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Apple, Tesla and Nvidia were among 2023’s ‘magnificent seven’ stocks – here’s what to expect from them all in 2024.

In the 1960 western The Magnificent Seven, a group of seven gunfighters protect a village from bandits. Only three survive to ride out of town at the end of the movie. The odds look much better for the seven tech companies recently dubbed the magnificent seven after dominating US stock markets in 2023. But there are problems that could ambush some of these companies in 2024.

Apple, Alphabet, Microsoft, Amazon, Meta, Tesla and Nvidia have driven a rally in US stocks in 2023. They now make up nearly a third of the S&P 500 measure of the largest listed US companies, which has risen more than 20% since January. These tech stocks had provided shareholders with a whopping 71% return by mid-November while the other 493 names added just 6%.

This impressive performance led Bank of America analyst Michael Hartnett to name these companies the magnificent seven earlier this year. Goldman Sachs soon followed, calling their massive outperformance the “defining feature” of the equity market in 2023.

But as dramatic as this performance has been – and although they’re all essentially tech companies – don’t make the mistake of thinking they’re all the same. In fact, the outlook for the magnificent seven next year is mixed, particularly in light of expected changes in their core markets.

Rising competition in the EV market

Let’s start with the bad news first. Electric vehicle (EV) manufacturer Tesla Motors will continue to lose market share in 2024. While chief executive Elon Musk has been dealing with advertising problems on X (formerly Twitter), one of his other businesses, over the first three quarters of this year, Tesla has seen its US market dominance shrink from 62% to just over 50% of the market. Both BMW Group and Mercedes-Benz Cars have expanded their footprints.

And over the next few years, the growing global heft of Chinese manufacturers looks hard to beat. Chinese EV players such as BYD, Nio, Wuling and Xpeng produced almost 60% of the world’s EVs in 2022 – and they have been doing so in a very affordable manner. In the first half of 2023, the average cost of an EV in China was US$33,000 (£26,040), more than half the US$70,700 (£55,800) people pay for EVs in Europe and the US$72,000 (£56,800) paid in the US.

US president Joe Biden has proposed strict new car pollution controls that will require almost two-thirds of new cars sold in the US to be electric by 2032. But the cost of EVs will need to come down if they are to achieve mass market appeal.

Sunny outlook for cloud computing

Magnificent seven members Amazon, Microsoft and Alphabet make up two-thirds of the cloud computing market, which will continue to grow in 2024, although perhaps not quite as much as in the past.

Still, the market for cloud infrastructure services is expected to expand from US$122 billion in 2023 to US$446 billion by 2032. In particular, concerns about the macroeconomic environment have seen some customers focus on using the cloud more to reduce costs in recent years, although this has yet to have any meaningful impact on revenues.

And for Amazon in particular, there are some niggling questions around its outlook. Although its cloud business remains solid, its original e-commerce business has seen growing competition recently, notably from rival retail giant Walmart, which is eating into its business in the US.

This is one reason why holding Amazon shares provided an annual return over the past two years of -16.7%, as of early December, according to my calculations.

Unstoppable AI

Also linked to the cloud computing industry, California-based chip maker Nvidia Corporation has been the runaway success of the magnificent seven this year. This is all thanks to its dominance in processing AI workloads on the cloud. The majority of cloud players use Nvidia graphics processing units (GPUs).

But while its two-year return of 43.3% is the most impressive of the seven tech companies, there are competitors on the horizon that could nibble away at some market share.

Nvidia’s nearest rival AMD drew attention with its latest chip offering in 2023 – it’s betting the market will be worth US$400 billion by 2027. A number of other start-ups are also developing chips for niche AI fields.

Can Nvidia maintain its dominance? If it does, its earnings will skyrocket
alongside the growth of AI. But even if it loses some market share, the AI market will boom for years.

The outliers

For those keeping track, that just leaves two final members of the magnificent seven.

Apple Inc – the world’s largest company by market capitalisation – consistently delivers solid returns: 16.2% over the past two years by my calculations. At the other end of the scale, social media company Meta (owner of Facebook, Instagram, Threads and WhatsApp) is the only one of the group to have shown an essentially flat stock market performance over the past two years.

