The impact data is making in the financial world is more of a splash than a ripple. The technology is scaling at an exponential rate and the consequences are far-reaching. Increasing complexity and data generation is transforming the way industries operate including the financial sector.
At present, we humans create 2.5 quintillion bytes of data daily ( it’s more like stacking up 700,000 Blu ray discs filled with data up to the height of the Burj Khalifa every 10 minutes.) which in turn has created a lump sum opportunity in terms of processing, analyzing and leveraging the data in useful ways. Machine learning and AI are increasingly being used in financial trading to process a vast amount of data to create valuable insights and predictive models that are beyond ordinary human’s capacity in order to make better data-driven decisions better than ever before.
Finance and trading rely on accurate inputs into business decision-making models. For decades numbers were crunched by humans and decisions made based on inferences drawn from calculated risks and trends. Now, these functionalities are usurped by computers and software like excel (they too have their downside), which could compute massive scale of data, and draw from a multitude of sources to draw more accurate conclusions.
There are three ways data is influencing financial trading, and here they are.
1. Leveraging data analytics in financial models
Analytics in finance is not just limited to price examination or price behavior anymore but a process that integrates the principles that affect prices, social and political trends and other factors at different levels.
Data analytics tools with AI can be used in predictive models to estimate the rates of return and probably outcomes on investments. Increasing access to data results in more precise predictions and thus the ability to more effectively mitigate the inherent risks associated with financial trading.
High frequency trading has been used quite successfully up until now, with machines trading independently of human input. However, the computing timeframe habitually puts this method out of the game as literally seconds are of the essence with this type of trade and data usually means increasing processing time. The paradigm is changing though, as traders realize the value and advantages of accurate extrapolations they achieve with data analytics.
2. Real-time analytics
At the moment AI, Big Data & Machine Learning might be just a mere buzzword in finance. However, machine learning is enabling computers to make human-like decisions, executing trades at rapid speeds and frequencies that people cannot. The business archetype incorporates the best possible prices, traded at specific times and reduces manual errors that arise due to behavioral influences.
Real-time analytics has the potential to improve the investing power of HFT firms and individuals alike, as the insights gleaned by algorithmic analysis has leveled the playing field providing all with access to powerful information.
The power of algorithmic trading lies in the almost limitless capabilities. Structured and unstructured data can be used and thus social media, stock market information and news analysis can be used to make intuitive judgments. This situational sentiment analysis is highly valuable as the stock market is an easily influenced archetype.
3. AI & Machine learning
The complete potential of these technologies hasn’t yet been realized but the prospects for the application of these innovations are immeasurable. AI & Machine learning enables machines to actually learn and make decisions based on new information by learning from past mistakes and employing logic UNBIASEDLY. Yes, these technologies tend to remove the human emotional response from the model and makes decisions based on information without bias.
Although the technology is still developing, the possibilities are promising.
There is inordinate potential for machines to take over the financial sector in the near future. The concept Big data or data analytics allows more information to be fed into a machine thrives on knowledge of all possible influencers. The data analytical revolution coupled with AI & Machine learning makes trading more accurately and informedly; impacting dramatically on how financial transactions are executed.
As data continues to reform the framework of various industries, the financial sector is adopting data analytics to maintain a competitive edge in the trading environment. It is doubtful that it will be very long before this technology becomes a mainstream necessity for financial firms.