Data

Factiva

by

Factiva

Nithisha CHALLA

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024) delves into the essentials of Factiva, its features, and its applications, showcasing why it remains indispensable for professionals and academics alike working in business and finance.

Introduction

In the fast-paced world of business and finance, access to accurate, reliable, and up-to-date information is paramount. Factiva, a subscription-based database owned by Dow Jones & Company, is a cornerstone for researchers, financial analysts, and business professionals seeking high-quality data for decision-making.

The History of Factiva

Factiva was launched in 1999 as a joint venture between Dow Jones & Company and Reuters, two industry titans in financial news and information services. The aim was to create a unified platform catering to the growing need for consolidated global news and business data access. By integrating Dow Jones’s deep archives and Reuters’ real-time data capabilities, Factiva emerged as a pioneering solution for professionals in any sector, especially finance.

Logo of Factiva.
Logo of Factiva
Source: the company.

Factiva is a premier business intelligence platform offering access to a vast array of global content, including news, company information, market data, and industry insights. It integrates thousands of sources from over 200 countries in more than 30 languages. These sources include major newspapers, trade journals, industry publications, and multimedia content.

In 2006, Dow Jones acquired full ownership of Factiva, streamlining its integration with other Dow Jones products, including The Wall Street Journal. Over the years, Factiva has evolved into a sophisticated tool incorporating artificial intelligence (AI) and machine learning for advanced data analytics, thus staying ahead in a competitive information services market.

Key Features

Certain key features of Factiva make it very useful as a database such as its Extensive Content Coverage, Search and Filter Options, Data Analytics and Visualization, Company Profiles, and Industry Reports.

As an example, the picture below presents the news about Apple in the Factiva Interface.

Factiva Interface
Factiva Interface
Source: the company.

Extensive Content Coverage

Factiva includes over 33,000 sources, such as The Wall Street Journal, The Financial Times, The New York Times, and Reuters. It features specialized publications in sectors like energy, healthcare, and technology. Archival content dates back decades, enabling trend analysis and historical research.

Search and Filter Options

Advanced search tools allow users to refine searches using keywords, topics, dates, or specific publications. Filters can narrow results by geography, industry, or company size.

Real-Time News

Factiva provides real-time updates on financial markets, economic changes, and global events. Alerts and notifications keep users informed of developments affecting their areas of interest.

Data Analytics and Visualization

Users can extract, analyze, and visualize data to identify patterns and insights. Tools include charts, graphs, and export options for seamless integration with other software.

Company Profiles and Industry Reports

Comprehensive profiles offer financials, competitors, and SWOT (Strengths Weakness Opportunity and Threats) analyses for thousands of companies. Industry reports provide market trends, regulatory updates, and forecasts.

Applications in Finance and Business

There are several applications of Factiva in finance and business such as Investment Research, Risk Management, Academic Research, and Public Relations and Marketing.

  • Investment Research: Financial analysts rely on Factiva for market trends, earnings reports, and competitor analysis to guide investment decisions.
  • Risk Management: Businesses use Factiva to monitor geopolitical events, economic risks, and compliance-related developments.
  • Academic Research: Factiva’s extensive archives are invaluable for finance students and researchers studying historical market behavior or conducting case studies.
  • Public Relations and Marketing: PR professionals use Factiva to monitor media coverage, track competitors, and evaluate public sentiment.

Advantages and Limitations of Factiva

Though there are multiple advantages of using this database there are also certain limitations which we have to consider.

Advantages of Factiva

  • Global Reach: Access to international publications ensures a well-rounded perspective.
  • Customizable Dashboards: Users can tailor the interface to prioritize relevant content.
  • Reliable Sources: Factiva aggregates data from reputable and verified sources.
  • Ease of Integration: APIs (Application Programming Interface) allow integration with other platforms for streamlined workflows.

