront this distrust head-on to avoid backlash. “AI aversion” (as another study called it) suggests that transparency is necessary but should be handled with care. Brands might want to frame AI’s role in a positive light by focusing on efficiency, sustainability, or co-creation to mitigate negative perceptions. Fashion Houses need to expertly blend AI with humans. The AI designs from the study could have been reviewed and improved by designers, I’m sure. Allowing designers to steer AI’s outputs could preserve the “luxury touch” that customers associate with human creativity while benefiting from AI’s efficiency. This may even lead to better margins and perhaps (?) better pay for artisans who surely deserve it. Fashion houses need to establish strict, all-encompassing guidelines to future-proof their expertise. This includes establishing standards for what training data is used… and what data cannot/should not be used. AI in creative industries often relies on massive datasets, some of which are sourced unethically. Ensuring AI is trained ethically on licensed or voluntarily contributed data is key to gaining consumer trust. Nothing says luxury like ethics… right? Right? Above all, consumers need to be educated. Brands should invest in educating consumers on the strengths of AI for design — including sustainability and efficiency, where algorithms are likely well-placed to have an impact. Highlighting how AI uses discarded materials creatively, as seen with Missoni, could positively shift consumer sentiment. Where the Research Paper Falls Short While Moreau, Prandelli, and Schreier’s study brings some interesting insights to light, it is not without flaws. For one, all participants were business school students — hardly representative of the global, diverse consumer base of luxury fashion. It also didn’t explore whether consumers would prefer designs if AI had been marketed as an “assistant” rather than a “creator”. Furthermore, the research only touched upon visual identity, ignoring other sensory elements such as fabric texture and craftsmanship, which are crucial in luxury fashion. For future research, I’d love to see how AI fares when tasked with creating high-fashion couture pieces rather than simple t-shirts. How does it navigate avant-garde aesthetics or evolving cultural nuances? Probably not very well. Are Designers Doomed? Generative AI in luxury fashion is like a bold new fabric. One that can dazzle but also fray under scrutiny. If handled with care, AI could provide a much-needed spark of innovation while pushing the boundaries of design. The key lies in its careful integration, transparency, and cooperation with human experts. A future where AI complements, rather than replaces, human creativity isn’t just desirable… it might be our only shot at saving the soul of luxury. Good luck out there. Sounds (“Music”) and Images (“Art”) have been somewhat conquered by GenAI; it’s never been easier to create slop that’s “ close enough ” to the real thing. Creatives have taken notice, with 73% of them now believing that AI will significantly impact their industry . Sounds Images close enough 73% of them now believing that AI will significantly impact their industry Fashion has up until now been somewhat disconnected from these conversations. Sure, it’s a creative sector, but it also has glamor… and a certain exclusivity that makes AI’s infinite abundance seem gauche in comparison. But times are changing, and even the Milanese are now asking if machines could craft the kind of cultural cachet luxury designers have spent lifetimes building.’ Three researchers — Page Moreau from the University of Wisconsin-Madison, Emanuela Prandelli from Bocconi University, and Martin Schreier from WU Vienna — recently added complexity to this debate with a research paper titled, “ Generative Artificial Intelligence and Design Co-Creation in Luxury New Product Development: The Power of Discarded Ideas . ” Page Moreau Emanuela Prandelli Martin Schreier Generative Artificial Intelligence and Design Co-Creation in Luxury New Product Development: The Power of Discarded Ideas Generative Artificial Intelligence and Design Co-Creation in Luxury New Product Development: The Power of Discarded Ideas . Their research explores whether generative AI can compete with human designers when it comes to luxury fashion… and how consumers may react to machine-made chic. Can AI Design Fashion Luxury? The researchers teamed up with AwayToMars , Missoni , and IBM Watson to create and sell t-shirts designed either by human designers or by AI. The AI, IBM Watson, was trained on designs previously discarded by AWTM, using machine learning to generate new design ideas based on recurring features and patterns. They conducted a controlled experiment with over 1,000 participants and analyzed real sales data of the t-shirts over 20 weeks. AwayToMars Missoni The results? Consumers preferred AI-designed t-shirts — but only if they didn’t know they were designed by AI . When the source was revealed, consumer enthusiasm waned. Sales data showed a whopping 127% increase for AI-designed shirts compared to human-designed ones. but only if they didn’t know they were designed by AI Turns out, when fashion is sold without disclaimers, AI might just have a better eye for the cutting edge. What Does This Mean for the Fashion Industry? AI-designed products can outsell human creations. In fact, AI-generated t-shirts outsold their human-designed counterparts by more than twofold in the study. This means that any sense of superiority capitalistically-inclined designers may have needed to be adjusted to a new reality. AI-designed products can outsell human creations. In fact, AI-generated t-shirts outsold their human-designed counterparts by more than twofold in the study. This means that any sense of superiority capitalistically-inclined designers may have needed to be adjusted to a new reality. AI-designed products can outsell human creations. Consumer perception of AI is still a barrier. When