Becky Berkman

Our new podcast deep-dives into whether increasingly higher stock market concentration is good or bad for Corporate America, institutional investors, and retail stock market investors worldwide. We pay particular attention the key economic policy implications of American exceptionalism.

$META $AAPL $MSFT $GOOG $GOOGL $AMZN $NVDA $TSLA $AMD $ARM $ORCL $CSCO $IBM $TSM 

$BABA $BIDU $TME $NIO $RIVN $XPEV $JD $PDD $KKR $IQ $BILI $NFLX $DIS $WRB $PARA $IONQ 

$WMT $TGT $COST $CVX $HD $PD $HPE $SNOW $CRWD $NET $AEO $AMC $BRK.A $BRK.B $AXP 

#size #value #momentum #profitability #assetgrowth #marketrisk #revenuegrowth #returnvariance #industry 

#dividendyield #residualvariance #shortreversal #longreversal #fibonacci #murphyrank #bollinger #sma #ema 

The original blog article is available on our AYA fintech network platform. https://ayafintech.network/blog/is-higher-stock-market-concentration-good-or-bad-for-corporate-america/

This fun podcast is about 10 minutes long (with smart AI podcast generation from Google NotebookLM). https://bit.ly/3F1fpgN

In recent years, S&P 500 stock market returns exhibit spectacular concentration in the top tech titans Meta ($META), Apple ($AAPL), Microsoft ($MSFT), Google ($GOOG), Amazon ($AMZN), Nvidia ($NVDA), and Tesla ($TSLA), also known as MAMGANT or Magnificent 7. In the past years from January 2022 to December 2024, the Magnificent 7 delivered a hefty stock market return of 41% (versus only 17% for the other 493 stocks in the S&P 500 index). As of early-March 2025, the S&P 500 index shows substantial stock market concentration. Specifically, the top 10 stocks account for more than 35% of S&P 500 market capitalization. Historically, the top 10 stocks represented more than 20% of S&P 500 market capitalization over the past few decades. Further, the market capitalization of the largest stock relative to the top quartile stock now shows the highest level of stock market concentration since 1932. Although there has been no clear relationship between stock market concentration and near-term return performance, some economists and institutional investors express their concern about increasingly higher stock market concentration in NYSE and Nasdaq. Solid sales and profits in Corporate America can help substantially boost the S&P 500 index, perhaps from 6,000 points to 6,500 points, in the next few years. In this positive light, we can expect S&P 500 stocks, specifically the Magnificent 7 tech titans, to out-perform with a hefty 9% average return per annum in the next few years. Our fundamental analysis combines our proprietary alpha stock signals with ESG scores to lend credence to some of the tech titans in S&P 500 and more broadly Corporate America, especially the top tech titans with significant AI-driven technological advancements.

Stock market investors need not worry about higher market concentration in the longer term. American history shows that high stock market concentration usually leads to lower average returns ceteris paribus over longer investment horizons. When we add market concentration as a distinct variable to the long-run stock market return model, the model forecasts average S&P 500 annual returns of 3%-5% in each decade. Although this subpar return performance falls short of the historical average return of 11% for S&P 500, this drag on long-run returns arises from the greater volatility of tech titan stock returns. In recent times, the current higher stock market valuations of the Magnificent 7 tech titans now appear to embed greater growth expectations in relation to the recent AI-led stock market rally. From Apple ($AAPL), Amazon ($AMZN), and Microsoft ($MSFT) to Meta ($META) and Google ($GOOG), these tech titans rely upon the steady flows of high-end semiconductor microchips, graphical processing units (GPU), and several other quantum advances to make iterative continuous improvements for their Generative AI large language models (Gen AI LLM). As of mid-March 2025, we believe the current Gen AI LLM bellwethers include: Google Gemini, Meta Llama, Microsoft-OpenAI ChatGPT, Anthropic Claude, Perplexity, Alibaba Qwen, DeepSeek, Amazon Nova, Mistral, and Twitter xAI Grok.

After all, stock market concentration per se should not be a major concern for U.S. investors. This concentration often turns out to be a mainstream mechanical result of winner-takes-all sales and profits in AI-driven markets such as Internet search, text, voice, vision, video, and some smart combinations of these common forms of content generation. Specifically, stock market concentration need not heighten the 2 key types of stock market risks: fundamental risks and disequilibrium risks. The former relate to unlikely structural declines in fundamental sales and profits for S&P 500 tech titans, and the latter relate to short-term deviations from fair market values. Although some of the S&P 500 tech stocks seem to reach a new steady state of stock market over-valuation, we believe the vast majority of S&P 500 tech titans can benefit substantially from the broader AI stock market rally to explore new niche markets for both institutional investors and retail investors. In a positive light, stock market concentration need not be a major concern for U.S. investors in a fundamental view. However, we believe U.S. investors should refrain from placing big bets on the recent extreme winners, because their substantially higher market valuations may or may not justify their fundamental forces in the broader context of medium-term competitive threats. At least some of the recent AI-driven winners cannot sustain their greater growth expectations and longer-term competitive advantages. It takes time for U.S. investors to better assess whether each of these AI-driven winners passes the baseline proof of concept for the optimal product-market fit.

