3 Key Ways Big Data Is Changing Financial Trading
Big data is changing financial trading in a myriad of ways. Here's what to know about why and how big data is making such a difference.
Big data is one of the internet-oriented developments that have caused enormous impact across all industries over the last couple of decades. The term big data refers to the gigantic amounts of information constantly collected by websites and search engines as people continue to use the internet for diverse purposes. It consists of all kinds of data – numbers, text, images, tables, audio, video and any other possible type of information. Big data analytics involves the use of a new set of analytical techniques to obtain value from this enormous amount of information. It is a complicated practice/expertise left to professionals such as data analysts, data engineers, and data scientists.
Growth of big data analytics
Big data analytics has experienced exponential growth over the recent past and it can rightfully be considered as a fully-fledged industry. The International Data Corporation (IDC) had predicted in 2016 that sales of big data analytics solutions would reach $187 Billion by 2019. Financial services institutions such as banks and investment firms are among the fastest growing markets for these solutions. Financial trading, from stock, bonds, commodity or Forex trading, is particularly the most impacted aspect of business by big data analytics. Below, we identify and explore three ways in which big data is changing financial trading. These include:
- Big data analytics is causing a market-wide shift from manual trading to quantitative trading
- Human error risk minimization and profitability maximization
- Application of sentimental analysis in financial trading opportunity analysis
1. Shift from manual to quantitative trading
Quantitative analysis is taking over manual trading strategies. More trades are now inspired by the number crunching ability of computer programs and quantitative models. These programs and models are designed to use all available patterns, trends, outcomes and analogies provided by big data. Big financial institutions and hedge funds were the first users of quantitative trading strategies but other kinds of investors including individuals Forex traders are joining in. Quantitative models for financial trading can be more accurate than human analysts in predicting the outcome of particular events that happen in the financial world. They are thus more reliable in making decisions about entering and exiting trade positions.
2. Risk minimization
Access to big data is making it possible to mitigate the critical risks human error represents in online trading. Financial analytics now integrates principles that influence political, social and commodity pricing trends. The application of machine learning in financial analytics is also making a huge impact on the practice of electronic financial trading. Through different machine learning technology, computer programs are taught to learn from past mistakes and apply logic using newer, updated information to make better trading decisions. Machine learning is often coupled with algorithmic trading to maximize profitability when trading financial instruments online. Algorithmic trading involves rapidly and precisely executing orders following a set of predetermined rules. This effectively removes human error and the dangers of emotional decision making. High-frequency trading (HFT) is one of the emergent strategies enabling split second trading decision-making. Theory supports the proposal that faster trading platforms generate more profits.
3. Sentimental analysis to complement financial analysis
Sentimental analysis, or opinion mining, is frequently mentioned in financial trading context. It is a type of data mining that involves identifying and categorizing market sentiments. Market sentiment, according to Investopedia, is the overall attitude of investors in the financial markets. It helps to reveal the traders’ attitudes toward a financial instrument. Popular market sentiment indicators include bullish percentage, 52 week high/low sentiment ratio, 50-day and 200-day moving averages. Thanks to big data analytics, opinion mining is combined with predictive models to complement financial analysis when making financial trading decisions. Another interesting utilization of sentimental analysis is by contrarian investors who prefer to follow the opposite direction to that of the general market sentiment. For instance, a contrarian Forex trader would theoretically sell a currency that everyone else is buying.
Big data impacts in many ways how financial trading transactions are carried out. It helps to make quicker and more accurate trades, thus reducing risk while maximizing the profitability of trading strategies. However, it is noteworthy that big data analytics cannot perfectly predict market scenarios all the time. It has imperfections such as incompleteness of data patterns. In the overall, however, big data analytics presents far more benefits than disadvantages to financial trading. That is why it is increasingly becoming an inevitable necessity for financial institutions.
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