US government bonds rallied Friday, adding to their monthly gain, after benign inflation data kept alive predictions that the Federal Reserve will cut interest rates at least once this year.

Treasury Yields Dip After Inflation Gauges Match Estimates - Bloomberg

Before it’s here, it’s on the Bloomberg Terminal

Trump Raises $34.8 Million in Less Than a Day Off Guilty Verdict - Bloomberg

Fed’s Favored Inflation Gauge Cools, Spending Unexpectedly Drops - Bloomberg

European stock futures were flat and Asian shares rose as traders awaited key inflation data in the region as well as the US later on Friday.

Stock Market Today: Dow, S&P Live Updates for May 31 - Bloomberg

The guilty verdict in Donald Trump’s Manhattan criminal trial injects an explosive and unpredictable new variable into a presidential race locked in stasis, distinguished primarily by voter frustrations with their choice of two well-known and unpopular major candidates.

Trump Guilty Verdict Adds Twist to 2024 Race Against Biden - Bloomberg

The former president faced the reckoning he has long deserved, but the more critical verdict will come on Election Day.

Trump Fought the Law and the Law Finally Won - Bloomberg

Donald Trump Becomes First Former US President Guilty of Crimes

While the benchmark for US equities hasn’t moved, individual stocks have continued to trade. Photographer: Scott Eells/Bloomberg

S&P 500 (SPX), Dow Jones (INDU) Indices Live Pricing Issues Reported by Traders - Bloomberg

Dell’s $3.8 Billion AI Server Backlog Fails to Impress Investors - Bloomberg

A Nvidia Corp. artificial intelligence supercomputing graphics processing unit.Photographer: I-Hwa Cheng/Bloomberg

US Is Slowing AI Chip Exports to Middle East by Nvidia, AMD - Bloomberg

Bloomberg LP

bloomberg

On March 30th, Bloomberg, a top provider of information for professional financial market participants, unveiled a new artificial intelligence model named . This new AI model is set to change the finance industry. BloombergGPT BloombergGPT is specifically designed to understand financial language and can process complex financial information, such as market trends, risk assessments, and portfolio optimization. This technology can be used in a variety of financial applications, including asset management, risk management, and trading. Finance is one of the most data-intensive industries, and the use of industry-specific GPTs has the potential to revolutionize the way financial analysis, risk assessment, and decision-making are done. Judging by , the highly specialized neural network outperforms its competitors in solving specific financial problems. initial feedback A published by the company details the development of "BloombergGPT," a new LLM model that has been trained to handle the vast amount of financial data to solve various natural language processing (NLP) challenges in the financial industry. research paper The authors explained model choice, learning process, and evaluation methodology. As a next step, they plan to release training logs (chronicles) detailing  BloombergGPT training experience. Bloomberg built a 50 billion parameter LLM which is significantly smaller than GPT4 but trained on a wide range of financial data, and their findings show that the mixed data sets that they used outperform existing models. It took developers 53 days to train the neural network - during which time 569 billion tokens of information were circulated. The team created a data set of 363 billion tokens based on Bloomberg's own extensive data sources and augmented by 345 billion tokens from general-purpose datasets. What Is BloombergGPT Capable of? Generating draft routine market reports and summaries for clients, reducing the need for manual report generation, searching for information about various companies and their executives, creating a chart of an organization's executive-level structure and linkages based on the company names and names of executives, generating an initial draft of an SEC filing, generating headlines for news from longer financial blurbs, retrieving specific elements of financial statements for specific periods with a single prompt. BloombergGpt’s Potential Impact on the Fintech Industry Remains to Be Seen, but It Could Have Several Potential Uses: More informed investment decisions and reduce the risk of financial losses, more effective trading strategies, fraud detection and prevention, stronger customer relationships, personalized investment recommendations, advice, etc. Does It Make a Difference? says that they used diverse structured and unstructured financial datasets, including company filings, transcripts of Bloomberg News transcripts, opinions, press releases, etc. Bloomberg But the majority of this financial data is publicly available and can be obtained from other sources, except paid Bloomberg content. So, what is the real advantage of specific GPT if general ones could be trained on the same publicly available financial data? It's important to note that GPT models are typically pre-trained on vast amounts of data from diverse sources, and then fine-tuned for specific tasks. This means that even if GPT-4 were able to match the performance of finance-specific GPT models on financial tasks, it would still require specialized training and fine-tuning to achieve optimal performance in financial applications. Technically, BloombergGPT outperforms OpenAI's ChatGPT-3 and Google's LaMDA language engines but falls behind the larger LLaMA and PaLM. Bloomberg GPT isn’t that big compared to other general-purpose transformers(GPTs). The killing feature of this transformer for Bloomberg terminal users is the ability to generate , and this is a proprietary language. Bloomberg query language “The quality of machine learning and NLP models comes down to the data you put into them.” “Thanks to the collection of financial documents Bloomberg has curated over four decades, we were able to carefully create a large and clean, domain-specific dataset to train a LLM that is best suited for financial use cases. We’re excited to use BloombergGPT to improve existing NLP workflows, while also imagining new ways to put this model to work to delight our customers,”  explained Gideon Mann, Head of Bloomberg’s ML Product and Research team. BloombergGPT is a cutting-edge tool that has the potential to transform the finance industry. By using advanced AI techniques, it can help financial institutions analyze financial data with greater speed and precision, manage risks more effectively, and develop better trading strategies. Additionally, BloombergGPT can help enhance customer service, allowing financial institutions to better serve their clients. As this technology continues to evolve, we can expect to see more exciting advancements in the finance industry. Disclaimer: AI was used to write portions of this article. Specifically, it was used to write a number of key paragraphs, namely paragraphs 2,7,9,10.

