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

Liquidity Services reported $574.1M in Market Capitalization this April of 2024, considering the latest stock price and the number of outstanding shares.Data for Liquidity Services | LQDT - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last November in 2025.

Common-Stock Time Series for Liquidity Services Inc. Liquidity Services, Inc. provides e-commerce marketplaces, self-directed auction listing tools, and value-added services in the United States and internationally. The company operates through four segments: GovDeals, Retail Supply Chain Group (RSCG), Capital Assets Group (CAG), and Machinio. Its solutions enable government entities and commercial businesses to sell surplus property and real estate assets through GovDeals, Bid4Assets, and Sierra marketplaces. The company also offers a suite of services, including surplus management, asset valuation, asset sales, marketing, returns management, asset recovery, and ecommerce services; and operates Liquidation.com, a marketplace to sell excess, returned, and overstocked consumer goods. In addition, it operates a global search engine platform for listing used equipment for sale in the construction, machine tool, transportation, printing, laboratory/medical, and agriculture sectors. Further, the company provides Machinio System service that offers various software tools, such as website hosting, email marketing, and inventory management to equipment sellers. The company offers products for various industries, such as consumer electronics, general merchandise, apparel, scientific equipment, aerospace parts and equipment, technology hardware, real estate, energy equipment, industrial capital assets, heavy equipment, fleet and transportation equipment, and specialty equipment. The company was incorporated in 1999 and is headquartered in Bethesda, Maryland.

Market Liquidity: MSE: Shares Traded/All Shares Ratio: excl Non Malawi Registered Shares data was reported at 0.176 % in May 2016. This records a decrease from the previous number of 0.317 % for Apr 2016. Market Liquidity: MSE: Shares Traded/All Shares Ratio: excl Non Malawi Registered Shares data is updated monthly, averaging 0.183 % from Jan 2005 (Median) to May 2016, with 125 observations. The data reached an all-time high of 11.802 % in Mar 2013 and a record low of 0.006 % in Feb 2012. Market Liquidity: MSE: Shares Traded/All Shares Ratio: excl Non Malawi Registered Shares data remains active status in CEIC and is reported by Malawi Stock Exchange. The data is categorized under Global Database’s Malawi – Table MW.Z004: Malawi Stock Exchange: Market Liquidity.

Market Liquidity: MSE: Value of Trades/Market Cap: excl Non Malawi Registered Shares data was reported at 0.413 % in Oct 2018. This records an increase from the previous number of 0.262 % for Sep 2018. Market Liquidity: MSE: Value of Trades/Market Cap: excl Non Malawi Registered Shares data is updated monthly, averaging 0.139 % from Jan 2004 (Median) to Oct 2018, with 166 observations. The data reached an all-time high of 4.756 % in Apr 2015 and a record low of 0.008 % in Feb 2012. Market Liquidity: MSE: Value of Trades/Market Cap: excl Non Malawi Registered Shares data remains active status in CEIC and is reported by Malawi Stock Exchange. The data is categorized under Global Database’s Malawi – Table MW.Z004: Malawi Stock Exchange: Market Liquidity.

Malawi Market Liquidity: MSE: Market Cap/GDP Ratio data was reported at 190.400 % in May 2016. This records a decrease from the previous number of 190.450 % for Apr 2016. Malawi Market Liquidity: MSE: Market Cap/GDP Ratio data is updated monthly, averaging 307.110 % from Jan 2004 (Median) to May 2016, with 141 observations. The data reached an all-time high of 703.650 % in Dec 2006 and a record low of 128.960 % in May 2009. Malawi Market Liquidity: MSE: Market Cap/GDP Ratio data remains active status in CEIC and is reported by Malawi Stock Exchange. The data is categorized under Global Database’s Malawi – Table MW.Z004: Malawi Stock Exchange: Market Liquidity.

Liquidity Services reported 30.72M in Outstanding Shares in April of 2024. Data for Liquidity Services | LQDT - Outstanding Shares including historical, tables and charts were last updated by Trading Economics this last November in 2025.