Although Meta’s revenues and earnings have consistently beaten expectations this year, the threat of anti-trust legislation in the US and Europe hangs over the company, as does an advertising market that is bottoming out. Both of these issues could harm Meta’s revenue outlook next year.

So, the magnificent seven have all survived to ride out of town at the end of 2023, but it’s as clear as a tumbleweed rolling down a deserted main street that not all of them are in for a leisurely horseback ride through 2024. Saddle up, partners!

Karl Schmedders, International Institute for Management Development (IMD)

Karl Schmedders, Professor of Finance, International Institute for Management Development (IMD)

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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How government payments to the vulnerable can multiply to create economic growth for everyone.

The economic fallout of COVID-19 left people around the world facing a significant threat to their livelihood. As governments scrambled to mitigate the pandemic’s impact on their populations, many decided to use direct payments to support vulnerable citizens.

More than a sixth of the world’s population received some sort of cash transfer in 2020. These programmes were a key source of support for many people during the COVID-19 pandemic, with governments across the globe scaling up or introducing such payments.

Brazil, for example, introduced the Auxílio Emergencial programme, while the US implemented Economic Impact Payments. Both cash transfer programmes aimed to shield vulnerable populations. This was also not exclusive to middle- and high-income countries. Togo, for instance, implemented the Novissi cash transfer programme during the pandemic.

Using cash payments to protect people’s livelihoods and lift the poor out of poverty is not a novel strategy. It can be a simple way to provide basic social protection to people in need, helping citizens to withstand sudden shocks and also facilitating their recovery after a crisis.

Cash assistance as financial burden?

But cash transfers still attract a lot of debate. Besides typical concerns like creating dependency and reducing labour supply, these programmes are costly. This can cause concern about their sustainability and hinder the initial implementation and scale-up.

For example, the Social Assistance Grants for Empowerment programme in Uganda in 2010 became so politicised that it was challenged every step of the way to its implementation and later expansion. Even before its pilot programme, concerns regarding its financial sustainability and the potential creation of welfare dependencies were raised by politicians.

During periods of economic crisis, austerity policies can also directly influence social assistance initiatives. After the 2010 economic crisis, for example, Greece initially suspended and subsequently terminated its housing benefit programme, attributing this decision to budget constraints.

But cash transfer programmes aren’t “handouts”. The positive impacts on the people that receive them are well documented. They are powerful instruments for strengthening household resilience and fostering opportunities that can extend beyond the immediate recipients.

The multiplier effect

There is another vital element of social cash transfers that most people aren’t aware of: the economic multiplier effect. In a recent study with Ugo Gentilini, Giorgia Valleriani and Yuko Okamura of the World Bank, and Giulio Bordon of the UN’s International Labour Organization, we found the multiplier effect can greatly enhance the financial sustainability of social cash transfer programmes.

The core concept is that every dollar transferred that is spent rather than saved can increase the total income in the economy beyond its original value.

Consider a smallholder farmer who uses some of her grant to buy fertiliser at the local market. The local merchant profits from it and then spends this additional income, increasing profits for someone else and setting off a ripple effect through the economy. These taxable gains go beyond the people that get the payment, effectively “multiplying” the original grant’s worth for the economy.

Investing in the entire economy

We reviewed 23 studies of 19 cash assistance programmes across 13 countries and found substantial evidence of this multiplier effect from social cash transfers.

In Brazil, for example, Bolsa Família, the current national social welfare programme of Brazil and one of the largest cash transfer programmes in the world, was found to increase real GDP per R$1 (£0.16) spent by R$1.04. This is a small but positive spillover into the Brazilian economy.

Another noteworthy example is the GiveDirectly initiative in rural western Kenya, a pilot programme that offered a US$1,000 (£791) one-off transfer to 10,500 poor households. This programme led to a strong positive economic shock with a multiplier of 2.5 per US$1. So, every US$1 transferred generated a value of US$2.50 locally – a strong positive spillover to the local economy.

Social cash transfers have the potential to not only support the poor and vulnerable, but also to stimulate the wider economy. Rather than simply accepting the general perception of social transfers as an expense, we should start recognising their true value as an investment in a country’s entire economy.

