This dataset aggregates real-time sentiment scores and metadata for financial news headlines, enabling rapid detection of market-moving events and trends. It includes headline text, publication details, sentiment analysis, relevance to financial markets, and links to affected stocks and sectors. Ideal for quantitative trading, risk monitoring, and financial news analytics.

Introducing our comprehensive economic calendar, your ultimate resource for tracking major global economic events and their impact on currency and stock market prices. With a vast array of fields including event name, country, previous and current values, and more, our calendar provides you with essential data to make informed financial decisions. Stay ahead of the curve with our real-time updates, ensuring you have access to the latest information every 15 minutes. With this powerful tool at your fingertips, you can confidently navigate the dynamic world of economic events and seize opportunities for success. Don't miss out on this essential resource for staying informed and making calculated moves in the market.

In 2025, stock markets in the United States accounted for roughly ** percent of world stocks. The next largest country by stock market share was China, followed by the European Union as a whole. The New York Stock Exchange (NYSE) and the NASDAQ are the largest stock exchange operators worldwide. What is a stock exchange? The first modern publicly traded company was the Dutch East Industry Company, which sold shares to the general public to fund expeditions to Asia. Since then, groups of companies have formed exchanges in which brokers and dealers can come together and make transactions in one space. Stock market indices group companies trading on a given exchange, giving an idea of how they evolve in real time. Appeal of stock ownership Over half of adults in the United States are investing money in the stock market. Stocks are an attractive investment because the possible return is higher than offered by other financial instruments.

Stock Analysis Software Market Outlook

The global stock analysis software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The growth of this market is driven by the increasing adoption of advanced analytics tools by individual investors and financial institutions to make informed investment decisions. The rising demand for automated trading systems and the integration of artificial intelligence (AI) and machine learning (ML) in stock analysis software are significant growth factors contributing to the market expansion.

One of the primary growth factors for the stock analysis software market is the increasing complexity and volume of financial data. With the exponential growth of data from various sources such as social media, news articles, and financial statements, investors and financial analysts require sophisticated tools to process and interpret this information accurately. Stock analysis software equipped with AI and ML algorithms can analyze vast datasets in real-time, providing valuable insights and predictive analytics that enhance investment strategies. Moreover, the growing trend of algorithmic trading, which relies heavily on high-speed data processing and automated decision-making, is further propelling the market growth.

Another crucial growth driver is the rising awareness and adoption of stock analysis software among individual investors. As more individuals seek to actively manage their investment portfolios, there is a growing demand for user-friendly and cost-effective stock analysis tools that offer comprehensive market analysis, technical indicators, and personalized investment recommendations. The proliferation of mobile applications and the increasing accessibility of cloud-based stock analysis solutions have made it easier for retail investors to access advanced analytical tools, thereby contributing to market expansion.

The integration of innovative technologies such as natural language processing (NLP) and sentiment analysis into stock analysis software is also a significant growth factor. These technologies enable the software to interpret and analyze unstructured data from news articles, social media, and other textual sources to gauge market sentiment and predict stock price movements. This capability is particularly valuable in today's fast-paced financial markets, where sentiment and news events can have a substantial impact on stock prices. The continuous advancements in AI and NLP technologies are expected to drive further innovations and improvements in stock analysis software, thereby boosting market growth.

In the evolving landscape of financial technology, Investor Relations Tools have become indispensable for companies seeking to maintain transparent and effective communication with their stakeholders. These tools facilitate seamless interaction between companies and their investors, providing real-time updates, financial reports, and strategic insights. By leveraging these tools, companies can enhance their investor engagement strategies, build trust, and foster long-term relationships with their shareholders. The integration of advanced analytics and AI-driven insights into Investor Relations Tools further empowers companies to tailor their communication strategies, ensuring that they meet the diverse needs of their investor base. As the demand for transparency and accountability in financial markets continues to grow, the adoption of sophisticated Investor Relations Tools is expected to rise, playing a crucial role in the broader ecosystem of stock analysis software.