consumers were informed that designs were AI-generated, their willingness to pay for the garments decreased (“Knowing that a luxury fashion item was designed by AI reduced consumers’ responses towards it.”) This reflects an ongoing distrust of AI’s creative capability. Some may be tempted to hide the use of AI in the face of these results, but consumers also value transparency, and laws are being proposed to improve that transparency. This no doubt will be the toughest nut to crack in the near future. Consumer perception of AI is still a barrier. When consumers were informed that designs were AI-generated, their willingness to pay for the garments decreased ( “Knowing that a luxury fashion item was designed by AI reduced consumers’ responses towards it.”) This reflects an ongoing distrust of AI’s creative capability. Some may be tempted to hide the use of AI in the face of these results, but consumers also value transparency, and laws are being proposed to improve that transparency. This no doubt will be the toughest nut to crack in the near future. Consumer perception of AI is still a barrier. “Knowing that a luxury fashion item was designed by AI reduced consumers’ responses towards it.”) AI can enhance brand identity. The AI-designed shirts more effectively embodied Missoni’s signature zigzag patterns and brand codes compared to the human designs. This can be well-received by customers and can reinforce a brand identity if used properly. AI can enhance brand identity. The AI-designed shirts more effectively embodied Missoni’s signature zigzag patterns and brand codes compared to the human designs. This can be well-received by customers and can reinforce a brand identity if used properly. AI can enhance brand identity. “Trash” is more useful than we think. The AI drew inspiration from designs that had been previously discarded — transforming “waste” into wearable art. This highlights how AI’s capacity to learn from mass data can unlock the value of overlooked ideas. This is without a doubt my favorite part of the study. “Trash” is more useful than we think. The AI drew inspiration from designs that had been previously discarded — transforming “waste” into wearable art. This highlights how AI’s capacity to learn from mass data can unlock the value of overlooked ideas. This is without a doubt my favorite part of the study. “Trash” is more useful than we think. How Do We Move Forward? This is a lot to take in; enough to give fashion executives some vertigo. While they get their bearings, there are a few things the fashion community can push for. We need to implement mandatory AI disclosure in product descriptions. We need to implement mandatory AI disclosure in product descriptions. Transparency is crucial. Consumers should know if an item was designed by AI, even if it impacts their perception negatively. If 44% of participants disliked AI-designed fashion when informed, brands need to confront this distrust head-on to avoid backlash. “AI aversion” ( as another study called it ) suggests that transparency is necessary but should be handled with care. as another study called it Brands might want to frame AI’s role in a positive light by focusing on efficiency, sustainability, or co-creation to mitigate negative perceptions. Fashion Houses need to expertly blend AI with humans. Fashion Houses need to expertly blend AI with humans. The AI designs from the study could have been reviewed and improved by designers, I’m sure. Allowing designers to steer AI’s outputs could preserve the “luxury touch” that customers associate with human creativity while benefiting from AI’s efficiency. This may even lead to better margins and perhaps (?) better pay for artisans who surely deserve it. Fashion houses need to establish strict, all-encompassing guidelines to future-proof their expertise. Fashion houses need to establish strict, all-encompassing guidelines to future-proof their expertise. This includes establishing standards for what training data is used… and what data cannot/should not be used. AI in creative industries often relies on massive datasets, some of which are sourced unethically. Ensuring AI is trained ethically on licensed or voluntarily contributed data is key to gaining consumer trust. Nothing says luxury like ethics… right? Right? Above all, consumers need to be educated. Above all, consumers need to be educated. Brands should invest in educating consumers on the strengths of AI for design — including sustainability and efficiency, where algorithms are likely well-placed to have an impact. Highlighting how AI uses discarded materials creatively, as seen with Missoni, could positively shift consumer sentiment. Where the Research Paper Falls Short While Moreau , Prandelli , and Schreier ’s study brings some interesting insights to light, it is not without flaws. For one, all participants were business school students — hardly representative of the global, diverse consumer base of luxury fashion. Moreau Prandelli Schreier It also didn’t explore whether consumers would prefer designs if AI had been marketed as an “assistant” rather than a “creator”. Furthermore, the research only touched upon visual identity, ignoring other sensory elements such as fabric texture and craftsmanship, which are crucial in luxury fashion. For future research, I’d love to see how AI fares when tasked with creating high-fashion couture pieces rather than simple t-shirts. How does it navigate avant-garde aesthetics or evolving cultural nuances? Probably not very well. Are Designers Doomed? Generative AI in luxury fashion is like a bold new fabric. One that can dazzle but also fray under scrutiny. If handled with care, AI could provide a much-needed spark of innovation while pushing the boundaries of design. The key lies in its careful integration, transparency, and cooperation with human experts. A future where AI complements, rather than replaces, human creativity isn’t just desirable… it might be our only shot at saving the soul of luxury. Good luck out there.