Over the past 60 years, no more than 3% of S&P 500 companies were able to sustain 20%+ sales growth for 10 consecutive years. We can back up this empirical result with Jim Collins’s seminal research on what makes great companies tick in his strategic management books: Built to Last, Good to Great, Great by Choice, and Beyond Entrepreneurship 2.0. Therefore, it is hard for the recent AI-driven tech winners to sustain their stock market outperformance in the long run. For at least some of these recent growth stocks, the probable mean reversion of returns can result in future under-performance, especially when their future fundamental sales and profits dwindle, dry up, and then fail to allow these recent winners to dominate in the respective AI-driven markets and adjacent niche segments.

The U.S. regulators should step in when the AI-driven tech titans use their market power to stave off both their rivals and competitors with higher product prices. To the extent that stock market concentration may stifle subsequent disruptive innovations, the Securities Exchange Commission (SEC), Federal Trade Commission (FTC), and Department of Justice (DoJ) etc should introduce new antitrust rules and regulations to make American tech titans face fierce competitive pressure with no clear dominance in any particular AI-driven market. The classic examples include: Apple App Store and Google Play in the mobile software market; Amazon e-commerce in the retail market for consumer goods; Nvidia GPUs, microchips, and several other hardware advances in the semiconductor industry; and Tesla in the global market for electric vehicles (EV) and autonomous robotaxis (AR).

This current high stock market concentration serves as one of the mainstream reasons for U.S. investors to further diversify exposures across asset classes, regions, and strategies. The historically optimal portfolio mix of 60% stocks and 40% bonds remains empirically valid, relevant, profitable, and reasonable in a fundamental view. In light of the still-solid sales and profits in the AI-driven sections of Corporate America, we believe the optimal portfolio combo of 60% stocks and 40% bonds continues to serve as the mainstream economic engine for the global asset management industry, specifically BlackRock, State Street, and Vanguard. U.S. investors need to revisit their optimal choices of AI-driven stocks with new fundamental competitive moats, substantial safety margins, positive network effects, cost economies, and information cascades.

Below we provide hyperlinks to many other recent podcasts, surveys, research articles, and blog posts on global macro-finance, asset return prediction, trade, technology, fiscal-monetary policy coordination, and fundamental industry analysis for stock market investors worldwide. Key technological advancements include generative artificial intelligence (Gen AI) large language models (LLM), electric vehicles (EV), autonomous robotaxis (AR), virtual reality (VR) headsets, semiconductor microchips, high-speed broadband networks, telecoms, cloud services, social media platforms, quantum computers, and pharmaceutical treatments, medications, and therapies.

American exceptionalism often turns out to be the heuristic rule of thumb for better economic growth, low and stable inflation, full employment, and macro-financial stability.

Podcast: https://bit.ly/4iuWuJ9

Article: https://ayafintech.network/blog/american-exceptionalism-turns-out-to-be-the-heuristic-rule-of-thumb-for-better-economic-growth-low-stable-inflation-full-employment-macro-financial-stability/

In the broader modern monetary policy context, central banks learn to weigh the trade-offs between output and inflation expectations and macro-financial stress conditions.

Podcast: https://bit.ly/42SwrXG

Article: https://ayafintech.network/blog/central-banks-weigh-the-monetary-policy-trade-offs-between-output-inflation-and-macro-financial-stress-conditions/

Today, tech titans, billionaires, serial entrepreneurs, and venture capitalists continue to reshape and even disrupt global pharmaceutical investments for both better healthspan and longer lifespan.

Podcast: https://bit.ly/41KDNLp

Article: https://ayafintech.network/blog/today-tech-titans-reshape-global-pharmaceutical-investments-for-both-better-healthspan-and-longer-lifespan/

Artificial intelligence continues to reshape the current global market for better biotech advances, medical innovations, and healthcare services.

Podcast: https://bit.ly/4hBVimM

Article: https://ayafintech.network/blog/the-new-integration-of-artificial-intelligence-reshapes-the-competitive-landscape-for-the-global-market-for-better-medical-innovations-and-healthcare-services/

The global market for GLP-1 anti-obesity weight-loss treatments now grows substantially to benefit more than 1 billion people worldwide by 2030.

Podcast: https://bit.ly/4bz6vmI

Article: https://ayafintech.network/blog/the-global-market-for-GLP-1-weight-loss-medications-grows-substantially-to-benefit-1-billion-people-worldwide-by-2030/

Is higher stock market concentration good or bad for Corporate America?

Podcast: https://bit.ly/3F1fpgN

Article: https://ayafintech.network/blog/is-higher-stock-market-concentration-good-or-bad-for-corporate-america/

Geopolitical alignment often reshapes and reinforces asset market fragmentation in the broader context of financial deglobalization.

Podcast: https://bit.ly/3ZpGMcD

Article: https://ayafintech.network/blog/geopolitical-alignment-often-reshapes-and-reinforces-asset-market-fragmentation-in-the-broader-context-of-financial-deglobalization/

The global cloud infrastructure helps accelerate the next high-tech revolutions in electric vehicles (EV), virtual reality (VR) headsets, artificial intelligence (AI) online services, and the metaverse.

