[Data Lab] Waiker Data Search is live — Explore all data in one place
Try searching for a stock or investor you’re interested in.
You can now view data from insiders, institutions, and politicians all at once—no more checking them separately.
By searching a stock name, you can view all related data at a glance — including insider transactions, institutional investor activity, and politician transactions etc.
You can also view key insider trades and related news for the stock all in one place.
Check out trades by institutional investors and politicians, as well as major shareholder ownership and treasury stock transactions for the stock!
Search an investor’s name to see a full summary of when and how much they bought or sold.
When data connects, insights follow. Experience faster, deeper discovery with Waiker Data Search.
Does insider transaction news actually impact investment decisions?
Now, you can check how much stock prices have risen after news about insider transactions is published.
To support better investment decisions, we now provide recent stock prices, average insider purchase prices, and transaction amounts directly within the insider trading news section. Our goal is to enhance the feature so that you can invest using just the news alone.
[Data QC] Corrected Disclosure Data Now Available with Waiker’s Data QC System
Because disclosure data is manually entered, errors occur countless times daily. While some are corrected through revised disclosures, many remain uncorrected, making it difficult to ensure data integrity using raw parsed data alone.
To address this issue, Waiker has been providing error codes for detected disclosure errors. With this update, we now provide not only error codes but also corrected values, affected fields, and detailed error messages!
AS-IS: Disclosure Error Detection → Provides an Error Code
While many stock data providers display disclosure data as-is despite potential errors, Waiker goes further by offering detailed error information and corrected values, ensuring higher data reliability.
Hello, this is Waiker team with the announcement of our second release of 2025.
[Data Lab] Want to Uncover the Meaning Behind Hidden Trades? ‘Insider Transaction News’ Launched
We received feedback from users who were curious about whether insider trades held any significant meaning and what the recent trends among insiders were.
Reading through all the news articles to find reasons behind these trades can be time-consuming. To help with this, Waiker uses AI technology to gather and analyze essential information related to trades and summarize it into concise articles.
Insider Transaction News will provide compact insights from insider trading data, recent trends, and AI expert analyses.
[Data Lab] The Secret of Top Investors: ‘Insider Transaction Screener’ Launched
Discover insights from real-time insider transaction data using the Insider Transaction Screener.
You can search by ticker, stock, insider name, and more, along with filters suggested by Waiker’s expertise. This tool is designed to help uncover valuable data-driven insights. A 7-day free trial is available now, so try it out today.
Today, we would like to introduce our newly released Passive Fund or ETF (Exchange-Traded Fund) holdings data and discuss how to use this data effectively.
First, passive investing is an investment strategy that seeks to track the performance of a specific index or benchmark. Unlike actively managed funds, passive funds don’t involve portfolio managers actively selecting stocks or timing the market. Instead, they simply reflect the market index.
Commonly known ETF products fall under passive investing and are typically associated with lower management fees and the potential for stable, long-term returns.
1. Cost Efficiency: Passive funds generally have lower management fees compared to active funds, positively impacting investors’ long-term returns.
2. Transparency: Since these funds track the the entire market or a specific index, the composition of the investment portfolio is clear and transparent.
3. Diversification: Investing in a wide range of assets allows effective risk diversification across different stocks or industries.
Market Shift: From Active Funds to Passive Funds
Recent Passive Fund Trends
Traditionally, fund managers have preferred active investment strategies. However, as the graph above shows, passive fund growth has surged in recent years.
ETFs, in particular, offer investors convenient access to various asset classes, attracting both individual and institutional investors.
Even in Korea, retail investors investing in U.S. markets have become familiar with tickers like SPY and QQQ, reflecting the growing popularity of passive investing.
How to Use ‘Passive Fund Holdings’ Data?
Example: Passive Funds Tracking the S&P 500
The first way to utilize Waiker’s Passive Fund Holdings Data is by easily identifying ETFs and index funds that track a specific index.
The image above shows search results for passive funds that track the S&P 500 or its derivatives. With Waiker’s data, you can easily view a list of passive funds following the index and gain quick insights into their size (assets, liabilities, etc.).