Would You Still Love a $500 T-Shirt If You Knew AI Designed It?

Sal jy steeds van 'n $500 T-hemp hou as jy geweet het dat KI dit ontwerp het?

AI ዲዛይኑን ካወቁ አሁንም የ500 ዶላር ቲሸርት ይወዳሉ?

¿Aún te gustaría una camiseta de 500 dólares si supieras que fue diseñada por una IA?

اگر می دانستید که هوش مصنوعی آن را طراحی کرده است، آیا هنوز عاشق یک تی شرت 500 دلاری هستید؟

यदि आपको पता हो कि 500 डॉलर की टी-शर्ट को AI ने डिज़ाइन किया है, तो क्या आप उसे अब भी पसंद करेंगे?

Ti piacerebbe ancora una maglietta da 500 dollari se sapessi che è stata progettata dall'intelligenza artificiale?

AI がデザインした 500 ドルの T シャツをまだ気に入っていますか?

კიდევ გიყვართ 500 დოლარიანი მაისური, თუ იცოდით, რომ ხელოვნური ინტელექტის დიზაინია?

AI가 디자인했다고 해도 500달러짜리 티셔츠를 여전히 좋아할까요?

ایا تاسو به بیا هم د 500 ډالرو ټي شرټ خوښ کړئ که تاسو پوهیږئ چې AI یې ډیزاین کړی؟

Xana Wa Ha Swi Rhandza Xikipa Xa R1 000 Loko U Swi Tiva Leswaku AI Yi Xi Endlile?

Bạn có còn thích áo phông giá 500 đô la không nếu biết nó được thiết kế bởi AI?

Authors:
(1) Chengfeng Shen, School of Mathematical Sciences, Peking University, Beijing;
(2) Yifan Luo, School of Mathematical Sciences, Peking University, Beijing;
(3) Zhennan Zhou, Beijing International Center for Mathematical Research, Peking University. Table of Links Abstract and 1. Introduction 2 Model and 2.1 Optimal Stopping and Obstacle Problem 2.2 Mean Field Games with Optimal Stopping 2.3 Pure Strategy Equilibrium for OSMFG 2.4 Mixed Strategy Equilibrium for OSMFG 3 Algorithm Construction and 3.1 Fictitious Play 3.2 Convergence of Fictitious Play to Mixed Strategy Equilibrium 3.3 Algorithm Based on Fictitious Play 3.4 Numerical Analysis 4 Numerical Experiments and 4.1 A Non-local OSMFG Example 4.2 A Local OSMFG Example 5 Conclusion, Acknowledgement, and References 2.3 Pure Strategy Equilibrium for OSMFG As for pure strategy equilibrium, we require in addition that all agents choose the same control when they have multiple optimal controls. This is the simplest notion of equilibrium. Now we construct the pure strategy equilibrium in detail. Suppose that all agents apply the same optimal control, i.e. exit when (x, t) ∈ {u = ψ} and stay when (x, t) ∈ {u < ψ}, the actual crowd propagation will satisfy the following Fokker-Planck equation: When the actual crowd propagation matches the guessed one (i.e. m = µ), we can combine (2.2) and (2.3) to obtain the PDE system of the Nash equilibrium with pure strategy: We have mentioned that in some regions of the set {u = ψ}, agents are indifferent between exiting the game and staying in the game. And we have required all agents to leave in such cases. In other words, when an agent faces choices that bring the same cost, they should all choose a specific control rather than individually make a random choice. Therefore, we refer to (2.4) as the pure strategy equilibrium of mean field games with optimal stopping. However, the pure strategy equilibrium may not exist for mean field game models. In fact, we have the following result: Theorem 2.1 There exists f and ψ such that there is no solution for (2.4). We refer to [5] for the proof of such a non-existence result for the stationary case. The construction of f and ψ for the time-dependent case (2.4) is similar and we omit the details here. Instead of elaborating on the proof, we provide an economic intuition for such non-existence. Consider a game with a population of agents facing an optimal stopping problem, where agents experience crowd aversion. As we will show, there exists no pure strategy Nash equilibrium in this setting. Suppose all other agents follow the pure strategy of exiting at time t. For any individual agent, the cost of staying past t will be low due to crowd aversion. Thus, the agent’s best response is to deviate from the crowd and continue playing. On the other hand, if all agents use the pure strategy of staying past t, an individual agent will prefer exiting at t to avoid the crowd. In either case, no single pure strategy can be sustained in equilibrium, as agents have an incentive to deviate. The underlying issue is that the definition of pure strategy equilibrium requires all agents to make exactly the same decision, even when indifferent. If we relax this and allow some agents to exit at t while others stay, or have agents randomize between the two actions, an equilibrium may exist. This leads to the concept of mixed strategy equilibrium, where agents follow randomized policies that make opponents indifferent across actions. By permitting randomization, mixed equilibria can capture stable outcomes in games without pure strategy solutions. This provides an avenue to model strategic interactions in mean field games with crowd aversion and optimal stopping. This paper is available on arxiv under CC 4.0 license. Authors: (1) Chengfeng Shen, School of Mathematical Sciences, Peking University, Beijing; (2) Yifan Luo, School of Mathematical Sciences, Peking University, Beijing; (3) Zhennan Zhou, Beijing International Center for Mathematical Research, Peking University. Authors: Authors: (1) Chengfeng Shen, School of Mathematical Sciences, Peking University, Beijing; (2) Yifan Luo, School of Mathematical Sciences, Peking University, Beijing; (3) Zhennan Zhou, Beijing International Center for Mathematical Research, Peking University. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Model and 2.1 Optimal Stopping and Obstacle Problem 2 Model and 2.1 Optimal Stopping and Obstacle Problem 2.2 Mean Field Games with Optimal Stopping 2.2 Mean Field Games with Optimal Stopping 2.3 Pure Strategy Equilibrium for OSMFG 2.3 Pure Strategy Equilibrium for OSMFG 2.4 Mixed Strategy Equilibrium for OSMFG 2.4 Mixed Strategy Equilibrium for OSMFG 3 Algorithm Construction and 3.1 Fictitious Play 3 Algorithm Construction and 3.1 Fictitious Play 3.2 Convergence of Fictitious Play to Mixed Strategy Equilibrium 3.2 Convergence of Fictitious Play to Mixed Strategy Equilibrium 3.3 Algorithm Based on Fictitious Play 3.3 Algorithm Based on Fictitious Play 3.4 Numerical Analysis 3.4 Numerical Analysis 4 Numerical Experiments and 4.1 A Non-local OSMFG Example 4 Numerical Experiments and 4.1 A Non-local OSMFG Example 4.2 A Local OSMFG Example 4.2 A Local OSMFG Example 5 Conclusion, Acknowledgement, and References 5 Conclusion, Acknowledgement, and References 2.3 Pure Strategy Equilibrium for OSMFG As for pure strategy equilibrium, we require in addition that all agents choose the same control when they have multiple optimal controls. This is the simplest notion of equilibrium. Now we construct the pure strategy equilibrium in detail. Suppose that all agents apply the same optimal control, i.e. exit when (x, t) ∈ {u = ψ} and stay when (x, t) ∈ {u < ψ}, the actual crowd propagation will satisfy the following Fokker-Planck equation: When the actual crowd propagation matches the guessed one (i.e. m = µ), we can combine (2.2) and (2.3) to obtain the PDE system of the Nash equilibrium with pure strategy: We have mentioned that in some regions of the set {u = ψ}, agents are indifferent between exiting the game and staying in the game. And we have required all agents to leave in such cases. In other words, when an agent faces choices that bring the same cost, they should all choose a specific control rather than individually make a random choice. Therefore, we refer to (2.4) as the pure strategy equilibrium of mean field games with optimal stopping. However, the pure strategy equilibrium may not exist for mean field game models. In fact, we have the following result: Theorem 2.1 There exists f and ψ such that there is no solution for (2.4). Theorem 2.1 There exists f and ψ such that there is no solution for (2.4). We refer to [5] for the proof of such a non-existence result for the stationary case. The construction of f and ψ for the time-dependent case (2.4) is similar and we omit the details here. Instead of elaborating on the proof, we provide an economic intuition for such non-existence. f Consider a game with a population of agents facing an optimal stopping problem, where agents experience crowd aversion. As we will show, there exists no pure strategy Nash equilibrium in this setting. Suppose all other agents follow the pure strategy of exiting at time t . For any individual agent, the cost of staying past t will be low due to crowd aversion. Thus, the agent’s best response is to deviate from the crowd and continue playing. On the other hand, if all agents use the pure strategy of staying past t , an individual agent will prefer exiting at t to avoid the crowd. In either case, no single pure strategy can be sustained in equilibrium, as agents have an incentive to deviate. t t t t The underlying issue is that the definition of pure strategy equilibrium requires all agents to make exactly the same decision, even when indifferent. If we relax this and allow some agents to exit at t while others stay, or have agents randomize between the two actions, an equilibrium may exist. This leads to the concept of mixed strategy equilibrium, where agents follow randomized policies that make opponents indifferent across actions. By permitting randomization, mixed equilibria can capture stable outcomes in games without pure strategy solutions. This provides an avenue to model strategic interactions in mean field games with crowd aversion and optimal stopping. t This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Fictitious Play for Mixed Strategy Equilibria in Mean Field Games: Strategy Equilibrium for OSMFG