Podcast: https://bit.ly/47pDk3z

Article: https://ayafintech.network/blog/the-global-cloud-infrastructure-helps-expand-what-can-be-made-digitally-viable-from-electric-vehicles-and-virtual-reality-headsets-to-artificial-intelligence-metaverse/

The new homeland industrial policy stance tilts toward greater global resilience across the major high-tech supply chains worldwide.

Podcast: https://bit.ly/3B6xY12

Article: https://ayafintech.network/blog/the-current-homeland-industrial-policy-stance-worldwide-seeks-to-embed-the-new-notion-of-global-resilience-into-economic-statecraft/

China poses new threats to the U.S. and its western allies.

Podcast: https://bit.ly/3XGWrD1

Article: https://ayafintech.network/blog/china-poses-new-economic-technological-and-military-threats-to-the-us-and-western-allies/

How can generative AI tools and LLMs help enhance human productivity?

Podcast: https://bit.ly/4elAFKv

Article: https://ayafintech.network/blog/generative-artificial-intelligence-uses-large-language-models-and-content-generation-tools-to-enhance-human-productivity/

What are the macrofinancial ripple effects of central bank digital currency (CBDC) design, issuance, and broad user adoption?

Podcast: https://bit.ly/3XNMwM8

Article: https://ayafintech.network/blog/central-banks-should-shape-cbdc-design-features-and-functions-to-reduce-any-adverse-impact-on-bank-intermediation/

Both BYD and Tesla have become serious global manufacturers of electric vehicles (EV) worldwide.

Podcast: https://bit.ly/3BgL0sL

Article: https://ayafintech.network/blog/mainstream-technological-advances-in-the-global-auto-industry/

With U.S. fintech patent approval, accreditation, and protection for 20 years, our AYA fintech network platform provides proprietary alpha stock signals and personal finance tools for stock market investors worldwide.

We build, design, and delve into our new and non-obvious proprietary algorithmic system for smart asset return prediction and fintech network platform automation. Unlike our fintech rivals and competitors who chose to keep their proprietary algorithms in a black box, we open the black box by providing the free and complete disclosure of our U.S. fintech patent publication. In this rare unique fashion, we help stock market investors ferret out informative alpha stock signals in order to enrich their own stock market investment portfolios. With no need to crunch data over an extensive period of time, our freemium members pick and choose their own alpha stock signals for profitable investment opportunities in the U.S. stock market.

Smart investors can consult our proprietary alpha stock signals to ferret out rare opportunities for transient stock market undervaluation. Our analytic reports help many stock market investors better understand global macro trends in trade, finance, technology, and so forth. Most investors can combine our proprietary alpha stock signals with broader and deeper macrofinancial knowledge to win in the stock market.

Through our proprietary alpha stock signals and personal finance tools, we can help stock market investors achieve their near-term and longer-term financial goals. High-quality stock market investment decisions can help investors attain the near-term goals of buying a smartphone, a car, a house, good health care, and many more. Also, these high-quality stock market investment decisions can further help investors attain the longer-term goals of saving for travel, passive income, retirement, self-employment, and college education for children. Our AYA fintech network platform empowers stock market investors through better social integration, education, and technology.

Our latest podcast deep-dives into American exceptionalism and economic growth outperformance both by historical standards and in comparison to the rest of the world in recent decades.

$META $AAPL $MSFT $GOOG $GOOGL $AMZN $NVDA $TSLA $COST $PG $HD $CVS $WMT $TGT $T 

$TMUS $VZ $SNOW $CRWD $NET $IONQ $ZIM $QCOM $TSM $BABA $BIDU $TME $BILI $JD $PDD $IQ 

$ORCL $IBM $CSCO $SNPS $AEO $AMC $PARA $NFLX $DIS $WRB $KKR $XOM $OXY $PSX $F $RACE 

#size #value #momentum #profitability #assetgrowth #marketrisk #revenuegrowth #residualvariance #industry 

#dividendyield #returnvariance #shortreversal #longreversal #shortrange #longrange #fibonacci #issuance 

The original blog article is available on our AYA fintech network platform.

https://ayafintech.network/blog/american-exceptionalism-turns-out-to-be-the-heuristic-rule-of-thumb-for-better-economic-growth-low-stable-inflation-full-employment-macro-financial-stability/

This fun podcast is about 10 minutes long (with smart AI podcast generation from Google NotebookLM). https://bit.ly/4iuWuJ9

We delve into the mainstream root causes of higher productivity growth in America despite several recent extraordinary events. These rare events include the dotcom stock market crash of 2001-2002, Global Financial Crisis of 2008-2009, Covid-19 pandemic crisis of 2020-2022, and the subsequent surge in inflation and domestic unemployment. In this wider macro context, we explain and discuss why American productivity growth has been so impressive in comparison to the rest of the world. We can draw some vital lessons from American productivity growth in global trade, finance, and technology.

With impressively higher productivity growth, America continues to lead the rest of the world. America’s recent rise as the world’s biggest shale oil producer continues to fuel its own economic growth in the next couple of decades. America’s deep and liquid capital markets for stocks, bonds, ETFs, and so on continue to deliver higher returns, cash dividends, and share repurchases for global investors. The American dollar continues to serve as the dominant global reserve currency for trade, finance, and technology. Only toxic politics can derail American economic growth, low and stable inflation, maximum sustainable employment, and macro-financial stability.

