Lance Rybka is a current finance major at Samford University. He hopes to start a business that uses data analytics to help fee-based financial advisers grow firms.
According to data from the most recent Tiburon Summit, small fee-based financial advisers are increasingly facing pricing pressures that place their businesses at risk. Since 2009 the average fees charged by these advisors have decreased from 1.2% of AUM to .96%,, and 86% of Tiburon CEOs believe that these fees will continue to decrease over the next five years. While robo-advisers and data analytics are partially responsible for this restrictive pricing trend, many traditionalists do not realize the potential they have to grow their practice by embracing this technology instead of resisting it.
Samford business school student A.K. Vance writes:
As technology and data have become more prevalent in our daily lives, concern about privacy grows. Governments and countries now worry about “commercial espionage” on citizens. In a recent Wall Street Journal article, Trefor Moss discusses the implications of the ability for companies like Tesla to collect data on its consumers. China is currently attempting to restrict its citizens’ access to Tesla cars which have the capability to track and collect data on its owner. The Chinese government cites the fear that data including images which can be taken by the cars will be sold or given to the American government. Beijing has gone so far as to restrict the use of Tesla cars “by military personnel or employees of some state-owned companies”. Elon Musk has publicly stated that no data will be released to the United States or any other nation. The results of selling or giving such data or information to other governments could lead to many negative effects for Tesla and could cause a huge loss of Tesla’s business. In the last year, China made up a quarter of Tesla car sales. If Tesla did use their data capabilities to collect and give information to the United States government, they would risk losing a huge market for their product. Musk goes on to claim that such a violation of privacy could lead to a “shut down everywhere which is a very strong incentive for us [Tesla] to be very confidential”.
Related concerns over the Chinese application, TikTok, led to an attempt to ban the application in the United States due to its potential of collecting data on American citizens which could be used by the Chinese government to spy. As products become more integrated into the internet, privacy concerns may affect international trade. Companies might have a private incentive to protect customers, but that might not be sufficient. Legislation is still catching up to the changes in technology and new capacity to track individuals via “commercial espionage”.
Sinclaire Green, a Samford business school student, writes:
Since Billy Beane transformed the 2002 Oakland Athletics baseball team by utilizing data analytics, propelling the team to a 20 consecutive game win streak, sports fans, coaches, and players have all become more attuned to the role data can play in baseball. As a result, professional basketball teams also began to use data analytics to improve their game plans, skills, and recruiting. In the past few years, Colton Houston and Matt Dover have begun to use data analytics to help college basketball teams similarly to how Billy Beane helped transform the Oakland A’s in the early 2000s.
Houston and Dover’s company, HD Intelligence, provides a service to college basketball that was previously only feasible for professional teams. HD Intelligence eliminates the need for internal data analysts. Analysists at HD Intelligence compile data and present it to college basketball coaches to improve decision making. HD Intelligence prides themselves on making meaningful insights that coaches can understand. Instead of coaches having to rely on watching video and looking at statistics from box scores, HD Intelligence provides reports for teams. Coaches can know their team better, know their opponent better, evaluate recruits effectively, and optimize their schedules.
In the 2019-2020 basketball season, HD Intelligence had two primary college basketball clients, The University of Dayton Flyers and the University of Alabama Crimson Tide. Similar to the Oakland A’s, Dayton does not have quite as robust of a budget as many of the nation’s other top programs. Also similar to the Oakland A’s, the Flyers had a 20-game win streak last year and many basketball aficionados think that Dayton would have won the NCAA tournament had the COVID-19 pandemic not halted the tournament. Similarly, Alabama basketball has had an excellent 2020-2021 season. The program has risen within the SEC to win the 2021 conference tournament. More importantly, the Tide made themselves a legitimate contender for the national title by making it to the Sweet 16 in the NCAA tournament. Even though Alabama did not make it to the Elite Eight, they had a great season with many wins. HD Intelligence helped both Dayton and Alabama optimize their talent and resources by providing data analysis of each game and by assisting with pre-season non-conference scheduling for the two programs. Looking to next basketball season, Houston and Dover have over 10 schools who they will assist with data analytics.
In January of this year, Apple made a big announcement. It wasn’t about a new iPhone. Apple announced that it will soon release an update to their software that allows users to choose whether they give permissions to apps such as Facebook to track their browsing history on other companies’ apps and websites.(WSJ) This has implications for data usage and availability in advertising. As technology has advanced, regulations surrounding exactly what a company is allowed to do with your data has stayed relatively stagnant, especially for smartphones. Companies such as Facebook and Twitter are allowed to monitor your searches not only on their apps, but also on your phone browser and other apps.
