Financial __data analytics is booming now, it’s actually been around finance for decades. The 1980’s had program trading and the 90’s saw the precursor to high-frequency trading (the SOES bandits).
Today, we have so-called 'black box' funds, totally driven by quantitative models and algorithms. They’ve gotten so powerful that roughly 3 out of every 4 trades are now executed via algorithms. Recently, we even saw the launch of an Artificial Intelligence ETF powered by IBM’s Watson platform. What’s next?!
The demand for professionals that can build financial analytics programs is booming. We foresee two main objectives-to< strong>predict market movement for profit, and to protect customer assets of banks.
Predict Predictive analysis is the Pandora’s Box of data science. With the ability to mine enormous data sets to uncover profitable insights, it’s no surprise that finance is interested. Globally, there are sixty major stock markets, listing hundreds of thousands of companies. These companies trade millions of shares per day, electronically file thousands of financial statements and have infinite behavioral inputs (social media, chatrooms and instant messages). In short, trillions of data points must be sifted through.
The ability to glean profitable insights through pattern recognition is a very valuable skill.While no one can predict the future, some firms are coming pretty darn close. Last year, high-frequency shop Virtu Financial reported to the SEC that it had been profitable 1237 out of 1238 trading days. It’s no surprise that assets under management at quant funds have soared to nearly $1 trillion.
Data scientists help devise trading strategies for these entities. The highest paid are on the buy-side (meaning the firm’s own capital at risk, not customers/depositors). These are hedge funds and proprietary trading firms. But major brokerages have internal ‘asset management’ departments as well.
On the sell side, the safety of customer information has become the top priority in banking. data scientists are being recruited to safeguard not only individual’s deposits but also sensitive personal information.When breaches do arise, regulators are quick to dole out punitive fines.
Further, the paradigm shift to mobile banking has opened the doors for hackers and thieves. data analytics helps detect when online activity is being made by bots, not humans. These threats aren’t likely to abate anytime soon.
Data science can help a bank minimize credit (lending) risk. A shrewd analytics program can uncover risky behavioral patterns by individual as well as institutional borrowers. To proactively mitigate risk, the bank can freeze associated credit lines or raise the interest rate to accommodate for the risk.
Organizations are discovering how forensic data analytics (FDA) can protect them against fraud. A staggering 89% of corporate respondents to an Ernst Big data report cited a key benefit of FDA as improving their ability to "detect potential misconduct that we couldn’t detect before."
The majority utilize FDA to detect financial statement fraud. It can assist accounting firms tasked with auditing complex SEC filings. FDA can also assist investment bankers with lengthy due diligence relating to M&A transactions, ensuring fair valuations.
Skills and Compensation
While filling analytic positions is a priority in finance, candidates must have the right skills. The programming language Python may be the most versatile with ever-increasing libraries for statistical analysis, machine learning and numerical computing. The industry is also looking for skills in regression analysis and database tech (Big Data). These may be met with R and Hadoop experience, respectively.
Data scientists should also understand basic business practices to see how their analyses ‘fit’ into the firm’s bigger picture. If you’re short on time, consider the CQF (Certificate in Quantitative Finance) designation or a boot camp. If you have more time and are considering management, consider a Masters in data Science degree.
Entry-level data scientists average $118,748 according to Glassdoor. At Robinhood, data scientists reported around $130k and $110k at Morgan Stanley. But if you join the buy-side with a hedge fund you’re likely to enjoy a salary around $150k plus the chance to bump that towards $200k based on the fund’s performance. Some are recruited for much more (one 32-year old was offered a $2 million annual salary to join Third Point, LLC).
As you move up to managerial roles, data science directors enjoy a national average of $185,000, according to Payscale. Experience managing a team of 10 or more reports reportedly can yield over $200,000. A final bonus according to McKinsey & Company-the role ‘managing others’ is the least likely job title to be lost to automation!