Stock Market Bubble Research Articles

On the relationship between policy uncertainty and sustainable investing

PurposeIn recent times, sustainable investment gaining much attention within the investors’ community and it is broadly driven by environmental, social and governance (ESG) factors. This study aims to examine the ESG-based sustainability index and economic policy uncertainty (EPU).Design/methodology/approachCorporate sustainability assessment procedure yields Dow Jones sustainability indexes (DJSIUS) and ESG compliant firms become a member of such indexes. To uncover the effects of policy uncertainty as follows: the study considers EPU index, equity market policy uncertainty index, economic and political events for the period 2000–2017. The authors present the study using a conditional volatility framework.FindingsThe correlation between the DJSIUS and policy uncertainty appears to be negative and statistically significant. It is apparent from the results that policy uncertainty does contain important ESG factors that explain the sustainable investment in US firms. Moreover, the stock market boom, credit crunch, Lehman collapse and fiscal crises have shown significant adverse effects on the sustainability index. More importantly, it is seen that investors’ sustainable investing considers presidential election years for portfolio planning; the uncertainty associated with the election years has also shown a negative impact on the sustainable returns.Practical implicationsFirst, sustainability is essential for the long-term stakeholders’ wealth maximization under governments’ policy uncertainty such as constrained resources, demographic and climate-change-policy, societal expectations, public-policies, regulatory structure. Second, EPU creates new opportunities and risks for sustainable firms and sustainable investing.Originality/valueThe study is novel in which the authors present the effects of uncertainty on socially responsible investing.

Bubble run-ups and sell-offs: a study of Indian stock market

PurposeThis paper tries to locate the sectorial bubbles and examines the possibility for investors making extra profit from these bubbles in the Indian stock market.Design/methodology/approachThe authors use two main indicators: (1) asset centrality and (2) relative value. Asset centrality signals crowded trading, which is associated with the formation of a bubble. Relative value separates the crowded trading during the bubble run-up from the sell-off. The authors observe whether these measures can detect the cycle of bubbles in each sector of the Indian stock market for the period 2004–2019.FindingsThe authors show the sectors going through the inflationary phase delivers much better performance than the index, whereas the sectors in their deflationary phase perform quite worse than the index. This provides attractive opportunities to investors, especially the institutional investors, and fund managers of the Indian market.Originality/valueTo the best of our knowledge, there is no study that looks into the idea of locating a sectoral bubble in the Indian financial stock market using the concept of centrality score and relative score. This work helps to locate a bubble and identify its phases successfully. Traders can enter a bubble in their inflationary period gain profit and exit the trade before the sell-off period begins.

Paradox of Stock Market Boom amidst Covid-19 Pandemic: An Indian Perspective in Global Context

If we compare the performance of the economy and the stock market in India vis-à-vis China during the pandemic, we find that the China was the only major economy in the world to grow in 2020-21 with growth rate of 2.3% (IMF) whereas Indian economy shrank sharply; still, since March 24, 2020, India's stock market has been performing far better than the Chinese stock market despite the fact that, by July 10, 2021, India has reported more than 30 million Covid-19 cases whereas China has reported only around 92 thousand cases. This may be due to different policies being introduced in both the countries. However, stock market boom in India is very similar to that in the US where stock market plunged initially and then soared during the pandemic .

How to identify the different phases of stock market bubbles statistically?

Eugene Fama once mentioned in 2016 that people have not come up with ways of identifying bubbles statistically. This paper presents the nonparametric change-point method to identify different stages of stock bubbles, and we derive its asymptotic distribution under the null hypothesis. By simulation, we obtain the corresponding critical value. In the empirical analysis, we employ this test and binary segmentation method to the 1990s Nasdaq bubble and get the same result as Phillips et al. (2011). We also apply this test to the S&P 500 index, the Shanghai stock index, the Nikkei 225 index, the FTSE 100 index, and the CAC 40 index respectively, and successfully identify the bubbles’ different phases in each stock market.

Stock Market Booms and Labor Bargaining Power

This essay argues that there is a connection among modern stock market booms, the rising income inequality trend of the last four decades, and labor bargaining power. The essay also contends that the institutional and policy backdrop in any given economic era impacts the ability of economic actors and groups to influence their relative income standing, and therefore the political-economic structure matters. The basic argument is that there is an interplay between changes in wages relative to profits with stock valuations, that is, there exists a bidirectionality between income distribution and stock price booms. The paper offers an explanation for the major stock price run-ups in the 21st century.

