Euro Area's main stock market index, the EU50, fell to 5336 points on July 31, 2025, losing 1.06% from the previous session. Over the past month, the index has climbed 1.01% and is up 11.96% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on July of 2025.

India's main stock market index, the SENSEX, fell to 80888 points on July 31, 2025, losing 0.73% from the previous session. Over the past month, the index has declined 3.36% and is down 1.20% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

In 2024, the Tokyo Stock Price Index (TOPIX) hit a daily closing low of ******** points on August 5, when Japan's stock market experienced a historic crash. TOPIX is a free-float adjusted market capitalization-weighted index that has been published by the Tokyo Stock Exchange (TSE) since 1969. The market capitalization as of the base date (January 4, 1968) is set at 100 points.

JO: Human Capital Index (HCI): Male: Lower Bound: Scale 0-1 data was reported at 0.516 NA in 2017. JO: Human Capital Index (HCI): Male: Lower Bound: Scale 0-1 data is updated yearly, averaging 0.516 NA from Dec 2017 (Median) to 2017, with 1 observations. JO: Human Capital Index (HCI): Male: Lower Bound: Scale 0-1 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank: Human Capital Index. The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful.; ; World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498; ;

Selected features using the RFS feature selection method for the stock indices investigated.

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.

What happens to gold if CPI increases?

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.

Brown Group, Inc. (BWNG): Has the Bear Market Hit the Bottom?

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

Egypt EG: Human Capital Index (HCI): Female: Lower Bound: Scale 0-1 data was reported at 0.490 NA in 2017. Egypt EG: Human Capital Index (HCI): Female: Lower Bound: Scale 0-1 data is updated yearly, averaging 0.490 NA from Dec 2017 (Median) to 2017, with 1 observations. Egypt EG: Human Capital Index (HCI): Female: Lower Bound: Scale 0-1 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.World Bank: Human Capital Index. The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful.; ; World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498; ;

Oman OM: Depth of Credit Information Index: 0=Low To 8=High data was reported at 6.000 NA in 2017. This stayed constant from the previous number of 6.000 NA for 2016. Oman OM: Depth of Credit Information Index: 0=Low To 8=High data is updated yearly, averaging 6.000 NA from Dec 2013 (Median) to 2017, with 5 observations. The data reached an all-time high of 6.000 NA in 2017 and a record low of 6.000 NA in 2017. Oman OM: Depth of Credit Information Index: 0=Low To 8=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Oman – Table OM.World Bank.WDI: Business Environment. Depth of credit information index measures rules affecting the scope, accessibility, and quality of credit information available through public or private credit registries. The index ranges from 0 to 8, with higher values indicating the availability of more credit information, from either a public registry or a private bureau, to facilitate lending decisions.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year. Data before 2013 are not comparable with data from 2013 onward due to methodological changes.

Background: Socioeconomic status (SES) is an important determinant of health and potential modifier of the effects of environmental contaminants. There has been a lack of comprehensive indices for measuring overall SES in Canada. Here, a more comprehensive SES index is developed aiming to support future studies exploring health outcomes related to environmental pollution in Canada. Methods: SES variables (n=22, Census Canada 2006) were selected based on: cultural identities, housing characteristics, variables identified in Canadian environmental injustice studies and a previous deprivation index (Pampalon index). Principal component analysis with a single varimax rotation (factor loadings=¦60¦) was performed on SES variables for 52974 census dissemination areas (DA). The final index was created by averaging the factor scores per DA according to the three components retained. The index was validated by examining its association with preterm birth (gestational age<37 weeks), term low birth weight (LBW, <2500 g), small for gestational age (SGA, <10 percentile of birth weight for gestational age) and PM2.5 (particulate matter=2.5 µm) exposures in Edmonton, Alberta (1999–2008). Results: Index values exhibited a relatively normal distribution (median=0.11, mean=0.0, SD=0.58) across Canada. Values in Alberta tended to be higher than in Newfoundland and Labrador, Northwest Territories and Nunavut (Pearson chi-square p<0.001 across provinces). Lower quintiles of our index and the Pampalon’s index confirmed know associations with a higher prevalence of LBW, SGA, preterm birth and PM2.5 exposure. Results with our index exhibited greater statistical significance and a more consistent gradient of PM2.5 levels and prevalence of pregnancy outcomes. Conclusions: Our index reflects more dimensions of SES than an earlier index and it performed superiorly in capturing gradients in prevalence of pregnancy outcomes. It can be used for future research involving environmental pollution and health in Canada. These metadata can also be found on SAGE's searchable metadata website: http://sagemetadata.policywise.com/nada/index.php/catalog/14