Since the early-1990s, America has grown substantially faster than most other rich countries. Also, the American economy has rebounded more significantly from its recent recessions along the way. In practice, U.S. economic growth has been best-in-class, and American strengths give grounds for greater optimism about the next likely economic power, productivity growth, macro-financial stability, and maximum sustainable employment. However, the U.S. fraction of global GDP has decreased incrementally from 21% in 1990 to 16% today in real purchasing-power parity (PPP) terms. Even the economic growth spurts of the world’s 2 most populous countries, China and India, lag behind American exceptionalism. China’s real GDP per capita remains less than one third of U.S. real GDP per capita. India’s real GDP per capita is still smaller today. In recent years, higher American productivity growth has been impressive by both historical standards and in comparison to the rest of the world. American exceptionalism often turns out to be the heuristic rule of thumb for wider economic growth, full employment, low and stable inflation, real productivity growth, technological advancement, and macro-financial stability.

On a per-person basis, American economic output is now more than 40% higher than economic output in Western Europe and Canada, and about 60% higher than economic output in Japan. Today, these economic output gaps are approximately twice as large as they were back in 1990. Average wages in the poorest American state, Mississippi, remain higher than average wages in Australia, Britain, Canada, France, and Germany. A recent IMF survey shows that America is the only country whose economic output and employment are above pre-pandemic expectations in the G20 club. This impressive productivity growth combines with the global reserve currency status of the U.S. dollar to entrench global economic heft for America and wealth creation for Americans.

We explain and discuss why American productivity growth has been so impressive for so long. Also, we explain and discuss why this growth is likely to continue in the next couple of decades. Some of the fundamental forces relate to the good fortune due to geography. As a quasi-continental economy with a big and broad consumer market, American companies benefit substantially from this sheer economic scale. A good product idea that some Silicon Valley tech startup hatches in California can often spread to the other 49 states in short order. Also, America integrates its labor market into the global economic ecosystem. This integration allows Americans to move to better jobs with higher wages. At the same time, this integration empowers Americans to gravitate toward more productive strategic sectors of high technology. These strategic sectors include semiconductor microchip production, higher-speed broadband cloud services, telecoms, electric vehicles (EV), autonomous robotaxis (AR), generative artificial intelligence (Gen AI) large language models (LLM), and biopharmaceutical medications, treatments, and therapies. In light of green energy transformation, the American-led improvements in techniques for extracting hydro-carbons from shale rocks have turned America into the world’s largest producer of oil and natural gas over the past couple of decades.

With its deep, broad, and liquid financial markets, America has made it both easier and faster for startups to raise new equity. In stark contrast to the alternative debt capital instruments such as bank loans and corporate bonds, this key equity capital availability serves as a better way for numerous American companies to get off the ground. In practice, this novel and non-obvious profusion of young companies has helped build out the broader vital, vibrant, and dynamic startup ecosystem for high technology in America. In addition, the dominant global reserve currency status of the American dollar helps further make global trade, finance, and technology more frictionless for American business. From Harvard, Yale, MIT, NYU, Columbia, and Chicago to Stanford and UC Berkeley, America has many of the world’s best think tanks, universities, and research institutes. These academic institutions attract the world’s best students, doctors, scientists, engineers, statisticians, economists, and other fresh talents worldwide.

In America, business rules and regulations are relatively lax, lenient, tolerant, and inclusive. This broader deregulatory business context has given high-tech startups ample room to grow their core business operations in the midst of significantly less economic policy uncertainty. In the recent couple of decades, the U.S. government has made bold, robust, and resolute interventions in response to some rare crises, disasters, and other extraordinary events. These rare crises span the dotcom stock market crash, Global Financial Crisis, Eurozone sovereign debt debacle, Covid-19 pandemic crisis, and subsequent inflationary outbreak worldwide. At any rate, it is impossible for us to explain America’s smarter, faster, and better economic growth engine without acknowledging the U.S. government’s open and inclusive attitude toward stepping on the financial accelerator pedal when the real economy sputters. The resultant positive government interventions often arise in the common form of better fiscal-monetary policy coordination. In modern economic history, there is an element of relentless dynamism in American business. This unique characteristic of the American economy continues to be the ultimate fundamental force in support of higher-quality economic growth over the next couple of decades.

Central banks learn to better weigh the monetary policy trade-offs between output and inflation expectations and macro-financial stress conditions.

https://ayafintech.network/blog/central-banks-weigh-the-monetary-policy-trade-offs-between-output-inflation-and-macro-financial-stress-conditions/

Geopolitical alignment often reshapes and reinforces asset market fragmentation in the broader context of financial deglobalization.

https://ayafintech.network/blog/geopolitical-alignment-often-reshapes-and-reinforces-asset-market-fragmentation-in-the-broader-context-of-financial-deglobalization/

In the modern monetary system, each central bank digital currency (CBDC) helps better anchor public trust in money in support of economic welfare, especially in a new cashless society.

https://ayafintech.network/blog/central-banks-should-shape-cbdc-design-features-and-functions-to-reduce-any-adverse-impact-on-bank-intermediation/

Economic policy incrementalism for better fiscal and monetary policy coordination

https://ayafintech.network/blog/economic-policy-incrementalism-for-better-fiscal-and-monetary-policy-coordination/

