Carriage Services stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

This dataset offers both live (delayed) prices and End Of Day time series on equity options

1/ Live (delayed) prices for options on European stocks and indices including: Reference spot price, bid/ask screen price, fair value price (based on surface calibration), implicit volatility, forward Greeks : delta, vega Canari.dev computes AI-generated forecast signals indicating which option is over/underpriced, based on the holders strategy (buy and hold until maturity, 1 hour to 2 days holding horizon...). From these signals is derived a "Canari price" which is also available in this live tables.
Visit our website (canari.dev ) for more details about our forecast signals.

The delay ranges from 15 to 40 minutes depending on underlyings.

2/ Historical time series: Implied vol Realized vol Smile Forward
See a full API presentation here : https://youtu.be/qitPO-SFmY4 .

These data are also readily accessible in Excel thanks the provided Add-in available on Github: https://github.com/canari-dev/Excel-macro-to-consume-Canari-API

If you need help, contact us at: contact@canari.dev

User Guide: You can get a preview of the API by typing "data.canari.dev" in your web browser. This will show you a free version of this API with limited data.

Here are examples of possible syntaxes:

For live options prices: data.canari.dev/OPT/DAI data.canari.dev/OPT/OESX/0923 The "csv" suffix to get a csv rather than html formating, for example: data.canari.dev/OPT/DB1/1223/csv For historical parameters: Implied vol : data.canari.dev/IV/BMW

data.canari.dev/IV/ALV/1224

data.canari.dev/IV/DTE/1224/csv

Realized vol (intraday, maturity expressed as EWM, span in business days): data.canari.dev/RV/IFX ... Implied dividend flow: data.canari.dev/DIV/IBE ... Smile (vol spread between ATM strike and 90% strike, normalized to 1Y with factor 1/√T): data.canari.dev/SMI/DTE ... Forward: data.canari.dev/FWD/BNP ...

List of available underlyings: Code Name OESX Eurostoxx50 ODAX DAX OSMI SMI (Swiss index) OESB Eurostoxx Banks OVS2 VSTOXX ITK AB Inbev ABBN ABB ASM ASML ADS Adidas AIR Air Liquide EAD Airbus ALV Allianz AXA Axa BAS BASF BBVD BBVA BMW BMW BNP BNP BAY Bayer DBK Deutsche Bank DB1 Deutsche Boerse DPW Deutsche Post DTE Deutsche Telekom EOA E.ON ENL5 Enel INN ING IBE Iberdrola IFX Infineon IES5 Intesa Sanpaolo PPX Kering LOR L Oreal MOH LVMH LIN Linde DAI Mercedes-Benz MUV2 Munich Re NESN Nestle NOVN Novartis PHI1 Philips REP Repsol ROG Roche SAP SAP SNW Sanofi BSD2 Santander SND Schneider SIE Siemens SGE Société Générale SREN Swiss Re TNE5 Telefonica TOTB TotalEnergies UBSN UBS CRI5 Unicredito SQU Vinci VO3 Volkswagen ANN Vonovia ZURN Zurich Insurance Group

