QUICK Data Factory | Japanese Alternative Data for Investment

Stay-at-home Rate Data Provided by AIoT Cloud

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available

REST API

Macro-level

Partner

Score
Maket Trends

Geolocation Data Provided by AIoT Cloud

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Geolocation Data Provided by AIoT Cloud<p>AIoT Cloud Inc., a wholly owned subsidiary of Sharp Corporation (Sharp), supports various companies’ marketing activities by utilizing lifestyle data acquired from AIoT<font size=-3>*1</font> home appliances.<br>
Geolocation information obtained through the smartphone app with users’ prior permission is statistically processed to provide data in a manner that individuals cannot be identified. Location information is obtained with the app running constantly in the background.<font size=-3>*2</font> The composition of the users’ residential areas is similar to the population distribution in Japan. Statistical data can be used to forecast the performance of companies in a given area.</p>

<p><font size=-1>*1 “AIoT” is a vision of combining artificial intelligence (AI) and the Internet of Things (IoT) to connect everything with AI in the cloud, transforming it into an entity closer to people. “AIoT” is a registered trademark of Sharp.<br>
*2 Except when a smartphone is turned off.</font></p>

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REST API

Ticker-level

Partner

Geolocation

Purchase Statistics Data Provided by Toshiba Data

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Purchase Statistics Data Provided by Toshiba Data<div class="heading">Description</div>
<p>Purchase statistics data is obtained from the Digital Receipt service developed by Toshiba Group, with its strength in point of sales (POS) systems. This service provides data such as “when, where, what, how much, and how many were purchased” in a manner that does not identify the retailer or purchaser. From changes in purchasing behavior, it can be used to predict the sales and business performance of the manufacturer.</p>
<div class="heading">Case Study</div>
<a href="https://corporate.quick.co.jp/en/japanmarketsview/equity/reading-ahead-food-manufacturers-performance-based-on-consumers-purchase-statistics-data/" style="display: inline-block;padding-right: 1.5em;background-position: right center;background-size: 1.3em;background-repeat: no-repeat;background-image: url(/data-factory/wp-content/uploads/dataSetBlank_qk.svg);color: #00464f;" target="_blank">Reading Ahead Food Manufacturers’ Performance</a>

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REST API

Ticker-level

Partner

POS

TSE Detail Breakdown Trading Data by Issue

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available

REST API

Ticker-level

QUICK

Equity

TONA Averages & TONA Index

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TONA Averages & TONA Index<div class="heading">Description</div>
<p>TONA Averages and TONA Index are the benchmarks published by the Bank of Japan that are calculated by compounding the uncollateralized overnight call rate. TONA is one of the alternative interest rates to the Japanese Yen LIBOR. However, when used as the substitute, it must be compounded using the daily TONA. Through the use of TONA Averages and TONA Index, market participants can share the daily compounded TONA levels and reduce the burden involved in calculation and reconciliation.</p>
<p><b>TONA Averages</b><br>
Interest rates derived from the daily compounded TONA for the period starting exactly 30 days, 90 days, and 180 days prior to the reference date of the benchmark (each business day) until the reference date</p>
<p><b>TONA Index</b><br>
Asset valuation on the reference date assuming the asset is managed using TONA, with 100 as of June 14, 2017 (the date on which the Bank of Japan established “Uncollateralized Overnight Call Rate Code of Conduct”)</p>
<p>*By specifying the start and end dates, the interest rate compounded by TONA for any given period can be calculated from the historical data of the TONA Index.</p>

available

REST API

Macro-level

QUICK

Interest Rate

Tokyo Term Risk Free Rate (TORF)

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Tokyo Term Risk Free Rate (TORF)<div class="heading">Description</div>
<p>Tokyo Term Risk Free Rate (TORF) is a Japanese yen short-term interest rate calculated and published by QUICK Benchmarks Inc. (QBS). TORF is one of the alternative benchmarks to replace the London Interbank Offered Rate (LIBOR), and is designated as a “Specified Financial Benchmark” stipulated by the Financial Instruments and Exchange Act.</p>
<p>It is calculated based on the data of derivative transactions whose underlying asset is the “uncollateralized overnight call rate,” which contains almost no credit risk of financial institutions. Like other interest rate benchmarks such as LIBOR, TORF has a feature in that the rate is fixed as of the start of the interest rate calculation period.</p>
<p>Today, it is widely used by financial institutions around the world as the base interest rate for loans with Japanese yen interest rates and derivatives transactions.</p>
<p>For more information, please see below.<br>
<a href='https://corporate.quick.co.jp/en/torf' target='_blank'>https://corporate.quick.co.jp/en/torf</a><p>
<p>* Use of TORF requires a license agreement with QUICK.</p>

