Nasdaq AMZN Amazon

InvestorPlace - Stock Market News, Stock Advice & Trading Tips

The end of June is almost upon us. Where did half of 2023 go?

In this podcast, Motley Fool host Deidre Woollard and senior analyst Matt Argersinger discuss:

You know it best as the king of e-commerce. But for the past several years, the crux of the bull thesis for Amazon (NASDAQ: AMZN) has been Amazon Web Services (AWS), which is not only the leader of the cloud computing market but also Amazon's biggest moneymaker ... by a country m

The crypto market has undergone many changes since Bitcoin first landed on the scene in 2009. The digital currency originally sold investors on the idea of a decentralized network that could make transferring money easier and quicker without the involvement of banks or governing

Markets pulled back this week, but the reason for the price action may not be clear. Some investors will point to remarks from Fed chair Jerome Powell who continued his “hawkish pause” language in remarks to Congress this week. Powell continues to say one or two more rate hikes m

Is a new bull market here for the S&P 500? It depends on whom you ask. Yes, the index has jumped more than 20% above its previous bear market low. That's enough to qualify as a new bull market for many investors. Others, however, require that the S&P also hit a new all-ti

In today’s stock market, the three largest ETFs by assets under management are all S&P 500 ( SPX ) ETFs. The massive SPDR S&P 500 ETF Trust ( NYSEARCA:SPY ) comes in first at nearly $410 billion. The iShares Core S&P 500 ETF ( NYSEARCA:IVV) comes in second with over $328 billion in assets under management, while the Vanguard S&P 500 ETF ( NYSEARCA:VOO ) is close behind in third place with $313 billion in AUM. Combined, these three S&P 500 giants have over $1 trillion in assets under management between them.  Why are S&P 500 ETFs Appealing to Investors? Why invest in the S&P 500? While it may not sound like the most exciting or exotic investing strategy, it is an effective one. While past performance is no guarantee of future results, the S&P 500 index itself has been a pretty good performer for a really long time. Going back to 1957, when the S&P 500 as we know it was made, the index has posted an average annualized return of 10.15%.  Harnessing the Power of the U.S.'s Top Companies Adding to this appeal, an investment in an S&P 500 ETF gives investors exposure to over 500 of the strongest companies in the United States. An investment in an S&P 500 ETF also gives you instant diversification across sectors and industries. SPY, the world’s largest ETF, holds 503 stocks, and its top 10 holdings account for 30.3% of the fund. Below, you can take a look at SPY’s top 10 holdings. Because the S&P 500 is the underlying index for all of these funds, differences between their portfolios are negligible. Also, because mega-cap tech stocks have come to dominate today's market as the largest stocks in the S&P 500, all three of these ETFs give investors significant exposure to these tech leaders, including Apple ( NASDAQ:AAPL ), Microsoft ( NASDAQ:MSFT ), Amazon ( NASDAQ:AMZN ), Nvidia ( NASDAQ:NVDA ), Tesla ( NASDAQ:TSLA ), Alphabet ( NASDAQ:GOOG ) ( NASDAQ:GOOGL ) and Meta Platforms ( NASDAQ:META ). IVV holds 502 stocks, and its top 10 holdings also make up 30.3% of its holdings. Take a look at IVV’s top 10 holdings below. Lastly, VOO is unsurprisingly similar, with 504 holdings, and its top 10 holdings make up 30.4% of the fund. As you can see, the funds are largely the same, although there are some minor differences based on when the funds rebalance. Dividends SPY, IVV, and VOO all pay dividends, and they currently all yield roughly 1.5%. Because of the S&P 500's strong performance in 2023 (it's up about 14% year-to-date), this yield is a bit low for dividend investors to get excited about, but holders will not be complaining. Long-Term Performance of S&P 500 ETFs Unsurprisingly, because these funds all invest in the S&P 500, there is a high degree of correlation in their long-term performances. However, most importantly, all three have delivered strong long-term returns for investors. As of the end of May, SPY had a three-year annualized total return of 12.8%, a five-year annualized total return of 10.9%, and a 10-year annualized total return of 11.9%. Meanwhile, IVV provided annualized total returns of 12.9% over three years, 11% over five years, and 12% over the last 10 years. Lastly, VOO posted annualized total returns of 12.8%, 11%, and 11.9% over the same time frames, respectively, so all three funds provided investors with very similar double-digit annualized returns over the course of the past decade.  Expense Ratios One area where there is some difference is when it comes to the fees they charge. IVV and VOO both have expense ratios of just 0.03%. Meanwhile, SPY comes in at 0.09%. While 0.09% is a fantastic expense ratio in the overall ETF landscape, it is quite a bit higher than that of IVV and VOO in this comparison. Investors in SPY are paying three times as much in fees as investors in IVV and VOO. See below for a comparison of SPY, IVV, and VOO based on their fees as well as other criteria using TipRanks' ETF Comparison Tool, which lets you compare up to 20 ETFs at a time based on a variety of customizable factors.   Hard to Go Wrong Investing in the S&P 500 has historically delivered strong results for investors for a long time, and investing in an S&P 500 ETF allows you to add the power and future growth of 500+ of the U.S.’s top companies to your portfolio with one investment vehicle.  As you can see in the comparison above, SPY, IVV, and VOO all feature identical ETF Smart Scores of 8. The Smart Score is TipRanks’ proprietary quantitative stock scoring system. It gives stocks a score from 1 to 10 based on eight market key factors. The score is data-driven and does not involve any human intervention. A Smart Score of 8 or above is equivalent to an Outperform rating. It's hard to go wrong with any of these ETFs, and picking between the three is a good problem to have. All three look like great choices for investors. Also, for investors just starting out, these ETFs are nice for instantly diversifying portfolios. With similar portfolios and long-term track records over the past decade, the key difference between these ETFs is their fees. While all have favorable expense ratios, SPY’s comes in three times higher than that of IVV and VOO, making those the best buys. SPY has the longest track record (it launched in 1993, long predating IVV and VOO, which launched in 2000 and 2010, respectively), the most assets under management, and incredible liquidity (it accounts for 20% of ETF trading volume on a daily basis), so there’s certainly nothing wrong with choosing it over its counterparts. Nonetheless, the fees will be a bit higher over time.  Disclosure