Conrad Nunnenmacher, United Nations University; Franziska Gassmann, Maastricht University, and Julieta Morais, United Nations University

Conrad Nunnenmacher, PhD Research Fellow in Innovation, Economics, Governance and Sustainable Development, United Nations University; Franziska Gassmann, Professor of Social Protection and Development, Maastricht University, and Julieta Morais, Researcher in Social Protection, United Nations University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Deceit pays dividends: How CEO lies can boost stock ratings and fool even respected financial analysts.

The multibillion-dollar collapse of FTX – the high-profile cryptocurrency exchange whose founder now awaits trial on fraud charges – serves as a stark reminder of the perils of deception in the financial world.

The lies from FTX founder Sam Bankman-Fried date back to the company’s very beginning, prosecutors say. He lied to customers and investors alike, it is claimed, as part of what U.S. Attorney Damian Williams has called “one of the biggest financial frauds in American history.”

How were so many people apparently fooled?

A new study in the Strategic Management Journal sheds some light on the issue. In it, my colleagues and I found that even professional financial analysts fall for CEO lies – and that the best-respected analysts might be the most gullible.

Financial analysts give expert advice to help companies and investors make money. They predict how much a company will earn and suggest whether to buy or sell its stock. By guiding money into good investments, they help not just individual businesses but the entire economy grow.

But while financial analysts are paid for their advice, they aren’t oracles. As a management professor, I wondered how often they get duped by lying executives – so my colleagues and I used machine learning to find out. We developed an algorithm, trained on S&P 1500 earnings call transcripts from 2008 to 2016, that can reliably detect deception 84% of the time. Specifically, the algorithm identifies distinct linguistic patterns that occur when an individual is lying.

Our results were striking. We found that analysts were far more likely to give “buy” or “strong buy” recommendations after listening to deceptive CEOs – by nearly 28 percentage points, on average – rather than their more honest counterparts.

We also found that highly esteemed analysts fell for CEO lies more often than their lesser-known counterparts did. In fact, those named “all-star” analysts by trade publisher Institutional Investor were 5.3 percentage points more likely to upgrade habitually dishonest CEOs than their less-celebrated counterparts.

Although we applied this technology to gain insight into this corner of finance for an academic study, its broader use raises a number of challenging ethical questions around using AI to measure psychological constructs.

Biased toward believing

It seems counterintuitive: Why would professional givers of financial advice consistently fall for lying executives? And why would the most reputable advisers seem to have the worst results?

These findings reflect the natural human tendency to assume that others are being honest – what’s known as the “truth bias.” Thanks to this habit of mind, analysts are just as susceptible to lies as anyone else.

What’s more, we found that elevated status fosters a stronger truth bias. First, “all-star” analysts often gain a sense of overconfidence and entitlement as they rise in prestige. They start to believe they’re less likely to be deceived, leading them to take CEOs at face value. Second, these analysts tend to have closer relationships with CEOs, which studies show can increase the truth bias. This makes them even more prone to deception.

Given this vulnerability, businesses may want to reevaluate the credibility of “all-star” designations. Our research also underscores the importance of accountability in governance and the need for strong institutional systems to counter individual biases.

An AI ‘lie detector’?

The tool we developed for this study could have applications well beyond the world of business. We validated the algorithm using fraudulent transcripts, retracted articles in medical journals and deceptive YouTube videos. It could easily be deployed in different contexts.

It’s important to note that the tool doesn’t directly measure deception; it identifies language patterns associated with lying. This means that even though it’s highly accurate, it’s susceptible to both false positives and negatives – and false allegations of dishonesty in particular could have devastating consequences.

What’s more, tools like this struggle to distinguish socially beneficial “white lies” – which foster a sense of community and emotional well-being – from more serious lies. Flagging all deceptions indiscriminately could disrupt complex social dynamics, leading to unintended consequences.

These issues would need to be addressed before this type of technology is adopted widely. But that future is closer than many might realize: Companies in fields such as investing, security and insurance are already starting to use it.

Big questions remain

The widespread use of AI to catch lies would have profound social implications – most notably, by making it harder for the powerful to lie without consequence.

That might sound like an unambiguously good thing. But while the technology offers undeniable advantages, such as early detection of threats or fraud, it could also usher in a perilous transparency culture. In such a world, thoughts and emotions could become subject to measurement and judgment, eroding the sanctuary of mental privacy.