Artificial intelligence (AI) has made its way into banking and decentralized finance, so it’s no wonder people are now using it to forecast economic trends. Can it trade stocks better than an expert? Will the federal government look kindly upon its role? While only time can tell, several signs provide insight into those questions. The Difference Between Econometric Models and AI Traditional economic forecasts rely on statistical models and analytics tools. They can't really make predictions unless input data is in a particular, structured format. They’re not data-driven to the extent AI is — few tools can keep pace with its processing speeds. Human intuition is far less accurate than econometric models, but it’s used just as often. Instead of plugging numbers into a spreadsheet and paying attention to market forecasts, people make predictions based on their expertise and gut feelings. These strategies are often inaccurate. Take forecasts on the United States Consumer Price Index, for example. While experts estimated prices would rise by 7.5% in 2022, they actually increased by 6.5%, according to the U.S. Bureau of Labor Statistics. AI outperforms econometric models and human intuition. It can interpret complex relationships between variables, even considering outliers on a weighted scale. Also, it mitigates loss aversion, overconfidence, and the gambler’s fallacy, preventing biases from skewing forecasts. AI enables automation, so it can execute trades, adjust prices, or transfer funds at the most advantageous times. Naturally, it also reduces administrative work. According to one estimate, it could automate up to 70% of the workday today. Examples of AI Techniques Used to Forecast Trends Whatever AI technique a person uses influences their forecast generation approach. With machine learning, they can uncover nonlinear relationships between variables. These models can __process complex data in milliseconds __and update it whenever they receive new information. A neural network uses a series of interconnected nodes to recognize patterns. It’s inspired by the human brain — the artificial neurons work together in real-time to solve problems. As a result, its forecasts are precise and reasonable. With natural language processing, someone can process text-based data to make forecasts. This system can also analyze sentiment to determine tone and mood. Instead of pressing buttons, updating a spreadsheet, or entering code, the user can use plain language to generate output. Image recognition works similarly, analyzing images and videos to reveal hidden patterns or forecast trends. For example, when leveraged in an AI-powered surveillance system, it could evaluate foot traffic and purchase frequency to predict the following month’s sales figures. The Implications of AI Forecasts on Various Sectors Naturally, the finance sector will be most impacted by AI-driven economic forecasts. It could automate stock trading, predict inflation, and estimate growth rates with unparalleled accuracy. However, no technology is infallible — an algorithm simply doesn’t have the expertise of an industry professional, even if it has access to massive amounts of data. A good hypothetical is necessary to explain. During the non-fungible token craze, an algorithm may have misinterpreted the fad as a valuable emerging alternative investment. It couldn’t have understood the general public’s negative sentiment, which would’ve skewed its findings. The same situation could happen in e-commerce, where forecasts are vital for knowing how much to order and when to change prices. On one hand, AI could align retailers’ actions with customer demand, improving brand loyalty. Conversely, it might misread the situation because of incomplete information, leading to poor business outcomes. Whether the implications are more positive or negative, big change is coming. As a result, government policies may evolve. They’ll likely target data privacy and copyright instead of policing the act of forecasting. The Senate Task Force on AI has passed at least 15 bills to date, with more on the way. It probably won’t be nearly as strict as European lawmakers. However, any privacy and copyright laws limit what data companies can use in training and forecasting, which would reduce model accuracy right off the bat. Will AI Become the Standard for Forecasting Trends? Since AI is relatively new, it likely won’t replace industry experts and professional stock traders anytime soon. However, whether its hype continues, it will likely have a permanent place in forecasting since it enables automation and rapid insight generation. Artificial intelligence (AI) has made its way into banking and decentralized finance, so it’s no wonder people are now using it to forecast economic trends. Can it trade stocks better than an expert? Will the federal government look kindly upon its role? While only time can tell, several signs provide insight into those questions. The Difference Between Econometric Models and AI Traditional economic forecasts rely on statistical models and analytics tools. They can't really make predictions unless input data is in a particular, structured format. They’re not data-driven to the extent AI is — few tools can keep pace with its processing speeds. rely on statistical models rely on statistical models Human intuition is far less accurate than econometric models, but it’s used just as often. Instead of plugging numbers into a spreadsheet and paying attention to market forecasts, people make predictions based on their expertise and gut feelings. These strategies are often inaccurate. Take forecasts on the United States Consumer Price Index, for example. While experts estimated prices would rise by 7.5% in 2022, they actually increased by 6.5% , according to the U.S. Bureau of Labor Statistics. would rise by 7.5% would rise by 7.5% actually increased by 6.5% actually increased by 6.5% AI outperforms econometric models and human intuition. It can interpret complex relationships between variables, even considering outliers on a weighted scale. Also, it mitigates loss aversion, overconfidence, and the gambler’s fallacy, preventing biases from skewing forecasts. AI enables automation, so it can execute trades, adjust prices, or transfer funds at the most advantageous times. Naturally, it also reduces administrative work. According to one estimate, it could automate up to 70% of the workday today. up to 70% of the workday up to 70% of the workday Examples of AI Techniques Used to Forecast Trends Whatever AI technique a person uses influences their forecast generation approach. With machine learning, they can uncover nonlinear relationships between variables. These models can __ process complex data in milliseconds __and update it whenever they receive new information. process complex data in milliseconds A neural network uses a series of interconnected nodes to recognize patterns. It’s inspired by the human brain — the artificial neurons work together in real-time to solve problems. As a result, its forecasts are precise and reasonable. With natural language processing, someone can process text-based data to make forecasts. This system can also analyze sentiment to determine tone and mood. Instead of pressing buttons, updating a spreadsheet, or entering code, the user can use plain language to generate output. Image recognition works similarly, analyzing images and videos to reveal hidden patterns or forecast trends. For example, when leveraged in an AI-powered surveillance system, it could evaluate foot traffic and purchase frequency to predict the following month’s sales figures. The Implications of AI Forecasts on Various Sectors Naturally, the finance sector will be most impacted by AI-driven economic forecasts. It could automate stock trading, predict inflation, and estimate growth rates with unparalleled accuracy. However, no technology is infallible — an algorithm simply doesn’t have the expertise of an industry professional, even if it has access to massive amounts of data. A good hypothetical is necessary to explain. During the non-fungible token craze, an algorithm may have misinterpreted the fad as a valuable emerging alternative investment. It couldn’t have understood the general public’s negative sentiment, which would’ve skewed its findings. The same situation could happen in e-commerce, where forecasts are vital for knowing how much to order and when to change prices. On one hand, AI could align retailers’ actions with customer demand, improving brand loyalty. Conversely, it might misread the situation because of incomplete information, leading to poor business outcomes. Whether the implications are more positive or negative, big change is coming. As a result, government policies may evolve. They’ll likely target data privacy and copyright instead of policing the act of forecasting. The Senate Task Force on AI has passed at least 15 bills to date, with more on the way. It probably won’t be nearly as strict as European lawmakers. However, any privacy and copyright laws limit what data companies can use in training and forecasting, which would reduce model accuracy right off the bat. passed at least 15 bills passed at least 15 bills Will AI Become the Standard for Forecasting Trends? Since AI is relatively new, it likely won’t replace industry experts and professional stock traders anytime soon. However, whether its hype continues, it will likely have a permanent place in forecasting since it enables automation and rapid insight generation.