Graph and download economic data for Money Market Funds; Total Financial Assets, Level (MMMFFAQ027S) from Q4 1945 to Q2 2025 about MMMF, IMA, financial, assets, and USA.

Graph and download economic data for Federal Government; Exchange Stabilization Fund Economic Recovery Programs Investment in Money Market Mutual Fund Liquidity Facility (MMLF); Asset, Level (BOGZ1FL313094213Q) from Q4 1945 to Q2 2025 about MMLF, program, funds, MMMF, liquidity, equity, investment, federal, assets, government, and USA.

Your feedback, including upvotes, helps improve data resources for the community

ICICIBANK OHLCV Data: Multi-Interval Historical Stock Prices (NSE)

Overview

This dataset provides comprehensive historical Open, High, Low, Close, and Volume (OHLCV) data for ICICI Bank (ICICIBANK), a prominent Indian stock listed on the National Stock Exchange (NSE). The data has been consolidated from various time intervals (1-minute, 5-minute, 15-minute, and 1-day), offering a granular yet unified view for diverse analytical needs, from high-frequency trading simulations to long-term trend analysis.

Data Source

The raw data was collected programmatically using the Groww API. The specific API endpoint used for fetching charting data is: https://groww.in/v1/api/charting_service/v4/chart/exchange/NSE/segment/CASH. While efforts have been made during data fetching and consolidation to ensure accuracy, please be aware that financial data can sometimes be subject to minor corrections or revisions by data providers.

Dataset Contents

This dataset is provided as a single, unified CSV file (e.g., unified_icicibank_ohlcv_data.csv), which has been consolidated from multiple original JSON files representing different time intervals (1m, 5m, 15m, 1d).

The unified CSV file contains the following columns:

• timestamp: The human-readable timestamp of the candle, precisely parsed to the second.
• open: The opening price of the stock during that interval.
• high: The highest price reached during that interval.
• low: The lowest price reached during that interval.
• close: The closing price of the stock during that interval.
• volume: The trading volume (number of shares traded) during that interval.

The dataset covers a substantial historical period, and the total number of records will be the sum of records from each interval file.

Potential Use Cases

This dataset can be highly valuable for various applications in quantitative finance and data science, including: * Algorithmic Trading Strategy Development: Backtesting and optimizing trading strategies across different timeframes for ICICIBANK. * Technical Analysis: Generating charts, calculating technical indicators (e.g., Moving Averages, RSI, MACD, Bollinger Bands) specific to ICICIBANK. * Machine Learning for Price Prediction: Training models to forecast future stock prices, trends, or volatility for ICICIBANK. * Market Trend Analysis: Studying short-term and long-term market behavior, liquidity, and price action of ICICI Bank. * Educational Purposes: A clean, multi-interval dataset ideal for learning and practicing data analysis with financial time series.

Disclaimer

This dataset is provided for informational and educational purposes only. It should not be considered financial advice, investment recommendations, or a solicitation to buy or sell any securities. Trading and investing in financial markets involve significant risk, and past performance is not indicative of future results. Always conduct your own thorough research and consult with a qualified financial advisor before making any investment decisions. The creators of this dataset are not liable for any losses incurred from its use.

In the stock market, return reversal occurs when investors sell overbought stocks and buy oversold stocks, reversing the stocks’ price trends. In this paper, we develop a new method to identify key drivers of return reversal by incorporating a comprehensive set of factors derived from different economic theories into one unified dynamical Bayesian factor graph. We then use the model to depict factor relationships and their dynamics, from which we make some interesting discoveries about the mechanism behind return reversals. Through extensive experiments on the US stock market, we conclude that among the various factors, the liquidity factors consistently emerge as key drivers of return reversal, which is in support of the theory of liquidity effect. Specifically, we find that stocks with high turnover rates or high Amihud illiquidity measures have a greater probability of experiencing return reversals. Apart from the consistent drivers, we find other drivers of return reversal that generally change from year to year, and they serve as important characteristics for evaluating the trends of stock returns. Besides, we also identify some seldom discussed yet enlightening inter-factor relationships, one of which shows that stocks in Finance and Insurance industry are more likely to have high Amihud illiquidity measures in comparison with those in other industries. These conclusions are robust for return reversals under different thresholds.

