Big Data and Trading: Breaching New Frontiers

Big Data and Trading: Breaching New Frontiers

The global financial markets are a hive of activity. Information is being transmitted at a rate of knots every second of the day. Traders are faced with an information overflow; discerning what data is relevant and impactful on trading activity. Wall Street traders rely heavily on having access to the right information at the right time.

Traders and investors want as much relevant information as possible before they make their trades. Such soothsaying is disingenuous however – nobody has perfect information on how markets are going to move. However, that has not stopped analysts, investors, and market experts from developing artificial intelligence, and sophisticated algorithms that can understand market behavior and forecast accordingly.

Integrating Big Data into Financial Decision Making

Human advisors a.k.a. analysts are now integrating with the available technology to create the best possible financial data streams for traders. This comes in the form of Big Data. This data analyzes a myriad of sources, including market fundamentals, technical analysis, market sentiment on social networks et al. Big Data is increasingly being used at government level, for tracking things like demographic data, environmental changes, and market-related activity.

There is potential to use Big Data for demystifying the financial markets. The technological prowess available to us today, in the form of automated trading, sophisticated algorithms, predictive modeling, and various trading apps allow traders to profit off market movements as quickly as possible. Financial analysts and investment professionals make a point of staying ahead of the competition by having access to the right information as the news breaks.

How to Use Big Data to Make Financial Decisions?

The three Vs of Big Data include volume, variety and velocity. The variety of data is important in that it can be structured or unstructured, or a combination thereof. As for volume, this determines how many terabytes of data are going to be considered in financial decision-making processes. Banks and non-bank financial institutions go through huge quantities of Big Data on a daily basis, trying to gain the competitive edge over others in the industry. Finally, there is the velocity of big data.

This includes how quickly information is available, and how it is accessed. It behooves traders of all types to utilize the most sophisticated programs possible to track data flows effectively. Wall Street traders have been doing this for quite some time. High-frequency trading is considered a form of algorithmic trading, and this allows traders to have a competitive edge over others in the market. However, there are some reservations about the nature of high-frequency trading and its efficacy in the markets.

Experts Value Big Data for Trading Purposes

Everyone wants to know which assets will generate the best returns and which assets will provide the least exposure to risk, according to CFD Trading analyst Jackson Brown Sr. It is important to select the right financial instruments at the right time, and trade them with call or put options accordingly. There is limited efficiency in financial markets; the cause-effect relationship between macroeconomic variables and individual stocks, commodities, bonds, indices, currencies, or other assets is nonlinear. Programs which use this information as effectively as possible are self-learning, or AI algorithms.

It is possible to gauge market movement with trend patterns that form. Analysts use this data to determine whether financial instruments are trending, or reverting to the mean. That data needs to be considered according to the time frame in question. Traders tend to dabble in the markets when momentum is on their side. Depending on your preferred investment strategy, it is always important to focus on technical and fundamental factors when trading assets. Big Data is especially useful in generating the necessary information to make those informed decisions.

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