The Role of AI in Forecasting Economic Trends

Cryptocurrency has sparked intense debate since Bitcoin’s inception in 2009, with proponents hailing it as the future of money and detractors warning of its volatility. In 2024, the crypto landscape continues to evolve, shaped by technological innovations, regulatory developments, and global economic shifts. With all these changes, the question remains: Is cryptocurrency still a good investment in 2024? The Case for Cryptocurrency Investment in 2024 Maturing Market and Institutional Adoption The cryptocurrency market has matured significantly over the past few years. Institutional investors, such as BlackRock, Fidelity, and JPMorgan, are increasingly incorporating crypto assets into their portfolios. This growing institutional presence adds credibility and, in theory, should reduce volatility, making the crypto market more appealing to long-term investors. The introduction of Bitcoin ETFs in the U.S. and other financial products tied to cryptocurrencies signals a shift toward more mainstream acceptance. Major payment platforms, including PayPal and Visa, have integrated cryptocurrency payments, further expanding the utility of digital currencies beyond speculative investments. Diversification and Hedge Against Inflation Cryptocurrencies, particularly Bitcoin, have often been viewed as a "digital gold" or a hedge against inflation. With inflation concerns persisting globally due to the aftermath of the COVID-19 pandemic, rising energy prices, and ongoing geopolitical instability, some investors turn to Bitcoin and other cryptocurrencies to protect their wealth. Although Bitcoin has shown significant volatility, its limited supply and decentralized nature make it appealing to those looking to diversify their investment portfolios beyond traditional assets like stocks and bonds. Bitcoin, in particular, has demonstrated a strong ability to hold its value during inflationary periods. Despite short-term volatility, some analysts argue that its long-term trend has proven to be an effective store of value for investors concerned about the devaluation of fiat currencies. Technological Innovation and New Opportunities Beyond established assets like Bitcoin and Ethereum, the cryptocurrency market continues to introduce innovative projects that could revolutionize industries. Decentralized Finance (DeFi), Web3 applications, and non-fungible tokens (NFTs) are transforming sectors ranging from banking to entertainment. For example, DeFi projects offer services like lending, borrowing, and staking without the need for traditional banks, challenging traditional financial systems. Ethereum's transition to Proof of Stake (PoS) and advancements in layer 2 solutions have made the network more scalable and environmentally friendly, positioning it for future growth. Meanwhile, newer blockchain networks such as Solana and Polkadot are making significant strides in terms of speed, scalability, and security, which may present opportunities for tech-savvy investors willing to explore beyond the leading cryptocurrencies. Global Financial Inclusion Cryptocurrencies remain a powerful tool for financial inclusion, particularly in regions where access to traditional banking is limited or expensive. Cryptos allow individuals to store, transfer, and invest money without the need for a bank. As more countries experiment with central bank digital currencies (CBDCs) and stablecoins, this trend of providing financial access is likely to continue, making cryptocurrencies a long-term investment option in underserved markets. Market Analysis: Bitcoin Price Fluctuations and Global Instability in 2024 Bitcoin has always been known for its volatility, and 2024 is no exception. In early 2024, Bitcoin’s price experienced sharp swings due to a combination of macroeconomic factors, regulatory announcements, and shifting investor sentiment. After a significant rally in late 2023, Bitcoin briefly surpassed the $40,000 mark but then saw a pullback to around $30,000 amid concerns about inflation and tightening monetary policies by central banks globally. This price fluctuation can be attributed to several key factors: Global Economic Instability: Bitcoin’s value is often sensitive to global macroeconomic trends. The ongoing Russia-Ukraine conflict, coupled with rising tensions between the U.S. and China, has caused investors to reassess their risk tolerance. These geopolitical uncertainties contribute to price volatility as investors grapple with the broader implications for global markets. Interest Rates and
Inflation: Central banks, including the U.S. Federal Reserve, have maintained hawkish stances on interest rates to combat inflation. Higher interest rates tend to diminish the appeal of speculative assets like Bitcoin, as investors gravitate toward safer, income-generating assets like bonds. As a result, Bitcoin and other cryptos often experience sharp price declines when central banks signal continued rate hikes.
Investor Sentiment: The crypto market is still highly speculative, with prices often driven by sentiment and narratives. In 2024, concerns over crypto regulations, including potential restrictions on stablecoins and tightening of anti-money laundering (AML) policies, have contributed to periods of price weakness. However, bullish sentiment can also quickly return, particularly when favorable regulatory developments or institutional investments are announced. Impact of Global Instability The geopolitical and economic instability in 2024 is influencing Bitcoin’s price in complex ways. On one hand, ongoing conflicts, particularly in Europe and the Middle East, are increasing demand for alternative assets like Bitcoin that are perceived as safe havens in times of uncertainty. Countries with currency instability, such as Turkey and Argentina, have seen increased adoption of Bitcoin as people seek protection from devaluing national currencies. On the other hand, heightened global instability also leads to risk aversion among investors, especially institutional players, who may reduce their exposure to volatile assets like Bitcoin. This dynamic creates a push-pull effect on Bitcoin’s price: while some investors see Bitcoin as a hedge against economic collapse, others view it as too risky during periods of market turbulence. Future Outlook: Where Does Bitcoin Go From Here? Looking ahead, Bitcoin's future in 2024 hinges on several factors: Regulatory Developments: Governments around the world continue to explore crypto regulations. The European Union’s Markets in Crypto-Assets (MiCA) regulation, which was implemented in late 2023, aims to provide a framework for crypto assets across the bloc. The U.S. is also expected to introduce clearer crypto guidelines, which could either bolster or hinder market confidence, depending on the specifics.