Extrapolative Bubbles and Trading Volume

Abstract We propose an extrapolative model of bubbles to explain the sharp rise in prices and volume observed in historical financial bubbles. The model generates a novel mechanism for volume: because of the interaction between extrapolative beliefs and disposition effects, investors are quick to not only buy assets with positive past returns but also sell them if good returns continue. Using account-level transaction data on the 2014–2015 Chinese stock market bubble, we test and confirm the model’s predictions about trading volume. We quantify the magnitude of the proposed mechanism and show that it can increase trading volume by another 30$\%$.

Boom and Bust: A Global History of Financial Bubbles. By William Quinn and John D. Turner. New York: Cambridge University Press, 2020. Pp. 3296. $24.95, hardcover.

The end of the bicycle mania and the dot-com bubbles simply solidified the hold of leading companies in the technology that sparked the bubble, but the railway mania ended with an over-extended network that plagues the efficiency of the British rail system to this day, in the authors’ opinion, at least. (An American reviewer has a different perspective, it has to be said!) Government-inspired bubbles, unfortunately, can have long-term, deleterious consequences, as was the case for Australia’s land boom at the end of the nineteenth century, the Wall Street crash worldwide in the 1930s, the lost decade of growth for Japan in the 1980s, and the sub-prime crisis, which they extend beyond the United States to include Ireland, Spain, and Greece. By contrast, they find that the government-initiated, and propelled, stock market bubbles in China have, at least to date, ended with only minor consequences due to effective management of the effects by an increasingly authoritarian government.

Asset bubbles in explaining top income shares

This paper considers the role of asset price bubbles (crashes) as an important determinant in seeking a further explanation for top income shares. The asset price bubbles caused at least in part by monetary policies, along with other determinants such as top tax rates and innovativeness are the important drivers to explain the surge in top income shares. The empirical results show that correlation between asset bubbles and top inequality is positive and significant. The regression coefficient of stock and housing market bubbles have a positive effect on top income shares, while the stock and housing market crashes fail to reduce the surge in top income shares. In sum, as the asset markets grow, the share of income going to those at the very top increases and the accumulation of income accelerates if the duration of bubbles expands. Concentration of income at the very top is much more important when capital gains are counted as income.

Dynamical characteristics of global stock markets based on time dependent Tsallis non-extensive statistics and generalized Hurst exponents

We perform non-linear analysis on stock market indices using time-dependent extended Tsallis statistics. Specifically, we evaluate the q-triplet for particular time periods with the purpose of demonstrating the temporal dependence of the extended characteristics of the underlying market dynamics. We apply the analysis on daily close price timeseries of four major global markets (S&P 500, Tokyo-NIKKEI, Frankfurt-DAX, London-LSE). For comparison, we also compute time-dependentGeneralized Hurst Exponents (GHE) Hq using the GHE method, thus estimating the temporal evolution of the multiscaling characteristics of the index dynamics. We focus on periods before and after critical market events such as stock market bubbles (2000 dot.com bubble, Japanese 1990 bubble, 2008 US real estate crisis) and find that the temporal trends of q-triplet values significantly differ among these periods indicating that in the rising period before a bubble break, the underlying extended statistics of the market dynamics strongly deviates from purely stochastic behavior, whereas, after the breakdown, it gradually converges to the Gaussian-like behavior which is a characteristic of an efficient market. We also conclude that relative temporal variation patterns of the Tsallis q-triplet can be connected to different aspects of market dynamics and reveals useful information about market conditions especially those underlying the development of a stock market bubble. We found specific temporal patterns and trends in the relative variation of the indices in the q-triplet that distinguish periods just before and just after a stock-market bubble break. Differences between endogenous and exogenous stock market crises are also captured by the temporal changes in the Tsallis q-triplet. Finally, we introduce two new time-dependent empirical metrics (Q-metrics) that are functions of the Tsallis q-triplet. We apply them to the above stock market index price timeseries and discuss the significance of their temporal dependence on market dynamics and the possibility of using them, together with the relative temporal changes of the q-triplet, as signaling tools for future market events such as the development of a market bubble.

A Post-COVID Recovery is Unlikely to Resemble the Roaring 20s; The Years 1919 and 1999 Serve as More Insightful Comparisons

As of June 12, 2021, the end of the COVID-19 pandemic appeared to be fast approaching. As such, there is now increased debate with respect to the nature of a post-COVID economic recovery. Several financial writers have referenced the Roaring 20s as a historical period that may provide useful lessons. However, a deconstruction of the key economic drivers during the Roaring 20s suggests that this period provides few parallels to the circumstances in 2021. There are two historical periods, however, that do offer useful comparisons: the years 1919 and 1999. Drawing from the lessons from these two years, it seems plausible that a post-COVID recovery may combine a consumer spending rebound that is reminiscent of 1919 with increased speculation in the stock market that is reminiscent of 1999. The paper concludes by encouraging investors to exercise caution, noting that neither the spending rebound in 1919 nor the speculative stock market bubble of 1999 lasted long. Therefore, it seems conceivable that after the initial, post-pandemic euphoria wears off, a similarly painful economic contraction and market correction could soon follow. Should this scenario occur, readers may benefit from considering this possibility and preparing psychologically for the consequences.

