Between March 4 and March 11, 2020, the S&P 500 index declined by ** percent, descending into a bear market. On March 12, 2020, the S&P 500 plunged *** percent, its steepest one-day fall since 1987. The index began to recover at the start of April and reached a peak in December 2021. As of December 29, 2024, the value of the S&P 500 stood at ******** points. Coronavirus sparks stock market chaos Stock markets plunged in the wake of the COVID-19 pandemic, with investors fearing its spread would destroy economic growth. Buoyed by figures that suggested cases were leveling off in China, investors were initially optimistic about the virus being contained. However, confidence in the market started to subside as the number of cases increased worldwide. Investors were deterred from buying stocks, and this was reflected in the markets – the values of the Dow Jones Industrial Average and the Nasdaq Composite also dived during the height of the crisis. What is a bear market? A bear market occurs when the value of a stock market suffers a prolonged decline of more than 20 percent over a period of at least 2 months. The COVID-19 pandemic caused severe concern and sent stock markets on a steep downward spiral. The S&P 500 achieved a record closing high of ***** on February 19, 2020. However, just over 3 weeks later, the market closed on *****, which represented a decline of around ** percent in only 16 sessions.

Gold prices face a weekly decline as investor risk appetite grows due to strong tech earnings and resilient manufacturing data, affecting Federal Reserve rate-cut expectations.

April 4, 2025, the day U.S. President Trump announced tariffs on product product imported from all countries led to one of the largest falls in history on stock markets worldwide. The FTSE 100 lost ***** percent of its value in only one day. The FTSE 100 Index has experienced significant weekly falls since 1987, with the most substantial drop occurring during the global financial crisis in October 2008. This ** percent decline surpassed even the infamous Black Monday crash of 1987, highlighting the severity of the 2008 economic downturn. More recently, the COVID-19 pandemic triggered two of the largest weekly drops in the index's history.

Germany's main stock market index, the DE40, fell to 23561 points on August 1, 2025, losing 2.10% from the previous session. Over the past month, the index has declined 0.96%, though it remains 33.40% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on August of 2025.

Between Marc 23 and June 15, 2025 U.S. grocery stores recorded the highest growth in weekly visits in the week of April **, with an increase of ten percent compared to 2024. On the other hand, the week of March **, grocery stores saw their weekly visits decline by four percent.

Data Generation ProceduresThe data for this study was generated through a survey experiment designed to assess how participants evaluate stock risk based on nominal price changes. Participants were randomly assigned to one of four questionnaires, each presenting a hypothetical scenario involving a stock whose price had declined by 25%. The stocks were priced at $2,000, $200, $20, and $2. This setup allowed the researchers to observe how the nominal price influenced participants’ perceptions of risk and their likelihood to sell the stock. The survey included questions about participants’ risk assessments and their decisions regarding selling shares after being informed of the price decline. The data collection involved online survey tools, ensuring a diverse participant pool. Temporal and Geographical ScopeThe survey was conducted in September and December 2022. The geographical scope primarily encompasses participants from various regions in the USA. Tabular Data DescriptionThe survey results were compiled into tabular data containing several entries corresponding to each participant’s responses—the total number of data entries was 632. Row and Column Headers: Each row represents an individual participant’s response with their Participant ID, while columns include variables tested. Variables reported in the data: Variable Description Respondent ID Random number of the respondent’s randomly assigned ID Gender Male=0, Female=1. Region The location of the respondent: Middle Atlantic, West North Central, New England, South Atlantic, Mountain, West South Central, Pacific, East North Central, East South Central Age Age Groups are divided into four, numbered from 1 to 4, respectively: 18-29, 30-44, 45-60, and 60 and older. Stock Price: Q_2,Q_20,Q_200,Q_2000 Participants were randomly assigned a questionnaire and were asked about the stocks of $2, $20, $200, and $2,000. To construct this variable, it has four values range from 1 to 4, in which (1) $2; (2) $20; (3) $200; (4) $2,000. Income Reported annual income is divided into income groups from 1 to 8. Risk appetite/ Self-assessment of risk-taking behavior Participants were asked how much of a risk taker they consider themselves to be on a scale of 1 (risk averse) to 10 (risk lover). TraderD Participants were asked if they trade stocks or bonds and, if so, how often; the possible answers were, from 1 to 4, respectively: Trade regularly (daily or weekly[1]), trade a few times a month, rarely (a few times a year at most), or have never traded before. Follow news People were asked if they follow stock-related news and stock or bond price changes, and if so, how often. The possible responses were divided into five: I never follow news related to stocks or stock indices=1; Occasionally, a few times a year=2; Sometimes, a few times a month =3; Often, a few times a week=4; Always, daily=5. Risk Participants were asked about how risky they think the stock is on a scale of 1 (not risky) to 7 (high risk) Percent Sold Participants were asked how many shares they would sell due to the stock decline. Based on the answer, I calculated the percentage of stocks they sold out of their portfolio. Sold Based on the same question from the previous variable, I constructed another variable with the values of (1) for those who sold at least 10% of their portfolio and (0) otherwise. In some cases, the respondents answered they would purchase more stocks; in those few cases, I assumed their response was zero. Missing DataA few respondents skipped questions or provided incomplete answers. If present, missing data were excluded from the analysis. Description of Each Data FileThe primary data file generated from this study was structured as an Excel file containing all participant responses. Content: The file includes columns for participant demographics, stock prices presented, risk assessments, selling decisions, and number of shares sold.Format: Excel.Size: 64 kb. [1] In some questionnaires I divided this answer into daily or weekly to allow more variability, however, since the number of answers for “daily”, when constructing this variable and combined the responses from daily and weekly together.