Money Talks: How Bloomberg GPT Is Changing the Game

If you like stocks and are careful with the way you spend your money, (me saying it seems counter-intuitive given that I bought GME at the peak, I know) you know how much time goes into buying shares of stock. Check out the Gamestonk Terminal GitHub Link : Before Investing in Stocks, you need to Find stocks that are somehow undervalued; Research on the company, and its competitors; Check that the financials are healthy; Look into different technical indicators; Investigate SEC fillings and Insider activity; Look up for next earnings date and analysts estimates; Estimate market’s sentiment through Reddit, Twitter, Stocktwits; Read news;. … the list goes on. It’s tedious and I don’t have $24k for a Bloomberg terminal. Which led me to the idea during X'mas break to spend the time creating my own terminal. I introduce you to “Gamestonk Terminal” (probably should’ve sent 1 tweet every day to Elon Musk for copyright permission heh heh). As someone mentioned, this is meant to be like a swiss army knife for finance. My Gamestonk Terminal contains the following features/functionalities: Some features are: Top gainers; Sectors performance; upcoming earnings releases; top high shorted interest stocks; top stocks with low float; top orders on fidelity; and some SPAC websites with news/calendars. Discover New Stocks: Main features are: Scrolling through Reddit main posts, and most tickers mentions; Extracting trending symbols on stocktwits, or even stocktwit sentiment based on bull/bear flags; Twitter in-depth sentiment prediction using AI; Google mentions over time. Gauge Market Sentiment: List of good pages to do research on a stock, e.g. macroaxis, zacks, macrotrends, .. Research Web pages: Read financials from a company from Market Watch, Yahoo Finance, Alpha Vantage, and Financial Modeling Prep API. Since I only rely on free data, I added the information from all of these, so that the user can get it from the source it trusts the most. Also exports management team behind stock, along with their pages on Google, to speed up research process. Fundamental Analysis: The usual technical indicators: sma, rsi, macd, adx, bbands, and more. Technical Analysis: It has several features that I found to be really useful. Some of them are: Latest news of the company; Analyst prices and ratings; Price target from several analysts plot over time vs stock price; Insider activity, and these timestamps marked on the stock price historical data; Latest SEC fillings; Short interest over time; A check for financial warnings based on Sean Seah book. Due Diligence: The one I had more fun with. It tries to predict the stock price, from simple models like sma and arima to complex neural network models, like LSTM. The additional capability here is that all of these are easy to configure. Either through command line arguments, or even in form of a configuration file to define your NN. It also allows backtesting. Prediction Techniques: Allows you to run several jobs functionalities and write daily notes on a stock, so that you can assess what you thought about the stock in the past, to perform better decisions. Reports: Allows you to compare different stocks. Comparitive Analysis: : Cryptocurrencies, Portfolio Analysis, Credit Analysis. Feel free to add the features you'd like and we would happily work on it. On the ROADMAP This project will always remain open-source, and the idea is that it can grow substantially over-time so that more and more people start taking advantage of it. Feel free to contribute to the project. Feedback is extremely welcome!

Gamestonk Terminal: The Next Best Thing To A $24k Bloomberg Terminal

If you follow crypto news and crypto twitter (CT) closely, there’s a huge backlash against co-founder of Ethereum, Vitalik Buterin for his alternative comments on crypto boom in current scenario. Let’s see if this outrage makes sense or his comments make more sense In an interview with Bloomberg, Vitalik Buterin went on to make some sensible comments about the current state (awareness of cryptocurrency) and the possibility of Lambos or Moons as follows If you talk to the average educated person at this point, they probably have heard of blockchain at least once. There isn’t an opportunity for yet another 1,000-times growth in anything in the space anymore. With this comment, interviewers went crazy and the media around the world picked up this statement to kill the morale of every crypto believer or a newbie, which are now called crypto curious and made them lose confidence by saying “Vitalik himself has said that there’s no room for cryptocurrencies to grow” — which sounds quite pessimistic. However, Vitalik has clarified on his twitter by tweeting as below, To be clear, I never said that there is “no room for growth” in the crypto ecosystem. I said there is no room for *1000x price increases*. A 1000x price increase from today means $200T in crypto, or ~an entire 70% of today’s global wealth being in crypto. What I *actually* said is that, because large portions of the population have already heard of crypto, further growth of crypto in any sense must come from *depth* (ie. actual usage), and not bringing in more attention and to add it to the clarification, he made fun troll of Justin Sun of Tron Me: obviously, let’s be realistic, the entire world wealth is not going to turn into cryptocurrencies… Media: VITALIK IS A PESSIMIST!!! 1! 1 Guys, if you spin things this way you’re *incentivizing* people to act more like @justinsuntron Above stuff sounds funny given how media twists certain logical statements into pessimistic ones to spread FUD (Fear, Uncertainty, Despair)in the crypto crowd. It’s important for everyone to follow the actual people’s twitter to find the clarifications and statements as they usually never make it to the headlines — unless media has a chance to twist them too! An attempt to be part of crypto myth busters series! Follow me on Twitter and Medium for more such informational articles along with what we are working on to help the crypto community. Check out the tax calculation software, — we’ve launched for the benefit of crypto-community. This will reduce a lot of your effort in consolidating and reviewing trades on various exchanges and can easily calculate taxable gains in few minutes. Signup now on BearTax www.bear.tax _Simple & trusted platform for crypto traders, accounting professionals, CPAs to connect exchanges, import transactions…_bear.tax BearTax - Your Crypto Tax Assistant

Don’t take Vitalik’s 1000x comment in a negative way — it’s just sensible

Will the self-driving cars software market drive all of the transportation market? Incumbents in logistics always circle back to the one dashboard solution. If uber can achieve profitability with a large labor force (Dara doesn't definitively say it in the video but he implies it), what can they do with an a super fleet? How costly is a super fleet of self driving cars to buy and maintain and maintain, or to lease? And maintain. And what happens to the current drivers? The self driving cars company may be approaching one of the larger worker displacements events of our time.