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File (SFTP get)
REST API

Macro-level

QUICK

Interest Rate

QUICK Equity Risk Premium

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available

REST API

Ticker-level

QUICK

Equity

TV Metadata (1: TV Program Data / 2: TV-CM (Advertising) Data)

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TV Metadata (1: TV Program Data / 2: TV-CM (Advertising) Data)<div class="heading">Description</div>
<p>TV Metadata is a dataset that 24/7 TV broadcasts (programs and TV-CMs) convert into text and databases.</P>
<P>This data indicates when, where, what, how, and how many seconds were broadcast. You can aggregate broadcast volume and content by brands.</P>
<P>By observing the amount of marketing activity of companies, it is possible to use it as an alternative data as a leading indicator of stock prices and an upward signal, and it can be indexed as a TV index by aggregating the broadcast volume of each brand in TV metadata by time series change. With TV information that affects sentiment, it is possible to discover stocks and make investment decisions from various perspectives, and it is also possible to create new indicators in combination with other data.</p>
<div class="heading">Case Study</div>
<a href="https://corporate.quick.co.jp/en/japanmarketsview/equity/looking-ahead-at-corporate-performance-with-tv-data/" style="display: inline-block;padding-right: 1.5em;background-position: right center;background-size: 1.3em;background-repeat: no-repeat;background-image: url(/data-factory/wp-content/uploads/dataSetBlank_qk.svg);color: #00464f;" target="_blank">Looking Ahead at Corporate Performance with TV Data</a>
<a href="https://corporate.quick.co.jp/en/japanmarketsview/equity/reading-ahead-corporate-sales-based-on-tv-commercial-airing-data/" style="display: inline-block;padding-right: 1.5em;background-position: right center;background-size: 1.3em;background-repeat: no-repeat;background-image: url(/data-factory/wp-content/uploads/dataSetBlank_qk.svg);color: #00464f;" target="_blank">Reading Ahead Corporate Sales Based on TV Commercial Airing Data</a>

available

REST API

Ticker-level

Partner

Score
Maket Trends

Nikkei Earnings Summary

提供 Source：

Nikkei Inc.

available

REST API

Ticker-level

News
Corporate Earnings
Corporate Disclosure
NLP

Honda Drive Data Service (Customer Index)

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Honda Drive Data Service (Customer Index)<div class="heading">Description</div>
<p>This data set represents visiting information to parking lot associated with the stocks (retail or BtoB company), and that is calculated using the driving data of Honda car users who have given consent. Since the sampling frequency of the original data is high, it is possible to grasp the time spent in seconds.  it is possible to grasp the change in the average stay time even though the offering data set is statistically processed. It would be very helpful information not only for predicting the increase/decrease in the number of customers from the number of visiting vehicles, but also for predicting the increase/decrease in the customer unit price.</p>
<div class="heading">Case Study</div>
<a href="https://corporate.quick.co.jp/en/japanmarketsview/equity/reading-ahead-retail-performance-using-vehicle-driving-data/" style="display: inline-block;padding-right: 1.5em;background-position: right center;background-size: 1.3em;background-repeat: no-repeat;background-image: url(/data-factory/wp-content/uploads/dataSetBlank_qk.svg);color: #00464f;" target="_blank">Reading Ahead Retail Performance Using Vehicle Driving Data (1)</a>
<a href="https://corporate.quick.co.jp/en/japanmarketsview/equity/reading-ahead-retail-performance-using-vehicle-driving-data-2/" style="display: inline-block;padding-right: 1.5em;background-position: right center;background-size: 1.3em;background-repeat: no-repeat;background-image: url(/data-factory/wp-content/uploads/dataSetBlank_qk.svg);color: #00464f;" target="_blank">Reading Ahead Retail Performance Using Vehicle Driving Data (2)</a>
<a href="https://corporate.quick.co.jp/en/japanmarketsview/equity/manufacturers-production-forecasts-made-possible-using-factory-visitation-data/" style="display: inline-block;padding-right: 1.5em;background-position: right center;background-size: 1.3em;background-repeat: no-repeat;background-image: url(/data-factory/wp-content/uploads/dataSetBlank_qk.svg);color: #00464f; font-size: 15px;" target="_blank">Manufacturers’ Production Forecasts Made Possible Using Factory Visitation Data</a>

