Financial Stability in the Aftermath of the 'Great Recession'

Arestis, Philip; Karakitsos, Elias

doi:10.1057/9781137333964

Cited by 17 publications

(7 citation statements)

References 70 publications

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“…Under such circumstances, the recovery, such as it is, relies on private-sector borrowing. Outstanding household debt in the UK was 72.0 percent of GDP over the period 1998 to 2002 (in 1960 it amounted to less than 15 percent of GDP); between 2003 and 2007 it shot to 94.3 percent of GDP, and it has been increasing since (Arestis and Karakitsos, 2013). Bunn and Rostom (2014) state that "the household debt to income ratio rose from around 100% in 1999 to a peak of 160% in 2008" (p. 305).…”

Section: Current Uk Economic Situationmentioning

confidence: 99%

“…Fiscal austerity makes matters worse since the 'burden' of borrowing falls on the private sector, which is much more vulnerable in terms of the accumulation of liabilities. It was precisely this process of high private expenditure through borrowing that led to the 'great recession' (see Arestis and Karakitsos, 2013); and as Stiglitz (2014) argues, recovery of the economy through consumption is worrying for "It means return to unsustainable patterns of the kind marked the pre-crisis days" (p. 20).…”

Section: Current Uk Economic Situationmentioning

confidence: 99%

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Austerity Cannot Explain the Current UK Economic Growth

Arestis

Sawyer

2015

Challenge

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Section: Current Uk Economic Situationmentioning

confidence: 99%

Section: Current Uk Economic Situationmentioning

confidence: 99%

Austerity Cannot Explain the Current UK Economic Growth

Arestis

Sawyer

2015

Challenge

Self Cite

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“…7,8 Cases exemplifying this phenomenon are abundant in the financial crisis literature, such as the Asian crisis of 1997, the dot-com bubble of 2000, the global financial crisis of 2007-2008 (GFC of 2007-08 henceforth), and the European sovereign debt crisis of 2011-2013. [9][10][11] Considering the relevance of the problems and challenges aforementioned, the present paper then highlights the benefits of consistently disaggregating financial data into meaningful homogenous groups. Such a data disaggregation process aims to better understand similarity and clustering patterns between competing investment alternatives, particularly during periods of high volatility.…”

Section: Introductionmentioning

confidence: 99%

Harnessing the power of intersection for pattern recognition: a novel unsupervised learning method and its application to financial engineering

Haddad

2021

Engineering Reports

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In the present paper a data‐driven hard cluster analysis derived from a novel similarity measure is proposed to support financial investors in their portfolio management decision‐making process. The main objective of the proposed method is to provide a less arbitrary learning procedure to quantify similarity levels between investment alternatives (pairwise) as well as revealing clustering patterns (whole sample). This is especially useful during periods of high volatility, when investment alternatives tend to become more similar and, therefore, harder to distinguish between themselves. The method dynamics may be readily interpreted through a clear data visualisation. The advantages and caveats of the proposed method is compared to the most popular class of cluster analysis, applied to the well‐known Fisher's Iris dataset. Such results show a slightly superior performance of the proposed method but, most importantly, through remarkably different clustering allocation approaches. Moreover, further empirical results applied to daily data reflecting a period of 15 years of time series of economies/stock markets of the Group of Seven (G7) illustrate the potential practical usefulness of the proposed unsupervised learning method, particularly, for portfolio strategy, asset allocation, and investment diversification.

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“…The greater frequency and impact of financial and economic crises requires the development of feasible methods that aim to clearly distinguish investment alternatives in a robust, consistent, and coherent manner [[1], [2], [3], [4]]. The main goal of the proposed data-driven cluster-similarity risk method is to support financial investors to precisely measure the level of similarity between publicly listed companies through time - especially in turbulent periods, showing the outputs through a clear graphical representation as a by-product of the analysis.…”

Section: Introductionmentioning

confidence: 99%