Uber CEO Interview, and the Advent of Self Driving Car Technology

Almost 2 years ago, I started building my own investment research platform. 2 months later I named it Gamestonk Terminal, made it open source and shared it on Reddit. The rest is history. Since then, we have surpassed . Raised $ 8.8 million in our seed round. Build a very competitive team, and our OpenBB brand is now recognized by most in the financial sector space. You can read more about our story . 17,800 stars on Github here . Over the past few months, we have been heads down and building, and today, I’m excited to share with you the announcement of OpenBB Terminal 2.0. Our mission to democratize investment research has not changed The headline is: OpenBB Terminal 2.0 is more than an application, it’s a platform. A summary: We are releasing OpenBB SDK, which allows developers to use a single API to access the world’s raw financial data in order to build their own products/dashboards. The SDK will allow users to create report templates in a matter of minutes and run them for custom tickers at any time in a matter of seconds. Instead of spending hours and starting a report from scratch every single time. We envision a world where the community can share these and help each other at becoming better investors. We are also bringing a state-of-the-art AI / ML toolkit to the financial industry to be used alongside all the data sources our platform has access to (stocks, crypto, NFTs, options, forex, ETFs, mutual funds, macroeconomic data and even alternative data). For more information, you can read our announcement here: https://openbb.co/blog/openbb-terminal-2-acai Or even better, watch the announcement live ! More than 1100 participants have already signed up to join us. here For anything else, feel free to reach out to me directly on , or join the OpenBB journey . Twitter here Also Published Here

OpenBB Terminal 2.0 is More Than an Alternative for Bloomberg Terminal 

GM. This is Crypto Player One, the daily newsletter on crypto gaming, NFTs, and the Metaverse. We are the protein bars of crypto gaming - sweet and nutritious. This Wednesday comes with: Daily Trivia. Bloomberg's top finance writer talks NFTs and the Metaverse. In Other News. Meme of the Day. Daily Trivia What's the name of the first interactive analog computer game? Scroll down to find out the answer. Bloomberg's Top Finance Writer Talks NFTs and The Metaverse Matt Levine, one of the best finance writers in the world, wrote an entire 40,000-word issue of Bloomberg Businessweek covering all things crypto. This is the second time in the history of Bloomberg that a writer has written a cover-to-cover issue of the magazine. So, as fans of Matt, we had to read it. While he talked about a bunch of stuff that's outside of the scope of this newsletter, he discussed NFTs, the Metaverse, and online communities. Here are the best bits: 1. NFTs Matt points out that most NFTs don't live on the blockchain. How could that be? Well, Matt refers to a by the founder of Signal, Moxie Marlinspike. blog post In the piece, Moxie explains how NFTs contain an URL that points to the data storing your image, but that data doesn't live on-chain. So the NFT links to a server that stores its image, title, and description. And anyone with access to that server can change the NFT. So what does that mean for the NFT space in general? , intellectual property rights and royalties are not enforceable on the blockchain. As we recently found out Many NFT companies sell their projects by offering image rights to the IP or promising to transfer those rights to the holders. But all of that happens off-chain, and it's not legally enforceable. So, in the end, when you buy an NFT, you get a pointer to a piece of digital art. Matt puts it beautifully: "These NFTs may not represent ownership in any particularly binding sense, but they represent a feeling of ownership. The technological and legal connections between blockchain and JPEG and ownership are a bit thin, but the connections are enforced culturally." He gives an example with Bored Apes. It's an online community with its own real-life events and social norms (using a monkey NFT as your Twitter profile pic.) 2. The Metaverse Matt doesn't know what the Metaverse is. Let us help you, Matt. It's a term describing a potential future. A future in which we all live, work and play digitally. He does mention that if indeed that's the future, buying, monitoring, and distributing digital goods online will be important. And we can make all of these goods scarce with NFTs. 3. Online Communities Mark Zuckerberg built one of the most valuable companies in the world, Meta, by focusing on human connection. And now, his pivot to the Metaverse goes to show that his vision is that most, if not all, parts of our lives will take place online. And if crypto has taught us something is that the human social life going online has a sh*t ton of economic value. People form communities online, making them valuable and getting some of that value for themselves. Want to read the entire 40 000 word article? Click here! In Other News The Web3 ticketing company, YellowHeart, launched a metaverse music venue. Oasys, the blockchain focused on crypto gaming, launched its mainnet. Seba Bank has started offering institutional-grade, certified, and audited hot and cold storage custody for blue-chip NFTs. Meme of the Day Answer Tennis for Two Want to Stay On Top of All Things Crypto Gaming and NFTs? Sign up for the daily newsletter - it’s free!

