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This study aims to predict stock market crashes identified by the CMAX approach (current index level relative to historical maximum) during periods of global and local events, namely the subprime crisis of 2008, the political and social instability of 2011 and the COVID-19 pandemic.

Over the period 2004–2020, a log-periodic power law model (LPPL) has been employed which describes the price dynamics preceding the beginning dates of the crisis. In order to adjust the LPPL model, the Global Search algorithm was developed using the “fmincon” function.

By minimizing the sum of square errors between the observed logarithmic indices and the LPPL predicted values, the authors find that the estimated parameters satisfy all the constraints imposed in the literature. Moreover, the adjustment line of the LPPL models to the logarithms of the indices closely corresponds to the observed trend of the logarithms of the indices, which was overall bullish before the crashes. The most predicted dates correspond to the start dates of the stock market crashes identified by the CMAX approach. Therefore, the forecasted stock market crashes are the results of the bursting of speculative bubbles and, consequently, of the price deviation from their fundamental values.

The adoption of the LPPL model might be very beneficial for financial market participants in reducing their financial crash risk exposure and managing their equity portfolio risk.

This study differs from previous research in several ways. First of all, to the best of the authors' knowledge, the authors' paper is among the first to show stock market crises detection and prediction, specifically in African countries, since they generate recessionary economic and social dynamics on a large extent and on multiple regional and global scales. Second, in this manuscript, the authors employ the LPPL model, which can expect the most probable day of the beginning of the crash by analyzing excessive stock price volatility.

In this paper we provide an in‐depth comparative analysis of the shares of listed firms in three Southeast Asian stock markets, namely, Indonesia, Malaysia and Thailand, that had experienced the most violent fluctuations in the 1997 market crash. Our purpose is to present broad lessons from the experiences of these countries that could be helpful to understand the behavior of stock markets under severe financial crisis. Several new results are found: (1) There were local price bubbles prior to the market crash in each country. (2) Price momentum may have contributed to the share price increase prior to the crash but not during the period of crisis or the market reversal. (3) The price bubbles in these countries were mainly among the most liquid and most volatile shares. (4) Asset liquidity was found to cause returns to behave differently in quiet versus extraordinary period.

The occurrence and unpredictability of speculative bubbles on financial markets, and their accompanying crashes, have confounded economists and economic historians worldwide. The purpose of this paper is to diagnose and detect the bursting of shipping bubbles ex ante, and to qualify the patterns of shipping price dynamics and the bubble mechanics, so that appropriate counter measures can be taken in advance to reduce side effects arising from bubbles.

Log periodic power law (LPPL) model, developed in the past decade, is used to detect large market falls or “crashes” through modeling of the shipping price dynamics on a selection of three historical shipping bubbles over the period of 1985 to 2016. The method is based on a nonlinear least squares estimation that yields predictions of the most probable time of the regime switching.

It could be concluded that predictions by the LPPL model are quite dependent on the time at which they are conducted. Interestingly, the LPPL model could have predicted the substantial fall in the Baltic Dry Index during the recent global downturn, but not all crashes in the past. It is also found that the key ingredient that sets off an unsustainable growth process for shipping prices is the positive feedback. When the positive feedback starts, the burst of bubbles in shipping would be influenced by both endogenous and exogenous factors, which are crucial for the advanced warning of the market conversion.

The LPPL model has been first applied into the dry bulk shipping market to test a couple of shipping bubbles. The authors not only assess the predictability and robustness of the LPPL model but also expand the understanding of the model and explain patterns of shipping price dynamics and bubble mechanics.

We tested for evidence of stock price bubbles in the Malaysian stock market from 1978 to 2004. Four different tests were used namely excess volatility tests, unit root/co-integration tests, duration dependence tests, and the intrinsic bubbles model. All four tests indicate that during the sample period, there was evidence of stock price bubbles. All tests results conform to the theoretical literature on asset price bubbles except for the results on the intrinsic bubbles model, which concludes that Malaysian investors under react to information on dividends. We find this result hardly surprising as anecdotal evidence does indicate that Malaysian investors place more importance on capital gains as compared to dividends. Although we do not go into a debate on whether authorities should be prick the bubble to stem its negative effects, we argue that transparent information dissemination will ensure that the stock market becomes more efficient in pricing stocks.

Speculative bubbles have been occurring periodically in local or global real-estate markets and are considered a potential cause of economic crises. In this context, the detection of explosive behaviors in the financial market and the implementation of early warning diagnosis tests are of critical importance. The recent increase in Brazilian housing prices has risen concerns that the Brazilian economy may have a speculative housing bubble. In the present chapter, we employ a recently proposed recursive unit root test in order to identify possible speculative bubbles in data from the Brazilian residential real-estate market. The empirical results show evidence for speculative price bubbles both in Rio de Janeiro and São Paulo, the two main Brazilian cities.

