Using Power Laws and the Hurst Exponent to Identify Stock Market Trading Bubbles

Yalamova, Rossitsa; McKelvey, Bill

doi:10.1201/9781315585444-9

Cited by 4 publications

(5 citation statements)

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“…Tong et al, 2018). It is commonly used for evaluating fluctuations in water levels, climatic factors, marketing, and (Koutsoyiannis, 2003;Yalamova & McKelvey, 2018).…”

Section: Persistence Of Time Series Data With the Hurst Exponentmentioning

confidence: 99%

“…However, this model is limited in quantifying the variations in the trend of time-series data. In addition to time-series analysis, the Hurst exponent has been widely used as an effective method to quantitatively detect the persistence (i.e., sustainability) of trends in time series data in various fields, such as hydrology (Adarsh, Dharan, & Anuja, 2018;Koutsoyiannis, 2003), climatology (Karmakar, Goswami, & Chattopadhyay, 2019;Tatli, 2015), economics (Yalamova & McKelvey, 2018), and environment (S. Tong et al, 2018).…”

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confidence: 99%

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Characterising spatiotemporal vegetation variations using LANDSAT time‐series and Hurst exponent index in the Mekong River Delta

Tran

Nguyen

et al. 2021

Land Degrad Dev

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Spatiotemporal analysis and monitoring of vegetation help us investigate ecological health and guide better forest conservation and land management practices for sustainable development. This paper proposes the use of spatial analysis approaches (i.e., ordinary least squares [OLS] and the Hurst exponent) combined with time-series analysis using enhanced vegetation index (EVI) data, derived from LANDSAT via the Google Earth Engine, to estimate the trends and sustainability of vegetation dynamics in the Tra Vinh Province in the Mekong River Delta. We also assessed the EVI changes connected to land change issues to examine the influence of land use conversion on vegetation dynamics. Results show that a large portion of the study area was covered by abundant vegetation (over 50% of the total area), and the increased EVI area was about 5.5-times greater than the area of EVI reduction. Additionally, vegetation sustainability was being seriously compromised (e.g., a decrease in the total area of 8,275 ha) due to several land conversion drivers such as shrimp farming, urbanisation, and industrialisation. Furthermore, results obtained from this research provide insight into the spatiotemporal dynamics of vegetation coverage and reveal the consistency of future vegetation trends. Moreover, the study also quantitatively assessed the positive impacts of Buddhist doctrines on reducing the negative trend of vegetation change in the study area. These findings can lay the ground to formulate sustainable land and environmental plans that meet the 11th, 13th and 15th Sustainable Development Goals (SDGs) (i.e., the sustainable cities and communities, the climate actions, and the life on land). Besides, the analytical procedure adopted in this study can also be applicable to any other coastal areas that require the accurate assessment of vegetation status over time.

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“…Tong et al, 2018). It is commonly used for evaluating fluctuations in water levels, climatic factors, marketing, and (Koutsoyiannis, 2003;Yalamova & McKelvey, 2018).…”

Section: Persistence Of Time Series Data With the Hurst Exponentmentioning

confidence: 99%

mentioning

confidence: 99%

Characterising spatiotemporal vegetation variations using LANDSAT time‐series and Hurst exponent index in the Mekong River Delta

Tran

Nguyen

et al. 2021

Land Degrad Dev

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“…Yet, while many of these high-risk banks went bankrupt, the few that remained -Goldman Sachs, Morgan Stanley, Citigroup, Bank of America, and Wells Fargo -were able to exploit the Federal Reserve bailouts by engaging in merger and acquisition activity to emerge far stronger and larger than they had been. Here we see both positive and negative scalability dynamics at work, triggered by some early TIEs -the invention of derivatives in 1973 and of mortgage-backed securities c. 1985 (McKelvey andYalamova, 2011;Yalamova and McKelvey, 2011b). As indicated by Figure16.6, the Complexity Regime of the Ashby Space is sandwiched between order and chaos.…”

Section: An Examplementioning

confidence: 81%

Complexity and Organization–Environment Relations: Revisiting Ashby's Law of Requisite Variety

Boisot¹,

McKelvey²

2011

The Sage Handbook of Complexity and Management

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“…looking at the evolution of the shape (skewness) of the spectrum and relating it to market events and underling dynamics. Other studies have tried to associate a time-varying Hurst exponent as a measure of the dynamically changing scaling of a financial time-series, with the development of stock-market bubbles ( [33], [34] and references therein), trading signals ( [35]) and predictability of an index [36], raising the question whether scaling analysis can be used as a signaling tool for financial markets ( [37], [38], [39]).…”

Section: Introductionmentioning

confidence: 99%

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

Antoniades,

Brandi,

Magafas

et al. 2020

Preprint

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The dynamical evolution of multiscaling in financial time series is investigated using timedependent Generalized Hurst Exponents (GHE), H q , for various values of the parameter q.Using H q , 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 H q 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

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Using Power Laws and the Hurst Exponent to Identify Stock Market Trading Bubbles

Cited by 4 publications

References 0 publications

Characterising spatiotemporal vegetation variations using LANDSAT time‐series and Hurst exponent index in the Mekong River Delta

Characterising spatiotemporal vegetation variations using LANDSAT time‐series and Hurst exponent index in the Mekong River Delta

Complexity and Organization–Environment Relations: Revisiting Ashby's Law of Requisite Variety

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

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