🎮 Bloomberg's Top Finance Writer On NFTs and The Metaverse

When we think about sales, we generally don’t think of Michael Bloomberg. And when we think of Michael Bloomberg (MB), we may think of money, we may think of him being the ex-mayor of New York, a democratic candidate in the 2020 election, or a billionaire with a net worth of roughly $69 billion, but we don’t all think of how he got there. How he hustled and sold his first-ever terminal. But Michael (and the Bloomberg terminal) started somewhere. A quick history of MB — he was a Harvard MBA and started working at the investment bank Salomon Brothers. When Salomon Brother’s was acquired by Phibro, Michael Bloomberg got out with a nice $10m dollar paycheck. This first career milestone gave him the capital to develop the Bloomberg terminal. He spent over $4m creating the first terminal, with the only money he had at the time from his exit from Salomon Brother’s. (Does taking the proceeds from an initial successful foray and reinvesting it in another successful future endeavor ring any bells? cough *musk* cough). Now keep in mind, he had now spent $4m, was sustaining a fairly upscale lifestyle, and still didn’t have a single customer yet. This is where our sales story starts. Michael Bloomberg did not rush his first customer. Think of the next three years of his life as the type of work a company should do over the course of a sales cycle with a customer. These are the steps he took to land his first customer. Year 1 (Step 1) Sales Steps Taken: Build the relationship. Land a small deal to establish rapport with the customer. MB was doing odd consulting jobs, and one of his clients was Merrill Lynch & co. In terms of being the ideal customer, Merill checked all the boxes. The goal of this consulting work was to establish a relationship with the company and start to form relationships with champions and decision-makers within the organization. For MB, Merrill was the ideal customer profile. Over the course of the year, consulting paid MB roughly 100k and allowed him to continue his sales process. Year 2 (Step 2) Steps Taken: Identify buyer persona/decision-maker. Build rapport with the decision-maker to increase wallet share (the amount of money spent & products/services purchased from MB. MB didn’t have the resources to develop relationships with all potential buyers of the Bloomberg Terminal, so he decided to double down and focus entirely on the company he already had built some relationship capital with. He still had access to Merrill Lynch’s offices from the consulting, so every morning at 6 am he woke up, drive to the deli across the street from Merrill Lynch’s headquarters at six a.m. and buy coffee (with and without milk) and tea (with and without milk), plus a few sugars on the side. He roamed the halls and looked for anyone who was in the office reading a newspaper. He’d walk in and say… “Hi, I’m Mike Bloomberg. I bought you a cup of coffee. I’d just like to bend your ear.” His rationale? No one was going to tell him to go away if he brought them a coffee (law of reciprocity). The effort paid off. He got a meeting with Ed Moriarty, a managing director at Merill Lynch. This was the decision-maker he needed to convince. Year 3 (Step 3) Steps Taken: Pitch, mitigate risk & go above and beyond the buyers' expectations. Bloomberg finally had a chance to pitch. It went horrible. The Merill Lynch exec team saw the terminal as a great idea but also something they could do themselves. They forecasted that in 6 months, they could build an alternative internally…. even better than what MB pitched. MB took a bet and eliminated all downside risks associated with his own product. If it can’t install it, and you don’t love it in 6 months, you don’t have to pay for it. If you want to sell more, take notes. Eliminate risk. It was one of the easiest decisions the firm ever made. And the exec team immediately signed off on the project, giving MB 6 months to prove himself. When the 6 months were up, MB tested the first iteration of the Bloomberg terminal at Merill Lynch. Although there were some software bugs (which were quickly ironed out), the Merill Lynch team loved that he had fulfilled in the time frame they allotted and saved them the trouble from having to produce a complicated product from scratch. They proceeded with the entire order of 20 units and even added 2 more. With MB selling his terminal to one of the top 5 banks in the world as his client, this was just the beginning of the Bloomberg terminal and Michael Bloomberg’s legacy as a business leader and magnate. Also published on . https://newsletter.roioverload.com/p/using-coffee-and-tea-to-close-deals

Startup Lessons: How to Sell Your Product Micheal Bloomberg's Way

On June 20, 2016 I published the article “ ” On the Rebound from Epic Failure” describing my failure as CEO of Fab.com. Today, . The Bloomberg story is also . Bloomberg published a follow-up to that article along with a podcast re-posted on Medium . My response follows I wish to commend , , and the team at Bloomberg for their fair reporting. Sarah Frier Brad Stone As I wrote , I am sorry to our investors, our employees, and everyone involved with Fab. I failed and I take full responsibility for the . in my Medium post failure I am currently focusing all of my efforts towards rehabilitating my reputation by building a product, , that I hope people will value and appreciate, and by steering the Company, Pepo Inc., in a manner that I hope earns the trust and respect of employees, partners, investors, and onlookers. I know that will take time and that actions speak louder than words. Pepo At the end of the Bloomberg podcast Sarah & Brad question whether employees and investors would ever follow me again. That’s a fair question that I earned. Rebounding from epic failure should be hard and it should come with skepticism. Or, put another way, I started Fab with the benefit of the doubt based on my most recent prior history founding , while I start Pepo with mountains of doubt to overcome based on my most recent prior history founding Fab — as it should be. socialmedian In the course of her reporting, Sarah asked me what lessons I would impart to founders who hear my story. While I don’t pretend to have all the answers, here is some of my perspective: Go to great lengths to remain humble and fight hubris at all costs. Run your company on day 365 and day 1000 with the same startup mentality you began with on day 1. Companies are founded with a blend of optimism and fear: “We think this can work, we believe this can work, but we have no idea if it will work.” Retain that same mentality no matter how big you grow or how much success you achieve. Don’t ever allow yourself to slip into thinking “we figured it out” or you risk losing it all. If you are ambitious and if you grow your company fast, this will be your own personal epic battle. It is extremely hard to remain humble when you are piloting a rocket ship. It is extremely hard to remain humble when the world is telling you you are winning. Always remember your startup roots, and surround yourself with advisors who challenge you and who see dangers where you see opportunity. Listen to the pockets of doubt and criticism and understand where they are coming from. Acknowledge and address the weaknesses of your business model more than your strengths. The ultimate respect you can demonstrate to your team and to your investors is to carefully protect their investments. You can only do that if you protect your blindside, embrace voices of dissent, fight arrogance, and remain humble. -jason