From a regional perspective, North America is anticipated to hold the largest market share due to the high concentration of financial institutions, brokerage firms, and individual investors in the region. The presence of key market players and the early adoption of advanced technologies also contribute to the dominant position of North America in the global stock analysis software market. Additionally, the Asia Pacific region is expected to witness significant growth during the forecast period, driven by the increasing number of retail investors, rapid economic development, and the growing financial markets in countries such as China and India.

Component Analysis

Dataset Description: 34-year Daily Stock Data (1990-2024)

Context and Inspiration

This dataset captures historical financial market data and macroeconomic indicators spanning over three decades, from 1990 onwards. It is designed for financial analysis, time series forecasting, and exploring relationships between market volatility, stock indices, and macroeconomic factors. This dataset is particularly relevant for researchers, data scientists, and enthusiasts interested in studying: - Volatility forecasting (VIX) - Stock market trends (S&P 500, DJIA, HSI) - Macroeconomic influences on markets (joblessness, interest rates, etc.) - The effect of geopolitical and economic uncertainty (EPU, GPRD)

Sources

The data has been aggregated from a mix of historical financial records and publicly available macroeconomic datasets: - VIX (Volatility Index): Chicago Board Options Exchange (CBOE). - Stock Indices (S&P 500, DJIA, HSI): Yahoo Finance and historical financial databases. - Volume Data: Extracted from official exchange reports. - Macroeconomic Indicators: Bureau of Economic Analysis (BEA), Federal Reserve, and other public records. - Uncertainty Metrics (EPU, GPRD): Economic Policy Uncertainty Index and Global Policy Uncertainty Database.

Columns

1. dt: Date of observation in YYYY-MM-DD format.
2. vix: VIX (Volatility Index), a measure of expected market volatility.
3. sp500: S&P 500 index value, a benchmark of the U.S. stock market.
4. sp500_volume: Daily trading volume for the S&P 500.
5. djia: Dow Jones Industrial Average (DJIA), another key U.S. market index.
6. djia_volume: Daily trading volume for the DJIA.
7. hsi: Hang Seng Index, representing the Hong Kong stock market.
8. ads: Aruoba-Diebold-Scotti (ADS) Business Conditions Index, reflecting U.S. economic activity.
9. us3m: U.S. Treasury 3-month bond yield, a short-term interest rate proxy.
10. joblessness: U.S. unemployment rate, reported as quartiles (1 represents lowest quartile and so on).
11. epu: Economic Policy Uncertainty Index, quantifying policy-related economic uncertainty.
12. GPRD: Geopolitical Risk Index (Daily), measuring geopolitical risk levels.
13. prev_day: Previous day’s S&P 500 closing value, added for lag-based time series analysis.

Key Features

• Cross-Market Analysis: Compare U.S. markets (S&P 500, DJIA) with international benchmarks like HSI.
• Macroeconomic Insights: Assess how external factors like joblessness, interest rates, and economic uncertainty affect markets.
• Temporal Scope: Longitudinal data facilitates trend analysis and machine learning model training.

Potential Use Cases

• Forecasting market indices using machine learning or statistical models.
• Building volatility trading strategies with VIX Futures.
• Economic research on relationships between policy uncertainty and market behavior.
• Educational material for financial data visualization and analysis tutorials.

Feel free to use this dataset for academic, research, or personal projects.

📹Project Video available on YouTube - https://youtu.be/WmJYHz_qn5s

Realistic Synthetic - AI Financial & Market Data for Gemini(Google), ChatGPT(OpenAI), Llama(Meta)

This dataset provides a synthetic, daily record of financial market activities related to companies involved in Artificial Intelligence (AI). There are key financial metrics and events that could influence a company's stock performance like launch of Llama by Meta, launch of GPT by OpenAI, launch of Gemini by Google etc. Here, we have the data about how much amount the companies are spending on R & D of their AI's Products & Services, and how much revenue these companies are generating. The data is from January 1, 2015, to December 31, 2024, and includes information for various companies : OpenAI, Google and Meta.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

This analyse will be helpful for those working in Finance or Share Market domain.

From this dataset, we extract various insights using Python in our Project.

1) How much amount the companies spent on R & D ?