Carriage Services reported $382.7M in Market Capitalization this April of 2024, considering the latest stock price and the number of outstanding shares.Data for Carriage Services | CSV - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Carriage Services reported $26.06M in Trade Debtors for its fiscal quarter ending in September of 2023. Data for Carriage Services | CSV - Trade Debtors including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Carriage Services reported $15.05M in Trade Creditors for its fiscal quarter ending in September of 2024. Data for Carriage Services | CSV - Trade Creditors including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Stock market prediction remains active research in a quest to inform investors on how to trade (buy/sell) at the most opportune time. The prevalent methods used by stock market players in trying to predict the likely future trade prices are either technical, fundamental or time series analysis. This research wanted to try out machine learning methods, in contrast to the existing prevalent methods. Artificial neural networks (ANNs) tend to be the preferred machine learning method for this type of application. However, ANNs require some historical data to learn from, in order to do predictions. The research used an ANN model to test the hypothesis that the next day price (prediction) can be determined from the stock prices of the immediate last five days. The final ANN model used for the tests was a feedforward multi-layer perceptron (MLP) with error backpropagation, using sigmoid activation function, with network configuration 5:21:21:1. The data period used was a 5-year dataset (2008 to 2012), with 80% of the data (4-year data) used for training and the balance 20% used for testing (last 1-year data). The original raw data for Nairobi Securities Exchange (NSE) was scrapped from a publicly available and accessible website of a stock market analysis company in Kenya (Synergy, 2020). This daily prices data was first exported to a spreadsheet, then cleaned off headers and other redundant information, leaving only the data with stock name, date of trade and the related data such as volumes, low prices, high prices and adjusted prices. The data was further sorted by the stock names and then the trading dates. The data dimension was finally reduced to only what was needed for the research, which was the stock name, the date of trade and the adjusted price (average trade price). This final dataset was in CSV format, as hereby presented. The research tested three NSE stocks with the mean absolute percentage error (MAPE) ranging between 0.77% to 1.91%, over the 3-month testing period, while the root mean squared error (RMSE) ranged between 1.83 and 3.07. This raw data can be used to train and test any machine learning model that requires training and testing data. The data can also be used to validate and reproduce the results already presented in this research. There could be slight variance between what is obtained when reproducing the results, due to the differences in the final exact weights that the trained ANN model eventually achieves. However, these differences should not be significant. List of data files on this dataset: stock01_NSE_01jan2008_to_31dec2012_Kakuzi.csv stock02_NSE_01jan2008_to_31dec2012_StandardBank.csv stock03_NSE_01jan2008_to_31dec2012_KenyaAirways.csv stock04_NSE_01jan2008_to_31dec2012_BamburiCement.csv stock05_NSE_01jan2008_to_31dec2012_Kengen.csv stock06_NSE_01jan2008_to_31dec2012_BAT.csv References: Synergy Systems Ltd. (2020). MyStocks. Retrieved March 9, 2020, from http://live.mystocks.co.ke/

Carriage Services reported 15.15M in Outstanding Shares in April of 2024. Data for Carriage Services | CSV - Outstanding Shares including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Carriage Services reported $7.61M in Stock for its fiscal quarter ending in December of 2022. Data for Carriage Services | CSV - Stock including historical, tables and charts were last updated by Trading Economics this last July in 2025.

