This article is authored to highlight the difficulties encountered in conducting of research on Latin America capital markets by a non-Spanish speaking individual. This summer I was excited to get an internship with Arquants, a fintech start-up that designs and develops algorithmic trading software. The company was founded and is headquartered in Buenos Aires, Argentina. While their sole clientele base is based in Argentina, the company hopes to extend its operations to other Latin American countries such as Colombia, Brazil and Mexico. In addition, the company envisages a global expansion to markets in other regions such as the United States, Europe, Asia, Middle East and Africa. To assist them with research into the new capital markets and regions, Arquants offered me an internship position to research the breadth and depth of algorithmic trading in the key regions of their expansion. In the subsequent paragraphs, I intend to highlight and summarize my internship journey and highlight various challenges that hampered my extensive research and data collection.
Content and data access restriction.
In the first half of my internship I was investigating algorithmic trading in Latin America markets. This mainly comprised of: Argentina, Brazil, Colombia and Mexico. Majority of these countries are located in South America with Mexico being in North America. My primary resource for research was Google and other search engines. Algorithmic trading is not extensive part of the global capital markets and thus access to data is very limited, especially for Latin American companies. In addition, business intelligence data is highly restricted for free public access. For instance, there are companies whose sole business is to collect data and conduct research then sell the data to financial companies. Examples of these companies are Bloomberg, Thomas Reuters among others.
Companies like Business Wire compound reports on various industries like algorithmic trading then sell it to companies at high prices. For instance, their report on key trends in algorithmic trading retails between $5000-$8000, which is extremely expensive to obtain for many start-ups. Other resources for data access like Bloomberg cost more than
$20,000 for annual subscription, again, a price that is beyond reach for start-up companies. Later, I was able to acquire Bloomberg access from my school graduate school of business. But again, given the shallow extent of algorithmic trading Bloomberg did not have much data about the subject.
Communication and language barriers.
Understanding a language of your research is really important in developing intensive and extensive research content. Latin America is a country that predominantly speaks and writes in Spanish with the exception of Brazil whose official language is Portuguese. Therefore, when conducting research on Latin American markets, most of the information for example for Argentina, Colombia and Mexico was in Spanish.
For example, when I was going to the main markets in Argentina and Colombia to find research content such as annual reports that might contain important data, most of them were in Spanish. As a result, I had to use Google translate to translate the reports which resulted in distortion and omission of key facts of the research. In addition, most companies’ websites are in Spanish because their target market and population is Hispanic my research into the companies was highly restricted to my understanding given that I am not even fluent in basic Spanish.
For a non-Spanish speaker or reader, research Latin American markets is extremely difficult as most content is in Spanish and Portuguese for Brazil
Another aspect of communication difficulty that I encountered was speaking to my colleagues via email and other video conferencing platforms. Due to communication barriers, sometimes I found it difficult to understand what was really needed of me and hence had to seek clarification to a lot of things that I feel like I missed. While this is not that big of an issue, it definitely poses potential challenges in conducting an effective research because sometime you might findyourself doing tasks that are inaccurate to what is expected.
This paper highlighted challenges encountered while examining algorithmic trading in Latin American markets. The main challenges are illustrated were limited access to data and language barriers. Data for such kind of research is business intelligence data and is available for sale by companies that conduct business intelligence research. In addition, platforms that have financial data like Bloomberg have limited access to data on algorithmic trading because it is not mainstream finance. Another challenge established is communication and language barriers. For a non-Spanish speaker or reader, research Latin American markets is extremely difficult as most content is in Spanish and Portuguese for Brazil.
The language is a barrier to conduct proper research on Latin American countries for non-Spanish speakers, but is that not the opposite? Or, in other words, it is a way to be more closed-minded to the global world?