2) Revenue Earned by the companies

3) Date-wise Impact on the Stock

4) Events when Maximum Stock Impact was observed

5) AI Revenue Growth of the companies

6) Correlation between the columns

7) Expenditure vs Revenue year-by-year

8) Event Impact Analysis

9) Change in the index wrt Year & Company

These are the main Features/Columns available in the dataset :

1) Date: This column indicates the specific calendar day for which the financial and AI-related data is recorded. It allows for time-series analysis of the trends and impacts.

2) Company: This column specifies the name of the company to which the data in that particular row belongs. Examples include "OpenAI" and "Meta".

3) R&D_Spending_USD_Mn: This column represents the Research and Development (R&D) spending of the company, measured in Millions of USD. It serves as an indicator of a company's investment in innovation and future growth, particularly in the AI sector.

4) AI_Revenue_USD_Mn: This column denotes the revenue generated specifically from AI-related products or services, also measured in Millions of USD. This metric highlights the direct financial success derived from AI initiatives.

5) AI_Revenue_Growth_%: This column shows the percentage growth of AI-related revenue for the company on a daily basis. It indicates the pace at which a company's AI business is expanding or contracting.

6) Event: This column captures any significant events or announcements made by the company that could potentially influence its financial performance or market perception. Examples include "Cloud AI launch," "AI partnership deal," "AI ethics policy update," and "AI speech recognition release." These events are crucial for understanding sudden shifts in stock impact.

7) Stock_Impact_%: This column quantifies the percentage change in the company's stock price on a given day, likely in response to the recorded financial metrics or events. It serves as a direct measure of market reaction.

Japan's main stock market index, the JP225, fell to 45355 points on September 26, 2025, losing 0.87% from the previous session. Over the past month, the index has climbed 6.67% and is up 13.87% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on September of 2025.

In the first quarter of 2020, global stock indices posted substantial losses that were triggered by the outbreak of COVID-19. The period from March 6 to 18 was particularly dramatic, with several stock indices losing more than ** percent of their value. Worldwide panic hits markets From the United States to the United Kingdom, stock market indices suffered steep falls as the coronavirus pandemic created economic uncertainty. The Nasdaq 100 and S&P 500 are two indices that track company performance in the United States, and both lost value as lockdowns were introduced in the country. European markets also recorded significant slumps, which triggered panic selling among investors. The FTSE 100 – the leading share index of companies in the UK – plunged by as much as ** percent in the opening weeks of March 2020. Is it time to invest in tech stocks? The S&P 500 is regarded as the best representation of the U.S. economy because it includes more companies from the leading industries. However, helped in no small part by its focus on tech companies, the Nasdaq 100 has risen in popularity and seen remarkable growth in recent years. Global demand for digital technologies has increased further due to the coronavirus, with remote working and online shopping becoming part of the new normal. As a result, more investors are likely to switch to the tech stocks listed on the Nasdaq 100.

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

France's main stock market index, the FR40, rose to 7862 points on September 26, 2025, gaining 0.86% from the previous session. Over the past month, the index has climbed 1.53% and is up 0.90% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on September of 2025.

While the global coronavirus (COVID-19) pandemic caused all major stock market indices to fall sharply in March 2020, both the extent of the decline at this time, and the shape of the subsequent recovery, have varied greatly. For example, on March 15, 2020, major European markets and traditional stocks in the United States had shed around ** percent of their value compared to January *, 2020. However, Asian markets and the NASDAQ Composite Index only shed around ** to ** percent of their value. A similar story can be seen with the post-coronavirus recovery. As of November 14, 2021 the NASDAQ composite index value was around ** percent higher than in January 2020, while most other markets were only between ** and ** percent higher. Why did the NASDAQ recover the quickest? Based in New York City, the NASDAQ is famously considered a proxy for the technology industry as many of the world’s largest technology industries choose to list there. And it just so happens that technology was the sector to perform the best during the coronavirus pandemic. Accordingly, many of the largest companies who benefitted the most from the pandemic such as Amazon, PayPal and Netflix, are listed on the NADSAQ, helping it to recover the fastest of the major stock exchanges worldwide. Which markets suffered the most? The energy sector was the worst hit by the global COVID-19 pandemic. In particular, oil companies share prices suffered large declines over 2020 as demand for oil plummeted while workers found themselves no longer needing to commute, and the tourism industry ground to a halt. In addition, overall share prices in two major stock exchanges – the London Stock Exchange (as represented by the FTSE 100 index) and Hong Kong (as represented by the Hang Seng index) – have notably recovered slower than other major exchanges. However, in both these, the underlying issue behind the slower recovery likely has more to do with political events unrelated to the coronavirus than it does with the pandemic – namely Brexit and general political unrest, respectively.