U.S. Federal Trade Commission Chair Lina Khan is set to testify on July 13 before the House Judiciary Committee led by Republicans who have sharply criticized her leadership of the antitrust enforcement agency, the committee and commission confirmed on Friday.

Chinese markets and stocks fell this week on lighter-than-hoped-for stimulus measures. China’s economic recovery continues to face a number of challenges as global macro growth slows and geopolitical tensions remain elevated. There is second-half opportunity for investors, particularly given the bargain in current valuations of China stocks. China remains at significantly reduced valuations. Brendan Ahern, [...] Read more at ETFtrends.com.

Amazon AMZN is making every effort to strengthen its presence in the global cloud market, backed by its Amazon Web Services (AWS) portfolio strength, which has been driving customer momentum.One of AWS’ existing customers, namely

Amazon (NASDAQ: AMZN) has dropped out of the spotlight this year as its big tech peers, Microsoft and Google parent Alphabet, fight an intense battle over artificial intelligence (AI). Microsoft recently acquired a large stake in OpenAI, and it has integrated the ChatGPT chatbot

Quick, name the large-cap e-commerce company with triple-digit earnings growth in recent quarters, revenue growth of 35% or more, and analyst estimates for an 85% increase this year in earnings per share. If you guessed Argentina-based MercadoLibre (NASDAQ: MELI), well, there’s

InvestorPlace - Stock Market News, Stock Advice & Trading Tips

Below is Validea's guru fundamental report for AMAZON.COM, INC. (AMZN). Of the 22 guru strategies we follow, AMZN rates highest using our P/B Growth Investor model based on the published strategy of Partha Mohanram. This growth model looks for low book-to-market stocks that exhi

InvestorPlace - Stock Market News, Stock Advice & Trading Tips

Amazon.com Inc isn't the only U.S. retailer accused of using deceptive tactics to sell memberships to shoppers.