Second Use Case: Identifying Tracking Errors
Waiker’s data also provides the fund’s Net Asset Value (NAV), making it easier to calculate tracking errors. This allows you to conveniently apply trading strategies based on the difference between market prices and NAV.
Tracking error is a crucial metric that shows how faithfully a fund replicates its target index. A lower tracking error means the fund reflects the index more accurately, which enhances investor confidence.
Third Use Case: Utilizing Rebalancing Dates
Index providers recalculate and manage the composition of the index on predetermined dates, such as quarterly or semi-annually.
Inclusions and exclusions of specific stocks in an index can be interpreted as major positive or negative signals, making it important for investors to monitor these changes and dates carefully.
The following example shows the holdings of a specific ETF. As of May 31, 2023, ProShares UltraPro QQQ held 1,249,343 shares of Tesla.
ETF Holdings Data
While passive funds primarily track specific indices, the composition and weighting of holdings can vary by fund.
For instance, while the S&P 500 consists of 503 stocks, an actual fund might only hold 501 stocks. Therefore, understanding how many funds hold specific stocks, and to what extent, can help estimate potential buying or selling volumes on rebalancing dates.
Passive Investing: Beyond Market Average Returns
While passive investing is generally a strategy aimed at achieving market-average returns, passive fund data has evolved beyond that purpose.
By applying precise data analysis, investors can optimize quantitative investment strategies using ETFs.
In this article, we briefly introduced several strategies:
1. Listing passive funds that track specific indices
2. Optimizing ETF selection using tracking error analysis
3. Developing precise investment strategies based on rebalancing timing
How are you using ETF data? If you have your own passive investment strategies, feel free to share them!
John C. Bogle:
“Don’t look for the needle in the haystack. Just buy the haystack!”
Cluster trading refers to the scenario where insiders of a company engage in similar transactions (buying or selling) within a specific period. When insiders, who have deeper knowledge of a company’s internal matters, engage in such actions, their trading behavior can provide useful insights into the company’s future prospects, potentially influencing stock prices. Furthermore, if multiple insiders simultaneously engage in transactions in the same direction, it can increase the reliability of the information from those insider trades.
This hypothesis was empirically tested using insider trading data from Waiker.
Analytical Method
Definition of Cluster Trading:
A cluster trade is defined when at least 4 insiders trade in the same direction (buying or selling) within 30 days of the initial insider trade date.
Performance Measurement:
The performance of stock price is tracked for up to 60 trading days following the initial insider trade date to calculate cumulative returns.
Cluster vs Non-Cluster:
Cluster trades are compared with non-cluster trades (defined as insider trades that do not meet the 4 insiders threshold).
Average cumulative returns for both cluster and non-cluster trades are calculated for comparison.
Comparison of Results:
The performance of cluster trades is compared with non-cluster trades over various timeframes (20, 40, 60 trading days) to analyze the potential benefit of cluster trading.
Results of the Analysis
Cluster Trading Outperforms Non-Cluster Trading:
Non-cluster trades exhibit lower returns compared to cluster trades at 20, 40, and 60 trading day intervals (as seen in the blue-highlighted cells in the table below).
Annualized Returns:
At the 20 trading-day mark, cluster trading yields 11.31% higher returns than non-cluster trading.
At the 40 trading-day mark, cluster trading outperforms by 14.53%.
At the 60 trading-day mark, cluster trades result in 9.05% higher returns.
Cluster Trades Show Consistent Performance Over Time:
Cluster trades demonstrate a steady performance over time, while non-cluster trades do not show similar long-term results.
Optimal Holding Period:
The best performance for cluster trades occurs with a 20 trading-day holding period, which indicates that the value of the insider information from cluster trading diminishes over time.
Key Findings
Cluster Trading Signals:
When 4 or more insiders engage in buying transactions within the past month and there are no selling trades, the likelihood of a positive performance is high.
Cluster Trading Stability:
Cluster trades tend to provide more stable and consistent returns than non-cluster trades.