Common-Stock Time Series for The Bank of New York Mellon Corporation. The Bank of New York Mellon Corporation provides a range of financial products and services in the United States and internationally. It operates through Securities Services, Market and Wealth Services, Investment and Wealth Management, and Other segments. The Securities Services segment offers custody, trust and depositary, accounting, exchange-traded funds, middle-office solutions, transfer agency, services for private equity and real estate funds, foreign exchange, securities lending, liquidity/lending services, and data analytics. This segment also provides trustee, paying agency, fiduciary, escrow and other financial, issuer, and support services for brokers and investors. The Market and Wealth Services segment offers clearing and custody, investment, wealth and retirement solutions, technology and enterprise data management, trading, and prime brokerage services. This segment also provides integrated cash management solutions, including payments, foreign exchange, liquidity management, receivables processing, payables management, and trade finance, as well as U.S. government and global clearing, and tri-party services. The Investment and Wealth Management segment offers investment management strategies, investment products distribution, investment management, custody, wealth and estate planning, private banking, investment, and information management services. The Other segment provides leasing, corporate treasury, derivative and other trading, corporate and bank-owned life insurance, tax credit investment, other corporate investment, and business exit services. The company serves central banks and sovereigns, financial institutions, asset managers, insurance companies, corporations, local authorities and high net-worth individuals, and family offices. The Bank of New York Mellon Corporation was founded in 1784 and is headquartered in New York, New York.

Liquidity Services reported $14.18M in Stock for its fiscal quarter ending in September of 2025. Data for Liquidity Services | LQDT - Stock including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Retained-Earnings Time Series for Liquidity Services Inc. Liquidity Services, Inc. provides e-commerce marketplaces, self-directed auction listing tools, and value-added services in the United States and internationally. The company operates through four segments: GovDeals, Retail Supply Chain Group (RSCG), Capital Assets Group (CAG), and Machinio. Its solutions enable government entities and commercial businesses to sell surplus property and real estate assets through GovDeals, Bid4Assets, and Sierra marketplaces. The company also offers a suite of services, including surplus management, asset valuation, asset sales, marketing, returns management, asset recovery, and ecommerce services; and operates Liquidation.com, a marketplace to sell excess, returned, and overstocked consumer goods. In addition, it operates a global search engine platform for listing used equipment for sale in the construction, machine tool, transportation, printing, laboratory/medical, and agriculture sectors. Further, the company provides Machinio System service that offers various software tools, such as website hosting, email marketing, and inventory management to equipment sellers. The company offers products for various industries, such as consumer electronics, general merchandise, apparel, scientific equipment, aerospace parts and equipment, technology hardware, real estate, energy equipment, industrial capital assets, heavy equipment, fleet and transportation equipment, and specialty equipment. The company was incorporated in 1999 and is headquartered in Bethesda, Maryland.

INFY OHLCV Data: Multi-Interval Historical Stock Prices (NSE)

Overview

This dataset provides comprehensive historical Open, High, Low, Close, and Volume (OHLCV) data for Infosys (INFY), a prominent Indian stock listed on the National Stock Exchange (NSE). The data has been consolidated from various time intervals (1-minute, 5-minute, 15-minute, and 1-day), offering a granular yet unified view for diverse analytical needs, from high-frequency trading simulations to long-term trend analysis.

Data Source

The raw data was collected programmatically using the Groww API. The specific API endpoint used for fetching charting data is: https://groww.in/v1/api/charting_service/v4/chart/exchange/NSE/segment/CASH. While efforts have been made during data fetching and consolidation to ensure accuracy, please be aware that financial data can sometimes be subject to minor corrections or revisions by data providers.

Dataset Contents

This dataset is provided as a single, unified CSV file (e.g., unified_infy_ohlcv_data.csv), which has been consolidated from multiple original JSON files representing different time intervals (1m, 5m, 15m, 1d).

The unified CSV file contains the following columns: * timestamp: The human-readable timestamp of the candle, precisely parsed to the second. * open: The opening price of the stock during that interval. * high: The highest price reached during that interval. * low: The lowest price reached during that interval. * close: The closing price of the stock during that interval. * volume: The trading volume (number of shares traded) during that interval.