Institutional Adoption: More companies are likely to adopt Bitcoin and blockchain technologies, which could further stabilize prices. If institutional interest continues to grow, with pension funds and endowments allocating even small portions of their portfolios to Bitcoin, the long-term price could see steady growth.
Global Macroeconomic Conditions: Bitcoin’s price will also depend heavily on the global economy. If inflation continues to rise, Bitcoin could regain its appeal as a hedge against fiat currency devaluation. However, if central banks succeed in controlling inflation and stabilizing markets, riskier assets like cryptocurrencies may see reduced inflows. Is Cryptocurrency a Good Investment in 2024? Cryptocurrency remains a highly speculative and volatile asset class in 2024. While there are many opportunities for investors, especially those with a high risk tolerance and long-term perspective, the risks cannot be ignored. The recent price fluctuations in Bitcoin, driven by global instability and macroeconomic forces, highlight the importance of timing and market understanding. For investors seeking diversification and exposure to emerging technologies, cryptocurrencies like Bitcoin, Ethereum, and other blockchain projects still offer considerable potential. However, navigating this market requires careful research, a solid risk management strategy, and a willingness to endure periods of high volatility. Ultimately, cryptocurrencies can be a good investment in 2024 for those who are prepared to weather the uncertainties and embrace the long-term growth potential. Cryptocurrency has sparked intense debate since Bitcoin’s inception in 2009, with proponents hailing it as the future of money and detractors warning of its volatility. In 2024, the crypto landscape continues to evolve, shaped by technological innovations, regulatory developments, and global economic shifts. With all these changes, the question remains: Is cryptocurrency still a good investment in 2024? The Case for Cryptocurrency Investment in 2024 Maturing Market and Institutional Adoption The cryptocurrency market has matured significantly over the past few years. Institutional investors, such as BlackRock, Fidelity, and JPMorgan, are increasingly incorporating crypto assets into their portfolios. This growing institutional presence adds credibility and, in theory, should reduce volatility, making the crypto market more appealing to long-term investors. The introduction of Bitcoin ETFs in the U.S. and other financial products tied to cryptocurrencies signals a shift toward more mainstream acceptance. Major payment platforms, including PayPal and Visa, have integrated cryptocurrency payments, further expanding the utility of digital currencies beyond speculative investments. Diversification and Hedge Against Inflation Cryptocurrencies, particularly Bitcoin, have often been viewed as a "digital gold" or a hedge against inflation. With inflation concerns persisting globally due to the aftermath of the COVID-19 pandemic, rising energy prices, and ongoing geopolitical instability, some investors turn to Bitcoin and other cryptocurrencies to protect their wealth. Although Bitcoin has shown significant volatility, its limited supply and decentralized nature make it appealing to those looking to diversify their investment portfolios beyond traditional assets like stocks and bonds. Bitcoin, in particular, has demonstrated a strong ability to hold its value during inflationary periods. Despite short-term volatility, some analysts argue that its long-term trend has proven to be an effective store of value for investors concerned about the devaluation of fiat currencies. Technological Innovation and New Opportunities Beyond established assets like Bitcoin and Ethereum, the cryptocurrency market continues to introduce innovative projects that could revolutionize industries. Decentralized Finance (DeFi), Web3 applications, and non-fungible tokens (NFTs) are transforming sectors ranging from banking to entertainment. For example, DeFi projects offer services like lending, borrowing, and staking without the need for traditional banks, challenging traditional financial systems. Ethereum's transition to Proof of Stake (PoS) and advancements in layer 2 solutions have made the network more scalable and environmentally friendly, positioning it for future growth. Meanwhile, newer blockchain networks such as Solana and Polkadot are making significant strides in terms of speed, scalability, and security, which may present opportunities for tech-savvy investors willing to explore beyond the leading cryptocurrencies. Global Financial Inclusion Cryptocurrencies remain a powerful tool for financial inclusion, particularly in regions where access to traditional banking is limited or expensive. Cryptos allow individuals to store, transfer, and invest money without the need for a bank. As more countries experiment with central bank digital currencies (CBDCs) and stablecoins, this trend of providing financial access is likely to continue, making cryptocurrencies a long-term investment option in underserved markets. Market Analysis: Bitcoin Price Fluctuations and Global Instability in 2024 Bitcoin has always been known for its volatility, and 2024 is no exception. In early 2024, Bitcoin’s price experienced sharp swings due to a combination of macroeconomic factors, regulatory announcements, and shifting investor sentiment. After a significant rally in late 2023, Bitcoin briefly surpassed the $40,000 mark but then saw a pullback to around $30,000 amid concerns about inflation and tightening monetary policies by central banks globally. This price fluctuation can be attributed to several key factors: Global Economic Instability: Bitcoin’s value is often sensitive to global macroeconomic trends. The ongoing Russia-Ukraine conflict, coupled with rising tensions between the U.S. and China, has caused investors to reassess