Challenges and Limitations

  • Cost: Factiva’s subscription model can be expensive for individuals or small businesses. The pricing is on the request basis of the data.
  • Complexity: The platform’s depth may require training for optimal use.
  • Access Restrictions: Some content may have geographical or licensing restrictions.

Why Factiva Matters in 2024

With the explosion of information and the increasing risk of misinformation, Factiva’s role as a curated, reliable database is more critical than ever. Its ability to distill vast quantities of data into actionable insights makes it a vital tool for navigating the complexities of modern business and finance. Moreover, the integration of advanced technologies such as AI in Factiva enhances predictive analytics, enabling users to anticipate market movements and mitigate risks proactively.

Conclusion

Factiva exemplifies the power of information in driving informed decision-making. Its rich history, innovative features, and significant economic implications underscore its enduring relevance in a data-driven economy. Whether you’re a student aiming to excel in finance or a professional seeking a competitive edge, Factiva equips you with the tools to succeed in a knowledge-driven world.

Why should I be interested in this post?

By embracing Factiva, users, and students mainly gain not just data but the clarity and confidence to act on it effectively, ensuring better outcomes for businesses, academia, and industries at large.

Related posts on the SimTrade blog

   ▶ Nithisha CHALLA Datastream

   ▶ Louis DETALLE The importance of data in finance

   ▶ Nithisha CHALLA CRSP

   ▶ Nithisha CHALLA Compustat

   ▶ Nithisha CHALLA Statista

Useful resources

Dow Jones Factiva – Global News Monitoring, Business Intelligence Platform

Dow Jones What is Factiva?

European University Institute (EUI) Factiva news and company database

Wikipedia Factiva

About the author

The article was written in December 2024 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024).

Institutional Brokers’ Estimate System (IBES)

by

Institutional Brokers’ Estimate System (IBES)

Nithisha CHALLA

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024) presents Institutional Brokers’ Estimate System (IBES), which provides market consensus for the financial community.

Introduction

In the fast-paced world of finance, the Institutional Brokers’ Estimate System, commonly known as IBES (often written as “I/B/E/S.”), stands as a linchpin in providing investors, analysts, and financial professionals with reliable forecasts and estimates.

IBES, with its roots in comprehensive data aggregation, takes center stage in providing a panoramic view of financial analysts’ estimates and forecasts. IBES acts as a centralized repository of earnings estimates, revenue projections, and other key financial metrics, serving as a vital resource for investors seeking actionable insights.

History

IBES was created in 1976 by the investment research firm, Lynch, Jones & Ryan (LJR). It was developed to address the need for a centralized system to collect and distribute earnings estimates from brokerage analysts. The database offers historical data from its inception and extends internationally from 1987. Over the years, IBES went through several ownership changes. In 1988, LJR was acquired by Primark Corporation. Primark Corporation later merged with Thomson Financial Services in 1990, and IBES became part of Thomson Financial. Thomson Financial subsequently merged with Reuters Group in 2008 to form Thomson Reuters. Thus, IBES became a part of Thomson Reuters. Then Thomson Reuters was acquired by private equity firm Blackstone Group and rebranded as Refinitiv in 2018. Refinitiv was later acquired by the London Stock Exchange Group (LSEG) in 2020. Therefore, IBES is currently owned by the London Stock Exchange Group (LSEG) through its subsidiary, Refinitiv.

Key Components of IBES

Earnings Estimates

IBES compiles earnings estimates from a multitude of financial analysts, providing a consensus view on the expected earnings of a company. As of the latest data, it aggregates forecasts from over 16,000 analysts worldwide, providing a robust consensus on expected earnings for companies across the globe.

Revenue Projections

Beyond earnings, IBES includes revenue projections, offering a comprehensive view of a company’s expected top-line growth. Analysts’ forecasts are aggregated to provide a consensus estimate, aiding investors in gauging revenue expectations.