The bank-credit-card model and fintech platforms have adapted well to the recent digitization of cashless finance.

https://ayafintech.network/blog/the-bank-credit-card-model-and-fintech-platforms-have-adapted-well-to-the-recent-digitization-of-cashless-finance/

Paul Morland shows that demographic changes lead to modern economic growth in the current world.

https://ayafintech.network/blog/paul-morland-argues-that-demographic-changes-lead-to-modern-economic-growth-in-the-current-world/

New Keynesian monetary policy framework

https://ayafintech.network/blog/new-keynesian-monetary-policy-framework/

Michael Woodford provides the theoretical foundations of monetary policy rules in ever more efficient financial markets.

https://ayafintech.network/blog/michael-woodford-provides-the-theoretical-foundations-of-monetary-policy-rules-in-efficient-financial-markets/

Government intervention remains a major influence over global trade, finance, and technology.

https://ayafintech.network/blog/government-intervention-remains-a-core-influence-over-global-trade-finance-and-technology/

Peter Isard analyzes the proper economic policy reforms and root causes of global financial crises of the 1990s and 2008-2009.

https://ayafintech.network/blog/peter-isard-analyzes-the-proper-economic-policy-reforms-and-root-causes-of-global-financial-crises/

Ray Fair applies his macroeconometric model to study the central features of the real economy such as price stability and full employment in the dual mandate.

https://ayafintech.network/blog/ray-fair-applies-his-macro-model-to-study-the-central-features-of-the-economy-price-stability-full-employment-dual-mandate/

Carmen Reinhart and Kenneth Rogoff analyze long-run crisis data to find the root causes of recent financial crises for better bank capital regulation and asset market stabilization.

https://ayafintech.network/blog/carmen-reinhart-and-kenneth-rogoff-analyze-long-run-crisis-data-to-find-the-root-causes-of-financial-crises-for-better-bank-capital-regulation-and-asset-market-stabilization/

Anat Admati and Martin Hellwig raise critical issues about bank capital regulation and asset market stabilization.

https://ayafintech.network/blog/admati-and-hellwig-raise-issues-about-bank-capital-regulation-and-asset-market-stabilization/

American bank failure resolution and financial risk management for Silicon Valley Bank, Signature Bank, and First Republic Bank

https://ayafintech.network/blog/bank-failure-resolution-financial-risk-management-silicon-valley-bank-first-republic-bank/

Timothy Geithner shares his personal reflections on the post-crisis macro financial stress tests for U.S. banks.

https://ayafintech.network/blog/timothy-geithner-shares-his-reflections-on-the-macro-financial-stress-tests-for-american-banks/

The Federal Reserve System conducts modern monetary policy decisions, interest rate adjustments, and inter-bank payment operations.

https://ayafintech.network/blog/federal-reserve-conducts-monetary-policy-decisions-interest-rate-adjustments-and-inter-bank-payment-operations/

Barry Eichengreen compares the Great Depression of the 1930s versus the Great Recession of 2008-2009 as historical episodes of economic woes.

https://ayafintech.network/blog/barry-eichengreen-compares-the-great-depression-and-global-financial-crisis-as-episodes-of-economic-woes/

Former Bank of England Governor Mervyn King provides his substantive analysis of the Global Financial Crisis of 2008-2009.

https://ayafintech.network/blog/former-bank-of-england-governor-mervyn-king-provides-his-deep-substantive-analysis-of-the-global-financial-crisis/

Michel De Vroey discusses the global history of macro-economic theories from real business cycles to persistent non-neutral monetary policy effects.

https://ayafintech.network/blog/de-vroey-delves-into-the-global-history-of-macroeconomic-theories-from-real-business-cycles-to-persistent-monetary-effects/

With U.S. fintech patent approval, accreditation, and protection for 20 years, our AYA fintech network platform provides proprietary alpha stock signals and personal finance tools for stock market investors worldwide.

We build, design, and delve into our new and non-obvious proprietary algorithmic system for smart asset return prediction and fintech network platform automation. Unlike our fintech rivals and competitors who chose to keep their proprietary algorithms in a black box, we open the black box by providing the free and complete disclosure of our U.S. fintech patent publication. In this rare unique fashion, we help stock market investors ferret out informative alpha stock signals in order to enrich their own stock market investment portfolios. With no need to crunch data over an extensive period of time, our freemium members pick and choose their own alpha stock signals for profitable investment opportunities in the U.S. stock market.

Smart investors can consult our proprietary alpha stock signals to ferret out rare opportunities for transient stock market undervaluation. Our analytic reports help many stock market investors better understand global macro trends in trade, finance, technology, and so forth. Most investors can combine our proprietary alpha stock signals with broader and deeper macrofinancial knowledge to win in the stock market.

Through our proprietary alpha stock signals and personal finance tools, we can help stock market investors achieve their near-term and longer-term financial goals. High-quality stock market investment decisions can help investors attain the near-term goals of buying a smartphone, a car, a house, good health care, and many more. Also, these high-quality stock market investment decisions can further help investors attain the longer-term goals of saving for travel, passive income, retirement, self-employment, and college education for children. Our AYA fintech network platform empowers stock market investors through better social integration, education, and technology.

