Does the family office need independent resources for economic modeling?

Given today’s conditions in the financial markets and the state of technology, does the family office need independent resources for economic modeling? Or should it rely on the models built by the financial institutions that manage its wealth?

I. Let us consider the context first.

The largest wealth transfer in history is underway.

It has been estimated that 59 trillion dollars of wealth would be transferred from the Baby Boomers to the Millennials, in the United States, over the course of the 55-year period 2007 — 62. The Baby Boomers did not inherit any significant wealth from their parents, the Greatest Generation. Nearly all of their wealth was created after the Second World War. Hence, the current transfer that is underway from the Baby Boomers to the Millennials is the largest wealth transfer in history.
For most Baby Boomer families, their total wealth does not exceed a million dollars. This wealth could be in the form of a primary residence, a vacation home, a retirement account with 401(k) and IRA in stocks and bonds, with supplemental income from social security. Thus most families are on auto-pilot when it comes to this massive wealth transfer from the Baby Boomer generation to the Millennial generation.
The situation is drastically different for the high net worth / ultra-high net worth individuals and their families. Moreover, these wealthy families are the ones involved with the major share of the 59 trillion dollars of wealth transfer. So, the function of coordinating this wealth transfer falls essentially to the family office space.

II. Economic Modeling for the long-run.

So, the largest wealth transfer in history is underway. What is the environment in which this process is happening?

In a globalized post-war world which has free floating currency regimes since 1971, for the first time in world history. Before the First World War (1914 — 17), the currencies of the world’s major economies were tied to gold or silver. Between the wars (1917 — 45), some countries experienced hyperinflation due to loose monetary policy, and other countries experienced depression partly due to rigid adherence to the Gold Standard. After the Second World War (1939 — 45), under the Bretton Woods framework, the currencies of the capitalist economies were managed as a single flexible system, with the dollar’s value held at 35 an ounce of gold. After 1971, all major currencies have been freely floating.
With Wall Street financial institutions only able to focus on short-term asset management, partly due to limitations on their technological / organizational capabilities. Long-term wealth planning has not made heavy use of economic modeling or number-crunching technology. Hitherto, wealth advisors have preferred ‘golden rules’ (i.e., rules of thumb) in guiding their clients.
And the central banks committed to addressing unemployment and inflation domestically
To help Main Street businesses recover from the economic crisis of 2008 — 09.
Amidst, major demographical shifts and population aging that would unfold over the next 50 years.
With internationally traded commodities like oil, copper, gold, silver, etc., and agricultural products like corn, wheat, rice, coffee, tea, etc., showing cycles of boom and bust that are increasingly volatile in the last 70 years.
In this situation, who would provide the financial services that are needed to manage wealth transfers over multiple generations? Is there a vacuum here?
Does the current phase of wealth transfer run the risk of repeating the experience of many cultures in the past? ‘Shirtsleeves to shirtsleeves in three generations’?

III. Economic modeling on Wall Street / City of London.

Are Wall Street financial institutions capable of managing the complexity of this massive inter-generational wealth transfer?

Large teams of risk, technology and business professionals, in a big firm, with offices all over the globe, building mathematical models to capture the pattern of events in a particular part of the economy.
Much of the process involves the manual intervention of 1,000s of people working in teams of sizes ranging from 4 to 60.
Each team could be operating a handful of applications spanning up to a million lines of code. Each application could be using several different software and hardware configurations. So, the process is quite error-prone with long timelines for delivery and updates.
Each model takes a life of its own, often taking several years to be developed and validated, and then going through many years with only minor revisions.
Models are independently owned and operated by different sets of teams. With the complexity of running each model being already so huge, only partial views of the economy could be captured by these models.
Given this situation, it is safe to say that it is questionable whether Wall Street institutions are equipped to implement global models that take into account the connectedness of economic events.

IV. Economic modeling on Wall Street / City of London.

Case Study: pricing of derivatives by Wall Street firms.

