Humans have always welcomed other beings in finance: over twenty years ago, some of the best Wall Street traders were outsmarted by Raven, a chimpanzee who picked stocks by throwing darts. Her index, called MonkeyDex, became one of the biggest sensations at the turn of the century after delivering a 213% gain. Perhaps because animals are not so easy to fit in offices, people have turned to other kinds of brains to choose equities. Big institutions are resorting to artificial intelligence (AI) to analyse stocks collating all sorts of information coming from a plethora of sources. In fact, while investments could previously be assessed based on financial reports and share price movement – what is called structured data – markets have been heavily influenced by unstructured data over the past few years.
Today's digital economy is blurring the boundaries between computer science and economics -- in Silicon Valley, on Wall Street, and increasingly on university campuses. Yale undergraduates interested in both fields can pursue the Computer Science and Economics (CSEC) interdepartmental degree program, which launched in fall 2019, with coursework covering topics such as machine learning and computational finance. Philipp Strack, CSEC's inaugural director of undergraduate studies, is comfortable straddling multiple disciplines. With an academic background in economics and mathematics, his research reflects this broad and interdisciplinary outlook -- ranging from behavioral economics and neuroscience to auction design, market design, optimization, and pure probability theory. Strack, an associate professor of economics in the Faculty of Arts and Sciences, recently spoke to YaleNews about the real-world implications of this work, what the CSEC program offers students, and how it bridges these critical fields.
The COVID pandemic may be receding, but it has left a mark on across multiple aspects of our lives. From mask mandates to travel restrictions, we chafe at some of the changes – but in the business world the use of artificial intelligence (AI) systems has dramatically expanded in the past year. This was probably inevitable – but AI brought advantages in coping with the pandemic for companies that could make use of it, and the expansion accelerated. AI has found its place in a huge range of applications, at both the front and back end of businesses. It’s prevalent in software management and data systems, as well as in communications, where AI systems filter emails and conduct robochats. And this has not been ignored by Wall Street. Analysts say that plenty of compelling investments can be found within this space. With this in mind, we’ve opened up TipRanks’ database, and pulled two stocks which are stand to benefit from AI technology. Importantly, both have amassed enough bullish calls from analysts to be given “Strong Buy” consensus ratings. Nuance Communications (NUAN) We’ll start with Nuance, a company in the communications software niche. This Massachusetts-based company offers solutions for business clients in the healthcare and customer service industries, with products that enhance speech recognition, telephone call steering systems, automated phone directories, medical transcription, and optical character recognition. It’s a full range of AI-powered, cloud communications software, applied in real time. Nuance’s flagship product, the Dragon Ambient eXperience (DAX) is marketed to the healthcare industry, where it uses AI to automate the paperwork burdens on physician practices and hospitals. This streamlines operations allow doctors more time and resources to spend on patients, and provides greater satisfaction to health care providers and users. The applications of Nuance’s product and solution lines to the current environment is clear: when the pandemic locked down so many people at home, businesses still had to maintain their customer-facing systems, and software automation, based on AI tech, made that possible with fewer personnel. Since the pandemic started last winter, the company seen its shares grow tremendously, up 205% in the last 12 months, far outpacing the overall stock market. The most recent quarterly report, for fiscal Q1, showed quarterly revenues above the forecast at $81.4 million. EPS showed a net loss, as expected, but at 27 cents the loss was a 28% sequential improvement from Q3. The company’s balance sheet is strong, with zero debt, $256 million cash on hand, and a credit facility up to $50 million. The company’s most recent quarterly report, for fiscal Q1, beat the forecasts on both the top and bottom lines. Earnings beat expectations by 11%, coming in at 20 cents per share, while revenues of $345.8 million were a modest 2% above the estimates. As a result, operating cash flow grew 22% year-over-year, to $54.6 million for the quarter. Among the bulls is 5-star analyst Daniel Ives, of Wedbush, who rates NUAN shares an Outperform (i.e. Buy), and his $65 