My Response to Bloomberg’s Article: “How Not to Strangle Your Unicorn — Jason Goldberg’s Fab.com

There are certain times in the history of investing when trends turn into full market swings, and it’s hard to find a better example than Environmental, Social, and Governance investing strategies. The focus for ESG investing was always on sustainability, corporate responsibility, and ethical governance, and it’s really reshaped how both individuals and institutional investors have allocated capital in the last decade. In recent years, though, ESG investing has been turned on its head. There have been many critics of the strategy, especially on performance metrics, and concerns about “greenwashing” have raised massive doubts about the true impacts of these strategies as well. It’s led to the growing debate: Is ESG investing losing its appeal, or is it evolving into a more mature and transparent market? The Rise of ESG Investing In the 1960s, there was a term for investing that was similar, called Socially Responsible Investing (SRI). This was really the beginning of ESG, but SRI investing mainly avoided certain industries, like tobacco and weapons. Over the decades, SRI evolved into the broader ESG framework, which gained significant traction in the 2000s. After the 2008 financial crisis, it grew even further as investors wanted safer, more responsible investment options. And by the 2010s, ESG transformed into a mainstream investment strategy from the niche offering it started as. The COVID-19 pandemic further accelerated growth in the area, as it highlighted how corporations can be important players in addressing global challenges. Maxim Manturov, head of investment research at Freedom24, explained: “The COVID-19 pandemic has made it clear that corporations play a crucial role in solving global problems. Governments alone cannot solve problems such as disease, climate change, workplace safety, or pay inequality based on gender and race. These problems affect people around the world and require the resources and expertise of corporations to address them. So, what makes a stock a good ESG investment? A good ESG company focuses on supporting environmental programmes, caring for its employees, making a positive contribution to local communities and delivering value to shareholders. It is important to validate its reputation through ESG rating agencies such as MSCI and Sustainalytics. Financial performance remains critical to ESG investing. As an ESG investor, you can promote responsible corporate behaviour without sacrificing returns. Institutional investors use ESG ratings to support the high standards of companies, and individual investors can do the same. By investing in leading ESG companies, you support initiatives such as energy efficiency, waste reduction, safer workplaces, and ethical business practices.” Globally, ESG investments are projected to continue with expansion. According to Bloomberg, ESG assets are expected to “reach over $40 trillion by 2030 – over 25% of” global assets under management. Europe is expected to be the leader of this sector with about $18 trillion in ESG assets, while the US may have slower growth due to political and economic challenges. Challenges Facing ESG Investing Recent years, however, haven’t been so rosy in the ESG investing space. The biggest challenge has been the issue of ‘greenwashing’, where companies falsely claim or exaggerate their environmental and social credentials to attract those ESG-conscious dollars. Many calls have been made to increase regulatory scrutiny and also standardisation of ESG reporting frameworks so there’s more transparency and accountability. According to Bloomberg Intelligence, this is exactly what will be needed to give more credibility to ESG investments and should help address some concerns about false claims and inconsistent ESG practices. The next issue for ESG investing is financial performance. A report from the Financial Times noted that ESG ETFs have underperformed their traditional counterparts over the past decade annually by 0.2 percentage points. Much was made about the outperformance in 2020 - often cited as ESG’s financial success, but it was largely a statistical outlier driven by sector biases, as ESG tends to be overweight in technology and underweight in energy, which drove much of the year’s success. Investor sentiment has since been mixed. Some remain committed to ESG for ethical reasons, while others question whether these funds are really delivering long-term returns. Is ESG Dead or Evolving? Despite the challenges facing ESG investing, it is likely evolving rather than dying as it enters a phase of slower growth and consolidation. It’s natural for any growth area or investing. As the market starts to mature, it needs better regulation and transparency. Once it has that and restores investor confidence, it may continue to grow. The core principles of sustainability, corporate responsibility, and governance aren’t going anywhere, especially in this transparent and data-driven digital age. However, to regain credibility, better analysis and accountability are needed. As for performance, there is likely a reason that ESG strategies are failing to outperform, according to Scientific Beta. They found that many ESG strategies rely on superficial ratings rather than the deep, fundamental analysis of companies - reinforcing the need for better ESG strategies that go beyond simple criteria. Evolution can lead to better outcomes for everyone in this scenario. Conclusion To bring it all together, ESG has had significant growth, but now, in its mature stage, there are many challenges. None of which are bigger than greenwashing and underperformance of assets. That being said, ESG is likely not dead - it is evolving. Long-term growth projections, alongside increased regulatory scrutiny, suggest that ESG investing should remain a big part of financial markets in the future. Investors, either ones that want to invest online or offline, should stay informed, understand the limitations of ESG strategies, and be mindful of greenwashing risks. There are certain times