Cerexio Logo

21, Woodlands Close, #05-47 Primz Bizhub, Singapore 737854


+(65) 6762 9293

Close this search box.

Advantages And Disadvantages Of High-frequency Data

Advantages And Disadvantages Of High-frequency Data

In an age where data drives the industrial and commercial realms, understanding the ‘whats and whys’ of high-frequency data is crucial for many business players. The more the data-driven markets advance towards a digital era, the need for better data collections and wielding of amazing analytical powers has never been this strict and demanding. This is why many companies in the new era are compelled to learn more and exploit the capabilities of High-frequency Data.

High-frequency Data refers to data fluctuations observed at intricate and refined time intervals at a convenient rate for advanced data analytics capabilities. The ceaseless advancement of computer analysis resulted in high-frequency data management, which was a breakthrough in gaining benefits during data processes and data acquisition at extreme fine scales. The study of high-frequency data is monumental in probing the limitations and space of advancement of market microstructures and complex omnichannel trading processes.

Therefore, learning about high-frequency data can help statisticians, economists, market strategists, and many data-driven specialists promptly narrate lucrative stories by manipulating colossal amounts of business data. In this article, we focus on allowing the reader to understand the use, advantages, and disadvantages of high-frequency data and identify the role of high-frequency data in the current disruptive business world.

Importance of High-frequency Data in The Dynamic Business

High-Frequency Data is majorly used in intraday observations of transactions and other rapidly fluctuating datasets (mainly in financial domains). High-Frequency Data primarily refers to financial analysis of data at an efficient rate in a particular time frame (initially referred to as a ‘tick’). This method is the out-turn of a data collection method that was called tick-by-tick data. A ‘tick’ refers to collecting data from a single event to another; an event can be a transaction, a change in data behaviour, a quote, or any other event that can be measured as a separate occurrence. But due to the limitations of this method- such as collecting copious amounts of ticks for a day- the emergence of High-frequency Data was encouraged. Every passing minute leads to the accumulation of an abundance of data in the business world; therefore, High-frequency Data becomes crucial in many unstable financial environments.

The importance of observing data at a finer time scale is much higher, especially when they are capitalised on to understand the realised volatility and the microstructures of markets. High-frequency Data is used to analyse the dynamics of intraday transaction-by-transaction of certain aspects like prices, volumes, quantities, and other measurable values. For example, this data collection method does not stop by vaguely saying that a specific value meets equilibrium; it takes the analysis a step further by explaining the speed and routes taken in reaching equilibrium. These capabilities enabled by High-frequency Data lead to interestingly challenging econometrics with fresher and innovative ways of data analytics. Employing High-frequency Data helps everyday investors to understand dynamic markets (like various financial markets) since these environments tend to present thousands of prices in a single market per day.

The importance of High-frequency Data is primarily highlighted in Market Microstructure. Market Microstructure can be generally defined as a study of dynamic and convoluted financial markets and illustrate and define how they operate in different events. Market Microstructure is one of the most recognised and vastly used economic research methods used since it capitalises on advanced algorithms and the benefits of electronic trading. It studies many events and financial situations such as determinants of quotes, exchange structures, intraday trading behaviours, transaction costs, trading venue changes, and more. These economic events are mushrooming in the Business Finance World; therefore, High-frequency Data is used by financial researchers and analysts in gaining a solid analytical approach and gaining advantages in sophisticated financial decision-making in real-time. To better understand High-frequency Data, it is essential to independently understand the advantages and disadvantages, which will be further explained in this article.