$BFLY $CLDX $NVO $LLY $JNJ $BMY $PFE $ABBV $MRK $AMGN $UNH $MRNA $AZN $WMT $TGT 

$COST $CVS $CSCO $ORCL $IBM $ASML $SNPS $NET $CRWD $AEO $AMC $PARA $NFLX $DIS 

$T $VZ $TMUS $C $BAC $JPM $WFC $MS $GS $PNC $BLK $STT $DOCU $IONQ $QBTS $QUBT 

The original blog article is available on our AYA fintech network platform. https://ayafintech.network/blog/innovative-investment-theory-and-practice/

This fun podcast is about 10 minutes long (with smart AI podcast generation from Google NotebookLM). https://bit.ly/42Anene

Corporate investment can be in the form of real tangible investment or intangible investment. The former concerns real asset expansion through capital expenditures and mergers and acquisitions while the latter involves corporate innovation through research and development (R&D). This literature review focuses on the empirical corporate finance studies on the key implications of R&D intensity for firm performance. The other empirical corporate finance studies on capital expenditures, mergers and acquisitions, and real asset growth flow into the other separate literature reviews. These literature reviews jointly paint a more holistic picture of the empirical facts about corporate investment decisions.

Eberhart, Maxwell, and Siddique (2004) examine a sample of 8,313 cases between 1951 and 2001 where firms unexpectedly increase their R&D expenditures by a significant margin. Both the long-run buy-and-hold portfolio strategies and Fama-French-Carhart (1993, 1996, 1997) time-series regressions show that significantly positive abnormal stock returns follow these R&D increases. Also, there is robust evidence that the same sample firms experience significant improvements in long-run operating performance after the R&D increases. These empirical results reject the null efficient-markets hypothesis and thus suggest that the market is slow to recognize both the nature and extent of R&D increases as valuable investments.

Eberhart, Maxwell, and Siddique (2004) contribute to the behavioral finance literature on investor under-reaction that occurs in response to key corporate events such as SEOs, stock repurchases, and stock splits (e.g. Eberhart and Siddique (2002), Ikenberry, Lakonishok, and Vermaelen (1995, 2000), Ikenberry and Ramnath (2002), and Loughran and Ritter (1995)). This contribution echoes Daniel and Titman’s (2006) thesis that investors tend to underreact to intangible information but not to tangible information. In this sense, R&D increases provide a natural experiment to test the market’s capability to correctly incorporate the intangible content of R&D increases. To the extent that investors underreact to the intangible benefit of R&D increases, significantly positive abnormal stock returns follow these R&D increases up to the 5-year time horizon. In the larger context of a unified theory of gradual news diffusion (e.g. Jegadeesh and Titman (1993, 2001), Hong and Stein (1999), and Hong, Lim, and Stein (2000)), Eberhart, Maxwell, and Siddique’s (2004) study corroborates the behavioral story of investor underreaction to the positive effect of R&D investments on shareholder value.

Although shareholders and bondholders both benefit from an increase in firm value due to an increase in R&D investment, shareholders benefit at the expense of bondholders from a concomitant increase in firm risk because stocks are analogous to call options that bondholders implicitly sell on the underlying firm value. The benefit of an increase in R&D investment to shareholders that some earlier studies report may reveal the effect of a wealth transfer from bondholders to shareholders (Shi, 2003), not any benefit to the entire firm value. Eberhart, Maxwell, and Siddique (2008) gauge R&D investment intensity as the ratio of R&D to sales or R&D to assets and report a positive relationship between these R&D intensity metrics and bond returns. The net effect of higher R&D investment intensity is positive for bondholder value. In fact, the negative nexus between R&D intensity and default risk accords with the recent investment asset pricing conjecture (Berk, Green, and Naik, 1999; Gomes, Kogan, and Zhang, 2003; Carlson, Fisher, and Giammarino, 2004; Zhang, 2005; Cooper, 2006; Li, Livdan, and Zhang, 2009; Liu, Whited, and Zhang, 2009; Anderson and Garcia-Feijoo, 2006). As a firm invests and exercises real growth options via R&D innovation, this investment transforms the real growth options into safer assets in place with steady cash flows. As a result, the firm’s relative risk declines. Nonetheless, Eberhart, Maxwell, and Siddique’s (2008) evidence suggests a positive relation between R&D investment expansion and subsequent stock return. This evidence seems more consistent with the alternative behavioral mispricing hypothesis in contrast to the rational pricing theory.

R&D usually provides strong positive externalities because R&D investment yields benefits that accrue to parties other than the R&D investor (Bernstein and Nadiri, 1988; Jaffe, 1986). Given the positive spill-over effects of R&D investment, Chen, Chen, Liang, and Wang (2014) empirically report that there is a positive relationship between R&D incoming spillovers and firm performance improvements. This nexus indicates that the market does not immediately incorporate positive R&D externalities into stock market valuation. Chen, Chen, Liang, and Wang (2014) attribute this evidence to the behavioral story that many investors underreact to firm-specific increases in R&D investment with both positive long-term abnormal stock returns and operating performance improvements (Eberhart, Maxwell, and Siddique, 2004, 2008).

Chen, Chen, Liang, and Wang (2014) use a stochastic frontier production function to capture R&D spill-overs, which can be estimated as non-negative stochastic random covariates. Firms with high R&D spill-overs tend to recruit more key employees from other firms. Thus, firms that hire more key personnel to take advantage of technical expertise from other firms enjoy higher R&D incoming spillovers.

