The importance of big data and how it impacts the world can be seen in the changes it is introducing to several industries.
The marketing landscape, for one, has been irrevocably changed thanks to big data. Where once marketers simply recycled former streamlined processes that had worked in the past, big data now implements substantial algorithmic processes that improve key areas of performance such as customer engagement, loyalty, and overall marketing optimisation.
From healthcare to manufacturing and government services, data has influenced how people work and understand processes better, spurring transparency, heightened risk awareness, and capital availability.
In the ever-changing industry of financial markets, which already implement large data sets, the power of big data influences industries to act smarter. By placing big data front and center in more aspects of financial operations, trading decisions are further calculated, and it provides the necessary tools for investors, with the help of their trusted online broker, to make better and more informed decisions. After all, there’s a ton of information out there.
Here are five key factors to watch out for when it comes to data-based trading decisions so you can make these work for you.
The 3 Vs: Volume, Velocity, and Value
Often, big data is defined by volume, velocity, and variety, but in the case of trading decisions, value is the real trade-off here as the information gleaned from data provides the necessary tools to yield better decisions. By utilising these three main defining properties of data, understanding how each dimension contributes to data-based trading decisions, and leveraging that technology, investors gain a competitive advantage over their competitors.
Data-based technology supports informed decision-making as data is proficient in not only finding patterns, but also managing voluminous data such as ticker data or vast historical data. This allows individuals and financial institutions to focus on quickly and efficiently processing trades.
Data also provides active risk management, supporting decisions with quantifiable data to optimize portfolios. While the quantity of data is important, according to Tech-Tonics Advisors Chief Executive Officer Gabriel Lowy, the quality of data is what improves a process and meets “the three criteria for return on investment (ROI): (1) reduced costs, (2) enhanced productivity, and (3) incremental revenue generation.”
Leveraging Big Data Analytics
By using big data analytics in predictive models, investors can integrate examining prices and price behaviour with principles that affect prices as well as social and political trends. These predictive models provide estimates on the rates of return and probable outcomes for the investments they make on trading platforms, leading to more precise predictions and effectively mitigating inherent risks often associated with financial trading.
An effective and optimal trading strategy utilises powerful technical analysis that provides likely rates of return. Big data allows the accuracy of this technical analysis to increase, allowing for accurate probabilities that specific outcomes will occur. This makes trading more consistent, mitigating risk and giving investors the tools necessary to manipulate the data as machines trade collaboratively with human input. This, in turn, increases the value and accuracy of extrapolations achieved with big data analytics.
The leveraging of big data can be applied across multiple financial markets such as the gold markets, as tools like “trend-determining” can analyse the trading of gold through techniques such as price-pattern behaviour, rates of change, and moving averages.
Algorithmic Trading
Algorithmic trading, also known as algo-trading or black-box trading, is a system that uses complex mathematical formulas and models to make expeditious decisions and transactions in financial markets at a speed and efficiency not manageable by a human trader. By using complex algorithms and a defined set of rules, algorithmic trading provides determined trading strategies for optimal returns.
Aside from optimal profit returns, algorithmic trading is also a key factor in making data-based trading decisions as it eliminates the emotional human impacts which, at times, hinder optimal returns.
Algorithmic trading can also enhance certain processes and investment and trading strategies such as arbitrage, watching the relative strength index (RSI) for gold trading, trading before index fund rebalancing, intermarket spreading, and mean reversion, all the while limiting speculation. Algorithmic trading works on certain defined sets of rules based on quantity, price, timing, and specific mathematical models. These models allow investors to maximise the best possible price without significantly impacting the stock’s price and increasing purchasing costs.
In 2015, the New York Stock Exchange was capturing 1 terabyte of information every day. And at the same time, globally, 2.5 quintillion bytes of data were created each day. According to research firm International Data Corp., big data will only continue to exponentially grow at an annual rate of 23% through 2019, with securities and investment services and banking being two of the fastest industries growing big data usage.
Big Data is big business, and this rapid growth continues to transform the financial landscape. Opportunities are being created for maximised returns and minimised human emotions, bias, and geopolitical pressures, leading to great financial success by simply tapping into the power of data.