in the history of investing when trends turn into full market swings, and it’s hard to find a better example than Environmental, Social, and Governance investing strategies. The focus for ESG investing was always on sustainability, corporate responsibility, and ethical governance, and it’s really reshaped how both individuals and institutional investors have allocated capital in the last decade. In recent years, though, ESG investing has been turned on its head. There have been many critics of the strategy, especially on performance metrics, and concerns about “greenwashing” have raised massive doubts about the true impacts of these strategies as well. It’s led to the growing debate: Is ESG investing losing its appeal, or is it evolving into a more mature and transparent market? The Rise of ESG Investing In the 1960s, there was a term for investing that was similar, called Socially Responsible Investing (SRI). This was really the beginning of ESG, but SRI investing mainly avoided certain industries, like tobacco and weapons. Over the decades, SRI evolved into the broader ESG framework, which gained significant traction in the 2000s. After the 2008 financial crisis, it grew even further as investors wanted safer, more responsible investment options. And by the 2010s, ESG transformed into a mainstream investment strategy from the niche offering it started as. The COVID-19 pandemic further accelerated growth in the area, as it highlighted how corporations can be important players in addressing global challenges. Maxim Manturov, head of investment research at Freedom24, explained: “The COVID-19 pandemic has made it clear that corporations play a crucial role in solving global problems. Governments alone cannot solve problems such as disease, climate change, workplace safety, or pay inequality based on gender and race. These problems affect people around the world and require the resources and expertise of corporations to address them. So, what makes a stock a good ESG investment? A good ESG company focuses on supporting environmental programmes, caring for its employees, making a positive contribution to local communities and delivering value to shareholders. It is important to validate its reputation through ESG rating agencies such as MSCI and Sustainalytics. Financial performance remains critical to ESG investing. As an ESG investor, you can promote responsible corporate behaviour without sacrificing returns. Institutional investors use ESG ratings to support the high standards of companies, and individual investors can do the same. By investing in leading ESG companies, you support initiatives such as energy efficiency, waste reduction, safer workplaces, and ethical business practices.” Globally, ESG investments are projected to continue with expansion. According to Bloomberg , ESG assets are expected to “reach over $40 trillion by 2030 – over 25% of” global assets under management. Europe is expected to be the leader of this sector with about $18 trillion in ESG assets, while the US may have slower growth due to political and economic challenges. Bloomberg Bloomberg Challenges Facing ESG Investing Recent years, however, haven’t been so rosy in the ESG investing space. The biggest challenge has been the issue of ‘ greenwashing ’, where companies falsely claim or exaggerate their environmental and social credentials to attract those ESG-conscious dollars. Many calls have been made to increase regulatory scrutiny and also standardisation of ESG reporting frameworks so there’s more transparency and accountability. According to Bloomberg Intelligence , this is exactly what will be needed to give more credibility to ESG investments and should help address some concerns about false claims and inconsistent ESG practices. greenwashing greenwashing Bloomberg Intelligence Bloomberg Intelligence The next issue for ESG investing is financial performance. A report from the Financial Times noted that ESG ETFs have underperformed their traditional counterparts over the past decade annually by 0.2 percentage points. Much was made about the outperformance in 2020 - often cited as ESG’s financial success, but it was largely a statistical outlier driven by sector biases, as ESG tends to be overweight in technology and underweight in energy, which drove much of the year’s success. Financial Times Financial Times Investor sentiment has since been mixed. Some remain committed to ESG for ethical reasons, while others question whether these funds are really delivering long-term returns. Is ESG Dead or Evolving? Despite the challenges facing ESG investing, it is likely evolving rather than dying as it enters a phase of slower growth and consolidation. It’s natural for any growth area or investing. As the market starts to mature, it needs better regulation and transparency. Once it has that and restores investor confidence, it may continue to grow. The core principles of sustainability , corporate responsibility, and governance aren’t going anywhere, especially in this transparent and data-driven digital age. However, to regain credibility, better analysis and accountability are needed. sustainability As for performance, there is likely a reason that ESG strategies are failing to outperform, according to Scientific Beta. They found that many ESG strategies rely on superficial ratings rather than the deep, fundamental analysis of companies - reinforcing the need for better ESG strategies that go beyond simple criteria. Evolution can lead to better outcomes for everyone in this scenario. Conclusion To bring it all together, ESG has had significant growth, but now, in its mature stage, there are many challenges. None of which are bigger than greenwashing and underperformance of assets. That being said, ESG is likely not dead - it is evolving. Long-term growth projections, alongside increased regulatory scrutiny, suggest that ESG investing should remain a big part of financial markets in the future. Investors, either ones that want to invest online or offline, should stay informed, understand the limitations of ESG strategies, and be mindful of greenwashing risks.

