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“Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, and everyone thinks everyone else is doing it, so everyone claims they are doing it.” – Dan Ariely
Data Science has become the new buzzword in the industry today and everyone wants to make use of this power. Gartner recently released a report saying that 64% of companies are deploying Data Analytics project, yet 56% struggle to know how to get value from their data.[1] Many universities including CMU now have specialized data science courses and degrees. A lot of research work has begun in this field which are funded by many prestigious institutions and governments. The future for Data Science as such is very bright and fruitful.
Also, from our previous blogs, we have seen that Data science has tremendous potential even in the ever changing industry of Finance. The Financial Services industry has realized this potential and has started harnessing data in the form of the transaction data, real time market feeds, social media trends etc to help them realize their potential. Data science in financial industry can not only help create a very customer driven enterprise but it will also help in optimizing risk management, making intelligent decision and streamlining the operations for any financial institution.
But for any institution to be able to make full use of the potential of data analytics, it is very important to determine the use cases that will generate significant business value.
The areas where financial industry would want to focus their attention on are:
- Leveraging Mobile wallet for marketing their services better.
- Fraud Detection
- Risk management
- Customer segmentation and targeting.
- Pricing securities and derivatives
- Competition analysis
Here are some of the areas where innovation can be applied to make the combination of data science and finance more powerful.
An Ensemble of Sentiment and Scenario Analysis
Traders of today are constantly on the look-out for new insights that would give them an edge on the trading platform. This is where scenario and sentiment analysis could be used effectively. Sentiment analysis thrives on data analyzed from social media and news platforms and plays a vital role in the financial industry, considering the sensitivity of market trends with respect to investor sentiment [1]. Price of a specific stock is usually determined by the speculation surrounding the company which is spread across the community of investors through platforms like Facebook, Twitter, financial blogs, RSS news feeds.
Scenario analysis too plays a major role in the field of finance especially when it comes to predicting stock prices using a simulation model. A investor usually inputs a specific scenario data depending upon previous market trends along with the outcome observed at that time.
However all possible scenarios are created and input by the analyst based on past trends in the market data and is usually specific to financial data alone. Here no consideration is given to the sentiment of the market at the time of analysis. Sentiment analysis is usually conducted separately and the judgement of whether a stock should be sold or bought is left to the investor to decide .
What if Sentiment analyzed data is used to predict a scenario? For example facebook and twitter data feed at the time of the recession could be used and the scenario of a recession can be used in a simulation model in the future if the sentiment analysis data is similar to that at the time of recession. [2] Sentiment Analysis in our opinion should be a part of scenario analysis so that it can eventually be used in the simulation models to determine the risk of a particular trade.
Intelligent Trading Models
Decision Models that execute a trade on its own (ranges of stock price values are predicted)
Today Algorithmic Trading has revolutionized the concept of buying and selling of stocks. High frequency trading has taken trading to an entirely new level but also has made it risky. The ranges usually chosen in the algorithm used for trading is entered by the trader themselves after analyzing market trends using financial models. However this process can be simplified if the ranges predicted by the financial models were directly put into the algorithm without human intervention. Further more the results of past trades could be used in the financial model being used so that future predicted trades would improve.However such a scenario may not involve human judgement which at crucial times may be required for making major financial decisions. The instinctive nature humans possess of making a decision in terms of trading a stock is something a model might not be able to replicate.
Challenges :
Most financial firms are jumping on the data analytics bandwagon. However, this does not necessarily mean that they are leveraging tools & data effectively. Here are some barriers to effectively implementing data analytics in financial institutions.
- Lack of a centralized approach to capture and analyse financial data.
- Insufficient infrastructure and technologies to capture and handle transactional data and customer data on a massive scale.
- Leadership does not support the use of data analytics and are skeptic about the impact it could have on their predictions.
- Dearth of talent to deal with the data and derive meaningful patterns to corroborate evidences towards predicting market shifts and financial meltdowns.
- Defining metrics to measure the role of analytics in transforming the financial sectors.
The future of data science with respect to the financial services industry is moving towards a model that is easy for the average analyst—and company—to use. The goal is for you to get usable, real-time, easy-to-understand insights using the cutting edge technologies and techniques to overcome the aforementioned challenges [3] Use of analytics is becoming a necessity in the financial services industry and using it appropriately, will serve as the key differentiator between firms that become successful and firms that fail in the long run.
References:
[1]http://tomfishburne.com/2014/01/big-data.html
[3]http://blogs.adobe.com/digitalmarketing/analytics/future-analytics-adobe-summit/