Forecasts for Key Financial Metrics

IBES extends beyond earnings and revenue, encompassing a spectrum of financial metrics. This includes estimates for operating margins, cash flows, and other key indicators, providing a holistic view of analysts’ expectations. The sheer volume of data—covering over 18,000 companies—ensures a comprehensive snapshot of financial expectations.

Coverage

There are certain types of coverage when we are talking about data coverage such as the Number of Companies Covered, geographic Coverage, Market Capitalization Coverage, Industry Coverage, Depth of Coverage, and Historical Coverage.

According to LSEG data analytics, I/B/E/S Estimates data is displayed at the company level, screened with rigorous quality control methods across more than 23,400 active companies in more than 90 countries, and sourced from over 19,000 analysts.

Estimates Data from over 950 firms contribute data to I/B/E/S, from the largest global houses to regional and local brokers, with U.S. data back to 1976 and international data back to 1987.

Period

IBES has evolved since its introduction in 1976 and includes international data from 1987. I/B/E/S Global Aggregates, with over 30 years of history, facilitates top-down analysis to better assess potential growth and risk as well as future value across industry, sector, index, country, and region.

Frequency

Initially focusing on annual earnings estimates, the database subsequently broadened to encompass quarterly earnings forecasts.

Firms and Financial analysts

IBES covers a wide range of firms across different industries, sectors, and regions. This includes companies listed on major stock exchanges in numerous countries and markets around the world. 950+ contributors, across 90+ in developed and emerging markets, totaling over 19,000 individual analysts – the most in the industry. Unmatchable history across more than 60,000 companies back to 1976 for North America, 1987 for other markets.

Pricing

The specific pricing for accessing I/B/E/S data is not explicitly mentioned on the IBES website and you have to make a request through the LSEG website which manages IBES. The cost likely varies based on the package and offerings selected, which can include different data sets, access methods, and service frequencies. For detailed pricing information, it’s recommended to directly contact LSEG or the respective service providers you’re interested in, as they can provide more precise details based on your specific requirements and the scale of your intended use.

For academic and non-commercial research purposes, access might be available through institutional subscriptions with specific terms of use, as seen with Aalto University’s subscription financed by the Aalto University Data Hub for its users. This indicates that the availability and cost of I/B/E/S data may vary significantly based on the type of use and the access platform. For precise pricing and package options, directly contacting the service providers is the best approach.

Use of IBES by the Financial Community

Benchmark for Analysis

IBES serves as a benchmark for investors and analysts, quantifying market reactions, it dives into the numbers behind market reactions. According to Faster Capital, studies reveal that stocks experiencing positive earnings surprises, surpassing IBES estimates, tend to outperform the market. These numerical insights underscore the practical implications of aligning investment decisions with IBES consensus.

Market Expectations

Analysts and fund managers utilize IBES to gauge market expectations for specific companies. Understanding consensus estimates aids in forming investment strategies aligned with prevailing market sentiments.

Earnings Season Preparation

During earnings seasons, IBES becomes a critical tool for investors preparing for companies’ financial releases. It provides a consolidated view of analysts’ forecasts, helping investors assess potential surprises or disappointments.

IBES and Tests of Market Efficiency

Academic works

The data was subsequently used as the basis for articles in academic finance journals attempting to demonstrate that changes in consensus earnings estimates could identify opportunities to capture excess returns in subsequent periods.

Information Dissemination

IBES plays a pivotal role in disseminating timely information. As estimates are constantly updated based on new information, IBES ensures that market participants have access to the latest insights, contributing to market efficiency.

Pros and Cons

Given its history and operations in huge industries and markets, we certainly need to know the pros and cons of the IBES estimates. In terms of accuracy metrics, IBES relies on the accuracy of analysts’ forecasts. Statistical metrics, such as the mean absolute error (MAE), offer a quantitative evaluation of the system’s precision.

Conclusion

IBES, when viewed through a data-driven lens, transforms into more than a system and becomes a useful tool for decision-makers navigating the intricacies of financial markets.