their risk tolerance. These geopolitical uncertainties contribute to price volatility as investors grapple with the broader implications for global markets. Interest Rates and Inflation: Central banks, including the U.S. Federal Reserve, have maintained hawkish stances on interest rates to combat inflation. Higher interest rates tend to diminish the appeal of speculative assets like Bitcoin, as investors gravitate toward safer, income-generating assets like bonds. As a result, Bitcoin and other cryptos often experience sharp price declines when central banks signal continued rate hikes. Investor Sentiment: The crypto market is still highly speculative, with prices often driven by sentiment and narratives. In 2024, concerns over crypto regulations, including potential restrictions on stablecoins and tightening of anti-money laundering (AML) policies, have contributed to periods of price weakness. However, bullish sentiment can also quickly return, particularly when favorable regulatory developments or institutional investments are announced. Global Economic Instability: Bitcoin’s value is often sensitive to global macroeconomic trends. The ongoing Russia-Ukraine conflict, coupled with rising tensions between the U.S. and China, has caused investors to reassess their risk tolerance. These geopolitical uncertainties contribute to price volatility as investors grapple with the broader implications for global markets. Interest Rates and Inflation: Central banks, including the U.S. Federal Reserve, have maintained hawkish stances on interest rates to combat inflation. Higher interest rates tend to diminish the appeal of speculative assets like Bitcoin, as investors gravitate toward safer, income-generating assets like bonds. As a result, Bitcoin and other cryptos often experience sharp price declines when central banks signal continued rate hikes. Investor Sentiment: The crypto market is still highly speculative, with prices often driven by sentiment and narratives. In 2024, concerns over crypto regulations, including potential restrictions on stablecoins and tightening of anti-money laundering (AML) policies, have contributed to periods of price weakness. However, bullish sentiment can also quickly return, particularly when favorable regulatory developments or institutional investments are announced. Impact of Global Instability The geopolitical and economic instability in 2024 is influencing Bitcoin’s price in complex ways. On one hand, ongoing conflicts, particularly in Europe and the Middle East, are increasing demand for alternative assets like Bitcoin that are perceived as safe havens in times of uncertainty. Countries with currency instability, such as Turkey and Argentina, have seen increased adoption of Bitcoin as people seek protection from devaluing national currencies. On the other hand, heightened global instability also leads to risk aversion among investors, especially institutional players, who may reduce their exposure to volatile assets like Bitcoin. This dynamic creates a push-pull effect on Bitcoin’s price: while some investors see Bitcoin as a hedge against economic collapse, others view it as too risky during periods of market turbulence. Future Outlook: Where Does Bitcoin Go From Here? Looking ahead, Bitcoin's future in 2024 hinges on several factors: Regulatory Developments: Governments around the world continue to explore crypto regulations. The European Union’s Markets in Crypto-Assets (MiCA) regulation, which was implemented in late 2023, aims to provide a framework for crypto assets across the bloc. The U.S. is also expected to introduce clearer crypto guidelines, which could either bolster or hinder market confidence, depending on the specifics. Institutional Adoption: More companies are likely to adopt Bitcoin and blockchain technologies, which could further stabilize prices. If institutional interest continues to grow, with pension funds and endowments allocating even small portions of their portfolios to Bitcoin, the long-term price could see steady growth. Global Macroeconomic Conditions: Bitcoin’s price will also depend heavily on the global economy. If inflation continues to rise, Bitcoin could regain its appeal as a hedge against fiat currency devaluation. However, if central banks succeed in controlling inflation and stabilizing markets, riskier assets like cryptocurrencies may see reduced inflows. Regulatory Developments: Governments around the world continue to explore crypto regulations. The European Union’s Markets in Crypto-Assets (MiCA) regulation, which was implemented in late 2023, aims to provide a framework for crypto assets across the bloc. The U.S. is also expected to introduce clearer crypto guidelines, which could either bolster or hinder market confidence, depending on the specifics. Institutional Adoption: More companies are likely to adopt Bitcoin and blockchain technologies, which could further stabilize prices. If institutional interest continues to grow, with pension funds and endowments allocating even small portions of their portfolios to Bitcoin, the long-term price could see steady growth. Global Macroeconomic Conditions: Bitcoin’s price will also depend heavily on the global economy. If inflation continues to rise, Bitcoin could regain its appeal as a hedge against fiat currency devaluation. However, if central banks succeed in controlling inflation and stabilizing markets, riskier assets like cryptocurrencies may see reduced inflows. Is Cryptocurrency a Good Investment in 2024? Cryptocurrency remains a highly speculative and volatile asset class in 2024. While there are many opportunities for investors, especially those with a high risk tolerance and long-term perspective, the risks cannot be ignored. The recent price fluctuations in Bitcoin, driven by global instability and macroeconomic forces, highlight the importance of timing and market understanding. For investors seeking diversification and exposure to emerging technologies, cryptocurrencies like Bitcoin, Ethereum, and other blockchain projects still offer considerable potential. However, navigating this market requires careful research, a solid risk management strategy, and a willingness to endure periods of high volatility. Ultimately, cryptocurrencies can be a good investment in 2024 for those who are prepared to weather the uncertainties and embrace the long-term growth potential.