This is the second article in a series. The first article can be found here: " Although AI is a Bubble, It’s Still Investible." Generative artificial intelligence (Generative AI) has been in the works for several years, but the general public and the stock markets didn’t pay much attention until ChatGPT came along, demonstrating the immense opportunities of the new technology. Within just five days, the generative AI language model from OpenAI, which produces original material in response to user requests, has attracted one million users. Now, it seems that we cannot get enough of AI -- it’s all over the headlines. It takes first place in Google searches, and it’s garnering investor attention and, well, money. Click the image above to compare top artificial intelligence stocks on TipRanks. Let’s Get Generative  Long before the Generative AI advance, many economic sectors had been exploiting Predictive AI – models that analyze existing massive amounts of data to detect patterns, provide the basis for making decisions, produce analytics, classify data, and detect discrepancies, problems, or fraud.   Predictive AI has been providing massive amounts of economic value by cutting costs, optimizing processes, and, in general, making use of Big Data possible as well as effective. Predictive, or “traditional”, AI has been extensively used in autonomous vehicles, medical diagnosis, stock trading, video games, and automating numerous routine knowledge-based tasks.   Now, we are at the dawn of Generative AI. As its name suggests, it can generate new and original content based on its learning of large datasets, just like humans do -- but at a vast scale. Generative AI has captured the general public’s attention by its ability to create art, compose music, and write texts (and even pass Wharton School’s MBA exams); its future applications may exceed entertainment and education uses.   Futurology aside, one of the most important tasks that Generative AI has performed so far – through its missionaries ChatGPT, DALL-E, and Midjourney – was to draw attention, and, consequently, money, to the AI technology and its vast capabilities.  The cutting-edge AI technology can be used in virtually everything, from providing full-scale customer support to from-scratch software generation to the creation of 3D worlds models for simulations for car development to developing new protein sequences to aid in drug discovery to helping with accurate weather forecasting and natural disaster prediction. With these capabilities, the economic added value of AI is potentially unlimited.    According to McKinsey’s latest research, AI can add up to $4.4 trillion (more than the UK’s GDP) annually to the global economy. The technology can automate up to 70% of work activities, with the largest contribution in the near term seen in banking, tech, and life sciences. In the near future, we will see the emergence of new business models and applications and even brand-new industries. The AI era is in its infancy, but the potential of AI cannot be overestimated.   The Hard Base of AI  Artificial Intelligence is a very complex technology, requiring significant research and development as well as vast investments. Besides the cutting-edge software models, it cannot develop without appropriate hardware, infrastructure, and data storage. Because of that, it’s not only the companies developing Generative AI models that are benefiting from the vast influx of investments.   While companies like ChatGPT-developer OpenAI draw most of the public’s attention, the whole AI ecosystem is developing around the technology, with many more companies expected to find their place in the AI value chain.   The footing for all things AI is hardware – the chips that form the computational basis of the technology. AI-computing workload has been doubling every three to four months up to now and will undoubtedly accelerate further; AI-spurred demand for essential GPUs (graphic processing units) will bring immense benefits to chipmakers. That is why Nvidia ( NVDA ) has seen its stock surge by over 200% year-to-date.   Although Nvidia’s hardware underpins the lion's share of AI applications, it is not the only producer of chips that are designed to handle the intensive computations required for training AI models. Such a lucrative field already draws competition from existing leaders, and many new ones will come along.   One of the main competitors to NVIDIA in the GPU space is Broadcom ( AVGO ), a major supplier of chips for Ethernet switches in data centers. In April, the company revealed its new chip for supercomputer connection that will add more capacity to handle AI-driven network load.   Alphabet ( GOOGL ) has been designing and deploying Tensor Processing Units (TPUs) since 2016 for its own use; in April, the company revealed its new TPU-based AI supercomputer, saying that it’s faster and more efficient than Nvidia’s systems. Meta Platforms ( META ) has entered the hardware race as well, scooping up the whole AI-chip team from a British startup Graphcore to design and develop supercomputing systems to support AI in its data centers. Meta was slow to adopt expensive AI-supporting hardware, but now it is all in the game, boosting investment in everything AI-related. Two previously-sleepy giants, Advanced Micro Devices ( AMD ) and Intel ( INTC ), are also stepping up the AI chip race. AMD has just revealed its new chip, which it described as “the world’s most advanced accelerator for generative AI,” representing a challenge to NVIDIA’s dominance in the field. Intel, one of the largest players in the market with a long history of technology development, is working together with the AI leader ​Microsoft ( MSFT ) to enable AI applications for PCs; Intel is also shifting its chip-developing strategy to compete with NVIDIA and AMD.   AI Back Office  Besides the fastest and the most potent chips, AI needs infrastructure, including servers, data centers, and cloud systems that will house and run the AI. This infrastructure provides the necessary storage and processing capabilities, whether those are based on in-house servers or on cloud-based systems that provide storage, computing power, and various other services.  Major players include Amazon’s ( AMZN ) Web Services (AWS), Google Cloud, and Microsoft Azure. Infrastructure as a service (IaaS) allows users to access computational resources on demand, which is vital for the large-scale computations needed for AI.   Two other major players in the infrastructure field are IBM ( IBM ) and Oracle ( ORCL ). IBM offers a suite of AI tools, pre-built models, and robust infrastructure services through Watson (IBM’s data analytics processor that uses natural language processing) and IBM Cloud. Oracle provides cloud infrastructure along with a suite of AI tools, databases, and machine learning services. Oracle Cloud Infrastructure (OCI) provides the foundation for cloud-based data management powered by AI and machine learning (ML).   