Why should I be interested in this post?

In essence, this article discovers how the global data powerhouse, backed by impactful statistics, empowers investors, providing a data-driven lens into market expectations and offering actionable insights for informed decision-making in the dynamic world of finance.

Related posts on the SimTrade blog

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   ▶ Louis DETALLE The importance of data in finance

   ▶ Bijal GANDHI Earnings per share

Useful resources

London Stock Exchange Group (LSEG) I/B/E/S Estimates

Wikipedia Institutional Brokers’ Estimate System

Market consensus What is market consensus?

Faster Capital Navigating Markets: The Power of Market Analysis and Consensus Estimates

About the author

The article was written in March 2024 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024).

Reuters

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Reuters

Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains everything there is to know about Reuters, the international giant in the data-providing market…

Quick presentation of the company

Thomson Reuters is a leading provider of business information services. As one of the main competitors of Bloomberg, their products include highly specialized information-enabled software and tools for legal, tax, accounting and compliance professionals combined with the world’s most global news service – Reuters.

Reuters is organized in 5 different business units:

Legal Professionals: This business unit serves law firms and governments with research products, focusing on intuitive legal research powered by emerging technologies and integrated legal workflow solutions that combine content, tools and analytics.

Corporates: Designed for corporate customers from small businesses to multinational organizations, this business unit provides its clients with a full suite of content-enabled technology solutions for in-house legal, tax, regulatory, compliance and IT professionals.

Tax & Accounting Professionals: This business provides its customers with research that focuses on intuitive tax offerings and automating tax workflows.

Reuters News: Supplies business, financial and global news to the world’s media organizations, professionals and news consumers through their many platforms.

Global Print: Provides legal and tax information primarily in print format to customers around the world.

Type of people working at Bloomberg (types of jobs)

Nearly 2/3 of Reuters’ employees work in the US, the remaining third working in Asia and in Europe. The careers available at Reuters are therefore numerous and very diverse.

Indeed, the profiles needed by Reuters consists in legal professionals, corporate professionals, tax & accounting professionals and journalists. Thomson Reuters also employs many software designers to help design the Reuters’ terminals, as well as sectorial legal and corporate specialists in order to provide precise and adequate analysis.

Main competitors

As Thomson Reuters’ activities are very diverse, we will classify the main competitors of the firm in respect to the activities.

For Thomson Reuters’ business that consists of software-design, Bloomberg LLP is the most natural competitor in this space with its very famous Bloomber Terminal. The terminal business is built on a fantastic technology platform that provides comprehensive financial information. There are other competitors, such as Dow Jones Industrial Average FX Trader, which have specialized in one type of industry whereas Reuters and Bloomberg remain generalists.

Reuters’ editorial branch’s main competitors would be Bloomberg News, the Financial Times (FT), the Wall Street Journal, and other traditional financial news companies. The same goes for their TV/radio operation (their competitor would be CNBC).

Use of data in financial markets

The explosion of financial data, enabled by the Internet tremendous potential, caused an explosion of demand for financial data. As evidenced in 2006 by the British mathematician and Tesco marketing mastermind Clive Humby’s quote, “Data is the new oil”, the data market seems to be limitless.

In addition, as Bloomberg acquires many of his competitors, such as BNA and BusinessWeek, this contributes to curbing the number of data providers and improving the monopoly of Bloomberg on the data-providing market. Reuters struggles to keep up the pace of its competitor which is very well established in this market.

Useful resources

Bloomberg

Reuters

Related posts on the SimTrade blog

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About the author

The article was written in March 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Big data in the financial sector

by

Big data in the financial sector

Rayan AKKAWI

In this article, Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022) explains the role of big data in the financial sector.