Is ESG Investing Dead? The Rise and Fall of Sustainable Investing

¿Ha muerto la inversión ESG? El auge y la caída de la inversión sostenible

Onko ESG Investing kuollut? Kestävän sijoittamisen nousu ja lasku

האם ESG Investing מת? עלייתה ונפילתה של השקעות בר קיימא

ESG投資は終わったのか？持続可能な投資の興亡

Maty ve ny ESG Investing? Ny fiakarana sy ny fianjeran'ny fampiasam-bola maharitra

ESG Investing үхсэн үү? Тогтвортой хөрөнгө оруулалтын өсөлт ба уналт

Investițiile ESG sunt moarte? Creșterea și scăderea investițiilor durabile

ESG ኢንቨስትመንት ሞይቱ ድዩ? ምልዓልን ምውዳቕን ዘላቒ ወፍሪ

Patay na ba ang ESG Investing? Ang Pagtaas at Pagbagsak ng Sustainable Investing

Xana ESG Ku Vekisa Swi file? Ku Tlakuka na ku Wa ka Vuvekisi lebyi nga heriki

کیا ESG سرمایہ کاری ختم ہو گئی ہے؟ پائیدار سرمایہ کاری کا عروج اور زوال

ESG 投资已死？可持续投资的兴衰

Newer technology is always a game-changing thing. In the case of video games, it’s literally so. And what other new piece of tech to talk about than what’s all the rage at the moment—artificial intelligence or AI? Companies have been quick to adopt AI for various purposes. Whereas the majority of corporations from Bloomberg to Disney see it as a tool to cut costs, the game development industry has a very different take on all this. This take is more creative in nature. It’s being used to develop new story arcs, generate new gameplay ideas, and analyze existing player data at a whole other level. Some companies are even experimenting with AI-based NPCs (non-playable characters) where these seemingly harmless folks can now hold entire conversations and retain context throughout their adventures in-game. We’re at a very early level here. In this post, we’ll focus on a new aspect that can be revolutionized with AI—virtual economies. Let’s hop right into it, shall we? What are Virtual Economies? Believe it or not, virtual economies are one of the most lucrative businesses today. A lot of gamers are a part of at least one such economy, sometimes unknowingly. A virtual economy is an in-game trading system. It ranges from the player-based economy of trading posts in classic MMOs such as World of Warcraft and Elder Scrolls Online to Loot Boxes in CS:GO and Valorant. A strong virtual economy is at the heart of these games. These don’t necessarily have to involve real money transactions. There’s a lot of in-game currency transactions, trading, and item barter involved here. But yes, the most popular forms of virtual economies are tied to real money. The best case would be CS:GO skins that you can buy from reputed sellers. These are skins that you buy with real money for a nice change of clothes or a weapon loot box. It’s an active, thriving community of transactions and items. AI in Virtual Economies Developers are quickly realizing the potential of leveraging AI-based technologies and deep learning models to optimize these in-game virtual economies. The only current problem is the scale. To really scale it up, a developer will need to make some serious investments in AI. Today, suffice it to say that justifying this cost in front of stakeholders isn’t going to go very smoothly. But the potential is there, and we all know it. AI has already shown us how we can build better games. Smart NPCs, dynamic procedural environments, choice-based challenges, matchmaking balances, data analysis, anti-cheat, etc., are all very serious real-world use cases where AI is already doing a lot of work or is poised to be a strong factor in the coming years. Now, let’s shift our focus toward virtual economies. What exactly can AI do in this space? Players trade in-game items ranging from armor and weapons in some games to skins and cosmetics in others. These are facilitated mainly with the help of real-world money or some kind of virtual currency that can only be purchased using real money. With that in mind, here’s how AI can help. Dynamic Pricing AI models can be trained to be good at understanding the virtual economy’s market demand. Many financial models are already available, often proprietarily, to do this for real-world markets. These algorithms will be able to maintain pricing stability just by looking at the market trends, user demand, and trading history of these virtual items. Trend Prediction With all that data, such algorithms or models can easily predict the rise and fall in demand for certain skins or cosmetics, which will allow the marketplace to adjust the listings and prices accordingly. For example, we know that a lot of people buy bundles and treasures in Dota around The International, only to sell them at a higher price later. But when you make all this formulaic, suddenly you can predict a lot more trends than just that. Preventing Fraud AI can also help in detecting instances of fraudulent transactions and suspicious activities. This is a huge benefit for any marketplace moderator or game developer. The strength of an AI model is the training or inferencing part. This is why AI can predict weather better than a supercomputer. It relies on past data. The more data it is fed, the more accurate it will be. So, if a company like Valve trains an AI algorithm on fraudulent transactions and suspicious users, it will quickly learn to spot future offenders, which will be an immense help for all the other players. Leading CS:GO skin selling platforms can develop their own algorithms this way to make sure that their marketplace remains spotless. Such a case has already been implemented by sites such as skinsmonkey. Where Do We Go From Here? The times are changing fast. The future is full of AI, even in the world of video games. Though there’s still time before we can reach a world akin to Cyberpunk 2077, it’s much easier to accept a worldview where we will have AI-controlled virtual economies that can be easily scaled. AI-driven price predictions, automated price setting, authenticity verification, highly optimized trade algorithms, and so on. Artificial intelligence models and algorithms can bring about a net positive change in the world of in-game economies that so many gamers rely on. It won’t be an overstatement to say that games will be much better for player transactions and economies with AI in the picture. Major Concerns in AI Gaming AI Needs Data: AI requires heaps of data and training. Any company that wishes to make a foray here needs to properly label and deconstruct its existing data and then train a model on it. If it’s done in a shallow or incorrect way, such an algorithm will be able to do nothing good for the players.


