Stock Market Bubble Research Articles

Фундаментальные факторы технико- экономической цикличности

The paper examines the phenomenon of stages in technical and economic development in the context of the theory of technological paradigm. On the basis of a critical rethinking of the approaches of Dosi, Perez, Glazyev et al., the marxist tendency of the rate of profit to fall, as well as an empirical analysis of the US economy, the authors built an econometric model that makes it possible to determine the factors that affect both the change of technological structures themselves and their phases. The incentive that pushes capital to the path of investment in new technologies is the dynamics of the rate of return, which tends to decline and stagnate at this stage. After the new technology was mastered by national producers, and the concentration of capital created national leaders with sufficient competitiveness. foreign economic relations between countries are intensifying, which expresses competition for the development of new markets. The growing rate of return is pushing investors to capitalize on it in the form of further investment in industries where new technology can increase the return on capital. At the same time, this creates the prerequisites for overaccumulation, which is expressed in a gradual drop in the marginal profitability in production, which directs capital to the financial sector. The financialization of the economy and the formation of stock market bubbles are taking place. After their collapse, the economy enters the stage of a fall in the average rate of profit, which again starts the process of searching for a new revolutionary technology that can ensure an increase in labor productivity. The research results make it possible to more fully explain the reasons for the change in the phases of technological orders, as well as predict these processes.

Dynamics of the price-volume information flow based on surrogate time series.

This paper uses transfer entropy and surrogates to analyze the information flow between price and transaction volume. We use random surrogates to construct local random permutation (LRP) surrogates that can analyze the local information flow in detail. The analysis based on the toy models verifies the effectiveness of the LRP method. We further apply it to analyze three financial datasets, including two index datasets and one stock dataset. Empirical analysis shows that both the S&P500 index data and SSEC index data include rich information flow dynamics. There was a stronger information flow during the stock bubble burst or the financial crisis. In addition, tests based on stock data suggest that market crises may lead to changes in the relationship between prices and trading volume. This paper provides a new way to analyze the price-volume relationship, which can effectively detect the drastic changes in the local information flow, thereby providing a method for studying the impact of events.

The use of scaling properties to detect relevant changes in financial time series: A new visual warning tool

The dynamical evolution of multiscaling in financial time series is investigated using time-dependent Generalized Hurst Exponents (GHE), Hq, for various values of the parameter q. Using Hq, we introduce a new visual methodology to algorithmically detect critical changes in the scaling of the underlying complex time-series. The methodology involves the degree of multiscaling at a particular time instance, the multiscaling trend which is calculated by the Change-Point Analysis method, and a rigorous evaluation of the statistical significance of the results. Using this algorithm, we have identified particular patterns in the temporal co-evolution of the different Hq time-series. These GHE patterns, distinguish in a statistically robust way, not only between time periods of uniscaling and multiscaling, but also among different types of multiscaling: symmetric multiscaling (M) and asymmetric multiscaling (A). Asymmetric multiscaling can also be robustly divided into three other subcategories. We apply the visual methodology to time-series comprising of daily close prices of four stock market indices: two major ones (S&P 500 and Tokyo-NIKKEI) and two peripheral ones (Athens Stock Exchange general Index and Bombay-SENSEX). Results show that multiscaling varies greatly with time: time periods of strong multiscaling behavior and time periods of uniscaling behavior are interchanged while transitions from uniscaling to multiscaling behavior occur before critical market events, such as stock market bubbles. Moreover, particular asymmetric multiscaling patterns appear during critical stock market eras and provide useful information about market conditions. In particular, they can be used as ’fingerprints’ of a turbulent market period as well as provide warning signals for an upcoming stock market ’bubble’. The applied visual methodology also appears to distinguish between exogenous and endogenous stock market crises, based on the observed patterns before the actual events. The visual methodology is sufficiently general to be applicable for the description of the dynamical evolution of multiscaling properties of any complex system.

