Tech Bubble Research Articles

Heterogeneity in bubble experience across firms: an examination of the tech bubble

ABSTRACT We study heterogeneity in bubble experience across individual stocks. Applying the date-stamping technique and using the tech bubble in late 1990s as our laboratory, we find that tech firms vary in whether, when and how long they experience bubbles. In multivariate regressions, we find that bubbles are more likely to happen and they on average last longer in more liquid stocks, in smaller firms, in fast-growing firms and in firms with higher ownership by institutional non-blockholders rather than blockholders.

Are There Dragon Kings in the Stock Market?

In this study, we undertake a systematic study of historic market volatility spanning roughly five preceding decades. We focus specifically on the time series of the realized volatility (RV) of the S&P500 index and its distribution function. As expected, the largest values of RV coincide with the largest economic upheavals of the period: Savings and Loan Crisis, Tech Bubble, Financial Crisis and Covid Pandemic. We address the question of whether these values belong to one of the three categories: Black Swans (BS), that is, they lie on scale-free, power-law tails of the distribution; Dragon Kings (DK), defined as statistically significant upward deviations from BS; or Negative Dragons Kings (nDK), defined as statistically significant downward deviations from BS. In analyzing the tails of the distribution with RV>40, we observe the appearance of “potential” DK, which eventually terminate in an abrupt plunge to nDK. This phenomenon becomes more pronounced with the increase in the number of days over which the average RV is calculated—here from daily, n=1, to “monthly”, n=21. We fit the entire distribution with a modified Generalized Beta (mGB) distribution function, which terminates at a finite value of the variable but exhibits a long power-law stretch prior to that, as well as a Generalized Beta Prime (GB2) distribution function, which has a power-law tail. We also fit the tails directly with a straight line on a log-log scale. In order to ascertain BS, DK or nDK behavior, all fits include their confidence intervals and p-values are evaluated for the data points to check whether they can come from the respective distributions.

ANALYSIS OF EMOTIONAL EXHAUSTION THROUGH DOWNSIZING WITH JOB SATISFACTION AS MEDIATION

Nowadays, many tech companies are on a downward trend. This can be referred to as a tech bubble burst. This makes it very difficult for companies, especially those in the e-commerce industry, financially. These companies are constantly looking for ways to make efficiencies in order to save costs and streamline the company. There are many ways to do that, but the most commonly used is downsizing. Downsizing is a way of efficiency by removing unnecessary employees to avoid unnecessary costs. Although downsizing is effective in saving costs, there are some negative effects of downsizing, namely its effect on emotional exhaustion and job satisfaction of the remaining employees in the company or can be said to be layoff survivors. Emotional exhaustion can be interpreted as emotional exhaustion towards their work while job satisfaction is the feeling of satisfaction that a person has when doing their job. The purpose of this study is to determine and analyze the effect of downsizing on emotional exhaustion and job satisfaction of employees working in the e-commerce industry in Indonesia. This study uses quantitative research methods with a cross sectional approach and non-probability sampling techniques with data obtained by means of questionnaires distributed to 180 respondents. The results of this study Downsizing has a significant effect on emotional exhaustion, Downsizing has a significant effect on job satisfaction, Job Satisfaction has a significant effect on emotional exhaustion, Job Satisfaction can mediate the effect of downsizing on emotional exhaustion.