On July 28, 2025, the Brent crude oil price stood at 69.68 U.S. dollars per barrel, compared to 66.71 U.S. dollars for WTI oil and 70.98 U.S. dollars for the OPEC basket. Brent and OPEC prices rose slightly that week, while WTI prices fell.Europe's Brent crude oil, the U.S. WTI crude oil, and OPEC's basket are three of the most important benchmarks used by traders as reference for oil and gasoline prices. Lowest ever oil prices during coronavirus pandemic In 2020, the coronavirus pandemic resulted in crude oil prices hitting a major slump as oil demand drastically declined following lockdowns and travel restrictions. Initial outlooks and uncertainty surrounding the course of the pandemic brought about a disagreement between two of the largest oil producers, Russia and Saudi Arabia, in early March. Bilateral talks between global oil producers ended in agreement on April 13th, with promises to cut petroleum output and hopes rising that these might help stabilize the oil price in the coming weeks. However, with storage facilities and oil tankers quickly filling up, fears grew over where to store excess oil, leading to benchmark prices seeing record negative prices between April 20 and April 22, 2020. How crude oil prices are determined As with most commodities, crude oil prices are impacted by supply and demand, as well as inventories and market sentiment. However, as oil is most often traded in future contracts (where a contract is agreed upon while product delivery will follow in the next two to three months), market speculation is one of the principal determinants for oil prices. Traders make conclusions on how production output and consumer demand will likely develop over the coming months, leaving room for uncertainty. Spot prices differ from futures in so far as they reflect the current market price of a commodity.

Matson Inc. experiences a sharp decline in share price amid ongoing U.S. trade tensions, dropping nearly 30% from its 52-week high.

Analysts forecast a drop in U.S. crude oil inventories, with distillate and gasoline stocks also declining. Rising refinery utilization suggests higher demand expectations.

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.

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.

The Decline of U.S. Natural Gas Exports: What Does It Mean for the World?

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 MAISON-LLF dataset was collected from 10 older adult participants living alone in the community following lower limb fractures. Each participant contributed data for over 8 weeks, beginning from their first-week post-discharge. This resulted in a total of 574 days of continuous multimodal sensor data, complemented by biweekly clinical questionnaire data.

