Summary statistics on sample of WSJ articles in 1970 as well as NYSE-listed firms that existed in 1970.

According to Cognitive Market Research, the Trading Software market size will be USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of XX% from 2024 to 2033. Market Dynamics Key Driver

Surge in retail investor participation is fueling the trading software market 

The rise in retail investor participation in the market is driving the demand for trading software. The driving forces behind the Retail Investor Boom are technology and accessibility, commission free trading software, social media influence, and financial literacy and awareness. The rising demand for trading software is mainly driven by the increasing demand for higher returns. • For instance, the number of demat accounts in India has seen a significant surge, reaching 179 million by October 2024, driven by increased awareness and adoption of equity investments, particularly among young investors. • In the last two years, approximately 30 million new retail investors opened brokerage accounts in United States. The percent of households with stock holdings increased to an all-time high of 58 percent as of 2022, according to the Federal Reserve’s Survey of Consumer Finances, up from 49 percent in 2013. Social media platforms have further fueled the demand for the trading software market. Retail investors are influenced by the Platforms such as Twitter, Reddit, and YouTube have created a community-driven investing culture, where retail traders share strategies, discuss stocks, and even coordinate market moves. (source- https://www.wsj.com/finance/stocks/stocks-americans-own-most-ever-9f6fd963) Technical Innovation is driving the market for trading software Advancements in technology have transformed trading software, enabling automation, real- time analytics and enhanced security. AI-powered insights have democratized finance, allowing anyone with a smartphone to participate in the stock market. Technology has enabled algorithmic trading systems to execute trades at high speeds leveraging automation to place orders, monitor markets, and execute complex strategies within milliseconds. For example, u Trade Algos provides users with capability to access their strategy ‘s historical performance accurately through precise historical data. Prior to engaging in live market trading, traders can conduct backtesting of their strategies to know their hypothetical performance. • For instance, the fusion of AI with crypto trading has given rise to AI crypto trading bots, which currently make up 60% of trading volumes on major exchanges. • AI-powered platforms like DeepSeek in China are being used to predict market movements and enhance decision-making.
The increase in technological advancement has enabled trading systems to execute trades in milliseconds. This speed has reduced transaction costs and enhanced market liquidity which has led to increase in demand for trading software. (source-https://www.debutinfotech.com/blog/what-are-ai-crypto-trading-bots)

Restraints

Cyber security Risks and Data Breaches

The increasing reliance on digital trading platforms has made them prime target for cyberattacks as these platforms handle large volumes of transactions and store sensitive financial data, breaches can lead to financial losses, identify theft, and loss of investor trust. The rising cases of data breaches are one of the prominent restraints in the market. Cybercriminals target trading platforms to steal funds, manipulate markets and disrupt services. • For instance, the collapse of FTX, a major trading platform, was partly due to internal mismanagement and security lapses that led to billions in investors losses. • In October 2022, the Binance exchange experienced hack after an unauthorized third party discovered a vulnerability in the cross-chain bride of system. By exploiting the flaw, the hacker was able to create and withdraw an extra two million Binance coins(BNB). Traders often fall victim to fake trading apps, scam emails, or fraudulent brokerages. Algorithmic trading all over the world has increased with increased penetration, low-cost trading platforms. Algorithmic trading and decentralised finance (DeFi) platforms rely on APIs and smart contracts, which can b...

Series Is Presented Here As Two Variables--(1)--Original Data, 1897-1916 (2)--Original Data, 1914-1958 20 Stocks Are Used Through September, 1928 And 30 Stocks Thereafter. A Detailed Description Of Methods Of Constucting Averages Is Given In "Basis Of Calculation Of Dow-Jones Average" Available From The Wall Street Journal. For A More Detailed Description Of The Series, See Business Cycle Indicators, Vol. Ii, Moore, NBER. This Index Is Based On Daily Closing Prices On The New York Stock Exchange. Through 1948, Averages Of Highest And Lowest Indexes For The Month Are Used. For 1949-1968, Averages Of Daily Closing Indexes Are Used. Source: Data Were Compiled By Dow Jones And Company From Quotations In The Wall Street Journal. Through June, 1952, Data Are From The Dow-Jones Averages, 13Th Edition, 1948, And Supplementary Averages (Barron'S Publishing Company). Thereafter, Through 1968, Data Are From Barron'S National Business And Financial Weekly.

This NBER data series m11009b appears on the NBER website in Chapter 11 at http://www.nber.org/databases/macrohistory/contents/chapter11.html.

NBER Indicator: m11009b

Bush job performance (6); public figures (12); Clinton (4); economy (28); taxes (24); stock market (4); energy crisis (5); important issues (7).

This paper looks at the relationship between negative news and stock markets in times of global crisis, such as the 2008/2009 period. We analysed one year of front page banner headlines of three financial newspapers, the Wall Street Journal, Financial Times, and Il Sole24ore to examine the influence of bad news both on stock market volatility and dynamic correlation. Our results show that the press and markets influenced each other in generating market volatility and in particular, that the Wall Street Journal had a crucial effect both on the volatility and correlation between the US and foreign markets. We also found significant differences between newspapers in their interpretation of the crisis, with the Financial Times being significantly pessimistic even in phases of low market volatility. Our results confirm the reflexive nature of stock markets. When the situation is uncertain and unpredictable, market behaviour may even reflect qualitative, big picture, and subjective information such as streamers in a newspaper, whose economic and informative value is questionable.

Direction of country (1); George W. Bush job performance on certain issues (1); approval of congress doing job (1); opinion of public figures and groups (1); vote in possible presidential election (3); familiarity with economic plans proposed by democratic candidates (1); appeal of democratic nominee Wesley Clark (1); opinion on Bush policies (1); qualities admired about George W. Bush (2); Bush and Republicans vs. Democrats in Congress (1); U.S. and war with Iraq (7); opinion and spending $87 billion in Iraq (4); opinion of role and soldiers in Iraq (2); effect of capturing Osama Bin Laden on end of war (1); U.S. role in Middle East (1); opinion on nation's economy (2); most important economic issue (1); investment in stock market (2); greatest cause of recent recession and economic downturn (1); efficiency of Bush plan to strengthen economy (1); opinion on canceling tax cuts (1); controlling budget deficit (2); effect of spending in Iraq on U.S. economy (1); congress on Medicare (3); impact of NAFTA agreement with Mexico and Canada (1); allowing individuals control Social Security payroll taxes into retirement accounts (1).