Genealogy of Finance: Long-term History and Alternatives

In periods of history with rapid social and economic change, it is useful to examine the long-term history of particular arrangements. Money is one of these institutions that seems eternal and unchanging, but that has changed dramatically over time. For example, money was typically conceived as coins made of precious metal and stamped with the image of the ruler. As long-distance trade expanded in the early modern period, money was represented by paper, most often letters of exchange among merchants who trusted one another because of common membership in a community. The emergence of the fiscal/military state enabled populations to have some role in the issue and uses of money, such as the Italian city-states of Republican form, like Florence and Venice. After the “Glorious Revolution” in 1688, the Bank of England (BOE) innovated with merchant monopoly corporations engaging in state-supported long-distance trade, the stock of which backed public debt. The political controversies surrounding the formation of the BOE reveal some of the alternative possibilities, a merchant-oriented bank, supported by the Whigs, or a land bank supported by the Tories. After the industrial revolution, the use of credit for real investment aided the accumulation of surplus, by the productivity treadmill. After the Great Depression, the extension of finance into the lives of the citizens proceeded, with debt financing for housing, education, and retirement, encompassing the entire life cycle. The dominance of the United States after World War II enabled that country to sustain a hegemonic currency, based on the expansion of trade and supply chains to Emerging Market Countries. Along with the tech bubble in the second decade of the twenty-first century, the crypto bubble expanded hopes for an alternative form of money. Such a long-term history can be informed by analysis of the role of money as a symbol, mobilized by meanings enacted in human institutions. Such a view can provide a method of interpreting the long term evolution of money, restoring the political dimension and human agency to the abstract impersonal mechanical notion of the market. Such a perspective can better inform consideration of alternative institutions to accommodate challenges like geopolitical competition, war, and climate change. JEL Classification: B51, B52, G20, N20, P48

The Long-Term Solvency of Teacher Pension Plans: How We Got to Now and Prospects for Recovery

The COVID-related financial market decline and economic recession have raised new concerns regarding the financial sustainability of retirement plans for state and local government employees, the largest group of whom is public school teachers. Using data from the Public Plans Database and the National Income and Product Accounts, I analyze teacher pension plans over the 2001–2019 period, seeking to answer questions regarding teacher pensions’ funded status, investment decisions and returns, adequacy of contributions, and generosity of benefits. These data show that teacher pension funding peaked at the beginning of the 2001–2019 period due to the tech bubble’s inflation of asset values, but then it declined thereafter due to investment returns that significantly underperformed assumptions, failures by sponsoring governments to consistently make full contributions, and increases in the generosity of pension benefits. School districts will face substantial funding challenges in the post-COVID period, as investment losses are factored into contribution rates, government revenues available to make contributions shrink, and education funding from state governments comes under pressure. I outline several policy alternatives that policymakers may consider, but none would make restoring teacher pensions to full funding a painless process.

Labor in the Age of Finance: Pensions, Politics, and Corporations from Deindustrialization to Dodd-Frank

Labor in the Age of Finance: Pensions, Politics, and Corporations from Deindustrialization to Dodd-Frank

NetVIX — A network volatility index of financial markets

We construct a network volatility index (NetVIX) via market interconnectedness and volatilities to measure global market risk. The NetVIX multiplicatively decomposes into a network volatility effect and a network contagion effect. It also additively decomposes into volatility contributions of each market. We apply our measure to study the relationship between the interconnectedness among 20 major stock markets and global market risks over the last two decades. We show that the NetVIX has a strong relationship with the VIX index, and therefore able to reliably signal changes in global market volatility. We also show that while the NetVIX tracks to some extent the VIX, it provides much more information about the level of volatility and contagion effects in financial markets. The result shows that during crisis periods, particularly the tech bubble, the global financial crisis, and the Covid-19 pandemic, stock market interconnectedness contributes to global market turmoil by amplifying average market volatility with over 400 percent multiplier. Also during crisis times, the level of risk is relatively higher and more persistent in the US and German markets, which implies market losses for investors with long exposures. The results also reveal that the highest risk-contributing markets are the US, Brazil, Hong Kong, France, and Germany.

Formirovanie cifrovyh otraslej ekonomiki v konce XX v.