United Kingdom's main stock market index, the GB100, rose to 9285 points on September 26, 2025, gaining 0.77% from the previous session. Over the past month, the index has climbed 0.32% and is up 11.59% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on September of 2025.

Media information plays an essential role in the stock market. Recent financial research has verified that media information could shock stock price by influencing investors’ expectation. Now, a new type of interactive media, called Digital Interactive Media (DIM), is popular in Chinese stock market and becomes the main channel for investors to understand listed companies. Unlike general news media or investor forums, DIM enables direct interaction between listed companies and investors. In the modern society where digital economy is booming, media information would largely affect investors’ decisions. Therefore, it is urgent to use natural language processing (NLP) technology to deconstruct the massive questions and answers (Q&A) interactive information in DIM and extract valuable factors that affect stock prices and stock performances to explore the influence mechanism of digital interactive information on stock performances. This paper firstly uses web crawling technology to obtain approximately 110000 Q&A text information from the digital interactive platform (‘Panoramic Network’) from 2015 to 2021. Then we use big data text analysis technology and emotional quantification technology to extract valuable influencing factors from the massive text. A Multiple Linear Regression (MLR) model was created to explore specific influence mechanism of digital interactive information on stock price performance. The empirical results show that the emotions implicit in investors’ questions do not significantly impact stock performance. However, the emotions and attitudes of the answers by listed companies can significantly affect corresponding stock prices, which indirectly confirms the Proximate Cause Effect of behavioral finance. This effect is particularly evident in the stock prices on the current trading day and the next trading day. In the Robustness Test, this paper replaces dependent variable and adds relevant control variables, and the conclusion remains valid. In the Endogeneity Test, this paper selects sample data before the launch of Panorama Network in 2014 as a comparison, and uses a Difference-in-Difference (DID) model to prove the significant impact of the launch of Panorama Network on Chinese stock market. In the Heterogeneity Test, the paper classifies the market value, region, and industry of listed companies and regressed the sub samples, once again confirming the reliability of the empirical conclusions. The results of Robustness Test, Endogeneity Test, and Heterogeneity Test conducted in this paper all support empirical conclusions.

The global stock market, a dynamic ecosystem driven by economic indicators, investor sentiment, and technological advancements, is poised for significant growth. While precise figures for market size and CAGR are absent from the provided data, a reasonable estimation, considering typical growth in mature markets and the influence of factors like increasing global wealth and the rise of fintech, suggests a 2025 market size in the trillions of dollars, with a conservative CAGR of 6-8% projected through 2033. Drivers include expanding access to investment platforms, the increasing popularity of algorithmic trading, and a growing focus on ESG (environmental, social, and governance) investing. Trends point towards increased volatility due to geopolitical uncertainty and the growing influence of retail investors, alongside a continued shift towards passive investing strategies such as ETFs. Restraints include regulatory hurdles, cybersecurity risks, and the potential for market bubbles. Market segmentation by type (equities, derivatives, bonds etc.) and application (institutional, retail) reveals significant differences in growth rates and profitability, with technological advancements impacting all segments. The competitive landscape is shaped by established brokerages alongside innovative fintech companies, creating a dynamic environment. Regional variations are expected, with North America and Europe maintaining leading positions due to established market infrastructures and investor sophistication. However, rapid growth is anticipated in Asia-Pacific markets, fueled by expanding middle classes and increased participation in financial markets. The forecast period (2025-2033) will witness a complex interplay of macroeconomic conditions, technological disruption, and evolving investor behavior. Sophisticated analytical tools, such as those offered by companies like Interactive Data, VectorVest, and Worden Brothers, will play a crucial role in navigating market complexities. Strategic investments in technological infrastructure and a proactive regulatory framework will be key to ensuring sustainable growth and stability across all regions.