Big data is a term used for contemporary technologies and methodologies that are used to collect, process, and analyze complex data. Today, data is being created at an exponential rate. In fact, and according to a 2015 IBM study, 90% of the data in the world has been created in the past two years. As big data gets bigger, it becomes even more important and essential for executives in the financial sector to stay ahead of the curve. Also, it is expected that data creation will continue to grow moving forward in time.

Big Data in The Financial Sector

For decades, financial analysts have relied on data to extract insights. Today, with the rise of data science and machine learning, automated algorithms and complex analytical tools are being used hand in hand to get a head of the curve in diferetn areas of the financial sector.

Fraud prevention

First, data has helped with fraud prevention such as identity theft and credit card schemes. Abnormally high transactions from conservative spenders and out of region purchases often signal credit card fraud. Whenever this happens, the card is automatically blocked, and a notification is sent out to the card owner. This protects users, insurance companies, and banks from huge financial loses in a small period. This also made things even easier and more practical avoiding the hassle of having to call and cancel the card. Data science comes in the form of tool like random forests that can detect a certain suspicion. In addition, and to lower the chance of identity theft, data has helped ease this process through 3D passwords, text messages, and PINT code which have backed up the safety of online transactions.

Anomaly detection

Second, data has helped the financial sector through anomaly detection. Data analysis is not only created to avoid a problem but also to detect it. For example, data today helps with catching illegal insider traders. To do so, data analysts created anomaly detection algorithms that can analyze history in trading patterns and thus detect and catch abnormal transactions of illegal traders.

Customer analytics

Third, data has helped with improving customer analytics. Data analyzes previous behavioral trends of consumers based on historical transactions and then makes future predictions of how consumers are likely to act. With the help of socioeconomic characteristics, we can create clusters of consumers and group customers based on how much money we expect to gain or lose from each client in the future. Following that, we can come up with decisions to focus on a certain type of clients to make profits and cut on other customers to make savings. Thus, financial institutions minimize human errors by utilizing data science. To achieve that, first, by identifying uncertain interactions and then monitor them going forward. Finally, prioritizing the investments most vulnerable at a given time. For example, banks use this approach to create adaptive real risk score time models to identify risky clients and those who are suitable for a mortgage or a loan.

Algorithmic trading

Fourth and most importantly data has created algorithmic trading. Machines make trading based on algorithms multiple times every second with no need for approval by a stand-by analyst. These trades can be in any market and even in multiple markets simultaneously. Thus, algorithmic trading has mitigated opportunity costs. Thus, there are algorithmic rules that can help in identifying if there is a need to trade or not to trade and reinforces business models where errors are highly penalized and then adjust hyper parameters. We can see algorithms that exploit arbitrage opportunities where they can find inconsistencies and make trades which can cause problems. The huge upside is that it is high frequency trading; whenever it will find an opportunity to make a trading, it will. However, the downside is that imprecision could lead to huge losses due to lack of human supervision. That is why sometimes human interventions are needed.

Conclusion

Thus, we can say that data has become the hottest commodity that results in getting an edge over competition. Financial institutions spend a huge amount of money to get exclusive rights to data. By having more information, they can construct better models. The most valuable commodities are not analysts but the data itself. That is how the data science has revolutionized finance.

Characteristics of Big Data

When talking about Big Data, four main characteristics need to be considered to understand the why Big Data plays a transformational role in the financial sector: volume, variety, velocity, and value.

Volume

First, the amount also known as volume of data being produced on daily basis by users has been increasing exponentially by users. This large output of data has helped create Zettabytes (1012 Gigabyte) and Yottabytes (1015 Gigabyte) of datasets in which companies can benefit by extracting knowledge and insights out of it. However, this amount of data cannot be processed using regular computers and laptops. Since they would require a lot of processing power.

Variety

Second, as the massive amount of data is being generated by multiple sources, the output of this data is unstructured making it hard to organize the data extract insights. Raw data extracted from the source without being processed does not provide any value to business as it does provide stakeholders with the ability to analyze it.

