In this study, a new market representation from persistence homology, known as the L1-norm time series, is used and applied independently with three critical slowing down indicators [autocorrelation function at lag 1, variance, and mean for power spectrum (MPS)] to examine two historical financial crises (Dotcom crash and Lehman Brothers bankruptcy) in the US market. The captured signal is the rising trend in the indicator time series, which can be determined by Kendall's tau correlation test. Furthermore, we examined Pearson's and Spearman's rho correlation tests as potential substitutes for Kendall's tau correlation. After that, we determined a correlation threshold and predicted the whole available date. The point of comparison between these correlation tests is to determine which test is significant and consistent in classifying the rising trend. The results of such a comparison will suggest the best test that can classify the observed rising trend and detect early warning signals (EWSs) of impending financial crises. Our outcome shows that the L1-norm time series is more likely to increase before the two financial crises. Kendall's tau, Pearson's, and Spearman's rho correlation tests consistently indicate a significant rising trend in the MPS time series before the two financial crises. Based on the two evaluation scores (the probability of successful anticipation and probability of erroneous anticipation), by using the L1-norm time series with MPS, our result in the whole prediction demonstrated that Spearman's rho correlation (46.15 and 53.85%) obtains the best score as compared to Kendall's tau (42.31 and 57.69%) and Pearson's (40 and 60%) correlations. Therefore, by using Spearman's rho correlation test, L1-norm time series with MPS is shown to be a better way to detect EWSs of US financial crises.

The United States recorded a Government Debt to GDP of 124.30 percent of the country's Gross Domestic Product in 2024. This dataset provides - United States Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Asia’s financial crisis in July 1997 affects currency, capital market, and real market throughout Asian countries. Countries in southeast region (ASEAN), including Indonesia, Malaysia, Philippines, Singapore, and Thailand, are some of the countries where the crisis hit the most. In these countries, where financial sectors are far more developed than real sectors and the money market sectors, most of the economic activities are conducted in capital market. Movement in the capital market could be a proxy to describe the overall economic situation and therefore the prediction of it could be an early warning system of economic crises. This paper tries to investigate movement in ASEAN (Indonesia, Malaysia, Philippines, Singapore, and Thailand) capital market to build an early warning system from financial sectors perspective. This paper will be very beneficial for the government to anticipate the forthcoming crisis. The insight of this paper is from Hamilton (1990) model of regime switching process in which he divide the movement of currency into two regimes, describe the switching transition based on Markov process and creates different model for each regimes. Differ from Hamilton, our research focuses on index return instead of currency to model the regime switching. This research aimed to find the probability of crisis in the future by combining the probability of switching and the probability distribution function of each regime. Probability of switching is estimated by categorizing the movement in index return into two regimes (negative return in regime 1 and positive return in regime 2) then measuring the proportion of switching to regime 1 in t given regime 1 in t-1 (P11) and to regime 2 in t given regime 2 in t-1 (P22). The probability distribution function of each regime is modeled using t-student distribution. This paper is able to give signal of the 1997/8 crisis few periods prior the crisis.

Following the drastic increase directly after the COVID-19 pandemic, the delinquency rate started to gradually decline, falling below *** percent in the second quarter of 2023. In the second half of 2023, the delinquency rate picked up, but remained stable throughout 2024. In the first quarter of 2025, **** percent of mortgage loans were delinquent. That was significantly lower than the **** percent during the onset of the COVID-19 pandemic in 2020 or the peak of *** percent during the subprime mortgage crisis of 2007-2010. What does the mortgage delinquency rate tell us? The mortgage delinquency rate is the share of the total number of mortgaged home loans in the U.S. where payment is overdue by 30 days or more. Many borrowers eventually manage to service their loan, though, as indicated by the markedly lower foreclosure rates. Total home mortgage debt in the U.S. stood at almost ** trillion U.S. dollars in 2024. Not all mortgage loans are made equal ‘Subprime’ loans, being targeted at high-risk borrowers and generally coupled with higher interest rates to compensate for the risk. These loans have far higher delinquency rates than conventional loans. Defaulting on such loans was one of the triggers for the 2007-2010 financial crisis, with subprime delinquency rates reaching almost ** percent around this time. These higher delinquency rates translate into higher foreclosure rates, which peaked at just under ** percent of all subprime mortgages in 2011.

Statistics on student debt, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of graduates with debt who had paid it off at the time of the interview, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

The spread between 10–year and two–year U.S. Treasury bond yields reached a positive value of 0.49 percent in June 2025. The 10–year minus two–year Treasury bond spread is generally considered to be an advance warning of severe weakness in the stock market. Negative spreads occurred prior to the recession of the early 1990s, the tech-bubble crash in 2000–2001, and the financial crisis of 2007–2008.