Performance Summary:
Cluster Period
Number of Insiders
Sample Size
Buy/Sell
20-Day Avg. Return(Annualized)
40-Day Avg. Return(Annualized)
60-Day Avg. Return(Annualized)
Cluster Trading
4 Insiders
32,524
Buy
2.49% (34.4%)
3.74% (24.65%)
4.34% (18.52%)
Non-Cluster
–
68,429
Buy
1.75% (23.09%)
1.62% (10.12%)
2.29% (9.47%)
Note: The table presents both the cumulative returns and their annualized values for comparison.
Time-series Changes in Average Return
Conclusion
Cluster Trading’s Advantage:
Insider transactions that occur in clusters (with 4 or more insiders trading in the same direction) significantly outperform non-cluster trades in terms of return.
Cluster trading tends to provide more consistent and stable returns, especially when compared to non-cluster trades.
Optimal Holding Period: The ideal holding period for cluster trades is 20 trading days, beyond which the value of the insider information tends to decrease.
Insights for Investors:
Cluster trades offer a strong signal for investors looking for stable returns and insider-driven insights into stock price movements.
Data Availability:
Detailed cluster trading data is provided by Weikher’s Insider Trading Database, allowing investors to access valuable insights on insider trading patterns and trends.
Hana Securities is committed to leveraging artificial intelligence (AI) technology and global financial data to offer investors more precise and reliable insights. In collaboration with Waiker, Hana Securities has newly launched the ‘AI Insider Signal’ service within its mobile trading system, OneQ Pro. This service visualizes stock trading data of key corporate insiders, helping investors better understand insider trading trends.
Waiker, a partner of LSEG (London Stock Exchange Group), specializes in AI-driven stock market data analysis and widget services. With its proprietary data verification system, Waiker analyzes insider trading information in real time using both domestic and international disclosure data. The ‘AI Insider Signal’ service provides not only key insiders’ stock transactions but also unique investment insights, such as consecutive trades, group transactions, and outperformance compared to AI-based indices. The data is visualized to enhance user experience, making it more accessible and intuitive for investors.
Through this collaboration, Hana Securities has expanded its AI-powered financial data offerings. The company stated, “We will continue to strengthen our AI capabilities to develop specialized financial market content that differentiates us in the industry.”
Shinhan Securities’ adoption of overseas news is a prime example of how AI technology can create tangible value in the financial industry. By utilizing Waiker’s AI technology, which offers innovative financial data solutions, Shinhan Securities has successfully implemented a service that translates and summarizes overseas stock news on their mobile trading system (MTS), Shinhan SOL.
The overseas stock news service is based on Reuters news provided by global financial data company LSEG (formerly Refinitiv). Waiker translates and summarizes these news articles in real-time using AI algorithms, helping investors quickly and efficiently grasp information about U.S.-listed stocks. The service is delivered alongside the original English text to enhance the reliability of the information and support investors in making more accurate decisions. Waiker’s AI technology goes beyond simple translation and summarization; it combines natural language processing (NLP) and data analytics to systematically organize and process vast amounts of financial data. This allows Shinhan Securities to offer a differentiated investment experience to clients, successfully enhancing their competitiveness in overseas stock investments.
Jeon Hyung-sook, Head of the Platform Group at Shinhan Securities, said, “Through AI-based overseas news provision and ‘similar businesses’ information services, we are now able to deliver both domestic and international investment information to clients quickly and easily. We will continue to leverage AI technology to provide even more valuable information to our clients in the future.”
Shinhan Securities’ implementation of overseas news is a prime example of how AI technology can create practical value in the financial industry.
Backtesting the stock trading data of U.S. House members from Waiker’s politician transaction dataset revealed that many U.S. politicians achieved market-beating returns in 2023. The dataset allows for the examination of returns by individual politicians and their affiliated parties. The results suggest that certain politicians may have benefited from an informational advantage inaccessible to the general public. Below are some particularly noteworthy statistics:
1. Key Returns and Performance
The top 5 House members by stock trading returns in 2023 are as follows:
Democratic Rep. Brian Higgins: 238.9%
Republican Rep. Mark Green: 122.2%
Republican Rep. Garret Graves: 107.6%
Republican Rep. David Rouzer: 105.6%
Democratic Rep. Seth Moulton: 80%
Additionally House Speaker Nancy Pelosi recorded a return of 65.5%, ranking among the higher-performing members.