This data set provides model output and post-processing files required to reproduce the results, tables, and figures in the paper "Bringing Hydrologic Realism to Water Markets" by Grogan et al. (in review). Other input data used in this study includes: Lisk, M., Grogan, D., Zuidema, S., Caccese, R., Peklak, D., Zheng, J., Fisher-Vanden, K., Lammers, R., Olmstead, S., & Fowler, L. (2023). Harmonized Database of Western U.S. Water Rights (HarDWR) (Version v1) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/2205619 Two models were used in this study: (1) The University of New Hampshire Water Balance Model WBM, and (2) a Water Market Model. Market model code and model output post-processing code that make use of these data can be found here Model output files are: 1. WBM output files: scenario[x]_wbm_output.zip Where [x] is one of 1, 2, 2a, 3, and 3a Each zipped directory contains 7 gridded NetCDF files, each reporting the 10-year annual average value of a given variable, in units of average mm/day: File Name: wbm_indUseGross_yc.nc; Description: Water withdrawals by industry (part of the urban sector) File Name: wbm_domUseGross_yc.nc; Description: Water withdrawals by the domestic sector (part of the urban sector) File Name: wbm_irrigationGross_yc.nc; Description: Water withdrawals for agriculture Filemore » Name: wbm_irrigationExtra_yc.nc; Description: Water withdrawals from unsustainable groundwater for agriculture File Name: wbm_indUseEvap_yc.nc; Description: Consumptive water use by industry File Name: wbm_domUseEvap_yc.nc; Description: Consumptive water use by the domestic sector File Name: wbm_irrigationNet_yc.nc; Description: Consumptive water use by agriculture The file full_cell_area.nc gives the area of each grid cell in km2, which is used for converting water depth to water volume. 2. Water market model output & post processing output Folder: marketTrdSummaries/ Description: Files in this folder are used as input to code 1_WelfareCalculation_actual_trades.R. They summarize historical water right trade transactions in each state. File Name: welfare_gain_by_state_sector.csv; Description: Welfare gains by state and sector, as shown in Figure 3F. Used in code Figure3.R and produced (as a .xlsx file) by code 2_DemandCurves_simulated_trades.R File Name: welfare_data_actual.rdata; Description: welfare gains by WMA from actual historical trades, as shown in Figure 3A. This data is the output of code 1_WelfareCalculation_actual_trades.R File Name: welfare_summary_simulated.xlsx; Description: Welfare gains by state as simulated by the market model in Scenario 1. Produced by code 2_DemandCurves_simulated_trades.R, and used in code 4_WelfareCalculation.R. File Name: welfare_summary_cutoffs.xlsx; Description: Welfare gains by state as simulated by the market model in Scenario 2. Produced by code 3_DemandCurves_simulated_trades_cutoffs.R, and used in code 4_WelfareCalculation.R. File Name: welfare_summary_cutoffs_SGMS.xlsx; Description: Welfare gains by state as simulated by the market model in Scenario 2a. Produced by code 3_DemandCurves_simulated_trades_cutoffs.R, and used in code 4_WelfareCalculation.R. File Name: welfare_data_actual.rdata; Description: Spatial data, actual historical welfare gains by WMA as shown in Figure 3A. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. File Name: welfare_data_simulated.rdata; Description: Spatial data, simulated Scenario 1 welfare gains by WMA as shown in Figure 3B. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. File Name: welfare_data_simulated_cutoffs.rdata; Description: Spatial data, simulated Scenario 2 welfare gains by WMA. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. File Name: welfare_data_simulated_cutoffs_SGMA.rdata; Description: Spatial data, simulated Scenario 2a welfare gains by WMA. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. Additional files are provided for efficient reproduction of tables and figures. These include: File Name: wma_thresold_dates_Scenario2(a).csv; Description: Wet vs. paper right threshold dates for each WMA. Shown in Figure 2A,B. Produced and used by code calculate_thresolds_Figure2.R File Name: WWRTradeBounds (directory); Description: Trade boundary shapefile required to reproduce Figure 3A-D. Used in code Figure3.R File Name: welfare_region_totals.csv; Description: Welfare gains for the entire study region, as shown in Figure 3E. Used in code Figure3.R File Name: Welfare_gain_by_state_sector.csv; Description: Welfare gains by state and sector, as shown in Figure 3F. Used in code Figure3.R and produced (as a .xlsx file) by code 2_DemandCurves_simulated_trades.R File Name: WECC_MERIT_5min_v3b_mask.nc; Description: Gridded file that identified which land grid cells are in the WBM model domain, used for processing in code Figure4.py File Name: Table_1.csv; Description: All data in Table 1, reproducible from WBM output files using code table_1.R« less