New Economy is a buzzword to describe new, fast-growing industries that are at the forefront of technology and are considered to be driving economic growth and productivity. The new economy was first announced in the late 1990s, when high-tech tools, especially the Internet and increasingly powerful computers, appeared in the consumer and business market. The new economy was seen as a transition from an economy based on production and commodities to an economy that uses technology to create new products and services at a speed that the traditional manufacturing economy could not match. The idea that a new economy had emerged was part of the hysteria associated with the tech bubble of the late 1990s and early 2000s. The new economy has been variously called the knowledge economy, the data economy, the e-commerce economy, and so on. Unfortunately, for the longterm health of the new economy that emerged in the 90s, investors and financial institutions raised the prices of technology sector stocks to unprecedented high levels, without fully taking into account fundamental factors. The hype around the tech sector has done more harm than good, and the speed at which these firms have been forced to become the next Microsoft has probably destroyed many potentially good business ideas in pursuit of great ones.

Initial Public Offerings and Local Housing Markets

This study explores the link between corporations’ initial public offering (IPO) activities and the housing markets in the MSAs where the IPO firms are headquartered. Using a sample of 4,500 U.S. IPOs occurring from 1990 to 2018, a period covering the tech bubble years and the 2007–2008 financial crisis, we examine the existence and magnitude of the link between IPOs and housing, and related hypotheses including the short-term expectations effects, the wealth effects after the IPO lock-up expiration dates, and the long-term effects. Our results provide strong support to these hypotheses. The effects are stronger during the tech-bubble years. In addition, the results are to some extent driven by IPOs in traffic-congested and/or low-affordability cities, and the effects are generally amplified by local housing supply rigidity and IPO stock performance. We find that the overall impact of IPOs on the housing market is economically significant with the most prominent influence linked to the changes in short-term expectations.

Value versus Glamour Stocks: The Return of Irrational Exuberance?

Value stocks have endured a period of severe underperformance until recently. This article shows that the value spreads between valuations of value stocks and their most expensive peers expanded in all regions and sectors during this period of underperformance, reaching the same extreme high levels last seen at the peak of the tech bubble in 2000. Investors have rerated expensive stocks relative to their value peers, thus reflecting an expanding difference in their respective earnings growth forecasts. There are signs this trend may now have changed. Value spreads may have started a new period of compression at the end of 2020, led by shrinking differences in earnings growth forecasts. A compression in value spreads would be favorable for value stocks, small-capitalization stocks, and multifactor strategies. <b>TOPICS:</b>Security analysis and valuation, analysis of individual factors/risk premia, financial crises and financial market history, performance measurement <b>Key Findings</b> ▪ The spread between valuations of value stocks relative to their expensive peers reached levels last seen in the 2000 tech bubble in every region and sector investigated. This expansion of value spreads explains the recent underperformance of value stocks. ▪ The expansion of value spreads reflected an increasing difference between earnings growth forecasts for expensive and value stocks. Value spreads and earnings growth forecast differences peaked in 2020, suggesting that a regime of value spread compression has begun. ▪ Value spread compression periods are characterized by strong outperformance of value stocks relative to expensive peers and by outperformance of smaller-cap stocks relative to larger-cap stocks. Other sector-neutral styles and their multifactor combinations also do well.

Value Investing: Capitulation or Opportunity?

Value stocks endured a period of severe underperformance until recently. We show that the value spreads between valuations of value stocks and their most expensive peers expanded in all regions and sectors during this period of underperformance, reaching the same extreme high levels last seen at the peak of the tech bubble in 2000. Investors have re-rated expensive stocks relative to their value peers reflecting an expanding difference in their respective earnings growth forecasts. There are signs this trend may now have changed. Value spreads may have started a new period of compression at the end of 2020, a trend led by shrinking differences in earnings growth forecasts. A compression in value spreads would be favorable for value stocks, smaller capitalization stocks and for multi-factor strategies.