Chen, Chen, Liang, and Wang (2014) run the Fama-French (1993, 1996) and Carhart (1997) time-series regressions of excess stock returns that the econometrician sorts on R&D incoming spillovers. The mean monthly alpha is 0.67%-1.08% for firms with higher R&D incoming spillovers in comparison to no more than 0.58% for firms with lower R&D incoming spillovers. Thereby, R&D investments are beneficial to other firms, but the market is slow to recognize the full extent of this benefit.

Following the convention of Denis and Sarin (2001), Chan, Ikenberry, and Lee (2004), and Titman, Wei, and Xie (2004), Chen, Chen, Liang, and Wang (2014) examine abnormal stock returns around earnings announcements over the post-R&D-increase 5-year period. The evidence suggests significantly positive earnings-announcement abnormal returns for firms with high incoming spillovers, but this evidence does not hold for firms with low incoming spillovers. Thereby, the long-term stock return outperformance of R&D-intensive firms with high incoming spillovers is unlikely to be driven by benchmark measurement noise (Lyon, Barber, and Tsai, 1999).

Brown, Fazzari, and Petersen (2009) explore whether supply shifts in finance can explain a significant portion of the 1990s R&D boom and subsequent decline. With a firm-level panel dataset of 1,347 high-tech publicly traded firms from 1990 to 2004, Brown, Fazzari, and Petersen (2009) use GMM estimation of dynamic R&D models to find sharp differences in R&D finance when Brown et al split the data into young and mature firms. For mature firms, the point estimates for the financial variables are insignificant. For young firms, the measures of access to internal and external equity finance have significantly positive effects on R&D intensity. The financial effects for the young high-tech firms alone are large enough to explain most of the aggregate R&D cycle in the 1990s. In the larger context of endogenous growth theory, stock markets provide an important source of external finance and in turn contribute to economic growth by directly funding R&D innovation, particularly for young firms.

Brown, Martinsson, and Petersen (2013) analyze the explanatory power of investor protection and access to stock market finance in capturing the variation in long-term R&D intensity. Legal rules and institutions and financial developments affect the availability of external equity finance. This empirical mechanism is particularly important for risky and intangible R&D investments that are not easily financed with debt. Brown, Martinsson, and Petersen’s (2013) empirical study connects both law and finance with firm-level innovative R&D investments that help promote economic growth.

An extensive literature suggests that countries with robust legal protection of minority shareholders have larger and more accessible stock markets (La Porta, Lopez-de-Silanes, and Shleifer, 2006, 2008). Several studies report evidence of a positive relationship between stock market development and broad measures of economic growth (e.g. Levine and Zervos (1998) and Bekaert, Harvey, and Lundblad (2005)). Legal contracting institutions help enhance stock market development (Acemoglu and Johnson, 2005), and also stock market liberalization boosts aggregate productivity (Bekaert, Harvey, and Lundblad, 2011). Access to stock market finance is particularly important for R&D investments because the intangible nature of R&D with little collateral value sharply limits the firm’s ability to use debt. Since creditors share only in downside returns, the design of standard debt contracts does not work well for financing innovative R&D investments that are characterized by a high probability of failure but only some thin chance of extremely large upside returns. Thereby, legal institutions and financial developments that better facilitate access to stock market equity are more important for R&D innovative productivity growth than for tangible capital accumulation.

Brown, Martinsson, and Petersen (2013) use several exogenous instruments for legal origin, enforcement, and anti-self-dealing protection in two-stage least squares (2SLS) regressions of R&D intensity and stock market development (Demirguc-Kunt and Maksimovic, 2002; Beck and Levine, 2005; La Porta, Lopez-de-Silanes, and Shleifer, 1997, 2008). The key interaction term between stock market development and industry dependence on external finance carries a significantly positive coefficient in the small-firm and young-firm subsamples. The R&D differential measure for the interquartile range of industry dependence on external finance is significant at 2% in these subsamples.

Brown, Martinsson, and Petersen’s (2013) evidence suggests that there is a micro-level channel through which stock market development causes economic growth by supplying critical external finance to fund intangible R&D investments (which in turn help spur productivity growth) (e.g. Levine (2005: 870) and Bekaert, Harvey, and Lundblad (2011)). Further, the same evidence suggests a new nexus between legal contracting institutions and innovative R&D activities (the latter of which in turn drive economic growth) (e.g. Acemoglu and Johnson (2005)). This nexus is particularly important when R&D firms are likely to face considerable difficulty in substituting debt for equity. In addition, the interaction between financial development and access to stock market finance matters more for the R&D investment intensity among small and young firms in comparison to large and mature firms (e.g. Beck et al (2008)). In essence, the market-centric system may have a significant advantage in spurring economic growth through a sequence of creative destruction that emerges from the continual innovative R&D investments of small and young firms (e.g. Brown and Petersen (2011)).