Can Adaptive Market Hypothesis Explain the Existence of Seasonal Anomalies? Evidence from Dhaka Stock Exchange, Bangladesh

This paper examines the behavior of seasonal anomalies in Dhaka Stock Exchange (DSE) of Bangladesh and whether the time varying nature of the anomalies is in line with Adaptive Market Hypothesis (AMH). With this aim the research investigated whether the changes in market conditions, for example: up and down market states, stock market bubbles and crashes, initiation of automated trading system and circuit breaker system can affect the behavior of calendar anomalies and therefore, can provide justification for the seasonal patterns in DSE. To achieve the stated objectives, this study utilizes daily general index values of DSE from 1993 to 2018, with GARCH (1,1) model, Markov switching model, subsample analysis and rolling window analysis. The findings support the existence of AMH at DSE in the form of time-varying nature of seasonal anomalies. However, not all seasonal anomalies examined in the study were found to grow weaker over time. The most important finding of this study is that the investors in emerging stock markets, for example DSE, may not learn from the past investment experiences and show the adapting ability towards changed market conditions in the same manner like the investors in a developed market.

Stock price bubbles, leverage and systemic risk

Using on a broad quarterly dataset of 25 developing and developed countries from 1975 to 2017, this paper empirically studies the impact of stock price bubbles on systemic risk and its transmission channel. Results from panel regressions and the Qual VAR model demonstrate that stock market exuberance is a prominent contributor of financial fragility, and credit-price feedback is the key transmission channel. It’s worth noting that systemic risk has already increased while the bubble is building up, and the stock bubble boom-bust cycle exhibits notably asymmetric influence on systemic risk with a larger risk increase during the bubble bust. We also find that the risk exposure induced by stock price bubbles is higher for countries with more developed stock markets and for periods when the economy is prospering.

The Souk Al-Manakh: The Anatomy of a Pure Price-Chasing Bubble

It is widely agreed that the Nasdaq during the dot-com era 20 years ago was a full-fledged stock market bubble. Recently, the US stock market according to many metrics has become significantly more speculative and overvalued than it was at the dot-com peak 20 years ago. In both instances, a very broad subset of stocks became so highly valued that speculation in them had to be untethered from all fundamentals: the essence of what we call a “pure price-chasing bubble.” This paper, drawn from a book in progress, examines the history of stock markets for comparable pure price-chasing bubbles, finding nine or so which have ever reached such a speculative extreme, with an over-the-counter market in Kuwait in the early 1980s called the “Souk al-Manakh” representing the most extreme example. Based on my personal exposure to this Souk al-Manakh almost 40 years ago, I describe this anatomy and thereby make transparent the recurrent dynamics—on the way up and on the way down—of these greatest asset bubbles in human history. When one applies this framework to the current US stock market, one sees that the stock market in the US today will likely follow the disastrous path of the dot-com market.

Measuring gambling activity in options market

PurposeThis paper investigates how the gambling measure captures market bubble events, and how it predicts stock return and option return.Design/methodology/approachThis paper proposes a gambling activity measure by jointly considering open interest and moneyness of out-of-the-money (OTM) individual equity call options.FindingsThe new measure, CallMoney, captures excessive optimism during the dot-com bubble, the oil price bubble and the pre-GFC stock market bubble. CallMoney robustly and negatively predicts both OTM and at-the-money call option returns cross-sectionally. The option return predictability of CallMoney is stronger when stock price is further from its 52-weeks high, capital gains overhang is lower, and when information uncertainty of the underlying stock is higher. CallMoney also robustly and negatively predicts cross-sectional stock returns.Originality/valueThe gambling measure has the advantages of being economically intuitive, model-free, easy to measure. The measure performs more robustly than existing lottery measures with respect to option and stock return predictability and more reliably captures the overpricing of options and stocks. The work helps understanding the gambling related anomalies in equity option returns and stock returns.

A Case Study of Stock Market Bubbles in the Indian Stock Market

This paper's objective was to analyze various stock market crashes to identify the stocks that offered a durable competitive advantage. To do so, stock market data were analyzed from January 2001 – December 2018, and it was found that Nifty observed 10 major crashes during this period. After identifying the stocks that outperformed or performed moderately during the crisis, the compounded annual growth rate (CAGR) was computed for them. The results exhibited that the stocks that survived the market crashes had high CAGR and were considered stocks with a durable competitive advantage (DCA). The results exhibited that in India, stocks with DCA belonged to consumer goods, pharma and healthcare, and financial services sector. The present paper underlined the sectors in the Indian market, which are relatively immune to economic downturns and provided a cushion of safety compared to their other sector equivalents. The paper presented indicators for investors to identify stocks with DCA.