Effects of US quantitative easing on emerging market economies

We estimate international spillover effects of US Quantitative Easing (QE) on emerging market economies (EMEs). Using a Bayesian panel VAR we find that an expansionary US QE shock has significant effects on financial variables in EMEs. It leads to an exchange rate appreciation, a reduction in long-term bond yields, a stock market boom, and an increase in capital inflows to these countries. These effects on financial variables are stronger for the “Fragile Five” countries compared to other EMEs, which we relate to their weak ex-ante fundamentals.

Brazilian stock market bubble in the 2010s.

Brazilian stock markets underwent a period of remarkable exuberance between early 2016 and March 2020, only to crash with the global turmoil related to health worries and oil prices. The Ibovespa index tripled its market value between a low point in January 2016 and its maximum in January 2020—by March 12, half those gains had been erased. Narratives about a bubble in Brazilian stocks before the global crash and its subsequent burst are plentiful in specialized media. In this paper, we explore this narrative from within the framework of strict local martingale financial bubbles. A key result in this literature states some financial asset price displays a bubble only if it follows a strict local martingale under the equivalent risk-neutral measure. A diffusion process is a strict local martingale if its volatility increases faster than linearly as its level grows. We first apply a nonparametric method to estimate the volatility function of Ibovespa daily prices, then fit a stochastic volatility model whose parameter values can discriminate the underlying price process as either a true martingale or a strict local martingale. Our results are negative towards the presence of a strict local martingale bubble in the Ibovespa index. Strict local martingale bubbles are related to a positive relationship between returns and volatility which does not seem present in the data at hand. We also performed a comparative analysis of the patterns found for the Ibovespa with the S&P500 index, spot Brent oil and gold prices.Electronic supplementary materialThe online version of this article (10.1007/s43546-020-00005-w) contains supplementary material, which is available to authorized users.

Fractal Characteristics, Multiple Bubbles, and Jump Anomalies in the Chinese Stock Market

To consider the jump problem of the Chinese stock market, this paper takes the CSI 300 Index from April 2005 to November 2015 as the research object, uses the rescaled range analysis (R/S analysis) method to examine the fractal characteristics of the Chinese stock market in the past ten years, and deduces the possibility of multiple bubbles in the Chinese stock market. Based on this, combined with the log-periodic power law (LPPL) model, the stock market bubbles are identified in different periods. The results show that China’s stock market has some anomalies in terms of positive bubbles, negative bubbles, and reverse bubbles, as well as the cross occurrence of reverse-negative bubbles. Besides, through a comparison with the major foreign stock markets, it is found that the fluctuation range of the Chinese stock market is much larger than that of the Dow Jones Industrial Average and the FTSE 100 indices in the same period and there are also more types of multibubbles, which is a connotative anomaly that makes the Chinese stock market different from other major stock markets. Furthermore, the bubble phenomenon in the Chinese stock market during the periods of 2005/4–2007/10 and 2015/6–2015/11 is studied, and it is found that there is a jump anomaly in the Chinese stock market. Finally, based on the above empirical analysis and the current state of the stock market, this paper provides some suggestions for improving the mechanism of the Chinese stock market.

Was there a stock market bubble in Hungary?

Bubble is one of the most frequently used and colorful terms in economics. However, it is rarely explained in detail, most economists more or less agree on what it means. In the following paper we are going to show that the widely accepted explanation of bubble contains controversial, tautological reasoning. It is challenged from the theoretical side, but practical consequences will also be mentioned. Two questions hiding in the title above will be answered. First is to give a conceptual framework for analyzing stock prices to decide whether we can label as a bubble particular movements, upward and downward tendencies in stock prices. Second, with a coherent and consistent definition we will be able to answer the question whether there was a bubble in the Hungarian stock market between 1995-2002.