Cryptocurrency in 2024: A Safe Haven or Risky Bet Amid Global Geopolitical Instability?

ክሪፕቶ ምንዛሬ በ2024፡ ደህንነቱ የተጠበቀ ቦታ ወይስ አደገኛ ውርርድ በአለምአቀፍ ጂኦፖለቲካል አለመረጋጋት መካከል?

العملات المشفرة في عام 2024: ملاذ آمن أم رهان محفوف بالمخاطر في ظل عدم الاستقرار الجيوسياسي العالمي؟

Criptomoneda 2024 maran: ¿Mä Refugio Seguro jan ukax Apuesta Riesgosa Inestabilidad Geopolítica Global taypin?

Криптовалута през 2024 г.: Сигурно убежище или рисков залог на фона на глобалната геополитическа нестабилност?

Kriptovaluta u 2024.: Sigurno utočište ili rizična opklada usred globalne geopolitičke nestabilnosti?

Criptomonedas en 2024: ¿Un refugio seguro o una apuesta arriesgada en medio de la inestabilidad geopolítica mundial?

Cryptocurrency nan 2024: yon refij ki an sekirite oswa parye ki riske nan mitan enstabilite jeopolitik mondyal?

2024 年の暗号通貨: 世界的な地政学的不安定さの中での安全な避難先か、それとも危険な賭けか?

រូបិយប័ណ្ណគ្រីបតូនៅឆ្នាំ 2024៖ កន្លែងសុវត្ថិភាព ឬការភ្នាល់ហានិភ័យចំពេលអស្ថិរភាពភូមិសាស្ត្រនយោបាយសកល?

Cryptocurrency pada 2024: Syurga Selamat atau Pertaruhan Berisiko Di Tengah-tengah Ketidakstabilan Geopolitik Global?

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