Given that the S&P 500 Index recorded an annual return of 23.8% in 2023, these results indicate substantial outperformance. Notably, Democratic Rep. Brian Higgins achieved a remarkable return of 238.9%, a performance level far beyond what most average investors could expect.
2. Party-Based Sectoral Investment Trends
From a party perspective:
Democratic House members recorded an average return of approximately 33.0%,
Republican House members achieved an average return of 19.0%.
The higher returns of the ruling Democratic Party members are analyzed as a result of the government promoting policies to support sustainable technological innovation. Democratic lawmakers tend to focus their investments in sectors such as artificial intelligence (AI), cloud computing, and renewable energy technologies.
The report shows that lawmakers have a tendency to concentrate their investments in specific industries. Notably, the technology and energy industries were the sectors that garnered the most interest among politicians throughout 2023, reflecting the policy differences and interests of each party in their trading tendencies.
Conclusion
Politician transaction data is not merely intriguing but serves as a valuable tool for investors to develop actionable strategies. Politicians are often directly or indirectly exposed to critical information related to economic policies, legislative changes, and industry regulations. Analyzing trading patterns based on this information offers practical insights for predicting market trends.
By studying the industries that politicians frequently trade in or the timing of their concentrated transactions, investors can better anticipate which sectors might experience future growth. Actively analyzing such data can enhance investors’ ability to predict significant market changes and adapt their strategies accordingly.
To analyze the relationship between information asymmetry and the returns generated when U.S. senators use political information to trade stocks, this review examines the study titled “A Dilemma of Self-interest vs Ethical Responsibilities in Political Insider Trading” by Jan Hanousek et al., published in the Journal of Business Ethics in October 2022. The study empirically investigates whether senators’ stock trades contradict social contract theory and explores the informational value of their transactions.
Methodology
This study is based on a dataset of 8,064 securities transactions, including 7,092 stock trades disclosed by U.S. senators under the STOCK Act between 2012 and 2019. To measure the information asymmetry associated with senators’ transactions, the study employs Abnormal Idiosyncratic Volatility (AIV), a metric that captures changes in stock volatility during specific events and is particularly suited for evaluating the likelihood of trades based on insider information.
Key Points of Methodology
Abnormal Idiosyncratic Volatility (AIV): This measures the level of information asymmetry by analyzing volatility changes around trading days compared to normal periods.
Event Window: A 5-day event window (2 days before and after the trade day, including the trade day) was used to calculate abnormal volatility and compare it with other periods.
Focus: The study sought to determine whether senators’ trades generated higher AIV than major corporate events like quarterly earnings announcements or mergers and acquisitions (M&A), linking political insider information to stock returns.
AIV Calculation
Transaction Volatility (IVATT): Daily volatility during the 5-day event window was measured, excluding market-wide factors.
Non-Transaction Volatility (IVNAT): Volatility during other periods of the year was calculated similarly.
AIV: The difference between IVATT and IVNAT. A higher AIV indicates a greater likelihood that specific information influenced the stock on the transaction day.
The study confirmed that senators’ stock trades are associated with significant abnormal returns. Specifically:
Stocks traded by senators recorded an average excess return of 4.9% over three months after the transaction date.
As shown in the data, senators’ trades consistently outperformed the market over holding periods of 1 week, 2 weeks, 1 month, 2 months, and 3 months.
In terms of information asymmetry, senators’ trades recorded an average Abnormal Idiosyncratic Volatility (AIV) of 3.6%, which is three times higher than the 1.1% AIV observed during major corporate earnings announcements. This indicates that senators’ stock transactions are accompanied by significant information asymmetry.
Such asymmetry suggests that the political information held by senators carries substantial value in the stock market and demonstrates that senators participate in market transactions from a privileged position of informational advantage over other investors.
Conclusion
This study confirms that senators’ stock trades induce information asymmetry and exhibit a strong correlation with stock price volatility. Specifically, the timing of their transactions aligns with heightened AIV, indicating that the information senators use is indeed reflected in stock prices.
The existence of information asymmetry between U.S. politicians and the general public, coupled with the fact that senators achieve market-beating returns by holding stocks for three months after their purchases, suggests that trading based on senators’ political information could hold value as an investment strategy.
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