Not All Bull and Bear Markets Are Alike: Insights From a Five-State Hidden Semi-Markov Model

This paper employs the hidden semi-Markov model and a novel model selection procedure to detect different states in the US stock market. The empirical results suggest that the market is switching between five states that can be classified into three bull states and two bear states. The three bull states are categorized as a low volatility bull market, a high volatility bull market, and a stock market bubble. One of the bear states represents a regular bear market, while the other one corresponds to either a stock market crash or a market correction. The paper demonstrates that the five-state model is consistent with a number of stylized facts and provides many valuable insights into the dynamics of the US stock market. Besides, the five-state model has clear implications for the success of some active strategies that aim to enhance returns and reduce losses.

Detection of Chinese stock market bubbles with LPPLS confidence indicator

We present an advance bubble detection methodology based on the Log Periodic Power Law Singularity (LPPLS) confidence indicator for the early causal identifications of positive and negative bubbles in the Chinese stock market using the daily data on the Shanghai Shenzhen CSI 300 stock market index from January 2002 through April 2018. We account for the damping condition of the LPPLS model in the search space and implement the stricter filter conditions for the qualification of the valid LPPLS fits by considering the maximum relative error, performing the Lomb log-periodic test of the detrended residual, and the unit-root tests of the logarithmic residual based on both the Phillips–Perron test and the Dickey–Fuller test to improve the performance of the LPPLS confidence indicator. Our analysis shows that with forward prediction, the LPPLS detection strategy based on the LPPLS confidence indicator can diagnose both the positive and the negative bubbles corresponding to well-known historical events, demonstrating its outstanding performance to foretell the bubbles in advance. We find that the probability density distribution of the estimated beginning time of bubbles appears to be skewed and the mass of the distribution is concentrated on the area where the price starts to have an obvious super-exponential growth. This study is the first work in literature that identifies the existence of bubbles in the Chinese stock market using the daily data of CSI 300 index with the advance bubble detection methodology of the LPPLS confidence indicator. By forecasting the prospective positive and negative bubbles and their impending crashes ahead of time, one can potentially help limit the bubble sizes and eventually minimize the damages from the bubble crashes.

Stock market bubbles and monetary policy effectiveness

In this paper, we provide evidence on the role of conventional monetary policy in the dynamics of stock market bubbles. We analyze the response of stock market returns to monetary policy shocks but condition the analysis on both the direction of monetary policy surprises and business conditions. Following a two-step approach, we first use a structural vector autoregressive (SVAR) model to identify a proxy variable of monetary policy shocks, and then we apply a conditional regression to contemporary stock market returns and these monetary policy shocks to extract the implicit relationship between these variables in different scenarios. Our results show that monetary policy does not impact on stock market returns in a significant form in the scenario defined by positive shocks and expansion periods, i.e. the lower effectiveness of restrictive monetary policy shocks coincides with the phase of the business cycle in which bubbles arise.

BUBBLE PREDICTION AND COMPARATIVE ANALYSIS OF EMERGING AND MATURE MARKETS

By analyzing the financial bubbles, it can be observed that every bubble burst causes financial turmoil and severe economic recession. The paper utilize the technical analysis indicators for providing warning conditions for predicting the stock market bubbles. The significance of the technical analysis indicators are that they can provide early warning system for financial crisis and can help in avoiding such problems. We applied the early warning technical indicators to the stock market of US, South Korea, Brazil, China, Germany, and Japan for the period of 1995 to 2018. We also made comparison of the bubble warning conditions of emerging markets and the mature markets. The standard bubble warning conditions include K value 90, Bias 10%, and RSI value of 90. Empirical results shows that mature market shows less degree of volatility and lowering the warning baseline can improve the accuracy of bubble predictions. Furthermore, results shows that mature markets bursting time is about 6 months while in emerging market, the time is reduced to only 3 months. These results also indicate that mature market has good antirisk ability compare to the emerging markets.

Systemic Risk Channels of Asset Managers: Evidence from Hedge Funds and Mutual Funds

Using a sample of hedge funds and mutual funds, I examine two channels through which asset managers can contribute to systemic risk: the service channel when funds act as liquidity suppliers and the asset liquidation channel when funds act as liquidity demanders. Consistent with the latter channel being more important, I find that contributions to systemic risk increase significantly when hedge funds demand liquidity. Conversely, no such effect exists for mutual funds. A decomposition of systemic risk reveals that the higher level of systemic risk for liquidity-demanding hedge funds can be explained by a higher degree of interconnectedness. Providing further evidence for the asset liquidation channel, I document that systemic risk is considerably larger when hedge funds demand liquidity in times of low funding liquidity and during stock market boom and bust phases.