Innovation on the choice of debt financing source: evidence from innovative firms

PurposeThe purpose of this paper is to investigate how the innovative firm’s proprietary information has an impact on its debt financing preference. This study also examines the impact of industry-level competition on the debt financing orders and investigates how two exogenous shocks impacted on innovative firms’ financing policies.Design/methodology/approachThis paper uses the three types of debt data, including bonds, private debt placements and bank loans and patent application data, in the USA from 1987–2008. The number of patents applications and industry-level competition are used as proxies for a firm’s innovation and industry-level sensitivity. In addition, to minimize endogenous concern, this study uses the propensity score matching analysis and difference-in-differences.FindingsThe patents are the primary determinants for innovative firms to choose the debt types. The paper shows that innovative firms have the debt preference order – public debt, private placement and bank loans. However, as competition increases, innovative firms devise the order reverse. Finally, the paper provides evidence that the American Inventor’s Protection Act (AIPA) and the tech bubble crash made investors depend more on firms with more patents.Originality/valueThis paper is the first to study the impact of the AIPA on innovative firms’ financial policies using the propensity score matching analysis. The findings imply that both patents and industry-level competition are important factors to understand the capital structures for innovative firms.

Covid-19 and the Technology Bubble 2.0: Evidence from DCC-MGARCH and Wavelet Approaches

There has probably never been as big a divergence between markets and economies as there is in the pandemic period. This paper is an attempt to test the ‘time-varying’ and ‘time-scale dependent’ volatilities of major technology stocks, FAANG and Microsoft, for analyzing the possibility of a second technology bubble in the markets. Consistent with the results of DCC-GARCH models, our analysis based on the application of the Wavelet approach also indicates that major technology behave and move as if they were all one stock in the pandemic period which makes us to be cautious about a second dotcom crisis since %26 of S&amp;P 500 market cap is driven by FAANG and Microsoft stocks. JEL classification numbers: C58, D53, O14. Keywords: Dot-com crisis, tech bubble, DCC-GARCH, FAANG, Wavelet.

Systemic Risk between Carry Trade and Stock Market

In this paper, we exploit the impacts of extreme asset prices (high-low ranges) on multiple horizons and use a conditional value-at-risk (CoVaR) model to evaluate the degree of risk contagion between stock markets and carry-trade markets. We associate the systemic risk of a financial market with conditions in related markets during periods of crisis. The model can be empirically applied to diversified portfolio strategies, but the systemic risk involved is conditional on closely related financial markets. Most notably, our evidence shows that this propagating effect was significant during the 2000-2001 tech bubble and 2007-2009 global financial crisis periods and not reveal negligible risk spillover effect during the 2015-2016 Brexit (British exit from European Union) and potential possible Grexit (Greece’s potential exit from European Union) periods. Moreover, the CoVaR value is shown to be a strong alternative indicator of risk management and asset allocation, especially for investments in carry-trade and stock markets.

Tech Bubble 2.0: How Price Driven Narratives and Return Chasing Behavior Have Created Unsustainably High Valuations for Late Stage Startups in Financial Markets

A narrative that the largest companies in public markets (aka the Big Five - Facebook, Amazon, Apple, Microsoft and Google) were at one point all VC-backed unicorn startups and achieved their success by blitzscaling has propagated a belief that money-losing startups pursuing growth at any cost can offer investors a chance to replicate the Big Five’s outsized returns. In recent years this narrative has led to an explosion in unicorn startups, alongside increasing capital inflows entering into private late-stage startups as a whole. New sources of capital flooding into private late-stage startup fundraising deals comes not just from VC funds but from “non-traditional” VCs, a group that includes mutual funds, hedge funds, sovereign wealth funds and corporations. Historically non-traditional VC investors focused more on stock market investing, but in recent years this group has increasingly participated directly in private market fundraising rounds of late stage startups as a strategy to boost reported returns. In doing so these investors have adopted the VC mindset for investment analysis, prioritizing near-term sales growth and potential for market dominance over profitability. This influx of capital has created an attractive environment for late stage startup fundraising, which has been a contributing factor to elevated valuation levels observed in the space by virtue of the common (but misleading) convention of substituting post-money value for fair value used by private market investors. This upward skew in valuations is a source of bias in the top line returns reported across this asset class, which further reinforces capital inflows to the space. The consequence of the collective dynamics described above has been a positive feedback loop in valuations for late-stage VC-backed startups in private financial markets. In the last few years, VC-backed IPOs have been growing in frequency and size as public market investors are succumbing to price-driven narratives that rely heavily on VC-style investment analysis and less on traditional valuation methods based on normalized earnings and cash flows. This has created the situation where equity valuations for late-stage VC-backed startups have been bid up by speculation and have decoupled from their underlying business value.