The MAISON-LLF dataset is organized into a directory tree, as shown below.

maison-llf/
├── sensor-data/
│ ├── p01/
.
.
.
│ ├── p10/
│ │ ├── acceleration-data.csv
│ │ ├── heartrate-data.csv
│ │ ├── motion-data.csv
│ │ ├── position-data.csv
│ │ ├── sleep-data.csv
│ │ ├── step-data.csv
├── features/
│ ├── p01/
.
.
.
│ ├── p10/
│ │ ├── acceleration-features.csv
│ │ ├── heartrate-features.csv
│ │ ├── motion-features.csv
│ │ ├── position-features.csv
│ │ ├── sleep-features.csv
│ │ ├── step-features.csv
│ │ ├── clinical.csv
├── dataset/
│ ├── all-features.csv
│ ├── all-features-imputed.csv
│ ├── dataset-daily.pt
│ ├── dataset-weekly.pt
│ ├── dataset-biweekly.pt
│ ├──

In ‘sensor-data’ folder, the dataset includes 60 CSV files containing data from six sensor types for 10 participants. Each file includes a ‘timestamp’ column indicating the date and time of the recorded sensor data, accurate to milliseconds (‘yyyy-MM-dd HH:mm:ss.SSS’), along with the corresponding sensor measurements. For instance, the ‘acceleration-data.csv’ files include four columns: timestamp, and x, y, and z coordinates, while the ‘heartrate-data.csv’ files contain two columns: timestamp and heart rate value.

The dataset also includes 70 CSV files containing daily features extracted from the sensor data, along with clinical questionnaire data and physical test results. Each feature CSV file includes a timestamp column representing the date (‘yyyy-MM-dd’) of the sensor data from which the daily features were extracted, alongside the corresponding sensor features. For example, the ‘acceleration-features.csv’ files contain eight columns: timestamp and the seven acceleration features and the ‘heartrate-features.csv’ files include five columns: timestamp and the four heart rate features. Additionally, the ‘clinical.csv’ files provide values for individual items of the SIS (‘sis-01’ to ‘sis-06’), OHS (‘ohs-01’ to ‘ohs-12’), and OKS (‘oks-01’ to ‘oks-12’) questionnaires, along with their final scores (‘sis’, ‘ohs’, and ‘oks’). These files also include results for the TUG and 30-second chair stand tests. Each participant has four sets of clinical data, with each set sharing the same ‘timestamp’ corresponding to the date (‘yyyy-MM-dd’) on which the clinical data were collected.

To provide a comprehensive overview of the dataset, the ‘all-features.csv’ and ‘all-features-imputed.csv’ files in ‘dataset’ folder combine all daily features, clinical data, and demographic information into single CSV files, representing the data before and after missing value imputation (as explained in subsection 2.2.4). Additionally, the Python PyTorch files are structured datasets designed to facilitate supervised and unsupervised machine learning model development for estimating clinical outcomes.

‘dataset-daily.pt’ in ‘dataset’ folder contains a NumPy array with dimensions num_days × num_features, representing the daily features for all 10 participants. Alongside this array, it includes a num_days IDs array that maps each day to a participant (IDs 1 to 10). Additionally, the file contains three separate num_days arrays for SIS, OHS, and OKS scores, each assigned to the corresponding days in the daily features array.

‘dataset-weekly.pt’ in ‘dataset’ folder provides an array with dimensions num_weeks × 7 × num_features, which includes the weekly sequential features for all participants. This file also includes a num_weeks IDs array to identify the participant (1 to 10) associated with each week in the samples array. Similar to the daily dataset, it contains three separate num_weeks arrays for the SIS, OHS, and OKS scores, each assigned to the respective weeks in the weekly features array.

‘dataset-biweekly.pt’ in ‘dataset’ folder provides an array with dimensions num_biweeks × 14 × num_features, which includes the biweekly sequential features for all participants. This file also includes a num_biweeks IDs array to identify the participant (1 to 10) associated with each biweekly period in the samples array. Similar to the daily dataset, it contains three separate num_biweeks arrays for the SIS, OHS, and OKS scores, each assigned to the respective biweekly periods in the biweekly features array.

Citation
Cite the related pre-print:

A. Abedi, C. H. Chu, and S. S. Khan, "Multimodal Sensor Dataset for Remote Monitoring of Older Adults Post Lower-Limb Fractures in the Community,"

In the week of February 6, 2023, superstores recorded the highest decline in weekly visits compared to the same period in 2022, down *** percent. Positive growth was only observed in the weeks of January 2 and February 13, when visits to superstores increased by *** and *** percent, respectively, compared to a year earlier.