It must be said that the above-mentioned giants not only provide the underlying hardware and infrastructure but also offer higher-level services like pre-built machine learning models, AI tools, and development environments. They effectively span multiple stages of the AI value chain.  In addition to the infrastructure providers, numerous companies are serving as infrastructure facilitators. For instance, cloud-networking company Arista Networks ( ANET ) provides solutions that are designed to improve scalability, performance, and reliability, which are key factors in maintaining the robust infrastructure necessary for AI and machine learning workloads.   Its competitor Cisco Systems ( CSCO ) has launched a new generation of processors that can support massive GPU (graphic processing unit) clusters for AI workloads. Another networking company chipping in is Juniper Networks ( JNPR ), with its AI-driven switches and software for AI-enabling networks.   Data is Power  AI requires large amounts of data to train and function effectively. Therefore, this stage involves collecting, storing, and processing this data. Companies gather data through various means, from user interactions on digital platforms to IoT devices. The data is then stored securely and in a way that it can be easily accessed for training AI models.  However, just collecting vast amounts of data is not enough. Raw data is often messy and contains lots of irrelevant information. Therefore, it’s necessary to clean the data, deal with missing values, and standardize it into a format that can be used to train AI models. Data processing might also involve anonymizing data to ensure privacy and comply with data protection regulations.  Many companies facilitate data collection and storage; many of them also provide data cleaning, processing, and management solutions. Most of the companies in this field are the same one-stop shops mentioned earlier: Alphabet, Microsoft, Amazon, Oracle, and IBM. These giants not only provide the infrastructure and services necessary for data collection and storage, but they also use AI to improve their own products and services, making them key players in the AI training landscape.  However, there are several companies whose main business is data. Palantir ( PLTR ) specializes in big data analytics; Splunk ( SPLK ) provides software for harnessing the value of big data; Teradata ( TDC ) specializes in data warehousing and analytic applications; Iron Mountain ( IRM ) engages in the provision of storage and information management solutions; Equinix ( EQIX ) provides collocation space and develops data center solutions; Informatica ( INFA ) supplies AI-powered platform that connects, manages, and unifies data; and NetApp ( NTAP ) provides cloud data services, data storage systems, and data management solutions.   While the main business of these companies is centered around data collection, storage, and analysis, it's important to note that they usually serve a broader range of industries and are not solely focused on AI training. Nevertheless, their services form a critical part of the infrastructure needed to train AI models.  Putting AI to Work  After the data is collected, stored, cleaned, dissected, and analyzed, it must be put to work. At this stage, the data is used to train AI models. This involves selecting an appropriate model, defining a loss function, and training the model using machine-learning frameworks. This step also includes testing and validating the model to ensure it performs well.   The main players in this field are IBM, NVIDIA, and the creator of ChatGPT, OpenAI. IBM is a well-known player in the field of AI, with a history that dates to the 1950s; its AI platform, IBM Watson, is used across various industries for tasks like data analysis, automation, and predictive insights.   NVIDIA is primarily known for its graphics processing units (GPUs), but the company also provides a range of software and hardware tools that are widely used in AI model development. Microsoft-backed OpenAI is currently the most influential company in the AI sphere, as its highly-advanced models have a significant impact on the AI field. Another notable influencer is Google’s subsidiary DeepMind.  Once a model is trained, it needs to be integrated into an application that can use the AI's capabilities. This could be anything from a voice assistant to a self-driving car. It also involves creating user interfaces and APIs so that the AI can interact with users and other systems.   As for AI application development, there are numerous companies in this less capital-intensive playground, ranging from mega caps to tiny startups. The most notable companies in this field are Salesforce ( CRM ), which develops cloud-based enterprise software for customer relationship management, and Adobe ( ADBE ), which integrates AI into its digital marketing and media solutions and provides an AI-powered platform to automate tasks and offer predictive analytics in its applications. There are many other companies involved in AI models and application development. As AI technology advances, we’ll likely see even more companies entering these fields.  The final stage in the AI value chain is the provision of AI as a service (AIaaS), a cloud-based service offering artificial intelligence outsourcing, making it available to end-users or businesses. This can involve providing cloud-based AI services or implementing AI directly into products like voice assistants, recommendation systems, or advanced analytics tools.   Most of the top players in the AIaaS sphere are the AI stalwarts and their divisions -- Microsoft, Alphabet, IBM, Oracle, Salesforce, and Amazon. However, countless startups are coming down to play – maybe some of them will steal the crown from the giants of AI.  Investing in the Future  In the swiftly advancing landscape of AI, the entire value chain presents a cornucopia of investment opportunities -- from firms fueling the engine of AI through hardware and infrastructure development to those that help manage and analyze data, and further down the value chain to companies at the forefront of AI model development and application, as well as to those that are democratizing access to AI and offering scalable solutions for businesses.   However, the stage is far from monopolized. Startups and innovators with breakthrough ideas are finding their niches, potentially offering high-return investment prospects. As we venture deeper into the era of AI, each link in the value chain represents a fertile ground for investment, promising lucrative returns and a stake in shaping the future of technology. Disclaimer

Growth in cloud computing was a boon for cloud stocks in recent years, but another wave of growth is coming as more companies ramp up their investments in artificial intelligence (AI).

Workers at aerospace supplier Spirit AeroSystems were the latest U.S. union employees to reject a contract their leaders negotiated with their employer, joining freight railroad employees, airline pilots and others who are growing more fed up with stagnant pay, high healthcare costs, scanty sick time and uncertain scheduling.