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REST API

Ticker-level

Partner

Geolocation

I Know First Stock Forecast Based On a Predictive Algorithm

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I Know First Stock Forecast Based On a Predictive Algorithm<div class="heading">Description</div>
<p> I Know First is a fintech company that provides state of the art self-learning, AI based algorithmic forecasting solutions for the capital markets to uncover the best investment opportunities. The company provides daily investment forecasts based on an advanced, self-learning algorithm. The AI Algorithm was developed by Dr. Lipa Roitman, a scientist with over 20 years of research and experience in artificial intelligence (AI) and machine learning (ML) fields. The concept of the current algorithm has crystallized following years of prior research into the nature of chaotic systems. 
 The underlying technology of the algorithm is based on artificial intelligence and machine learning, and incorporates elements of artificial neural networks and genetic algorithms through which we analyze, modelize, and predict the stock market. The algorithm is adaptable, scalable, and features a Decision Support System (DSS) to optimize the information produced by the years of data inputted.
 The algorithm generates daily market predictions for stocks, commodities, ETF’s, interest rates, currencies, and world indices for the short, medium and long-term time horizons.
 The I Know First algorithm is designed for large financial institutions, banks, and hedge funds in the capital market as well as private investors looking for an advanced algorithmic support system. The algorithm is currently tracking and predicting a growing universe of over 10,000 financial assets.
 The I Know First team is focused on providing comprehensive algorithmic solutions to its investors. Employees in each of our R&D, Operations, Analytics, and Marketing departments are dedicated to providing the best products and support to our clients, while maintaining the highest level of ethical conduct and transparency.</p>

available

Application

Ticker-level

Partner

Application
Global
Score
Equity

SMACOM（SMART COMPASS）

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available

REST API

Ticker-level

Partner

Application
Score
News
Corporate Earnings
Corporate Disclosure

KDDI Location Data (location-specific movement data)

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available

REST API

Ticker-level

Partner

Geolocation

QUICK Forecast

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available

REST API

Ticker-level

QUICK

Corporate Earnings

News analysis

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Others

Ticker-level

QUICK

Score
News
NLP

QUICK Finer Compass (Bank-Specific Financial Data)

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available

REST API

Macro-level

QUICK

Corporate Earnings
Corporate Disclosure
Regulatory

Share Buybacks Data

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Share Buybacks Data<div class="heading">Description</div>
<b>Summary</b>
Specialized data only in share buybacks of listed companies in Japan

<b>Point</b>
1. Provide well-maintained share buybacks data
2. Include original items in addition to public information
3. Monitor the progress and predict the timing based on our share buybacks data

<b>Data Items (Example)</b>
･Announcement date
･Purchase method
･Share buyback progress

<b>Data Collection & Process Method</b>
QUICK extracts share buybacks data from corporate disclosure information, and calculates original items."

<div class="heading">Case Study</div>
Based on Share Buybacks data and financial condition, we evaluated the predictability of future share buybacks. The study found that buybacks can be predicted with a high degree of accuracy and that the predictions can be used for investment strategies.
For details: <a href="/en/japanmarketsview/equity/evaluating-predictability-of-share-buybacks/" style="display: inline-block;padding-right: 1.5em;background-position: right center;background-size: 1.3em;background-repeat: no-repeat;background-image: url(/wp-content/themes/quickDataFactory/images/dataSetBlank.svg);color: #00464f;" target="_blank">Evaluating Predictability of Share Buybacks</a>
<a href="/en/japanmarketsview/equity/evaluating-predictability-of-share-buybacks/" target="_blank"><img src="/data-factory/wp-content/uploads/241106_jmv_img3.png" alt=""></a>

available

REST API

Ticker-level

QUICK

Corporate Disclosure
Equity

Results Forecast Comment

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available

REST API

Ticker-level

QUICK

Corporate Earnings
Corporate Disclosure
NLP

Qr1 Access Ranking

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Qr1 Access Ranking<div class="heading">Description</div>
<b>Summary</b>
Access ranking for all listed stocks in Japanese market every 30 minutes

<b>Point</b>
1. Check the market trend directly where market interest is going
2. Qr1 is the top share information terminal for financial advisors in Japan
3. Provide the ranking data daily and 30-min basis

<b>Data Items (Example)</b>
･ Rank
･ Stock code
･ Access ratio

<b>Data Collection & Process Method</b>
QUICK collects usage logs from Qr1 and calculate access ranking of individual stocks with eliminating individual information.