Guyana GY: Logistics Performance Index: 1=Low To 5=High: Competence and Quality of Logistics Services data was reported at 2.659 NA in 2016. This records an increase from the previous number of 2.270 NA for 2014. Guyana GY: Logistics Performance Index: 1=Low To 5=High: Competence and Quality of Logistics Services data is updated yearly, averaging 2.270 NA from Dec 2007 (Median) to 2016, with 5 observations. The data reached an all-time high of 2.659 NA in 2016 and a record low of 1.950 NA in 2007. Guyana GY: Logistics Performance Index: 1=Low To 5=High: Competence and Quality of Logistics Services data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guyana – Table GY.World Bank.WDI: Transportation. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2009 round of surveys covered more than 5,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluate eight markets on six core dimensions on a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Details of the survey methodology are in Arvis and others' Connecting to Compete 2010: Trade Logistics in the Global Economy (2010). Respondents evaluated the overall level of competence and quality of logistics services (e.g. transport operators, customs brokers), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents.; ; World Bank and Turku School of Economics, Logistic Performance Index Surveys. Data are available online at : http://www.worldbank.org/lpi. Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.; Unweighted average;

Uzbekistan UZ: Logistics Performance Index: 1=Low To 5=High: Frequency with which Shipments Reach Consignee within Scheduled or Expected Time data was reported at 2.832 NA in 2016. This records a decrease from the previous number of 3.083 NA for 2014. Uzbekistan UZ: Logistics Performance Index: 1=Low To 5=High: Frequency with which Shipments Reach Consignee within Scheduled or Expected Time data is updated yearly, averaging 2.960 NA from Dec 2007 (Median) to 2016, with 5 observations. The data reached an all-time high of 3.720 NA in 2010 and a record low of 2.730 NA in 2007. Uzbekistan UZ: Logistics Performance Index: 1=Low To 5=High: Frequency with which Shipments Reach Consignee within Scheduled or Expected Time data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Uzbekistan – Table UZ.World Bank.WDI: Transportation. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2009 round of surveys covered more than 5,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluate eight markets on six core dimensions on a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Details of the survey methodology are in Arvis and others' Connecting to Compete 2010: Trade Logistics in the Global Economy (2010). Respondents assessed how often the shipments to assessed markets reach the consignee within the scheduled or expected delivery time, on a rating ranging from 1 (hardly ever) to 5 (nearly always). Scores are averaged across all respondents.; ; World Bank and Turku School of Economics, Logistic Performance Index Surveys. Data are available online at : http://www.worldbank.org/lpi. Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.; Unweighted average;

Italy IT: Human Capital Index (HCI): Male: Lower Bound: Scale 0-1 data was reported at 0.752 NA in 2017. Italy IT: Human Capital Index (HCI): Male: Lower Bound: Scale 0-1 data is updated yearly, averaging 0.752 NA from Dec 2017 (Median) to 2017, with 1 observations. Italy IT: Human Capital Index (HCI): Male: Lower Bound: Scale 0-1 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank: Human Capital Index. The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful.; ; World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498; ;

Georgia GE: Human Capital Index (HCI): Lower Bound: Scale 0-1 data was reported at 0.596 NA in 2017. Georgia GE: Human Capital Index (HCI): Lower Bound: Scale 0-1 data is updated yearly, averaging 0.596 NA from Dec 2017 (Median) to 2017, with 1 observations. Georgia GE: Human Capital Index (HCI): Lower Bound: Scale 0-1 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Georgia – Table GE.World Bank: Human Capital Index. The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful.; ; World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498; ;

Benin BJ: Logistics Performance Index: 1=Low To 5=High: Overall data was reported at 2.900 NA in 2022. This records an increase from the previous number of 2.750 NA for 2018. Benin BJ: Logistics Performance Index: 1=Low To 5=High: Overall data is updated yearly, averaging 2.750 NA from Dec 2007 (Median) to 2022, with 7 observations. The data reached an all-time high of 2.900 NA in 2022 and a record low of 2.428 NA in 2016. Benin BJ: Logistics Performance Index: 1=Low To 5=High: Overall data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Benin – Table BJ.World Bank.WDI: Transportation. The Logistics Performance Index overall score reflects perceptions of a country's logistics based on the efficiency of customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance. Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report.;Data are available online at: https://lpi.worldbank.org/. Summary results are published in World Bank (2023): Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators.;Unweighted average;