The dataset covers a substantial historical period, and the total number of records will be the sum of records from each interval file.

Potential Use Cases

This dataset can be highly valuable for various applications in quantitative finance and data science, including: * Algorithmic Trading Strategy Development: Backtesting and optimizing trading strategies across different timeframes for INFY. * Technical Analysis: Generating charts, calculating technical indicators (e.g., Moving Averages, RSI, MACD, Bollinger Bands) specific to INFY. * Machine Learning for Price Prediction: Training models to forecast future stock prices, trends, or volatility for INFY. * Market Trend Analysis: Studying short-term and long-term market behavior, liquidity, and price action of Infosys. * Educational Purposes: A clean, multi-interval dataset ideal for learning and practicing data analysis with financial time series.

Disclaimer

This dataset is provided for informational and educational purposes only. It should not be considered financial advice, investment recommendations, or a solicitation to buy or sell any securities. Trading and investing in financial markets involve significant risk, and past performance is not indicative of future results. Always conduct your own thorough research and consult with a qualified financial advisor before making any investment decisions. The creators of this dataset are not liable for any losses incurred from its use.

BAJFINANCE OHLCV Data: Multi-Interval Historical Stock Prices (NSE)

Overview

This dataset provides comprehensive historical Open, High, Low, Close, and Volume (OHLCV) data for Bajaj Finance (BAJFINANCE), a prominent Indian stock listed on the National Stock Exchange (NSE). The data has been consolidated from various time intervals (1-minute, 5-minute, 15-minute, and 1-day), offering a granular yet unified view for diverse analytical needs, from high-frequency trading simulations to long-term trend analysis.

Data Source

The raw data was collected programmatically using the Groww API. The specific API endpoint used for fetching charting data is: https://groww.in/v1/api/charting_service/v4/chart/exchange/NSE/segment/CASH. While efforts have been made during data fetching and consolidation to ensure accuracy, please be aware that financial data can sometimes be subject to minor corrections or revisions by data providers.

Dataset Contents

This dataset is provided as a single, unified CSV file (e.g., unified_bajfinance_ohlcv_data.csv), which has been consolidated from multiple original JSON files representing different time intervals (1m, 5m, 15m, 1d).

The unified CSV file contains the following columns:

• timestamp: The human-readable timestamp of the candle, precisely parsed to the second.
• open: The opening price of the stock during that interval.
• high: The highest price reached during that interval.
• low: The lowest price reached during that interval.
• close: The closing price of the stock during that interval.
• volume: The trading volume (number of shares traded) during that interval.

The dataset covers a substantial historical period, and the total number of records will be the sum of records from each interval file.

Potential Use Cases

This dataset can be highly valuable for various applications in quantitative finance and data science, including: * Algorithmic Trading Strategy Development: Backtesting and optimizing trading strategies across different timeframes for BAJFINANCE. * Technical Analysis: Generating charts, calculating technical indicators (e.g., Moving Averages, RSI, MACD, Bollinger Bands) specific to BAJFINANCE. * Machine Learning for Price Prediction: Training models to forecast future stock prices, trends, or volatility for BAJFINANCE. * Market Trend Analysis: Studying short-term and long-term market behavior, liquidity, and price action of Bajaj Finance. * Educational Purposes: A clean, multi-interval dataset ideal for learning and practicing data analysis with financial time series.

Disclaimer

This dataset is provided for informational and educational purposes only. It should not be considered financial advice, investment recommendations, or a solicitation to buy or sell any securities. Trading and investing in financial markets involve significant risk, and past performance is not indicative of future results. Always conduct your own thorough research and consult with a qualified financial advisor before making any investment decisions. The creators of this dataset are not liable for any losses incurred from its use.

HCLTECH OHLCV Data: Multi-Interval Historical Stock Prices (NSE)

Overview

This dataset provides comprehensive historical Open, High, Low, Close, and Volume (OHLCV) data for HCLTech (HCLTECH), a prominent Indian stock listed on the National Stock Exchange (NSE). The data has been consolidated from various time intervals (1-minute, 5-minute, 15-minute, and 1-day), offering a granular yet unified view for diverse analytical needs, from high-frequency trading simulations to long-term trend analysis.