<div class="heading">Case Study</div>
<a href="https://corporate.quick.co.jp/en/japanmarketsview/equity/investment-strategies-using-access-ranking-data/" style="display: inline-block;padding-right: 1.5em;background-position: right center;background-size: 1.3em;background-repeat: no-repeat;background-image: url(/data-factory/wp-content/uploads/dataSetBlank_qk.svg);color: #00464f;" target="_blank">Investment Strategies Using Access Ranking Data</a>

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REST API

Ticker-level

QUICK

Maket Trends
Statistics

Update on Shareholder Benefits

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available

REST API

Ticker-level

QUICK

Equity

Margin Trading Restriction (Includes Negative Interest per diem)

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available

REST API

Ticker-level

QUICK

Score
Regulatory

QUICK Themed Stocks

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available

REST API

Ticker-level

QUICK

Maket Trends
Equity

NQN News

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NQN News<div class="heading">Description</div>
This service is mainly Japanese market-related news provided by Nikkei QUICK News, Inc. (NQN) that is one of the most trusted news sources for market professionals in Japan.
Market outlook and schedules are provided before the Tokyo market opening. While the market is in session, news on indices, stocks for selective buying, and other topics are provided in timely manner. 
Around 12:00 JST, a summary of the morning session and an explanation of what to look out for in the afternoon session are offered. After 15:00 JST, the day's market is reviewed and commentary for deliberating the following day's strategies is delivered.

<div class="heading">Case Study</div>
Based on the assumption that stock prices fluctuate in response to NQN news, we constructed an investment method to follow the stock price trend before and after news had been released, and we evaluated investment performance. The top 10 stocks with the highest number of news releases outperformed the TOPIX return of -5.26% for the one-month investment period ending in December 2022.
For details: <a href="/en/japanmarketsview/equity/trend-following-strategy-using-nqn-news/" style="display: inline-block;padding-right: 1.5em;background-position: right center;background-size: 1.3em;background-repeat: no-repeat;background-image: url(/wp-content/themes/quickDataFactory/images/dataSetBlank.svg);color: #00464f;" target="_blank">Trend-following Strategy Using NQN News</a>
<a href="/en/japanmarketsview/equity/trend-following-strategy-using-nqn-news/" target="_blank"><img src="/data-factory/wp-content/uploads/231018_jmv_img4_3.png" alt=""></a>

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REST API

Ticker-level

QUICK

News

QUICK Licensed News

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available

REST API

Ticker-level

QUICK

News

Stock Regulatory Information File

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Stock Regulatory Information File<div class="heading">Description</div>
<p>This file service includes the following items;
Exchange code, Collection on the same day classification, Buying at the market forbidden classification, Buying at broker’s own account forbidden classification, Proprietary trading forbidden classification, Daily price limit classification, Base price for daily priced limit/Upper limit/Lower limit, Disclosure issue classification, Liquidation / supervision post classification, Assignment schedule classification, Assignment scheduled date, Daily disclosure / stock under restriction / stock under observance, Loans for margin transaction restriction classification, Selling short classification (restriction of stock loan applications), Physical settlement classification (restriction of stock loan applications), Effective date/notification date of restriction of loans for margin transaction, Margin requirement ratio change classification, Margin requirement ratio, Margin requirement-to-cash ratio, Margin requirement ratio change effective date, Buying on margin and selling shorts forbidden etc.</p>

available

File (SFTP get)

Ticker-level

QUICK

Equity
Regulatory

Corporate Action File

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available

File (SFTP get)

Ticker-level

QUICK

Equity

Stock Master File

提供 Source：

available

File (SFTP get)

Ticker-level

QUICK

Equity

QUICK Monthly Survey

提供 Source：

available

REST API

Macro-level

QUICK

Economic
Maket Trends

Company Disclosure Materials (TDnet, EDINET)

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Company Disclosure Materials (TDnet, EDINET)<div class="heading">Description</div>
<b>Summary</b>
All Japanese corporate action data

<b>Point</b>
1. Automatic analysis of the disclosures posted on TDnet and EDINET
2. Immediate deliver in text format
3. Cover all corporate actions in Japan

<b>Data Items (Example)</b>
･ Earnings forecast revision
･ Stock split

<b>Data Collection & Process Method</b>
QUICK analyzes PDF and XBRL files posted on "TDnet", the Tokyo Stock Exchange's service providing timely disclosure releases and "EDINET", the Financial Services Agency's electronic disclosure system for disclosure materials and convert into a text format.

