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This article was published on the Guardian website at 20.25 BST on Thursday 11 June 2009. A version appeared on p1 of the Main section section of the Guardian on Friday 12 June 2009. It was last modified at 12.21 BST on Monday 19 May 2014.
© 2014 Guardian News and Media Limited or its affiliated companies. All rights reserved.

Rising inflation rates was the biggest worry of people around the world at the beginning of 2023. ** percent of the respondents gave this as their biggest worry. Inflation rates rose through 2022 and 2023 following the COVID-19 pandemic and Russia's invasion of Ukraine in February 2022. The Russia-Ukraine war followed in second with **** of the respondents worrying about this, whereas climate change and the environment followed in third.

ABSTRACT This paper argues that several aspects of the productive structure and the macroeconomic policies of Latin American countries, when combined with a Taylor Rule, may produce too much output volatility and a bias towards real exchange rate overvaluation. Relaying on a simple Aggregate Demand - Aggregate Supply model, we show that this is a likely outcome when: a) the real interest rate elasticity of demand is low; b) depreciations have strong contractionary effects; and c) the exchange rate pass-through is relatively large. These conditions imply that depreciations are contractionary and a have a strong effect on inflation.

news_headline,article_text,inflation_sentiment "Oil prices surge, analysts fear inflation spike","Global oil benchmarks hit record highs, prompting concerns about consumer prices.","negative" "Steady job growth, no inflation pressure seen","Latest labor report indicates healthy employment without significant wage inflation.","neutral" "Central bank hints at dovish stance, market reacts positively","Statements from the recent policy meeting suggest potential rate cuts soon.","positive"

During a 2025 survey in the United States, marketers' optimism level about the American economy declined to **** points, down from **** in Fall 2024. Optimism was at its lowest level since Fall 2022 - that year, Russia's invasion of Ukraine led to global economic uncertainty, while high inflation and recession fears also added to a general negative sentiment.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

VIX: Fear or Opportunity?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The inflation rates of energy commodity prices in Germany began to significantly increase in 2021, a rise that continued throughout 2022. The gas inflation rate peaked in November 2022 at 82 percent. These increases in inflation were driven by tighter fossil fuel supplies since many economies began recovering from the coronavirus pandemic, and further worsened by supply fears following the Russia-Ukraine war. However, in 2024, the HCIP has decreased compared to the previous year.

This paper adopts and develops the ‘‘fear of floating’’ theory to explain the decision to implement a de facto peg, the choice of anchor currency among multiple key currencies, and the role of central bank independence for these choices. We argue that since exchange rate depreciations are passed-through into higher prices of imported goods, avoiding the import of inflation provides an important motive to de facto peg the exchange rate in import-dependent countries. This study shows that the choice of anchor currency is determined by the degree of dependence of the potentially pegging country on imports from the key currency country and on imports from the key currency area, consisting of all countries which have already pegged to this key currency. The fear of floating approach also predicts that countries with more independent central banks are more likely to de facto peg their exchange rate since independent central banks are more averse to inflation than governments and can de facto peg a country’s exchange rate independently of the government.

This paper adopts and develops the “fear of floating” theory to explain the decision to implement a de facto peg, the choice of anchor currency among multiple key currencies, and the role of central bank independence for these choices. We argue that since exchange rate depreciations are passed-through into higher prices of imported goods, avoiding the import of inflation provides an important motive to de facto peg the exchange rate in import-dependent countries. This study shows that the choice of anchor currency is determined by the degree of dependence of the potentially pegging country on imports from the key currency country and on imports from the key currency area, consisting of all countries which have already pegged to this key currency. The fear of floating approach also predicts that countries with more independent central banks are more likely to de facto peg their exchange rate since independent central banks are more averse to inflation than governments and can de facto peg a country's exchange rate independently of the government.

Linear regression analysis of anxious temperament, self-infection and COVID-19-related fear of loved ones’ infection as predictors of cyberchondria in the whole sample (N = 499) with variance inflation factor (VIF).

In a survey conducted by Infocus Mekong in January 2024, it was revealed that ** percent of respondents in Vietnam considered inflation to be their primary concern. This was followed by worries about unemployment, with ** percent of respondents, and pollution, which worried ** percent of respondents, respectively.

Based on the findings of a survey conducted among Hungarians in 2022, from the possible consequences of the Russia-Ukraine war, respondents were mostly worried about inflation and rising prices. A further ** percent of surveyed Hungarians were worried about the situation leading to a nuclear war.