Noise trading and stock market bubbles: what the derivatives market is telling us

PurposeThe authors examine the relation between noise trading in equity markets and stochastic volatility by estimating a two-factor jump diffusion model. Their analysis shows that contemporaneous price deviations in the derivatives market are statistically significant in explaining movements in index futures prices and option-market volatility measures.Design/methodology/approachTo understand the impact noise may have in the S&P 500 derivatives market, the authors first measure and evaluate the influence noise exerts on futures prices and then investigate its influence on option volatility.FindingsIn the period from 1996 to 2003, this study finds significant changes in the volatility and mean reversion in the noise level and a significant increase in its relation to implied volatility in option prices. The results are consistent with a bubble in technology stocks that occurred with significant increases in noise trading.Research limitations/implicationsThis study provides estimates for this model during the periods preceding and during the technology bubble. The study analysis shows that the volatility and mean reversion in the noise level are much stronger during the bubble period. Furthermore, the relation between noise trading and implied volatility in the futures market was of a significantly larger magnitude during this period. The study results support the importance of noise trading in market bubbles.Practical implicationsBloomfield, O'Hara and Saar (2009) find that noise traders lower bid–ask spreads and improve liquidity through increases in trading volume and market depth. Such improved market conditions could have positive effects on market quality, and this impact could be evidenced by lower implied volatility when noise traders are more active. Indeed, the results in this study indicate that the level and characteristics of noise trading are fundamentally different during the technology bubble, and this noise trading activity has a larger impact during this period on implied volatility in the options market.Originality/valueThis paper uniquely analyzes derivatives on the S&P 500 Index in order to detect the presence and influence of noise traders. The authors derive and implement a two-factor jump diffusion noise model. In their model, noise rectifies the difference of analysts' opinions, market information and beliefs among traders. By incorporating a reduced-form temporal expression of heterogeneities among traders, the model is rich enough to capture salient time-series characteristics of equity prices (i.e. stochastic volatility and jumps). A singular feature of the authors’ model is that stochastic volatility represents the random movements in asset prices that are attributed to nonmarket fundamentals.

Q: Risk, Rents, or Growth?

The dramatic stock market crash of March 2020 was preceded by a prolonged rise in Tobin's Q and a productivity slowdown. Do longer stock market booms and sharper corrections reflect structural changes in the US economy? We address this question about Q by estimating an endogenous growth model featuring realistic risk premia and markups. Our baseline estimates highlight the importance of rising market power for explaining increasing valuation ratios despite declining growth prospects with weakening investment and innovation over the past decade. High industry concentration exacerbates economic downturns and stock market corrections triggered by adverse economic shocks. We provide novel forecasts about growth expectations based on current market valuations using our structural model.

Financial Contagion During Stock Market Bubbles

We investigate the role of bubbles on financial contagion using a set of developed economies. First, using the recursive flexible window right-tailed ADF-based procedure, we date stamp bubble periods in stock index series. Second, we capture contagion with a DCC multivariate GARCH framework. In a third step, we construct a panel by pooling across the time-series dynamic conditional correlations and bubbles to estimate various dynamic panel specifications that consider the endogenous nature of bubbles. We find statistically significant decreases in the dynamic correlations during periods of bubbles, which shows that the financial contagion between pair of countries diminishes when any of the two countries in the pair is going through a bubble period. This implies that during bubble periods investors are looking for an investment opportunity within their economy and rely less on international diversification. However, decrease in contagion between two economies could provide ample diversification opportunities for portfolio managers.