Velocity

Third, to address the issue of processing technological advancements have brought us to the tipping point where technologies such as cloud computing have enabled companies to process this large amount of data by utilizing the ability to share computational power. Furthermore, cloud platforms have not only helped in the processing part of data but by the emergence or cloud solution such as data lakes and data warehouses. Businesses are able to store this data in its original from to make sure that they can benefit from it.

Value

Finally, this brings us to the most important aspect of Big Data and that in being able to extract insights and value out of the data to understand what it is telling us. This process is tedious and time consuming however with ETL tool (Extract Transform Load) the data in its raw format is transformed so that standardized data sets can be produced. Insights can be extracted through Business Intelligence (BI) tools to create visualization that help business decisions. As well as predictive artificial intelligence models that help business predict when to take a strategic decision. In the case of financial markets, these decisions are when to buy or sell assets, and how much to invest.

Challenges Solved by Big Data in the Financial Industry

Utilizing Big Data in the finance industry presents a lot of benefits and helps the industry to overcome multiple challenges.

Data Quality

As previously mentioned, the multiple data sources present a huge challenge from a data management standpoint. Making it an ongoing and a tedious effort to maintain the integrity and the reliability of the records collected. Therefore, adding information processing systems and standardizing the data gathering and transformation processes helps improve the accuracy of the decision-making process, especially in financial services companies where real-time data enables fast decision making and elevates the performance of companies.

Data Silos

Since financial data comes from multiple sources (applications, emails, documents, and more), the use of data integration tools help simplifies and consolidate the data of the institution. These technologies facilitate processes and make them faster and more agile, which are important characteristics in the financial markets.

Robo-Advisory

Big Data and analytics have had a huge impact on the financial advisory sector. Where financial advisors are being replaced by machine learning algorithms and AI models to manage portfolio and provide customers with personalized advice and without human intervention.

Why should I be interested in this post?

This article is just an eye opener on the trends and the future state of the financial industry.

Like many other industries, the financial sector is becoming one of the most data driven field. Therefore, as future leaders it is vital to keep track and push towards data driven solutions to excel and succeed within the financial sector.

Related posts on the SimTrade blog

   ▶ All posts about Financial techniques

   ▶ Louis DETALLE Understand the importance of data providers and how they influence global finance…

   ▶ Louis DETALLE The importance of data in finance

   ▶ Louis DETALLE Reuters

   ▶ Louis DETALLE Bloomberg

Useful resources

The Future of Cognitive Computing

Five Ways to Use RPA in Finance

About the author

The article was written in May 2022 by Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022).

Bloomberg

by

Bloomberg

Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains everything there is to know about Bloomberg LP, the international leader in the data-providing market…

Quick presentation of the company

Bloomberg LP is an American financial group specialized in services to financial market professionals and in economic and financial information. Bloomberg operates as a news agency and via numerous media such as TV, radio, press, internet, and books. The company was founded in 1981 by Michael Bloomberg, former mayor of New York City.

In its early days, Bloomberg LP’s activities were only based on the exploitation of a historical database of US Treasury yield curves, bought by the founder to its former employer, the investment bank Salomon Brothers. After that, the company added on its terminals a messaging system, retransmissions of financial assets’ prices and developed financial news flows long before the watershed of Internet.
In 1990, Michael Bloomberg installed his 1,000th terminal.

Type of people working at Bloomberg (types of jobs)

The careers available at Bloomberg LP are numerous and very diverse. The Board’s needs in terms of employees mainly consist of software designers to help design the Bloomberg’s terminals, sectorial financial specialists in order to provide precise and adequate analysis.
Finally, the last kind of profiles that Bloomberg needs are journalists and more broadly, people with great writing abilities since Bloomberg LP produces a huge flow of written articles every day. Bloomberg News for instance (one of many Bloomberg LP’s subsidiaries) has over 10 000 employees which gives an idea of the written flow emitted by the company.