<div class="heading">Case Study</div>
Focusing on dividend revision news from Company Disclosure Bulletin, we modeled the probability of a stock price increase toward the last cum-rights date, and assigned a “high impact” label to the news stories with a high probability of such an increase. The results show that desirable performance can be expected when investments are made based on the labeling results.
For details: <a href="/en/japanmarketsview/equity/impact-of-news-on-stock-price-fluctuations-and-investment-performance-%e2%80%95-stock-dividend-news/" style="display: inline-block;padding-right: 1.5em;background-position: right center;background-size: 1.3em;background-repeat: no-repeat;background-image: url(/wp-content/themes/quickDataFactory/images/dataSetBlank.svg);color: #00464f;" target="_blank">Evaluate Investment Performance with Dividend Revision News
</a>
<a href="/en/japanmarketsview/equity/impact-of-news-on-stock-price-fluctuations-and-investment-performance-%e2%80%95-stock-dividend-news/" target="_blank"><img src="/data-factory/wp-content/uploads/231031_jmv_img1.png" alt="Figure 1: Stock Prices and Dividend Yields after News Release on August 7, 2020, Regarding House Freedom’s Dividend Increase"></a>

available

REST API

Ticker-level

QUICK

News
Corporate Earnings
Corporate Disclosure

Monthly Sales & Business Activity Data

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Monthly Sales & Business Activity Data<div class="heading">Description</div>
<b>Summary</b>
Various corporate performance data in addition to monthly sales

<b>Point</b>
1. Reveal company business performance, without waiting for quarterly results
2. Different perspective from income statement
3. Cover 25 industries

<b>Data Items (Example)</b>
･Net sales
･Same-store sales
･Number of stores
･Number of subscribers etc.

<b>Data Collection & Process Method</b>
QUICK collects these data from companies' IR websites, standardizes into a text format.

<div class="heading">Case Study</div>
<a href="https://corporate.quick.co.jp/en/japanmarketsview/equity/reading-future-financial-results-for-food-service-industry-using-monthly-sales-business-activity-data/" style="display: inline-block;padding-right: 1.5em;background-position: right center;background-size: 1.3em;background-repeat: no-repeat;background-image: url(/data-factory/wp-content/uploads/dataSetBlank_qk.svg);color: #00464f;" target="_blank">Reading Future Financial Results for Food Service Industry</a>
<a href="https://corporate.quick.co.jp/en/japanmarketsview/equity/reading-future-earnings-revisions-based-on-monthly-sales-business-activity-data/" style="display: inline-block;padding-right: 1.5em;background-position: right center;background-size: 1.3em;background-repeat: no-repeat;background-image: url(/data-factory/wp-content/uploads/dataSetBlank_qk.svg);color: #00464f;" target="_blank">Reading Future Earnings Revisions</a>

available

REST API

Ticker-level

QUICK

Corporate Earnings
Corporate Disclosure

QUICK Consensus

提供 Source：

QUICK Consensus<div class="heading">Description</div>
QUICK Consensus has the widest coverage in Japanese equities including mid and small stocks in Japan since 2005. We calculate consensus on daily basis by averaging brokers’ estimates such as sales, net income, EPS, DPS, ROE, EBITDA etc. We offer point in time consensus data.

<div class="heading">Case Study</div>
Based on the Revision Index (RI) calculated from the QUICK consensus, we devised an investment strategy to determine when to buy stocks by RI calculated at the end of each month by industry. When we backtested using the RI, the performance of drugstores was the highest among the industries we evaluated.
For details: <a href="/en/japanmarketsview/equity/efficient-investment-strategies-using-quick-consensus/" style="display: inline-block;padding-right: 1.5em;background-position: right center;background-size: 1.3em;background-repeat: no-repeat;background-image: url(/wp-content/themes/quickDataFactory/images/dataSetBlank.svg);color: #00464f;" target="_blank">Efficient Investment Strategies Using QUICK Consensus</a>
<a href="/en/japanmarketsview/equity/efficient-investment-strategies-using-quick-consensus/"  target="_blank"><img src="/data-factory/wp-content/uploads/img-221201-2.png" alt=""></a>

available