Constant Learning: At its core, AI models are all about constant learning. If they are not allowed to hook into the economies in a friendly manner, then trends will change before they can pick up on them. This is especially challenging because so far, virtual economy data hasn’t really been intended for public use through APIs. Such APIs will be crucial if we want to use AI for continuous learning. This might require major rewrites of the game’s code before they can even be prepared for something like this. We Wrap Things Up Here It's all exciting, that’s for sure. But only time will tell how practical these AI use cases are, or when we finally achieve something valuable for the gamer. Until then, happy gaming. Newer technology is always a game-changing thing. In the case of video games, it’s literally so. And what other new piece of tech to talk about than what’s all the rage at the moment—artificial intelligence or AI? Companies have been quick to adopt AI for various purposes. Whereas the majority of corporations from Bloomberg to Disney see it as a tool to cut costs, the game development industry has a very different take on all this. Bloomberg Disney This take is more creative in nature. It’s being used to develop new story arcs, generate new gameplay ideas, and analyze existing player data at a whole other level. Some companies are even experimenting with AI-based NPCs (non-playable characters) where these seemingly harmless folks can now hold entire conversations and retain context throughout their adventures in-game. We’re at a very early level here. In this post, we’ll focus on a new aspect that can be revolutionized with AI— virtual economies . Let’s hop right into it, shall we? revolutionized virtual economies What are Virtual Economies? Believe it or not, virtual economies are one of the most lucrative businesses today. A lot of gamers are a part of at least one such economy, sometimes unknowingly. A virtual economy is an in-game trading system. It ranges from the player-based economy of trading posts in classic MMOs such as World of Warcraft and Elder Scrolls Online to Loot Boxes in CS:GO and Valorant . virtual economies MMOs World of Warcraft Elder Scrolls Online Loot Boxes CS:GO Valorant A strong virtual economy is at the heart of these games. These don’t necessarily have to involve real money transactions. There’s a lot of in-game currency transactions, trading, and item barter involved here. But yes, the most popular forms of virtual economies are tied to real money. The best case would be CS:GO skins that you can buy from reputed sellers. These are skins that you buy with real money for a nice change of clothes or a weapon loot box. It’s an active, thriving community of transactions and items. AI in Virtual Economies Developers are quickly realizing the potential of leveraging AI-based technologies and deep learning models to optimize these in-game virtual economies. The only current problem is the scale . To really scale it up, a developer will need to make some serious investments in AI. only current problem is the scale Today, suffice it to say that justifying this cost in front of stakeholders isn’t going to go very smoothly. But the potential is there, and we all know it. AI has already shown us how we can build better games. Smart NPCs , dynamic procedural environments , choice-based challenges , matchmaking balances , data analysis , anti-cheat , etc., are all very serious real-world use cases where AI is already doing a lot of work or is poised to be a strong factor in the coming years. Smart NPCs dynamic procedural environments choice-based challenges matchmaking balances data analysis anti-cheat Now, let’s shift our focus toward virtual economies. What exactly can AI do in this space? What exactly can AI do in this space? Players trade in-game items ranging from armor and weapons in some games to skins and cosmetics in others. These are facilitated mainly with the help of real-world money or some kind of virtual currency that can only be purchased using real money. With that in mind, here’s how AI can help. Dynamic Pricing AI models can be trained to be good at understanding the virtual economy’s market demand. Many financial models are already available, often proprietarily, to do this for real-world markets. These algorithms will be able to maintain pricing stability just by looking at the market trends , user demand , and trading history of these virtual items. market trends user demand trading history Trend Prediction With all that data, such algorithms or models can easily predict the rise and fall in demand for certain skins or cosmetics, which will allow the marketplace to adjust the listings and prices accordingly. For example, we know that a lot of people buy bundles and treasures in Dota around The International, only to sell them at a higher price later. Dota around The International, But when you make all this formulaic, suddenly you can predict a lot more trends than just that. Preventing Fraud AI can also help in detecting instances of fraudulent transactions and suspicious activities. This is a huge benefit for any marketplace moderator or game developer. The strength of an AI model is the training or inferencing part. This is why AI can predict weather better than a supercomputer. It relies on past data. The more data it is fed, the more accurate it will be. So, if a company like Valve trains an AI algorithm on fraudulent transactions and suspicious users, it will quickly learn to spot future offenders, which will be an immense help for all the other players. So, if a company like Valve trains an AI algorithm on fraudulent transactions and suspicious users, it will quickly learn to spot future offenders, which will be an immense help for all the other players. Leading CS:GO skin selling platforms can develop their own algorithms this way to make sure that their marketplace remains spotless. Such a case has already been implemented by sites such as skinsmonkey . skinsmonkey skinsmonkey . Where Do We Go From Here? The times are changing fast. The future is full of AI, even in the world of video games. Though there’s still time before we can reach a world akin to Cyberpunk 2077, it’s much easier to accept a worldview where we will have AI-controlled virtual economies that can be easily scaled. AI-driven price predictions , automated price setting, authenticity verification , highly optimized trade algorithms , and so on. Artificial intelligence models and algorithms can bring about a net positive change in the world of in-game economies that so many gamers rely on. AI-driven price predictions automated price setting, authenticity verification highly optimized trade algorithms It won’t be an overstatement to say that games will be much better for player transactions and economies with AI in the picture. Major Concerns in AI Gaming AI Needs Data: AI requires heaps of data and training. Any company that wishes to make a foray here needs to properly label and deconstruct its existing data and then train a model on it. If it’s done in a shallow or incorrect way, such an algorithm will be able to do nothing good for the players. Constant Learning: At its core, AI models are all about constant learning. If they are not allowed to hook into the economies in a friendly manner, then trends will change before they can pick up on them. This is especially challenging because so far, virtual economy data hasn’t really been intended for public use through APIs. Such APIs will be crucial if we want to use AI for continuous learning. This might require major rewrites of the game’s code before they can even be prepared for something like this. AI Needs Data: AI requires heaps of data and training. Any company that wishes to make a foray here needs to properly label and deconstruct its existing data and then train a model on it. If it’s done in a shallow or incorrect way, such an algorithm will be able to do nothing good for the players. AI Needs Data: Constant Learning: At its core, AI models are all about constant learning. If they are not allowed to hook into the economies in a friendly manner, then trends will change before they can pick up on them. This is especially challenging because so far, virtual economy data hasn’t really been intended for public use through APIs. Such APIs will be crucial if we want to use AI for continuous learning. This might require major rewrites of the game’s code before they can even be prepared for something like this. Constant Learning: We Wrap Things Up Here It's all exciting, that’s for sure. But only time will tell how practical these AI use cases are, or when we finally achieve something valuable for the gamer. Until then, happy gaming.

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