Large language models (LLMs), including ChatGPT, can extract profitable trading signals from the sentiment in news text. However, backtesting such strategies poses a challenge because LLMs are trained on many years of data, and backtesting produces biased results if the training and backtesting periods overlap. This bias can take two forms: a look-ahead bias, in which the LLM may have specific knowledge of the stock returns that followed a news article, and a distraction effect, in which general knowledge of the companies named interferes with the measurement of a text's sentiment. We investigate these sources of bias through trading strategies driven by the sentiment of financial news headlines. We compare trading performance based on the original headlines with de-biased strategies in which we remove the relevant company's identifiers from the text. In-sample (within the LLM training window), we find, surprisingly, that the anonymized headlines outperform, indicating that the distraction effect has a greater impact than look-ahead bias. This tendency is particularly strong for larger companies--companies about which we expect an LLM to have greater general knowledge. Out-of-sample, look-ahead bias is not a concern but distraction remains possible. Our proposed anonymization procedure is therefore potentially useful in out-of-sample implementation, as well as for de-biased backtesting.

At a time when companies are increasingly embracing technologies such as robotic process automation, natural language processing, and artificial intelligence, and as companies' automation efforts mature, findings from our second McKinsey Global Survey on the topic show that the imperatives for automation success are shifting. The online survey was in the field from February 4 to February 14, 2020, and garnered responses from 1,179 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. To adjust for differences in response rates, the data are weighted by the contribution of each respondent's nation to global GDP. Two years ago our survey found that making business-process automation a strategic priority was conducive to success beyond the piloting stage. 2 2. We define business-process automation as the use of general-purpose technologies (for example, bots and algorithms) to perform work that was previously done manually, in order to improve the functionality of a company's underlying systems. In the survey, automation did not include the use of automation that was custom built (for example, Excel macros and custom scripts) for organizations.

The current economic environment enables many large companies to succeed, while many small and medium businesses (SMBs) are suffering. Adding to this divide are the changes created by AI. Right now, larger companies are investing in AI because they recognize the opportunity to dominate a market using this technology. Big companies have many advantages when it comes to AI. They have more data (a critical factor for success), more talent, more opportunities for improvement, and more investment capital.

"What this means for CIOs is likely higher prices and less choice," said Crawford Del Prete, president of technology research firm International Data Corp. Mr. Del Prete said many large IT providers over the next few years will be looking to fill gaps or expand into new markets, in part by targeting embattled startups struggling to reignite sales and raise capital. The gaps include areas such as cloud computing, collaboration, access management and other business continuity tools that saw a surge in demand during regional lockdowns. Microsoft Corp. said Tuesday that it was acquiring Softomotive, a robotic-process-automation maker that enables businesses to automate workplace tasks, a capability many businesses have turned to in order to keep daily operations running with a thinner workforce. Financial terms of the deal weren't disclosed.

Some of the fastest-growing tech companies right now are reinventing web functions that have been with us since before the turn of the century. Not as famous as the first two, San Francisco-based Intercom has found favor as an online messaging platform that helps businesses engage and support their customers and prospects. The boom in online messaging among consumers and the rise of conversational computing in business has helped Intercom thrive since its foundation in 2011. It now has more than 30,000 paying customers globally and is backed by $241 million in venture funding. People increasingly expect to find a chat option for customer support when they visit a website or use an app, says Intercom's SVP of Marketing, Shane Murphy-Reuter: Given that every single company in the world has customer support -- typically by email, maybe calls for larger companies -- I do not see a world in the future where a messenger isn't on every single website and in every app.

An Algorithmia report released on Thursday revealed the challenges associated with increased machine learning use in 2020. Most companies will be in the early stages of machine learning developments in 2020, but to get to more advanced stages, organizations must overcome a variety of obstacles, the report found. Algorithmia's 2020 State of Enterprise Machine Learning report surveyed 745 tech professionals to determine how organizations plan on deploying machine learning in 2020, and the key issues that accompany the journey. The biggest challenges associated with machine learning deployments involved scaling, versioning, and budgeting, according to the report. "AI and machine learning is going to be the most impactful technological advance that we're going to see in our lifetime," said Diego Oppenheimer, CEO of Algorithmia. "The role of the data science is to grab a bunch of the data these companies have been collecting and make sense of it," and technological advances have caused companies to generate more data, which results in the need for more data scientists, Oppenheimer said.

Summary: Adoption of AI/ML by larger companies has more than doubled since last year according to these survey results from McKinsey and Stanford's Human-Centered AI Institute. This new data gives us a much better idea of which global regions and which industries are adopting which AI/ML techniques. We know we've entered the era of exploitation of AI/ML but the $64 Billion question is how far along the curve are we and who exactly has implemented and will implement? By the way, $64 Billion is a reasonable estimate of global market spend in roughly four or five years, about 6 times where we are today. And that investment should yield about $4 Trillion in business value in that same time frame according to Gartner.

Big tech companies are pouring tens of billions of dollars into research on artificial intelligence (AI). Can small startups hope to enter AI markets and compete effectively? A recent survey we did of commercial AI startups provides some intriguing insights into how they work, and how and where they compete with larger and more established firms. The picture that emerges is one of a robust, competitive market for startups, but also a market that is restricted in some important ways. Large incumbent tech firms have several advantages over startups: they can invest huge sums into R&D, they have access to large amounts of data, and they have complementary assets and established markets.

Professionals within larger organizations (25,000 employees or more) are significantly more satisfied with their machine learning progress than employees in smaller companies (500 employees or less), according to Algorithmia's 2018 State of Enterprise Machine Learning study released on Tuesday. The report surveyed 523 data science and machine learning professionals to learn how companies of different sizes are using machine learning technologies, said the release. Employees from larger companies were 300% more likely to consider their machine learning efforts "sophisticated" and 80% more likely to be "satisfied" or "very satisfied" with the progression of such efforts, in comparison to smaller companies, added the release. Some 92% of respondents from larger organizations said their organization's investment in machine learning has grown by at least 25% in the past year, said the release. Larger companies have been utilizing machine learning in three main ways: Increasing customer loyalty (59%), increasing customer satisfaction (51%), and interacting with customers (48%), according to the report.

Murphy: I believe that it is the number one least explored topic because it is not "sexy." When you read media columns, they spend significantly more time discussing machine learning than they do about the mountain of technology that will never get cleaned up and is degrading every day. Unfortunately, that mountain prevents organizations from innovating because it blocks their ability to take artificial intelligence [AI] or machine-learning and apply it to their business. These larger companies have massive built-in advantages, such as customers and network effects that should typically make startups irrelevant. However, they have done a poor job of cleaning up the anchor of their technical systems, organizational systems, and old processes that should have been updated.