The purpose is to review what is known about property bubbles and their causes.

The method has been to review the literature on bubbles in the property and other asset markets to examine their likely causes and whether there are specific aspects of the property market that make it more prone to bubbles.

The property market has features that make it susceptible to bubbles, particularly inelasticity in supply and the absence of short selling. Bubbles can develop where there are heterogeneous beliefs. The way in which property tends to be financed helps to facilitate bubbles and transmit their effects onto the wider economy.

The collapse in property prices after the financial crisis of 2008, like previous bubble collapses, has inflicted serious damage on the wider economy through losses of banks' capital, reductions in lending, and increased risk aversion. Understanding why bubbles exist offers the potential to devise policies to limit the impact of their collapse.

Much of the literature on asset bubbles is based on securities markets. It is important to recognise the differences between the property market and securities markets, particularly how investment is financed.

Much of the author’s understanding of experimental asset market bubbles is based on the Smith, Suchanek and Williams (SSW) design. The purpose of this paper is to find alternative bubble-producing designs, which is a promising path for new insights.

The Smith et al. (1988) experimental design has been widely used to study bubbles. This paper introduces a novel modification, where the asset has a binary liquidation value and no dividends. Dividends are replaced by the events affecting the liquidation value probability distribution.

Overpricing is common and consistent with subject optimism concerning the random liquidation value. Bubbles are also observed, as the degree of overpricing often rises and then fall during the experiments. However, crashes where the asset price drops below fundamental values are not observed.

Subject over optimism, speculation and/or subject confusion are possible bubble ingredients. More research is needed to determine how much the factors responsible for these bubbles differ from the factors responsible for the SSW design. However, it seems likely that there are at least some common factors given the structural similarities between the two designs.

The present design is novel and may provide a means to better generalize results from previous experiments based on the SSW design.

This paper tries to locate the sectorial bubbles and examines the possibility for investors making extra profit from these bubbles in the Indian stock market.

The 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.

The 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.

To 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.

The purpose of this paper is to examine the evolutionary nature of herding phenomenon in the context of a frontier stock market, the Colombo Stock Exchange of Sri Lanka.

This study applies the cross-sectional absolute deviation methodology for daily frequencies of data of all the common stocks listed during the period from April 2000 to March 2018. The regression coefficients are estimated by using both the ordinary least square and the quantile regression procedures.

The findings reveal significant changes to the pattern of herding over different market periods, each with specific characteristics. Herding is strongly evident in up and down market days in the 2000-2009 period, during which the market was highly uncertain with the impact of the political instability of the country due to the Civil War on the stock trading. Even after this Civil War period, herd tendency is strongly manifested toward the up market direction as a result of the investors’ optimism about the country’s economy and political stability, which caused to a speculative bubble in the market. After that, it is turned into negative herding due to the panic selling occurred in view of the uncertainty of the inflated prices, which led to a market crash. Notably, herding appears to be consistently absent over the period after the crash, despite the presence of herd motives such as high market uncertainties triggered by political instability and economic crisis during that period.

The findings suggest that herd behavior is an evolving phenomenon in financial markets. Consistent with the adaptive market hypothesis, the absence of herding evident after the market crash could be attributed to the investors’ learning of the irrationality of herding/negative herding for adapting to market conditions. As a result, herding and negative herding tendencies declined and disappeared at the aggregate market level.

This study contributes to the literature by providing novel evidence on the evolutionary nature of behavioral biases, particularly herding, as predicted by the adaptive market hypothesis. With the application of the quantile regression procedure, in addition to customary used ordinary least squares approach, it also provides robust evidence on this phenomenon.

The purpose of this paper is to provide the current state of knowledge about the Flash Crash. It has been one of the remarkable events of the decade and its causes are still a matter of debate.

This paper reviews the literature since the early days to most recent findings, and critically compares the most important hypotheses about the possible causes of the crisis.

Among the causes of the Flash Crash, the literature has propsed the following: a large selling program triggering the sales wave, small but not negligible delays suffered by the exchange computers, the micro-structure of the financial markets, the price fall leading to margin cover and forced sales, some types of feedback loops leading to downward price spiral, stop-loss orders coupled with scarce liquidity that triggered price reduction. On its turn leading to further stop-loss activation, the use of Intermarket Sweep Orders, that is, orders that sacrificed search for the best price to speed of execution, and dumb algorithms.

The results of the previous section are condensed in a set of policy implications and recommendations.