This study also raises ethical questions about using AI to measure psychological characteristics, particularly where privacy and consent are concerned. Unlike traditional deception research, which relies on human subjects who consent to be studied, this AI model operates covertly, detecting nuanced linguistic patterns without a speaker’s knowledge.

The implications are staggering. For instance, in this study, we developed a second machine learning model to gauge the level of suspicion in a speaker’s tone. Imagine a world where social scientists can create tools to assess any facet of your psychology, applying them without your consent. Not too appealing, is it?

As we enter a new era of AI, advanced psychometric tools offer both promise and peril. These technologies could revolutionize business by providing unprecedented insights into human psychology. They could also violate people’s rights and destabilize society in surprising and disturbing ways. The decisions we make today – about ethics, oversight and responsible use – will set the course for years to come.

Steven J. Hyde, Boise State University

Steven J. Hyde, Assistant Professor of Management, Boise State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Entrepreneurs, beware: Owning your own business can make it harder to get hired later.

If you’ve been thinking about starting your own business lately, you’re not alone. Americans began launching ventures in record numbers during the pandemic, with an above-trend pace continuing through 2023.

Unfortunately, many of of these enterprises won’t last long: 30% of new businesses fail within two years, and half don’t last past five, according to the Small Business Administration. While some of these unlucky founders will pursue new ventures, many others will try to rejoin the traditional labor force.

You can’t blame them. People often see “going back to work” as a safety net for risk-taking entrepreneurs. As professors of management who study entrepreneurship, we wanted to see if this was true.

Screened out

So we surveyed more than 700 hiring professionals to determine whether founders really can get new jobs that easily, as well as seven former entrepreneurs who successfully made the transition back into the workforce.

We found that former business owners were actually less likely to get interviews compared with applicants with only traditional experience. This was true regardless of whether they had sold or closed their businesses. And the longer they were out of the traditional workforce, the worse their chances of success were.

Why do employers hesitate to take a chance on former business owners?

It starts at the earliest stages, with the recruiters who screen people into – or out of – consideration for interviews. We found that recruiters worried that entrepreneurs would jump ship to start their own companies as soon as they can. This is a problem for employers, since hiring is a long, expensive process that can take months or even years to pay off.

For example, one recruiter told us, “I am looking for candidates that will be long-term employees, as we invest quite a bit into each hire. When I interview people, it is generally a red flag if they say they want to start their own business or already have a business on the side.”

A related fear: A worker who leaves to start a new venture might be tempted to poach talent, clients and tactics from their old employer.

Recruiters were also concerned that former entrepreneurs may refuse to take directions. Spending time as your own boss can make it difficult to adapt to a lower place on the organizational hierarchy. As one recruiter in our study put it, former business owners “are used to being the one who makes all the decisions.”

They also raised issues of job fit, questioning whether ex-entrepreneurs’ knowledge and abilities would translate to traditional work. “The concern would be the skills they have developed don’t transfer,” said one of our interviewees. In addition, for entrepreneurs who have worked alone, it can be difficult for recruiters to know how well they’ll perform with others.

Even when a former entrepreneur is a good match for a position, recruiters can fail to make the connection because of stereotypes or misunderstandings about their experience. A former bakery owner we interviewed recalled applying for a position and being pigeonholed based on their experience: “They said, ‘Oh, I wish we were hiring for a baker!’ and I said, ‘No, no, no, I’m applying for your front office.’ It was like they thought all I knew was just a baker, but that is far from the truth.”

Landing an interview

Our research adds to a growing body of evidence that ex-entrepreneurs struggle to get interviews and offers. Thankfully, it also offers insights that organizations can use to improve their applicant pool – and that enterprising job seekers can use to boost their odds.

Our study found that former entrepreneurs face less bias when they apply to roles that seem entrepreneurish – in other words, that are in line with stereotypes about business owners. So, for example, they’re more likely to land interviews when applying for positions with a lot of autonomy, such as in new business development, rather than those that require following lots of rules, such as in legal compliance.

Relatedly, our research suggests that recruiters – perhaps unintentionally – have biases against ex-entrepreneurs. Acknowledging such tendencies is a good first step toward minimizing their influence. Moreover, not all recruiters are equally affected: Another recent study showed that recruiters who also have prior entrepreneurial experience – as well as women and those who were recently hired – were less likely to screen out former business owners. So organizations with more diverse hiring teams and a deeper understanding of entrepreneurial experience might see less-biased results.