Stock Trading Training Market size is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2024 to 2031.

Global Stock Trading Training Market Drivers

The stock trading training market is influenced by a variety of factors that can drive its growth and development. Here are some key market drivers for this sector:

Increased Participation in Stock Markets: A growing number of retail investors entering the stock market, particularly due to the rise in online trading platforms, has led to a greater demand for training and educational resources. Technological Advancements: The proliferation of mobile trading applications, algorithmic trading, and advanced analytical tools has made stock trading more accessible. This prompts individuals to seek training to effectively use these technologies.

Global Stock Trading Training Market Restraints

The market drivers for the Stock Trading Training Market can be analyzed through various factors that influence the demand for training programs and platforms aimed at educating individuals in stock trading. Here are some key drivers:

Increasing Interest in Stock Market Investing: As more individuals seek to build wealth through investments, there is a growing interest in stock trading. Events like market rallies or economic events can drive people toward learning how to trade. Accessibility of Online Trading Platforms: The rise of user-friendly online trading platforms has made stock trading more accessible to the general public. This accessibility often leads users to seek out training resources to enhance their trading skills.

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.

Snapshot img { margin: 10px !important; } Overview of the global stock market software industry

Technavio’s market research analysts have estimated the global stock market software industry to grow moderately at a CAGR of above 8% by 2020. This market research study identifies the rising number of FTAs (free trade agreement) between emerging nations to be one of the primary drivers for this market. By simplifying procedures, these agreements improve transit times and the efficiency of business operations. Moreover, these agreements also help to remove complicated regulatory barriers, reduce trade tariffs, and assist in improving the investment environment of both parties in the agreement. These features have subsequently increased the demand for FTAs and will foster the growth prospects for this market during the forecast period.

The shift from traditional methods to open accounts is considered to be one of the major trends that will fuel the industry’s growth in the next four years. Factors such as reduced costs and improvements in efficiencies have prompted buyers and sellers to switch from letters of credits to open accounts. To meet this demand, banks have been compelled to improve their efficiency and reduce their reduce operational costs. Additionally, the adoption of bank payment obligations will also help to spur market growth as it provides a level of security that is not possible with pure open account transactions.

Segmentation by end user and analysis of the stock market software industry

Financials
Consumer goods
Industrials
Technology
Consumer services
Telecommunications
Healthcare
Basic materials
Oil and gas
Utilities

This industry research report identifies that the financials segment dominated the stock market software industry in 2015, accounting for almost 23% of the total market share. This market segment includes various sub-segments such as retail banking, cash management, and insurance agencies. The presence of companies that provide specialized financial services like security brokering, investment services, and commodity exchanges will contribute to the growth of this market segment during the forecast period.

Geographical segmentation and analysis of the stock market software industry

Americas
APAC
EMEA

In this market study, analysts have estimated that EMEA will dominate the global stock market software industry during the forecast period. Accounting for approximately 40% of the total market share in 2015, the introduction and adoption of the stock market software in different European languages will further increase the growth of this industry. Factors such as the higher adoption of this software among end users in the financial sector and the rising implementation of this software in local trade organizations will further lead to the growth of the market.

Competitive landscape and key vendors

Though competitive, the global stock market software industry is still in its growth phase. Regular investments from big vendors to acquire small vendors has compelled manufacturers to distinguish their product and service offerings through clear and unique value propositions. A rise in the number of new product and service offerings, new acquisitions, and technological advancements are likely to intensify the level of competition in the next four years.

The leading vendors in the market are -

Corporate Trading
Innovative Market Analysis
Interactive Data
Monex
Ninja Trader
VectorVest
Worden Brothers

The other prominent vendors in the stock market software industry are AbleSys, Alyuda Research, Bloombex Options, EquityFeed Workstation, Genesis Financial, Global Futures Exchange & Trading, INO, Interactive Brokers, Analyst International, MultiCharts, Muriel Siebert, OptionsHouse, ProfitSource, ThinkorSwim, Tradecision, and Wave59.