PURPOSE: To provide a permanent repository of key data series necessary to build a range-wide American eel stock assessment. DESCRIPTION: This collection presents data associated with the following report: Cairns, D.K. 2020. Landings, abundance indicators, and biological data for a potential range-wide American eel stock assessment. Canadian Data Report of Fisheries and Aquatic Science. No. 1311: v + 180 pp. Much of the data collection is from the Atlantic Provinces of Canada, particularly the Southern Gulf of St. Lawrence. The collection also includes data from elsewhere in the American eel's range in Canada, and also the United States and the Caribbean Basin. Files in the collection are as follows. Cairns2020_AnnexA_ReportTables.xlsx: This Excel file (file size 756 kb) contains all 37 tables in Cairns (2020) exactly as they appear in the report. Cairns2020_AnnexB_EelLengthsAgesEfishingRecords.xlsx: This Excel file (file size 3.1 mb) contains 20,047 records of American eel lengths and other biological data from the Canadian Atlantic Provinces, 1983-2017. Records include weights of 8,915 eels and ages of 2,212 eels. Records of 3,224 electrofishing sessions in the Miramichi River, New Brunswick, 1952-2019, and records of 2,590 electrofishing sessions in the Restigouche River, New Brunswick, 1972-2019 are included. Cairns2020_AnnexC_EelLengthsAgesDataDefinitions.csv: This .csv file (file size 4 kb) gives data definitions in English and French for the table of eel lengths and other biological data that is contained in Cairns2020_AnnexB_EelLengthsAgesEfishingRecords.xlsx and in Cairns2020_AnnexD_EelLengthsAges.csv. Cairns2020_AnnexD_EelLengthsAges.csv: This file (file size 2.0 mb) presents in .csv format the table of eel lengths and other biological data that is also presented in Cairns2020_AnnexB_EelLengthsAgesEfishingRecords.xlsx. Cairns2020_AnnexE_EelEFishingDataDefinitions.csv: This .csv file (file size 2 kb) gives data definitions in English and French for the table of eel electrofishing data that is contained in Cairns2020_AnnexB_EelLengthsAgesEfishingRecords.xlsx and in Cairns2020_AnnexD_EelLengthsAges.csv. Cairns2020_AnnexF_EelEFishing.csv: This file (file size 314 kb) presents in .csv format the table of eel electrofishing data that is also presented in Cairns2020_AnnexB_EelLengthsAgesEfishingRecords.xlsx. Cairns2020_AnnexG_OtolithImageMetadata.csv: This .csv file (file size 2 kb) provides metadata for the collection of eel otolith images. Files with names starting with EelOtos . . . . : These .tif, .jpg, and .bmp image files are in zipped format with a summed size of 5.3 gb. The files give magnified photos of 1,838 eel otoliths that have been prepared for age reading. Samples are from the Atlantic Provinces of Canada. Individual otolith codes in Cairns2020_AnnexB_EelLengthsAgesEfishingRecords.xlsx and in Cairns2020_AnnexC_EelLengthsAgesDataDefinitions.csv match the codes embedded in otolith image filenames. PARAMETERS COLLECTED: American eel landings, number caught, and effort of commercial and research fishing gear. American eel lengths, ages, sex and other biological data and sampling locations. NOTES ON QUALITY CONTROL: All keypunched records of landings, densities, and other data were verified against original sources. Landings and abundance indices were reviewed in a Department of Fisheries and Oceans scientific workshop and corrected as necessary. Length and age data were examined by length-weight and length age plots and implausible records were discarded. PHYSICAL SAMPLE DETAILS: No physical samples SAMPLING METHODS: Landings are from government fisheries agencies. Abundance indices are from commercial fyke, spear, and trap catch per unit effort, and from research ladder counts and electrofishing records. Mean elver lengths are compiled from published literature Sex ratios are compiled from published literature Locations of biological and genetic sampling are compiled from published literature American eel lengths are total length of live specimens. Ages are from otolith annulus readings Electrofishing records are from backpack electrofishing surveys in wadeable waters USE LIMITATION: To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

Carriage Services reported $22.9M in Operating Profit for its fiscal quarter ending in September of 2024. Data for Carriage Services | CSV - Operating Profit including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Our proprietary Skew-Adjusted Gamma Exposure measurements make adjustments to Naive GEX calculations to more accurately reflect actual gamma positioning of Market Makers who employ delta-hedging strategies. When Market Makers carry substantial negative gamma a security will often "over-react" to fundamental news. Conversely, when MMs carry substantial positive gamma a security will often "under-react" to news. Our data includes a quantified segmentation of a security's gamma distribution across all option strikes as well as across relevant expiration dates. Our website provides numerical, graphical, and historical views of all gamma data in our database. Additionally, our API access allows for easy download of csv files or import into Excel for further analysis and custom applications.

Carriage Services reported $1.28B in Assets for its fiscal quarter ending in September of 2024. Data for Carriage Services | CSV - Assets including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Monthly gold prices in USD since 1833 (sourced from the World Gold Council). The data is derived from historical records compiled by Timothy Green and supplemented by data provided by the World Bank...

Carriage Services reported $592.95M in Debt for its fiscal quarter ending in September of 2023. Data for Carriage Services | CSV - Debt including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Carriage Services reported $16.11M in EBIT for its fiscal quarter ending in September of 2023. Data for Carriage Services | CSV - Ebit including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Carriage Services reported $1.26M in Cash and Equivalent for its fiscal quarter ending in September of 2024. Data for Carriage Services | CSV - Cash And Equivalent including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Carriage Services reported 1.39 in Dividend Yield for its fiscal quarter ending in June of 2023. Data for Carriage Services | CSV - Dividend Yield including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Carriage Services reported $74.43M in Operating Expenses for its fiscal quarter ending in September of 2023. Data for Carriage Services | CSV - Operating Expenses including historical, tables and charts were last updated by Trading Economics this last July in 2025.