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The top generative artificial intelligence (Gen AI) tools include OpenAI’s ChatGPT, Google’s Gemini search engine, Microsoft’s Copilot, Amazon’s Alexa, Midjourney, Anthropic Claude, Synthesia, Jasper, and so forth. These Gen AI search engines apply large language models (LLM) and content generation tools to help enhance human lives with significantly greater productivity. Productivity gains often manifest in the common forms of AI-driven scripts, articles, images, podcasts, films, movies, and many other online contents. Gen AI avatars help translate text, imagery, and sound recognition into interpretable contents within only seconds. This recent Gen AI hype transforms and even revolutionizes the current high-tech stock market rally. Since the successful launch of OpenAI’s ChatGPT back in November 2022, these Gen AI virtual assistants have grown significantly to leverage the high performance of AI semiconductor microchips from Nvidia, AMD, Micron, TSMC, Pegatron, and many of their trade partners. In recent quarters, stock market investors revive their current interest in this modern Gen AI technology worldwide.

Foundational LLMs and Gen AI virtual tools, assistants, and avatars substantially enhance the high-skill process of online content generation. As a result, the stocks of Gen AI companies have had more than double-digit improvements in their stock market valuation, top-line sales revenue growth, and bottom-line profitability. Gen AI technology can help create new content generation in the form of text, imagery, video, audio, and code through natural language communication, rather than code snippets and arcane programming languages. These key transformative features distinguish the current Gen AI technology from the vast majority of its predecessors. We can view this Gen AI technology as the third generation of AI software research. Earlier iterations of AI technology either required computer scientists and software engineers to write deterministic code programs to perform specific tasks (Software 1.0), or required these specialists to statistically train complex neural networks on big data for specific tasks and predictions (Software 2.0). With Software 3.0 today, the foundational models leverage out-of-the-box capabilities to enhance their niche businesses with natural language reasons, explanations, and knowledge spillovers worldwide. These Gen AI base models help substantially transform both the logical and reasonable content generation through open-source application programming interfaces (API) without any laborious collection of big data.

This Gen AI transformation has begun to translate into a new economic reality. In some cases, developer productivity gains amount to double-digit percentages (at least 15% to 20% per annum) via Gen AI technology. This Gen AI technology can further enhance human-robot interactions and several other adjacent applications, especially in traditional service markets such as law, finance, medicine, illustration, as well as audio, voice, and video generation with smart data analytics. Many stock market investors regard this Gen AI technology as a major platform shift across all aspects of both the consumer and enterprise experiences.

This Gen AI transformation can cause new profound ripple effects, macroeconomic consequences, and policy implications. Recent empirical studies show that Gen AI usage can help boost U.S. annual labor productivity growth by 1.5-2.5 percentage points over the current decade. In the U.S. and many other rich economies, AI can eventually help raise annual global GDP by 7% to 15%. This global productivity lift can turn a relatively narrow AI-led U.S. stock market rally into a much broader one over the longer run. Specifically, S&P 500 stocks have already experienced 9% to 25% increases in fair asset market valuation in recent years, especially in the post-pandemic period. This Gen AI stock market trend turns out to be the new friend for many institutional investors worldwide.

However, the neural networks of these Gen AI tools differ from the neural networks of human brains. Gen AI machines can indeed perform reflexive statistical analysis, but these machines have virtually little capacity for human-like logical deliberations and reasons. Although these Gen AI machines learn and recognize patterns in text, imagery, audio, and video etc, the vast majority of machine-learning algorithms still revolve around the deep statistics of both words and proper responses to prompts. Specifically, these machine-learning algorithms cannot function like human brains to completely understand abstract concepts, such as the broad business judgment rule in law, dynamic equilibrium fair market valuation in finance, and the dopamine hypothesis in medicine. As a result, there is no internal model for these AI machine-learning algorithms to understand the world around them. One day the human race may achieve artificial general intelligence (AGI), but we are still far from AGI today. No finite dollar amount of stock market investment can change this current reality. When push comes to shove, stock market investors would need to make prudent investment decisions in support of better stock portfolio profitability, resilience, and diversification.

In terms of stock market valuation, the current AI stock market rally is pretty much like the past innovation booms. During the past innovation-led productivity booms, such as the widespread adoption of electricity from 1919 to 1929 and PCs and the Internet from 1996 to 2005, sharp and steady increases in stock market prices and returns turn into asset bubbles over the medium. These asset bubbles eventually burst in due course. In response to the widespread adoption of electricity, the Great Depression ensued in the 1930s. In response to the dotcom bubble, the subprime mortgage crisis and Global Financial Crisis arose in 2008-2009. Although the rising tide seems to lift all boats in the AI-led high-tech sector, the current AI stock market rally would eventually experience a more reasonable correction in due course.

In the meantime, the vast majority of Gen AI stocks continue to trade at reasonably attractive P/E and P/B multiples. It can be quite reasonable for many tech titans to drive the current powerful AI stock market rally. In light of this positive platform shift, the current AI stock market rally may or may not be a fundamentally shallow hype cycle. To the extent that many tech titans continue to apply AI foundational models for business purposes, the picks-and-shovels businesses are likely to benefit from this continuation. For AI, these fundamental picks-and-shovels businesses include semiconductor microchip manufacturers (Nvidia, AMD, TSMC, Micron, Intel, and so on), cloud computing hyperscalers (Amazon, Google, Microsoft, IonQ, Alibaba, and Tencent), and online infrastructure companies (Cisco, Oracle, AT&T, T-Mobile, and Verizon).  

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