Early warning effect based on traditional financial indicators and MD&A text tone indicator.

Inflation is generally defined as the continued increase in the average prices of goods and services in a given region. Following the extremely high global inflation experienced in the 1980s and 1990s, global inflation has been relatively stable since the turn of the millennium, usually hovering between three and five percent per year. There was a sharp increase in 2008 due to the global financial crisis now known as the Great Recession, but inflation was fairly stable throughout the 2010s, before the current inflation crisis began in 2021. Recent years Despite the economic impact of the coronavirus pandemic, the global inflation rate fell to 3.26 percent in the pandemic's first year, before rising to 4.66 percent in 2021. This increase came as the impact of supply chain delays began to take more of an effect on consumer prices, before the Russia-Ukraine war exacerbated this further. A series of compounding issues such as rising energy and food prices, fiscal instability in the wake of the pandemic, and consumer insecurity have created a new global recession, and global inflation in 2024 is estimated to have reached 5.76 percent. This is the highest annual increase in inflation since 1996. Venezuela Venezuela is the country with the highest individual inflation rate in the world, forecast at around 200 percent in 2022. While this is figure is over 100 times larger than the global average in most years, it actually marks a decrease in Venezuela's inflation rate, which had peaked at over 65,000 percent in 2018. Between 2016 and 2021, Venezuela experienced hyperinflation due to the government's excessive spending and printing of money in an attempt to curve its already-high inflation rate, and the wave of migrants that left the country resulted in one of the largest refugee crises in recent years. In addition to its economic problems, political instability and foreign sanctions pose further long-term problems for Venezuela. While hyperinflation may be coming to an end, it remains to be seen how much of an impact this will have on the economy, how living standards will change, and how many refugees may return in the coming years.

In 2018, the estimated average inflation rate in Pakistan amounted to about 3.93 percent compared to the previous year, a slight drop from 2017, but an ever sharper one compared to four years earlier. Over the next few years, forecasts estimate it to level off at around 6.5 percent.

Pakistan‘s more or less fragile economy

Pakistan is one of the most populous countries in the worldwith a large Muslim population and a rather low urbanization rate, which means that the majority of Pakistanis live in rural areas. However, the majority of the country's GDP is generated by the services sector, which also employs most of the workforce. As of now, Pakistan’s economic growth seems stable, but that wasn’t always the case.

Stable growth ahead?

Like many others, Pakistan’s economy suffered during the 2009 financial crisis, and while it has recovered today, inflation was still over 10 percent in 2012. GDP slumped during that time as well, but now, ten years later, it has almost tripled and seems to be on an upward trend. Although its GDP generation now mainly relies on services, Pakistan still exports agricultural goods like cotton. However, the country still struggles with an increasing trade deficit and thus rising national debt – two factors that could hinder economic growth in the future.

The data sets used in Figs 6–12 are all from https://datasetsearch.research.google.com/.

Early warning effect based on traditional financial indicators.

Conditional probabilities of crises given a signal associated with comprehensive indicators.

Predictive ability from the comprehensive indicators.

Descriptive statistics of traditional financial indicators and text-linguistic feature indicators.

Interpretation of individual indicators.

The selection of traditional financial indicators and formulas.

Performance of the individual indicators in the KLR tests.

The Global Financial Crisis of 2007-2008 wiped out US$37 trillions across global financial markets, this value is equivalent to the combined GDPs of the United States and the European Union in 2014. The defining moment of this crisis was the failure of Lehman Brothers, which precipitated the October 2008 crash and the Asian Correction (March 2009). Had the Federal Reserve seen these crashes coming, they might have bailed out Lehman Brothers, and prevented the crashes altogether. In this paper, we show that some of these market crashes (like the Asian Correction) can be predicted, if we assume that a large number of adaptive traders employing competing trading strategies. As the number of adherents for some strategies grow, others decline in the constantly changing strategy space. When a strategy group grows into a giant component, trader actions become increasingly correlated and this is reflected in the stock price. The fragmentation of this giant component will leads to a market crash. In this paper, we also derived the mean-field market crash forecast equation based on a model of fusions and fissions in the trading strategy space. By fitting the continuous returns of 20 stocks traded in Singapore Exchange to the market crash forecast equation, we obtain crash predictions ranging from end October 2008 to mid-February 2009, with early warning four to six months prior to the crashes.