Revenue for the Open-End Investment Funds industry has been increasing over the past five years. Open-end investment funds revenue has been growing slightly but remaining relatively steady at a CAGR of 0.0% to $196.1 billion over the past five years, including an expected increase of 4.2% in the current year. In addition, industry profit has climbed and comprises 33.1% of revenue in the current year. Overall, revenue has been increasing alongside overall asset growth, despite operators being forced to lower fees to meet shifting consumer preferences. The industry has encountered volatility due to the high-interest rate environment for most of the period. Higher interest rates reduce liquidity and make fixed income securities more attractive to investors due to less risk and more predictable interest payments. The industry has also encountered increased growth for ETFs and retail investors. The greatest shift in the industry has been an evolving investor preference for exchange-traded funds (ETFs). While mutual funds account for the majority of industry assets, growth in ETF assets has significantly outpaced that of mutual funds. Expenses that mutual fund investors incur have fallen from 0.5% of assets in 2018 to 0.4% in 2023, as industry operators have cut fees to attract new capital due to pressure from new funds (latest data available). Despite the high interest rate environment, the Fed slashed rates in 2024 and is anticipated to cut rates further in the latter part of 2025, which will boost asset prices. Open-end investment funds' revenue is expected to grow at a CAGR of 0.3% to $198.7 billion over the five years to 2030. The fears over inflation and a possible recession are expected to dominate the beginning of the outlook period. The Federal Reserve is expected to continue cutting interest rates as inflationary pressures ease. Investment companies' importance will continue to grow, with mutual funds and ETFs representing key channels for individual and institutional investors to access financial markets.

Professional employer organizations (PEOs) have fared with volatility in recent years. The labor market quickly recovered from the pandemic and the unemployment rate has hovered near record low levels, intensifying competition for talent. PEOs have been essential in a market where many companies struggled to staff up, while also helping businesses implement strategies to reduce attrition. At the same time, elevated interest rates in response to inflation have constrained growth among critical clients and tempered corporate profit, curtailing spending on PEO services. Overall, industry revenue is forecast to decline at a CAGR of 2.5% over the past five years to $215.9 billion, including growth of 1.7% in 2025. The importation of technology has already had a major impact on PEOs, with online job boards creating opportunities to match candidates with employers, while the adoption of automation technologies has improved the efficiency of matching candidates with job listings. The artificial intelligence (AI) revolution will streamline routine tasks like payroll processing, benefits administration and recruitment for PEOs, allowing companies to leverage new capabilities to improve strategic decision-making processes for clients. At the same time, the scale of change will encourage more companies to consolidate as larger companies are better able to invest in technology and spread administrative costs across a larger client base. Amid this changing landscape, profit margins will hold steady as growth opportunities will be counterbalanced by steep competitive pressures.PEOs will grow in the coming years, interest rates temper, strengthening corporate finances and better enabling companies to spend on external services. Economic expansion will encourage entrepreneurship, with a rise in the number of businesses creating new need for PEOs. As a result, industry revenue is forecast to increase at a CAGR of 1.6% over the next five years to 2030, although ongoing inflation fears alongside the imposition of tariffs could undermine these trends. PEOs will remain essential to helping companies navigate a changing economic landscape in the coming years, as the expansion of the gig economy reclassifies workers.

By April 2026, it is projected that there is a probability of ***** percent that the United States will fall into another economic recession. This reflects a significant decrease from the projection of the preceding month.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Stocks Take a Dive as Investors Fear Recession

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Fed, ECB, and BoE to Pause Rate Hikes Amid Recession Fears

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

In 2024, the salary increase across India was *** percent, a slight decline since last year. The salary increase in India was the highest among the Asia-Pacific countries. The salary growth is expected to stay the same in 2025 at *** percent. Sectors driving growth Sectors like Pharmaceuticals, manufacturing, insurance, captives, and SSO are projecting above the general industry salary median for 2025. With its highly skilled talent pool, India’s global capability centers (GCCs) are driving the projected salary increases. Outlook The projected increase in salary in 2025 is expected to be similar to 2024. Cost management, inflation, fear of recession, and a tighter labor market are some factors leading to 2025 projections. 2023 witnessed the highest salary increase during the recorded period, with a ** percent growth.

Graph and download economic data for Real-time Sahm Rule Recession Indicator (SAHMREALTIME) from Dec 1959 to Jun 2025 about recession indicators, academic data, and USA.

One of the major duties the Bank of England (BoE) is tasked with is keeping inflation rates low and stable. The usual tactic for keeping inflation rates down, and therefore the price of goods and services stable by the Bank of England is through lowering the Bank Rate. Such a measure was used in 2008 during the global recession when the BoE lowered the bank base rate from **** percent to *** percent. Due to the economic fears surrounding the COVID-19 virus, as of the 19th of March 2020, the bank base rate was set to its lowest ever standing. The issue with lowering interest rates is that there is an end limit as to how low they can go. Quantitative easing Quantitative easing is a measure that central banks can use to inject money into the economy to hopefully boost spending and investment. Quantitative easing is the creation of digital money in order to purchase government bonds. By purchasing large amounts of government bonds, the interest rates on those bonds lower. This in turn means that the interest rates offered on loans for the purchasing of mortgages or business loans also lowers, encouraging spending and stimulating the economy. Large enterprises jump at the opportunity After the initial stimulus of *** billion British pounds through quantitative easing in March 2020, the Bank of England announced in June that they would increase the amount by a further 100 billion British pounds. In March of 2020, the headline flow of borrowing by non-financial industries including construction, transport, real estate and the manufacturing sectors increased significantly.