This the replication data for Scale economies and decline of ride-pooling: A case study of New York City. The data has a csv with weekly aggregates for all TNC trips in NYC, starting from Feb 2019 to Mar 2023. The data about TNC trips is obtained from NYC TLC High Volume For-Hire Vehicles Trip Records. Additionally, there are hourly aggregates for 2019 and 2023, alongside hourly precipitation obtained from the Open-Meteo dataset.

According to a survey on coffee consumption habits conducted since 2002, an average of four mugs of instant coffee were consumed on a weekly basis per person in Japan in 2022. The survey revealed a gradual increase in the consumption of bottled coffee until 2020, while canned products showed a continuous decline in recent years

Gold rose to 3,294.43 USD/t.oz on July 31, 2025, up 0.60% from the previous day. Over the past month, Gold's price has fallen 1.31%, but it is still 34.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on July of 2025.

Newspapers and other printed publications that appear at least four times a week are printed in this sector. In contrast to other printing sectors, where digital printing is on the rise, newspaper printing still mainly uses the indirect flat printing process of offset printing. The demand for printed newspapers has been declining steadily for several years due to the advancing digitalisation and the resulting change in consumer and publisher behaviour. This trend was further intensified in 2020 by the measures taken to contain the coronavirus pandemic and has contributed to a sharp decline in sales. With more people working from home and fewer commuters travelling as a result, significantly fewer printed newspapers were sold. In the current year, industry sales are expected to fall by 1.7% to 954.7 million euros. In the period from 2018 to 2023, revenue in newspaper printing fell by an average of 6.5% per year.The only clients of the companies in this sector are the newspaper publishers. In recent years, they have felt the effects of increasing digitalisation in the form of falling sales figures and have reacted by printing smaller print runs. As a result of digitalisation, consumers are increasingly using the Internet as a source of information. The change in consumer behaviour has put publishers under pressure and they are increasingly shifting their offerings from printed to digital newspaper editions and additional online content. As a result, the volume of print orders continues to decline, which is having a negative impact on print shops' sales. At the same time, rising paper prices have put pressure on the profit margins of industry players in recent years. As paper, together with ink, is the most important input factor in newspaper printing, the industry's material costs often rise in line with the producer price for paper. Due to the declining demand for print products, there has also been an acute shortage of waste paper since last year, which is having a particularly severe impact on the industry as newsprint is primarily made from waste paper fibres.For the period from 2023 to 2028, IBISWorld anticipates an average decline in revenue of 5.5% per year to EUR 719.2 million in 2028. This development is due to the expected further decline in demand for print media in the future and the resulting decline in order volumes for newspaper printers. This declining order volume is likely to lead to further overcapacity and thus be the reason for the falling number of industry players over the next five years.

Graph and download economic data for US Regular Conventional Gas Price (GASREGCOVW) from 1990-08-20 to 2025-06-16 about conventional, gas, commodities, and USA.

We monitored northern bat Eptesicus nilssonii (Keyserling & Blasius, 1839) acoustically along a 27 km road transect at weekly intervals in 1988, 1989, and 1990, and again in 2016 and 2017. The methodology of data collection and the transect were the same throughout, except that the insect-attracting mercury-vapour streetlights along parts of the road were replaced by sodium lights between the two survey periods. Counts along sections of the transect with and without streetlights were analyzed separately. The frequency of bat encounters in unlit sections showed an average decline of 3.0% (± 0.4 SE) per year, corresponding to a reduction of 59% between 1988 and 2017. Sections with streetlights showed an 85% decline over the same period (6.3% ± 0.4 SE per year). The decline represents a real reduction in the abundance of bats rather than an artefact of changed distribution of bats away from roads, following the disuse of insect-attracting mercury-vapour streetlights. Our results conform with another long-term survey of the same species on the Baltic island of Gotland. This decline agrees with predictions based on climate change models, and may also be explained by increased competition from pipistrelle bats (Pipistrellus spp.) expanding from the south. However, the decline may also be due to changed availability of preferred prey (moths), a likely effect of the change in streetlighting. In the 1980´s, E. nilssonii was considered the most common bat in Sweden, but the subsequent decline would rather qualify it for vulnerable or endangered status in the national Red List of Threatened Species.

Average Weekly Hours in China remained unchanged at 48.50 Hours in June. This dataset includes a chart with historical data for China Average Weekly Hours.