Noteworthy Advantages of Using High-frequency Data

High-frequency Data gives the advantage for data maneuverers to pinpoint noteworthy moments of various events. It can be agilely and effortlessly implemented in significant caveats and circumstances in the financial market, such as technical problems and limitations in inaccurate data reporting processes. Some of the advantages of High-frequency Data are explained below.
Use A Narrow Time Window in Liquid Market Environments
High-frequency Data can be used as a data collection method to measure reactions in liquid markets. It provides a comprehensive time window to detect reactions of monetary policies in certain events- whether they are surprised or not when hearing specific news at one particular time. This provides data analysts the necessary insight to eliminate noises coming from irrelevant markets and narrow down analytical scopes with appropriate crowd-in or crowd-out effects. For example, in instances where the impact of exchange rate changes against the reaction of market domains are studied, High-frequency Data is monumentally advantageous.
Executing Comprehensive Government Surveys
This method allows thriving governmental officials, investors, academics, economists, and policymakers to gain insightful knowledge about certain economic activities in certain situations. Many countries’ better economic analytical methods are demanded, especially after the hindrance caused by the COVID-19 pandemic. For example, the government of the United Kingdom used High-frequency Data to find that clothing and footwear sales rapidly upsurge after relaxing the COVID-19 lockdown and restrictions in London.

What are The limitations of High-frequency Data?

Compared to other data collection methods, High-frequency Data stands ahead as an advantageous method of analysing and capturing actionable business insights for many companies. However, there are a few limitations in High-Frequency Data too. Two of the most famous limitations in High-frequency Data are explained in detail below.
Erroneous Data Collection
The probability of making errors in high-frequency datasets is higher than in low-frequency datasets because of a range of issues. Human errors are one of the drawbacks that lead to inaccuracy in high-frequency data collection. Human errors can be of two types: intentional human errors and unintentional human errors. International human errors refer to ‘dummy’ quotes and templates, and unintentional human errors refer to mistakes such as typing mistakes, unintentional data duplications, and more. Another type of error is computer errors which are also known as technical issues. These errors hinder the data analytical processes, especially when rapid data is accumulated in linear time frames.
Limitations in Data Management
High-frequency Data plays the primary role in cleansing data via a utilisation process of algorithmic functions. This helps in dispelling irrelevant data, unnecessary data, and inaccurate data from rich data sets. In cleansing, organising, and managing data to meet the specific needs of analysis, High-frequency Data is the best mode of data collection to execute the proper research. But due to various events, managing data in certain circumstances can be daunting, especially during irregular spacing, market openings and closing events, and bid-ask bounces. Therefore, High-frequency Data collection requires accurate and reliable data management efforts.

Where are We Heading with These Analytical Advancements?

Regardless of the limitations – such as trading errors, unfair market speed, denial of service, magnified market movements- of High-frequency Data, the uses and benefits indeed outweigh the cons of this data collection method. This method provides analytical breakthroughs that can help organisations turn tables in the data-driven digital markets in the new age, with the capabilities and functionalities of modern technologies to tackle High-frequency Data in critical market events. This method keeps advancing and disrupting with better data technologies that help companies resourcefully capture relatable and accurate insights that can help them forecast impactful market fluctuations and take futureproof corporate decisions. It also allows practitioners of dynamic markets to study market microstructures, proactively isolate unique data patterns and unusual data behaviours, and trim down business decisions making life cycles.

Cerexio is one of the advanced software solution providers in Singapore that enables modern data solutions for data-heavy establishments to remain relevant and stable in liquid and illiquid markets. Cerexio Data Streaming and Cerexio Multi-protocol Drivers are two of the most recognised solutions for tackling High-frequency Data sets and generate actionable business intelligence for data-dependent industrial practitioners worldwide. Connect with the Cerexio Client Support Team to learn more about how our solutions help your company tackle High-frequency Data in real-time.

The above information helps the reader to have a sound understanding of how modern companies capitalise on High-frequency Data and what the limitations are in High-frequency Data that they have to be careful about. In brief, High-frequency Data is the best method of data and insight collection in lucrative trading, financial, marketing, and purchasing capabilities for companies.

Search Blog Posts

Latest Blog Posts

What is Industry 4.0 in Procurement?

Procurement, or, in simple terms, acquiring goods, services, or raw materials needed for the business’s operational process, is something that receives much weight within the