For their part, ex-entrepreneur job applicants would be wise to highlight in-demand aspects of their work history. For instance, a recent survey by Boston Consulting Group found that executives rank innovation as one of their top three priorities. Former entrepreneurs should emphasize their many valuable characteristics – such as being passionate and creative – that contribute to innovation.

The lack of a traditional employment history may create obstacles for entrepreneurs trying to rejoin the workforce. Recruiters who overlook their value risk missing out on strong candidates.

Jacob A. Waddingham, Texas State University and Miles Zachary, Auburn University

Jacob A. Waddingham, Assistant Professor of Management, Texas State University and Miles Zachary, Associate Professor of Management and Entrepreneurship, Auburn University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Insider trading − the legal kind − is a lot more profitable if you work for a multinational company.

Corporate insiders who trade stocks based on the information they gain on the job earn a lot more if they work at multinational corporations than their peers at U.S. companies with no sales abroad. That’s the main finding of our new peer-reviewed research.

Insider trading happens when a director or employee trades their company’s public stock or other security based on important or “material” information about that business. Insider trading isn’t illegal as long as the person reports the trade to the Securities and Exchange Commission and the information is already in the public domain.

We wanted to know if multinational insiders stand to make more money because of the complexity of the information they could possess relative to outsiders.

So we examined returns from over 2.5 million trades reported to the SEC from 1987 to 2019 by insiders at over 10,000 companies. This is only a subset of all insider trades reported during the period because we focused on only those transactions most likely to be informed by the employee’s insight. We then compared monthly returns for insiders at multinational and domestic companies with those for a typical investor.

We found that all insiders beat the market, but those at multinationals did better – especially if they were on the highest rungs of the corporate ladder. While insiders at domestic companies typically obtained a return of 2.4% in the month following a stock purchase, those at multinational corporations reaped 2.8%. That may not sound like a lot, but, assuming consistent returns, it could amount to earning $170,000 more if an insider traded $1 million over several months. And it’s triple the typical stock market monthly gain of 0.9%

The most in-the-know insiders – executives and others with the most intimate knowledge of the company and its operations – at multinationals got an even bigger advantage, earning 3.6% per month vs. 2.7% at domestic companies.

Why it matters

Insider trading is familiar to most people from movies that portray it in criminal terms, such as Gordon Gekko of “Wall Street.” In the film, he makes millions off others’ inside information.

But even when it is legal, insider trading is very profitable. That’s because insiders trading on public information are more knowledgeable about their industry and process information more effectively than outside investors.

With global companies, the advantage of being an insider increases. Since multinational companies generate earnings in foreign countries, with different currencies, cultures, economies and operating environments, it can be hard for an outsider or analyst to accurately value the company and its stock price. This is especially true when the company does business in regions that are culturally and linguistically distinct from the U.S. This helps insiders trade more efficiently, by buying underpriced stocks at a bargain and selling them later for a windfall.

Companies often motivate their employees to work harder by offering them a stake in their success, but if insiders seem to be getting an unfair advantage over ordinary investors, it may undermine trust in financial markets. The size and profitability of such trades – particularly in light of our data – mean regulators and policymakers may want to consider whether new restrictions on insider trading are needed, such as placing additional limits on the timing or frequency of trades.

What other research is being done

Scholars, including us, are pursuing many avenues of research on insider trading, such as how insider trading restrictions are determined and how insider trades inform markets when news is limited. We’ve recently conducted research on how insider trades by colleagues at the same company tend to cluster together, and we are currently looking at how innovation affects insider trading.

Another recently published project relates to how information is incorporated into stock market prices and how investors underreact to news that may affect insiders’ ability to trade profitably. Similarly, ongoing research uses a GPT language model to assess the complexity of business regulatory filings and financial statements by analyzing technical jargon that can confuse investors, which could also affect how outside investors understand stock prices compared with insiders.

The Research Brief is a short take about interesting academic work.

D. Brian Blank, Mississippi State University and Dallin Alldredge, Florida International University

D. Brian Blank, Assistant Professor of Finance, Mississippi State University and Dallin Alldredge, Assistant Professor of Finance, Florida International University

This article is republished from The Conversation under a Creative Commons license. Read the original article.