Investor Emotions and Speculative Bubbles

Between June 2005 and October 2007, when it peaked, the Chinese stock market rose five-fold. It then went into free fall losing 70% of its value over the following year, more than China’s total GDP. A similar trajectory played out between July 2014 and January 2016.This paper describes the powerful emotions unleashed during market crises of this nature. Specifically, we adopt a five-stage path-dependent model relevant to other bubbles and financial crises and test this empirically using the Chinese 2005-2008 stock market bubble as a case study. Results are consistent with investors experiencing a range of highly-charged emotions directly inter-related with different market states. Our evidence suggests that if we wish properly to understand and explain such destructive events we also need to recognise the vital role investor emotions play in their aetiology.

Decision-making model for China’s stock market bubble warning: the CoCoSo with picture fuzzy information

With the financial liberalization and globalization, China’s financial system has been deepening, the scale of the stock market has been expanding, and the stock market bubble problem has gradually emerged. The overinflated stock market bubble will undoubtedly increase the risk of the stock market, seriously threaten the stability of the financial system, and even bring disastrous consequences to the economic system. Therefore, the stock market bubble warning evaluation is very important. In the case of considering stock market bubble warning evaluation, the fundamental issues involve strong fuzziness. Picture fuzzy set, depicted by three memberships (positive membership, neutral membership and negative membership), is a more resultful means for capturing fuzziness. In this paper, the novel picture fuzzy score function is given for dealing the comparison problem. Then, the objective weights are calculated by Renyi entropy method. Meanwhile, we develop combined weights, which can reflect both subjective preference and objective preference. Moreover, the picture fuzzy decision making algorithm based on Combined Compromise Solution is presented. Finally, the feasibility of algorithm is stated by the stock market bubble warning evaluation issue. The salient features of the proposed algorithm are that they have no counter-intuitive cases and antilogarithm or division by zero issues.

Revisit stock price bubbles in the COVID-19 period: Further evidence from Taiwan’s and Mainland China’s tourism industries

This study applies both the sup augmented Dickey–Fuller (SADF) and the generalized SADF tests to investigate whether bubbles exist in the Taiwan’s and Mainland China’s tourism stock markets, focusing on the COVID-19 period. We use the weekly stock price index during the periods of January 4, 2000 to April 27, 2020 in Taiwan and August 13, 2007 to April 27, 2020 in Mainland China, respectively. Empirical results demonstrate the existence of bubbles in the Taiwan tourism stock market during some subsample periods, including the recent COVID-19 outbreak. In contrast with our findings concerning Taiwan, our empirical results found that the COVID-19 outbreak had no impact on the tourism stock market of Mainland China. Especially important, we found that the fourth bubble in the Taiwan stock market began from March 23, 2020, and grew gradually, bursting on March 30, 2020. Our results have important policy implications for investors who attempt to invest in the tourism stock market.

Gamestonk: What Happened and What to Do about It

The price of GameStop stock exploded from $18.84 at the end of 2020 to a high of $483 on January 28, 2021, before collapsing to under $60 in early February. The stock had long been heavily shorted even before the pandemic. A stock bubble and its collapse inflict collateral damage even on those not participating in the bubble, including index-fund investors. The incident raises questions about the duties of brokers and financial service professionals in the age of social media, and the role of payment for order flow (PFOF). The price dislocation in GameStop highlights several leaks in the plumbing of the US equity market that need to be plugged or else similar dislocations will happen again. The antiquated T+2 settlement cycle needs to be shortened to reduce risk and attendant collateral needs in the system. Institutional short positions should be disclosed on SEC Form 13F just as long positions are. The frequency of short interest disclosure should be increased to daily from the current biweekly. The SEC should implement the Congressional mandate in Dodd-Frank §894 to increase transparency in the stock lending market by implementing a ticker tape for stock lending transactions. Short positions should be marked to market yearly for tax purposes yearly to eliminate the tax incentive to delay covering short positions. SEC rules 204 and 15c3-3 need to be modernized to help prevent spikes in prices far beyond any reasonable levels. Rule 606 should be extended to require brokers to report retail execution quality.

Paradox of Stock Market Boom Amidst COVID-19 Pandemic: An Indian Perspective in Global Context

Paradox of Stock Market Boom Amidst COVID-19 Pandemic: An Indian Perspective in Global Context