price target implies an upside potential of ~44%. (To watch Ives’ track record, click here) "We believe Nuance overall continues to be laser focused on building a global cloud healthcare and AI driven business with growing ARR and a sustainable revenue/ earnings stream going forward with larger deals in the field as more hospital- wide deployments shift to the cloud are playing out and gaining further momentum based on our checks," Ives opined. The analyst added, "From a valuation/ SOTP perspective, we believe over time the DAX business alone could be worth between $3 billion to $4 billion to NUAN's stock as this AI next generation platform represents a potential paradigm changer for hospitals/healthcare clinics/specialists over the coming years." Ives is no outlier on Nuance, as shown by the unanimous Strong Buy analyst consensus on the stock. Nuance has received 6 recent reviews, and all are to Buy. The shares are trading for $45.20, and the $59.67 average price target suggests a 32% one-year upside. (See NUAN stock analysis on TipRanks) Dynatrace, Inc. (DT) The second AI stock we’ll look at, Dynatrace, is another cloud software company – but Dynatrace’s products are designed to power business data. The company’s AI platform brings intelligent automation to network management and cloud monitoring. DT’s platform allows for cloud automation, business analytics, digital experience, application security, applications and microservices, and infrastructure monitoring. It’s sold as a one-stop-shop for network and system managers seeking an intelligent software agent. Dynatrace’s shares have been showing consistent growth over a long term. The stock is up a robust 133% in the past 12 months, and revenues have also been growing over that period. In the most recent report, for Q3 fiscal year 2021, the company showed $182.9 million in top-line revenue, beating the forecast by ~6% and growing 27% year-over-year. EPS came in at 6 cents, flat from Q2 and far better than the break-even reported for the year-ago quarter. Three key metrics stand out in the quarterly report, and both for the right reasons. Subscription revenue grew 33% year-over-year, to reach $170.3 million, and annual recurring revenue (ARR) – which is an important predictor of future performance – grew 35% yoy and came in at $722 million. At the same time, license revenue dropped by more than 93%, to just $300,000. Taken all together, these results point toward a strong shift toward recurring cloud customers – a common trend in the software space. Needham’s 5-star analyst Jack Andrews has been closely following Dynatrace, and he believes DT’s AI products may replace incumbent tools as customers expand to additional modules. “Embedded AIOps and automation creates a compelling value proposition… Compared to competitors in the market, DT's AI Engine is embedded within its core platform and can be levered across the portfolio to deliver answers from data. Moreover, its One Agent technology automatically discovers high-fidelity data from applications and thus can map the billions of dependencies in complex environments," Andrews said. The analyst summed up, "In our view, DT is well-positioned to serve as a single source of truth that can help users trace a line between written code and business outcomes (i.e. BizDevSecOps)." Andrews named Dynatrace as a top pick, and in line with this upbeat assessment, the analyst rates the stock a Buy along with a $66 price target. Ivestors stand to pocket ~28% gain should the analyst's thesis play out. (To watch Andrews’ track record, click here) Once again, we’re looking at a stock who strong performance has inspired unanimity from the Wall Street analysts. DT shares have 13 Buy reviews, for a Strong Buy consensus rating. The stock sells for $51.76 and its $59.69 average price target suggests ~15% upside from that level. (See DT stock analysis on TipRanks) To find good ideas for AI stocks trading at attractive valuations, visit TipRanks’ Best Stocks to Buy, a newly launched tool that unites all of TipRanks’ equity insights. Disclaimer: The opinions expressed in this article are solely those of the featured analysts. The content is intended to be used for informational purposes only. It is very important to do your own analysis before making any investment.
Recently public unicorn Upstart announced earnings that blew the socks off of Wall Street this week. After closing on Wednesday at around $61 per share, Upstart wrapped Thursday worth $115 per share. It turns out that all the blather we've had to endure about artificial intelligence (AI) in the past decade is coming true, at least in certain applications for select companies. But Upstart's blockbuster guidance for 2021 is just a sliver of the story. The AI-powered fintech is projecting a year so good that its valuation nearly doubled yesterday, but there are other shoots of life in the AI world worth discussing, and investors are taking note.