Macroeconomics, Firm Dynamics and IPOs

This paper extends a model of firm dynamics to incorporate heterogeneous privately held and publicly traded firms facing different financial frictions, and the decision to become publicly traded (Initial Public Offering, or IPO) is endogenous. This allows changes in the economic environment to affect these firms differently, impacting the selection into becoming publicly traded, and its macroeconomic outcomes. Firms are born privately held and small due to financial frictions. They finance investment with internal resources and debt and have the choice to go public (IPO). The main trade-off is access to external equity financing, at a one-off cost of IPO and an increased cost of operation. The calibrated model is successful in capturing the size distribution of firms, the share of publicly traded firms, and the dynamics around the IPO date. The decrease in corporate and dividend taxes experienced from the 1970s to the 1990s benefited more publicly traded firms financing with equity at the margin. This helps explaining the stock market boom, and the observed changes in the characteristics of firms going public, their investment and payout behavior. I perform some counterfactual exercises to understand what could be the reasons behind the decrease in publicly traded firms since the 2000s: increased cost of being public, increased access to debt, or changes in the idiosyncratic shock process. I find these changes are consistent with part (though not all) of the changes in IPO choice, payout and investment behavior of publicly traded firms in this period.

A study on comparison of mutual funds with reference to anand rathi shares & stock broking Ltd. Anantapuramu District

“A Mutual fund is an investment that pools yours money with the money of an unlimited number of other investors. In return, you and the other investors each own shares of the fund.” Many nationalized banks got into the mutual fund business in the early nineties and got off to a good start due to the stock market boom prevailing then. The money thus collected is investors by the fund manager in different types of securities depending upon the objective of the scheme. These could range from shares to debentures to money market instruments. In fact, they have forced the industry to upgrade itself and service levels of organizations like UTI have improved dramatically in the last few years in response to the competition provided by these. So many potential investors in India mutual funds can go a long way in getting established, plus with added set of alternatives within the mutual funds schemes each has a scheme ready for the specific needs.

Is the Federal Reserve Bank Stock Market Bubble-Neutral?

This paper argues that the Fed was not stock market bubble-neutral during the last several years. This nonneutrality implies two options: first, the Fed has used monetary policies to prevent the building of the stock market bubble or, second, the Fed has contributed to its development and subsequent deflation. We supply representative quotes from the FOMC transcripts to establish that the Fed has paid significant attention to the valuation of the stock market. These quotes confirm that the stock market's valuation was an important variable in the Fed's decision-making and its conduct of monetary policy. We also conduct econometric modeling, by extending the Taylor rule and using data for the 1987-2000 period, to argue that the Fed, perhaps unintentionally, has contributed to the stock market's overvaluation and subsequent decline.

Do stock price bubbles correlate between China and Pakistan? An inquiry of pre‐ and post‐Chinese investment in Pakistani capital market under China‐Pakistan Economic Corridor regime

AbstractThis paper examines the existence and coincidence of pre‐ and‐post stock price multiple bubbles in Chines and Pakistani capital markets under China Pakistan Economic Corridor regime using generalized supremum augmented Dickey–Fuller (GSADF) test with weekly closing prices for the period 2013–2018 for different market indices of Pakistan Stock Exchange (PSX), Shanghai Stock Exchange, and Shenzhen Stock Exchange. Empirical results depict that multiple stock bubbles exist in PSX at the end of 2016 and as well as in Chinese stock markets in the mid of 2014–2015 and these stock price bubbles are correlated between Pakistan and China. The present study is expected to be pioneer in its nature to apply GSADF for the identification of multiple stock bubbles in emerging stock markets of Pakistan and China which can be further used for comparison of stock bubbles in other regional markets such as BRICS or SAARC regions.

The impact of monetary policy shocks on stock market bubbles: International evidence

We extend previous research on monetary policy shocks and their impact on stock market bubbles, by considering a consistent data set of OECD countries in a time-varying BVAR framework. We also take into account the zero lower bound. We further determine whether the measured impact is related to variables such as the degree of financial development, credit market conditions, or the business cycle indicators and consumer confidence.