Comoros KM: Logistics Performance Index: 1=Low To 5=High: Ease of Arranging Competitively Priced Shipments data was reported at 2.490 NA in 2018. This records a decrease from the previous number of 2.576 NA for 2016. Comoros KM: Logistics Performance Index: 1=Low To 5=High: Ease of Arranging Competitively Priced Shipments data is updated yearly, averaging 2.501 NA from Dec 2007 (Median) to 2018, with 6 observations. The data reached an all-time high of 2.576 NA in 2016 and a record low of 1.810 NA in 2012. Comoros KM: Logistics Performance Index: 1=Low To 5=High: Ease of Arranging Competitively Priced Shipments data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Comoros – Table KM.World Bank.WDI: Transportation. Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents assessed the ease of arranging competitively priced shipments to markets, on a rating ranging from 1 (very difficult) to 5 (very easy). Scores are averaged across all respondents.;Data are available online at: https://lpi.worldbank.org/. Summary results are published in World Bank (2023): Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators.;Unweighted average;

Libya LY: Logistics Performance Index: 1=Low To 5=High: Ease of Arranging Competitively Priced Shipments data was reported at 2.397 NA in 2016. This records an increase from the previous number of 2.294 NA for 2014. Libya LY: Logistics Performance Index: 1=Low To 5=High: Ease of Arranging Competitively Priced Shipments data is updated yearly, averaging 2.346 NA from Dec 2010 (Median) to 2016, with 4 observations. The data reached an all-time high of 2.620 NA in 2012 and a record low of 2.280 NA in 2010. Libya LY: Logistics Performance Index: 1=Low To 5=High: Ease of Arranging Competitively Priced Shipments data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Libya – Table LY.World Bank.WDI: Transportation. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2009 round of surveys covered more than 5,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluate eight markets on six core dimensions on a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Details of the survey methodology are in Arvis and others' Connecting to Compete 2010: Trade Logistics in the Global Economy (2010). Respondents assessed the ease of arranging competitively priced shipments to markets, on a rating ranging from 1 (very difficult) to 5 (very easy). Scores are averaged across all respondents.; ; World Bank and Turku School of Economics, Logistic Performance Index Surveys. Data are available online at : http://www.worldbank.org/lpi. Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.; Unweighted average;

Nicaragua NI: Human Capital Index (HCI): Female: Lower Bound: Scale 0-1 data was reported at 0.533 NA in 2017. Nicaragua NI: Human Capital Index (HCI): Female: Lower Bound: Scale 0-1 data is updated yearly, averaging 0.533 NA from Dec 2017 (Median) to 2017, with 1 observations. Nicaragua NI: Human Capital Index (HCI): Female: Lower Bound: Scale 0-1 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nicaragua – Table NI.World Bank: Human Capital Index. The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful.; ; World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498; ;

Madagascar MG: Logistics Performance Index: 1=Low To 5=High: Quality of Trade and Transport-Related Infrastructure data was reported at 2.118 NA in 2016. This records a decrease from the previous number of 2.148 NA for 2014. Madagascar MG: Logistics Performance Index: 1=Low To 5=High: Quality of Trade and Transport-Related Infrastructure data is updated yearly, averaging 2.148 NA from Dec 2007 (Median) to 2016, with 5 observations. The data reached an all-time high of 2.630 NA in 2010 and a record low of 2.118 NA in 2016. Madagascar MG: Logistics Performance Index: 1=Low To 5=High: Quality of Trade and Transport-Related Infrastructure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Madagascar – Table MG.World Bank.WDI: Transportation. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2009 round of surveys covered more than 5,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluate eight markets on six core dimensions on a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Details of the survey methodology are in Arvis and others' Connecting to Compete 2010: Trade Logistics in the Global Economy (2010). Respondents evaluated the quality of trade and transport related infrastructure (e.g. ports, railroads, roads, information technology), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents.; ; World Bank and Turku School of Economics, Logistic Performance Index Surveys. Data are available online at : http://www.worldbank.org/lpi. Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.; Unweighted average;

Luxembourg LU: Human Capital Index (HCI): Lower Bound: Scale 0-1 data was reported at 0.684 NA in 2017. Luxembourg LU: Human Capital Index (HCI): Lower Bound: Scale 0-1 data is updated yearly, averaging 0.684 NA from Dec 2017 (Median) to 2017, with 1 observations. Luxembourg LU: Human Capital Index (HCI): Lower Bound: Scale 0-1 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Luxembourg – Table LU.World Bank: Human Capital Index. The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful.; ; World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498; ;