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Issue of Utilizing Big Data in Investment Decision

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Investment decision is considered a life-changing moment where two unavoidable options will be faced as the outcome of the invested asset, successful or failure. For years, People are trying to figure out how to develop an optimum model in eliminating the risk of failure by utilizing the available sources such as historical data, algorithm, etc.

However, people are facing a problem with the excess of data while only tiny of those can be derived as information. That’s how people start to employ an emerging method as their source in assisting the decision-making process by predicting the future which is the analysis of Big Data.

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At present time, most observations and records are uploaded on the internet as the advance of technology could capture those data using electronic devices such as mobile phone, scanner, satellite, and other devices. Hence, top financial institutions with huge market capitalization such as JP Morgan, Wells Fargo and Citibank is exploring this potential by employing data analysis approach to assist them in developing investment decision and predict the future. In general, they analyze the trend of stocks by observing their numerical data such as trading volume and stock pricing, and in addition they are collecting and analyzing the additional data derived from Big Data which is provided by data service companies such as Yodlee, Hadoop, Amazon, Google or even Facebook.

Technical analysis is undoubtedly serving as the first step of analysis performed by investment managers. Years ago, they analyzed time-series data related to market pricing and government data which are limited in determining the investment decision and economic indicator, but now they are utilizing the massive volume and velocity of data stored in servers of data provider companies such as Data Market, Influx Data and Dalmantin DB to retrieve even larger data set to assist their investment analysis.

The provided data has huge volume, real-time updated in high velocity, consist of variety of available data and the veracity is very accurate as these companies usually provide the tools and technology to ensure the accuracy of their data. In processing those data, financial institutions develop their own tools and strategies to bring together the data sets to allowing the derived information to be more accurate with more diverse data.

Financial institutions can also analyze people’s response for stock pricing which is posted on Facebook or Twitter according to their opinion, preference and views. The characteristics of this data is the variety-based value which enhance the quality of their analysis as an additional information and the volume of this data is massive as social media platforms are being used by billions of people around the world in real time, so the velocity is at the highest. Some people are using the good and bad news of a company as a basis of their investment decision and usually they would try to influence another or express their opinion about it in their social media, thus it may influence many other people which could affect the stock trading. Data sets gathered from social media can be used to predict whether the stock price will go up or fall correspond to the people’s opinion and view.

Harnessing Big Data approach to make investment decision requires advance technology instead of relies merely on human work. In doing so, investment managers develop systematic trade modeling to technically analysis the patterns, trends, and momentum of prices. Assisted by analysis tools and statistical methods, they carry out comparative assessment by comparing through different markets and variables then figure out the correlation between them. Machine Learning and Artificial Intelligent play significant role in performing the Big Data analysis because processing massive volume of data in high velocity and high varieties requires sophisticated processes which can be handled by super hi-tech computer intelligent to produce accurate information. At this stage, investor managers are able to generate the outcome of their analysis and also the predictive value information through complex Big Data analysis which has high accuracy in assisting their investment decision.

Behind these huge benefit in using Big Data approach for investment decision, financial institutions are dealing with several challenges. The necessity of hi-tech computer tools and data storage in assisting the Big Data analysis is inevitable to store and process Big Data. Therefore, they need the to hire a computer and data expert to utilize the costly technology in maximizing the value. They can also conduct training to develop their current employees to be able to handle the new technology adoption, but it will significantly cost them as well. By adopting a brand-new strategy in their business to embrace the advance of technology, organizations need to be ready at all cost.

Another challenge that financial institutions have to cope with is the growing data sets in Big Data are susceptible to error and sometimes deceptive. While developing a statistical modelling in their analysis, investment managers may accidently use inappropriate data sets resulting a bad decision outcome.

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