Key questions answered in the report include

What will the market size and the growth rate be in 2020?
What are the key factors driving the global stock market software industry?
What are the key market trends impacting the growth of the global stock market software industry?
What are the challenges to market growth?
Who are the key vendors in the global stock market software industry?
What are the market opportunities and threats faced by the vendors in the global stock market software industry?
Trending factors influencing the market shares of the Americas, APAC, and EMEA.
What are the key outcomes of the five forces analysis of the global stock market software industry?

Technavio also offers customization on reports based on specific client requirement.

Related reports

Global Trade Finance Market 2015-2019

Hong Kong's main stock market index, the HK50, fell to 26128 points on September 26, 2025, losing 1.35% from the previous session. Over the past month, the index has climbed 3.68% and is up 26.64% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on September of 2025.

As of November 14, 2021, all S&P 500 sector indices had recovered to levels above those of January 2020, prior to full economic effects of the global coronavirus (COVID-19) pandemic taking hold. However, different sectors recovered at different rates to sit at widely different levels above their pre-pandemic levels. This suggests that the effect of the coronavirus on financial markets in the United States is directly affected by how the virus has impacted various parts of the underlying economy. Which industry performed the best during the coronavirus pandemic? Companies operating in the information technology (IT) sector have been the clear winners from the pandemic, with the IT S&P 500 sector index sitting at almost ** percent above early 2020 levels as of November 2021. This is perhaps not surprising given this industry includes some of the companies who benefitted the most from the pandemic such as ************** and *******. The reason for these companies’ success is clear – as shops were shuttered and social gatherings heavily restricted due to the pandemic, online services such shopping and video streaming were in high demand. The success of the IT sector is also reflected in the performance of global share markets during the coronavirus pandemic, with tech-heavy NASDAQ being the best performing major market worldwide. Which industry performed the worst during the pandemic? Conversely, energy companies fared the worst during the pandemic, with the S&P 500 sector index value sitting below its early 2020 value as late as July 2021. Since then it has somewhat recovered, and was around ** percent above January 2020 levels as of October 2021. This reflects the fact that many oil companies were among the share prices suffering the largest declines over 2020. A primary driver for this was falling demand for fuel in line with the reduction in tourism and commuting caused by lockdowns all over the world. However, as increasing COVID-19 vaccination rates throughout 2021 led to lockdowns being lifted and global tourism reopening, demand has again risen - reflected by the recent increase in the S&P 500 energy index.

Text classification problems are quite successfully solved by current machine learning techniques. Text content such as consumer reviews, email content etc. can be classified as favorable/unfavorable, spam/not-spam, etc. with a high success rate. News content too is known to affect human sentiment leading to sharp, short term price movements in stocks that follows a positive/negative news. The attached sample dataset may be used to train a machine learning model to classify news text and predict its influence on stock price, and subsequently to deduce buy/sell recommendations. A predicted downward price movement may also help institutions engaged in lombard lending (securities lending) employ proactive risk mitigation. The dataset contains news articles and the empirical stock price movements following the news publication date. To attribute the stock price move to a specific news incident alone is difficult, as there are several factors influencing the stock price. However, we have selected stocks and incident dates, where the stock has significantly outperformed or underperformed its industry peers. Thus, the effects of broader market and industry factors can be assumed to have less significance, because such factors would cause all industry peers to rise/fall in tandem, if at all any cause-effect relationship exists. In other words, if the company's stock price showed a statistically significant up/downward change relative to its industry peers in the reference time period, only then such data points are taken in consideration. Secondly, earnings related news content (fundamental factor in attractiveness of a stock) is omitted from consideration, to keep the analysis limited in scope to incident news alone. Reference time period for evaluating the under/out performance is kept to a maximum of 10 days, to only capture "short-term" price movements. This helps omit the scenarios where stock price was affected by business operational realities of the company e.g. actual (not reported) success/failure of its product/service, as such events are relatively long term. In short, due care (feature engineering) has been employed to curate this dataset to serve its intended application. Please note that this is only a sample dataset of roughly 100 records. Full dataset can be requested for non commercial use. Please contact me via this platform or via Linkedin.