There have been moments in my career as a data analytics instructor that I have considered writing my own textbook, just so I could have one that works. When I started in 2017, Samford University was one of the first schools to seriously reshape the undergraduate business school curriculum in response to the increase in demand for analytics skills. The pickings for appropriate textbooks were slim. Students in my class have already taken “business statistics”, which is a class I had to take as an undergraduate as well. I was trying to smash together business case studies, analytics that was more advanced than basic stats but also not beyond the undergrads, all while using a software program for applications.
This semester, the textbook I am using to teach data analytics is Business Intelligence by Sharda, Delen, and Turban. In Chapter 3, the authors describe how a data warehouse fits into a business enterprise. A data warehouse (DW) is more than a spreadsheet. It is more than a two-dimensional transactional database. A DW takes expertise to build and maintain. If done correctly, users within the company will be able to quickly access important data that they need to make decisions. Having a good DW is essential for any large enterprise today.
Near the end of the chapter, the authors list problems that are encountered when technologists go in to build a DW for an enterprise.
My third post on Covid data heroes features Dr. Emily Oster. Emily is a mom. Lot’s of economists are moms, but few have incorporated it quite as much into their careers. Emily has written a book on pregnancy and a new one on what to do with the kids after they are born. She does a great job explaining scientific research in a way that is easy to understand.
Emily made a big push to collect data on schools and covid back when there was crippling uncertainty about how dangerous it is to let children go to school in person.
She has a great email newsletter and substack. Her latest post is called “Vaccines & Transmission Redux Redux”. In this post, she distills the latest research to give practical advice on when kids can see grandparents once the vaccines are out.
For a long time now, some families have been avoiding close contact with elderly relatives. When can we go back to normal?
With all the uproar around the election in December, the news of the SolarWinds data breach did not get the attention it deserved. Some well-resourced foreign organization, almost certainly in Russia, succeeded in infiltrating the data systems of an astounding 18,000 or more U.S. organizations. These included major federal agencies such as the Pentagon, the Department of Homeland Security, the State Department, the Department of Energy, the National Nuclear Security Administration, and the Treasury, and other big targets like Microsoft, Cisco, Intel, and Deloitte, and organizations like the California Department of State Hospitals, and Kent State University. Security watchdogs run out of adjectives (“11 out of 10”) in characterizing the magnitude of this hack.
At the same time, security experts cannot help admiring the sheer artistry of this exploit. Hackers themselves often view their codes as a work of art. According to one cybersecurity expert, “Programmers and hackers like to sign their work like artists…So they sign that code in various ways. Often, they’ll leave their initials or they’ll try to be cute and put some sort of cryptic message.” So how was this hack accomplished?
I am grateful to Yang Zhou for inviting me to talk about a working paper (with Gavin Roberts) on Friday. Yang told me that this audience is not familiar with lab experiments, so I’m going to take a few minutes out of my time to set the stage for my research.
There is a new book out, Causal Inference by Scott Cunningham, that is the talk of #EconTwitter (Cunningham, 2021). The book is 500 pages of dense prose and code. Here is a review saying that Cunningham left out many key things that a practitioner would need to know. Causal inference from naturally occurring data is hard!
Lab experiments bring something important to the research community. Lab experiments give the researcher a lot of control, which is why they are particularly useful for causal inference (Samek, 2019).
Who doesn’t want to be stronger? You can get on the floor and do 5 pushups right now. Did you do it? Probably not. (If you did, great work.) For most people, nothing is stopping you from getting strong, except yourself.
I just keep sitting around. Going to a gym and meeting with an instructor in person used to be a way around this problem. This takes our human foibles and makes them work to our advantage. The sunk cost fallacy can work for us.
If you bought a stock and it’s a loser, you should sell! Too many people keep holding and go down with the ship.
However, knowing themselves, many people also go to the gym and sign up for a class. Not wanting to walk away from their investment, they actually do the classes.
The WSJ reports that many gyms are closing after Covid-19 forced the customers out. The article describes the machines people have brought into their homes to replace gyms. The Peloton is a signature of the year 2020. The new trend brings a live human trainer into the process of exercising alone at home.
The new machines can collect data on the user. This data is transmitted to instructors and maybe even friends. Now, from the comfort of your own home, you can “sign up for a class” again.
Had Covid struck in 1980, people might have bought fitness machines for their basements and they might even have bought a VHS to pop in and exercise with. But they would have been missing the link to a human who knows where they are supposed to be, which apparently provides more motivation.
The market has loved Peloton and smart money seems to think it will continue to do well, even with a vaccine already rolling out.
Economist Writing Every Day 