Analyst Recommendations and Stock Price Bubbles

ABSTRACTThis paper documents how analyst recommendations are related to periods of bubbles. We find a strong positive relation between the concentration in analyst buy recommendations and bubble continuation in two settings. First, we show a positive association between the concentration in buy recommendations and the tech bubble; the crash was associated with changes in buy recommendation concentration. Second, in an out-of-sample analysis of firms in multiple industries from 1994 to 2009, we show that analyst buy recommendation concentration predicts future return patterns that exhibit characteristics of a rational speculative bubble. While the evidence is not sufficient to conclude that analyst buy recommendations are the causal factor that perpetuates the mispricing, our findings suggest that, at a minimum, analysts do not act proactively to correct this form of mispricing in a timely manner.JEL Classifications: G10; G12; G14; G20.

Equity Factor Investing: Historical Perspective of Recent Performance

We investigate the possible sources of the recent underperformance of multi-factor equity strategies reported by many equity quant managers. We considered the value, quality, low risk and momentum factor styles in mid to large-capitalisation World, USA and European stock universes. When looking at the historical performance of the factors and multi-factor combinations, we find that this is not the first time factor strategies have experienced a period of poor performance. The tech bubble of the late 90s and the great financial crisis of 2008 were other difficult periods for some of the factors and multi-factor combinations. What is different this time around is that poor performance can be mainly attributed to the underperformance of value factors. We also find that long-only portfolios, which tend to be exposed to smaller-capitalisation stocks in their construction, have suffered additionally from that exposure. Not only did the size factor fail to generate a premium in mid to large-capitalisation universes in the long term, but also the recent underperformance of smaller-capitalisation stocks and the consequent increase in the concentration of benchmarks was an additional source of difficulty in long-only benchmarked portfolios. Finally, we discuss the impact of a number of choices available to managers of factor strategies and show that the neutralisation of sectors, neutralisation of beta, control of tracking error and diversification of factors in styles play an important role in improving the performance of equity factor strategies.

Discussion

Discussion

Sentiment indices and their forecasting ability

AbstractThe success of any timing strategy depends on the accuracy of market forecasts. In this paper, we test five indices to forecast the 1‐month‐ahead performance of the S&amp;P 500 Index. These indices reflect investor sentiment, current business conditions, economic policy uncertainty, and market dislocation information. Each model is used in a logistic regression analysis to predict the 1‐month‐ahead market direction, and the forecasts are used to adjust the portfolio's beta. Beta optimization refers to a strategy designed to create a portfolio beta of 1.0 when the market is expected to go up, and a beta of −1.0 when a bear market is expected. Successful application of this strategy generates returns that are consistent with a call option or an option straddle position; that is, positive returns are generated in both up and down markets. Analysis reveals that the models' forecasts have discriminatory power in identifying substantial market movements, particularly during the bursting of the tech bubble and the financial crisis. Four of the five forecast models tested outperform the benchmark index.

Do institutional investors still encourage patent-based innovation after the tech bubble period?

Abstract This paper reexamines the effect of institutional ownership on corporate patent-based innovation. Using an updated sample, we confirm that higher institutional ownership leads to more innovations, including more citations received by patents of a firm, and higher patent generality and originality. However, we find that the impact of institutional ownership on patent-based innovation greatly decreases after the tech bubble bursts and this lower support is driven by both transient and non-transient institutional investors. We do not find that institutional investors intentionally lower their support of patent-based innovation for improving this less efficient innovation. Our results support the contention that the exorbitant litigation cost of patents lowers the incentives for institutional investors to invest in patent-based innovation after 2000.