Main competitors

As Bloomberg’s activities are very diverse, we will classify the main competitors of the American firm in respect to the activities.
For Bloomberg’s core business, which is the terminals, Thomson Reuters is the most natural competitor in this space (with products like Kobra, Eikon, D3000). The terminal business is built on a fantastic technology platform that provides comprehensive financial information. There are other competitors, such as Dow Jones Industrial Average FX Trader, which have specialized in one type of industry whereas Bloomberg remains a generalist.

Bloomberg’s editorial branch’s main competitors would be Reuters, the FT, the Wall Street Journal, and other traditional financial news companies. The same goes for their TV/radio operation (their competitor would be CNBC).

Use of data in financial markets

The explosion of financial data, enabled by the Internet tremendous potential, caused an explosion of demand for financial data. As evidenced in 2006 by the British mathematician and Tesco marketing mastermind Clive Humby’s quote, “Data is the new oil”, the data market seems to be limitless.

In addition, as Bloomberg acquires many of his competitors, such as BNA and BusinessWeek, this contributes to curbing the number of data providers and improving the monopoly of Bloomberg on the data-providing market.

Useful resources

Bloomberg

Thomson Reuters

Related posts on the SimTrade blog

   ▶ Louis DETALLE Understand the importance of data providers and how they influence global finance…

   ▶ Louis DETALLE Reuters

About the author

The article was written in March 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

The importance of data in finance

by

The importance of data in finance

Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains the importance of data-management for corporations and how they are used to improve profitability.

According to a study published by CapGemini untitled: The data-powered enterprise: Why organizations must strengthen their data mastery, it is estimated that the gain from efficient data-management would represent 22% in terms of firm profitability.

Why is data used?

The use of data in finance can also be very useful in finance for various reasons.

Indeed, the multitude of data available allows for a deeper understanding of the market in terms of risks and opportunities. This knowledge is accompanied by an important consideration of political, social and economic factors.

As early as 2006, British mathematician and Tesco marketing mastermind Clive Humby stated “Data is the new oil.” The companies with the largest market capitalizations also bear witness to this importance of data. The ranking shows of tradingstat shows a podium of Apple, Microsoft and Google: the predominance of data-driven companies is clearly observable here.

In which finance-related fields is data used?

In finance, it is especially in the trading rooms that data has become an absolutely indispensable tool. Indeed, it is thanks to Big Data – i.e. increasingly exhaustive data, at an ever faster pace – that high frequency trading has been developed. In short, high-frequency trading makes it possible to place several thousand buy and/or sell orders in a few seconds, or even milliseconds, while optimizing risk management in order to adapt the strategy to market responses. This trading strategy allows for buying and selling in a sufficiently short period of time to avoid a potentially negative market movement during the operation.

On the other hand, retail banks (i.e. banks for individuals) are also confronted with the challenges of data-management. The development of online services offers them a better knowledge of their customers, which leads to a change in the bank’s relationship with its customers. In doing so, banks improve their ability to adapt their offer to the customer profile. Big Data also enables banks to fight fraud. Banks are now able to monitor all bank card transactions and be alerted when a user makes a payment (particularly in terms of amount, time or geographical area). For investment banks, whether it is the implementation of a more reliable scoring of credit files, the pooling of data between banks, analysis of the “sentiment” of investors for traders or the compliance of data and its processing, the indispensable character of data is no longer to be proven.

The importance of data regulation though

The use of data in finance is very useful but can be problematic when the data concerns the personal data of users or customers. In this context, financial actors are subject to ever increasing regulation and the adoption of the EU’s GDPR, in 2016, seems to be a step in this direction.

Useful resources

BlackRock L’utilisation du Big Data dans un processus d’investissement

Related posts on the SimTrade blog

   ▶ Louis DETALLE Understand the importance of data providers and how they influence global finance…

   ▶ Louis DETALLE Reuters

   ▶ Louis DETALLE Bloomberg

About the author

The article was written in March 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).