The misery index is an economic indicator that combines the unemployment rate and the inflation rate. Although it is rare for both unemployment and inflation to be high at the same time, there have been instances of this occurring, such as during episodes of stagflation in the 1970s. Due to high levels of inflation since late 2021, the misery index in March 2023 is at a relatively high rate of 8.49 percent.

In 2024, Sudan was ranked as the most miserable country in the world, with a misery index score of 374.8. Argentina ranked second with an index score of 195.9. Quality of life around the worldThe misery index was created by the economist Arthur Okun in the 1960s. The index is calculated by adding the unemployment rate, the lending rate and the inflation rate minus percent change of GDP per capita. Another famous tool used for the comparison of development of countries around the world is the Human Development Index, which takes into account such factors as life expectancy at birth, literacy rate, education level and gross national income (GNI) per capita. Better economic conditions correlate with higher quality of life Economic conditions affect the life expectancy, which is much higher in the wealthiest regions. With a life expectancy of 85 years, Liechtenstein led the ranking of countries with the highest life expectancy in 2023. On the other hand, Nigeria was the country with the lowest life expectancy, where men were expected to live 55 years as of 2024. The Global Liveability Index ranks the quality of life in cities around the world, basing on political, social, economic and environmental aspects, such as personal safety and health, education and transport services and other public services. In 2024, Vienna was ranked as the city with the highest quality of life worldwide.

The misery index (the unweighted sum of unemployment and inflation rates) was probably the first attempt to develop a single statistic to measure the level of a population’s economic malaise. In this letter, we develop a dynamic approach to decompose the misery index using two basic relations of modern macroeconomics: the expectations-augmented Phillips curve and Okun’s law. Our reformulation of the misery index is closer in spirit to Okun’s idea. However, we are able to offer an improved version of the index, mainly based on output and unemployment. Specifically, this new Okun’s index measures the level of economic discomfort as a function of three key factors: (1) the misery index in the previous period; (2) the output gap in growth rate terms; and (3) cyclical unemployment. This dynamic approach differs substantially from the standard one utilised to develop the misery index, and allow us to obtain an index with five main interesting features: (1) it focuses on output, unemployment and inflation; (2) it considers only objective variables; (3) it allows a distinction between short-run and long-run phenomena; (4) it places more importance on output and unemployment rather than inflation; and (5) it weights recessions more than expansions.

Highest magnitude correlations at top. Standard deviations and confidence intervals on are estimated by 10,000 bootstraps (random sampling with replacement). Not shown are the literary scores which did not yield statistically significant correlations with economic misery: WNA misery (Fiction books), WNA joy, WNA fear, WNA surprise, WNA anger, WNA sadness, LIWC anxiety, LIWC anger, LIWC affect, and LIWC positive emotions.

Research has not yet explored whether the misery index or presidential rhetorical optimism about economic conditions can shape public opinion about the appropriate level of government involvement in domestic affairs in the United States. The time series analyses preformed here suggest prior change in the misery index and presidential rhetorical optimism about the economy produce shifts in public opinion, although the magnitude of the shift following changes in the misery index appears to be less substantial than the shift following changes in presidential rhetorical optimism about the economy.

In 2023, Argentina was the Latin American country with the highest misery index score of 321.8 points. Venezuela ranked second with around 276.3 points. During that same year, both countries also led the inflation ranking in Latin America.

The average for 2024 based on 138 countries was 5.56 points. The highest value was in Finland: 7.74 points and the lowest value was in Afghanistan: 1.72 points. The indicator is available from 2013 to 2024. Below is a chart for all countries where data are available.

This study explains how the economy affects the foreign policy rhetoric used by American presidents. When economic conditions deteriorate, presidents criticize foreign nations to boost their approval ratings. Presidents use this "diversionary cheap talk" in response to the misery index of unemployment plus inflation, which poses a unique threat to their popularity. They target historical rivals, which make intergroup distinctions most salient. Diversionary cheap talk is most influential for and most frequently used by Democratic presidents, whose non-core constituents prefer hawkish foreign policy but already expect it from Republican presidents. I test the observable implications of the theory with the American Diplomacy Dataset, an original record of 50,000 American foreign policy events between 1851 and 2010 drawn from a corpus of 1.3 million New York Times articles.

Leading Economic Index Turkey increased 2.53 percent in August of 2024 over the same month in the previous year. This dataset provides the latest reported value for - Turkey Leading Economic Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Data covers the panel time-series observations of laws sponsored-enacted ruling AK Party and public most-important problem survey between 2002-2023 period in Türkiye. More than 2500 laws and 20.000 MIP survey is content coded and assigned Comparative Agendas Project coding scheme. All observations are weighted as percentage and collapsed policy topic legislative year for panel analysis. Dataset also covers, type of regime, electoral democracy index, economic misery index, general election dummy and local election dummy consequently.

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.

NGMS: Next Generation Marvel Stock or Milepost of Market Misery?

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