The biggest energy transition in history is well and truly underway, and nowhere is the shift more readily apparent than in the transport industry. Wall Street is almost unanimous that electric vehicles are the future of the industry, with EV sales already outpacing ICE sales in markets such as Norway. That kind of exponential growth can only mean one thing: Explosive demand for the metals that go into those batteries. Demand for battery metals is projected to soar as the transport industry continues to electrify at a record pace. In fact, there's a real danger that current mining technologies might struggle to keep up with the demand for battery metals in the near future. Thankfully, Artificial intelligence (AI) can not only be deployed to help improve the way these crucial elements are mined but can replace them altogether.
Surrounded by rallies of "power to the people," a rag-tag group of scrappy underdogs recently managed to bring Wall Street to its knees through a dazzling display of disobedient investing that saw Gamestop stocks rocket Moonward. This unprecedented seizure of power by the proletariat has been lauded far and wide as a smack in the mouth for the establishment. Some say it's a warning shot to the financial kings and queens of the Earth. The "Gamestonk" legend will be told for years to come – Hollywood's already making sure of that. But the story is far from done.
For those who haven't heard, there's a bit of a brouhaha brewing with the video game retailer GameStop, which is publicly traded. Much of Wall Street soured on the company, believing it to be the next Blockbuster or Radio Shack: a dinosaur from a bygone era that has no hope of succeeding in the increasingly internet-run future. As a result, a major Wall Street hedge fund worth billions decided to make a bet that the company's already low stock price would just keep going lower. The traditional way to make money in stocks was to find a company that was worth more than what its stock price indicated, purchase the stock at a bargain, and then make your money either through the company's distribution of its profits back to its equity owners or the appreciation of its stock price. But you can also make money betting on a company to eventually circle the toilet.
Today, a war over the value of video game retailer GameStop's stock has caused what market guru Jim Cramer called "the squeeze of a lifetime." Howling with glee along the way, traders on the chaotic and obscene subreddit Wall Street Bets helped push GameStop's stock price up from $20 on January 11 to $73 after traditional analysts deemed the stock a clunker. While this isn't the first time Wall Street Bets has contributed to a surprising market shake-up, GameStop's unlikely trip to the moon is unique in both its velocity and allegations of harassment and hacking that accompanied it. Like other physical retailers, GameStop's business has suffered throughout the last year. Few gamers would rather hit the mall than Amazon's significantly safer "Buy Now" button.
CBS MarketWatch declared 2020: The Year of the SPAC (Special Purpose Acquisition Corporation). A record 219 companies went public through this fundraising vehicle that uses a reverse merger with an existing private business to create a publicly-listed entity. This accounted for more than $73 billion dollars of investment, providing private equity startups a new outlet to raise capital and provide shareholder liquidity. According to Goldman Sachs, the current trends represents a "year-over-year jump of 462% and outpacing traditional IPOs by $6 billion." In response to the interest in SPACs, the Securities and Exchange Commission agreed last week to allow private companies to raise capital through direct listings, providing even more access to the public markets outside of Wall Street's traditional institutional gatekeepers.
At NeurIPS, Nvidia AI presented a new method for creating high-quality synthetic images using a generative adversarial network (GAN) trained on 1500 source images. The neural network, StyleGAN2, usually requires a training data set of tens or hundreds of thousands of images to produce high-quality synthetic pictures. Nvidia's AI research used a dataset of 1500 images of faces from the Metropolitan Museum of Art to create new images that emulate artworks in the Museum's collection. While this breakthrough could be used to recreate the style of rare works and create new art inspired by historical portraits, there are wider implications for medical imaging AI. A key problem facing medical AI models is the lack of available training data due to privacy concerns, but especially for rare diseases where 100,000 images of a certain type of illness might not even exist.