Data Source

The raw data was collected programmatically using the Groww API. The specific API endpoint used for fetching charting data is: https://groww.in/v1/api/charting_service/v4/chart/exchange/NSE/segment/CASH. While efforts have been made during data fetching and consolidation to ensure accuracy, please be aware that financial data can sometimes be subject to minor corrections or revisions by data providers.

Dataset Contents

This dataset is provided as a single, unified CSV file (e.g., unified_hcltech_ohlcv_data.csv), which has been consolidated from multiple original JSON files representing different time intervals (1m, 5m, 15m, 1d).

The unified CSV file contains the following columns:

• timestamp: The human-readable timestamp of the candle, precisely parsed to the second.
• open: The opening price of the stock during that interval.
• high: The highest price reached during that interval.
• low: The lowest price reached during that interval.
• close: The closing price of the stock during that interval.
• volume: The trading volume (number of shares traded) during that interval.

The dataset covers a substantial historical period, and the total number of records will be the sum of records from each interval file.

Potential Use Cases

This dataset can be highly valuable for various applications in quantitative finance and data science, including: * Algorithmic Trading Strategy Development: Backtesting and optimizing trading strategies across different timeframes for HCLTECH. * Technical Analysis: Generating charts, calculating technical indicators (e.g., Moving Averages, RSI, MACD, Bollinger Bands) specific to HCLTECH. * Machine Learning for Price Prediction: Training models to forecast future stock prices, trends, or volatility for HCLTECH. * Market Trend Analysis: Studying short-term and long-term market behavior, liquidity, and price action of HCLTech. * Educational Purposes: A clean, multi-interval dataset ideal for learning and practicing data analysis with financial time series.

Disclaimer

This dataset is provided for informational and educational purposes only. It should not be considered financial advice, investment recommendations, or a solicitation to buy or sell any securities. Trading and investing in financial markets involve significant risk, and past performance is not indicative of future results. Always conduct your own thorough research and consult with a qualified financial advisor before making any investment decisions. The creators of this dataset are not liable for any losses incurred from its use.

Accounts-Payable Time Series for Liquidity Services Inc. Liquidity Services, Inc. engages in the provision of e-commerce marketplaces, self-directed auction listing tools, and value-added services in the United States and internationally. The company operates through four segments: GovDeals, Retail Supply Chain Group (RSCG), Capital Assets Group (CAG), and Machinio. Its solutions enable government entities and commercial businesses to sell surplus property and real estate assets through GovDeals, Bid4Assets, and Sierra marketplaces. The company also offers a suite of services, including surplus management, asset valuation, asset sales, marketing, returns management, asset recovery, and ecommerce services; and operates Liquidation.com, a marketplace to sell excess, returned, and overstocked consumer goods. In addition, it operates a global search engine platform for listing used equipment for sale in the construction, machine tool, transportation, printing, laboratory/medical, and agriculture sectors. Further, the company provides Machinio System service that offers various software tools, such as website hosting, email marketing, and inventory management to equipment sellers. The company offers products for various industries, such as consumer electronics, general merchandise, apparel, scientific equipment, aerospace parts and equipment, technology hardware, real estate, energy equipment, industrial capital assets, heavy equipment, fleet and transportation equipment, and specialty equipment. The company was incorporated in 1999 and is headquartered in Bethesda, Maryland.

Graph and download economic data for Net Percentage of Respondents Reporting an Improvement in Liquidity and Functioning in the Underlying Markets for: Agency RMBS (EXHE3C6Q65NP) from Q4 2011 to Q3 2025 about marketable, mortgage-backed, agency, liquidity, residential, Net, securities, percent, and USA.

Graph and download economic data for Net Percentage of Respondents Reporting an Improvement in Liquidity and Functioning in the Underlying Markets for: Consumer ABS (EXHE3C6Q77NP) from Q4 2011 to Q3 2025 about marketable, asset-backed, liquidity, Net, securities, percent, consumer, and USA.