The financial models and the computer algorithms that Wall Street firms use for the pricing of derivatives have been developed and implemented over the course of 35 years, starting from the late 70s.
This has involved the efforts of 100s of superstar PhD quants from ivy league schools. Initially, it was small teams of quants working in the nooks and corners of a few select investment firms.
This has grown into a gargantuan operation over the last 30 years. Today, to support the models and the algorithms developed by the quants, there are teams of 1000s of computer professionals, from all over the world, working around the clock.
Underlying market factors like interest rates, currency exchange rates, commodity prices are all treated as arising from independent stochastic phenomenon, and these market factors are calibrated from 3-month historical prices.
No consideration is given to the fact that, in the post-crisis environment, the central bank sets interest rates based on its estimations of inflation, unemployment and economic growth, and the expectations on all of them.
For example, price of a stock would simply be modeled as a geometric Brownian motion. No causal links in future time buckets between the stock price and economic phenomena, neither macroeconomic factors like unemployment, inflation, GDP growth, money supply, etc., nor microeconomic factors like earnings, historical performance, sector specific risks, etc.
Compare the rigid system that Wall Street has developed for pricing the value of derivatives in future time buckets by simulating the underlying market factors, with the concept of ‘stored program’ that was developed by von Neumann in 1944 for the modern electronic computer. There was no such general framework developed for pricing derivatives, instead a brute-force approach was adopted, that brought all events and possibilities into the purview of the efficient market hypothesis.

V. Impact of the great financial crisis of 2008 — 09 on economic modeling

Before the crisis

Assumption of the efficient market hypothesis was fundamental to nearly every model that Wall Street built before the crisis.
Under this hypothesis, all past information that impacts the pricing is already incorporated in current pricing, and all future movements in prices are completely random.
As a result, it didn’t matter whether it was long-term or short-term that one was simulating; simply generate random numbers to represent variability of prices in future tenor buckets.
No consideration was given to underlying causal economic phenomena. Limited technological resources available to build models that capture the inter-dependence of asset prices and economic phenomena.

After the crisis

Markets are no longer the sole determinant of prices.
Policies and guidelines provided by the regulatory agencies have largely shaped the activities of all financial institutions on Wall Street.
Governments and central banks have intervened massively in the economy, in an attempt to strengthen and speed up the economic recovery. This intervention has mainly been in the form of zero interest rate policy (ZIRP), quantitative easing (QE) and large fiscal deficits.
Short-term demand management has been the over-riding concern for the central banks in setting interest rates.
Models developed by different sectors in the finance industry — asset management, mutual funds, private banks, real estates, private equity, digital media, commodities, energy — today are overwhelmingly focused on short-term events.

VI. Parallel developments in technology that enable handling increased complexity

At the office

From personal computers in private cubicles, to thin clients on shared, on-the-go workspaces.
From heavy-duty grid-based high performance computing (HPC) to distributed Linux/Windows servers.
From coding standards and internet protocols to Big Data.

At home

From landlines to smartphones.
From instruction manuals to plug-and-play standards.
From digital equipment to ‘internet of things’.
From desktops to laptops to tablets.

VII. The Family Office

pre 2008 — 09 crisis

Family offices have a long history, going back several centuries. They are entities whose existence is tied to the needs of one or more wealthy families.
Tax filing, accounting, household staffing, health & wellness advisory, travel, art collections, personal security, along with asset management and wealth planning have been, traditionally, the main activities carried out by the family offices.
Today, some of these activities are outsourced to small boutique firms, or to professional organizations like private banks and hedge funds. Non-profit organizations also play important support functions, especially for philanthropy.
Over the course of the 19th and 20th centuries, the family office, as an institution, lost out steadily to the professional banking institutions that cater to the middle classes.
This was because the neighborhood bank around the corner provided transparency, security and documentation, whereas the family offices were prone to secrecy, mismanagement and infighting within the family.
This, in turn, contributed to the ‘democratization of finance’, with the upper middle class families of today being able to afford all the services a family office provides, through the private bank divisions of big financial institutions.

VIII. The impact of the 2008 — 09 crisis on conventional wisdom

Before the crisis

Younger generations, as a rule, were expected to become wealthier than older generations.
Recurrence of a catastrophe like the Great Depression was considered almost impossible (Robert Lucas, Presidential Address 2003, American Economic Association. Ben Bernanke, “The Great Moderation”, 2004).
National housing market was believed to go up indefinitely.
Inequality was expected to be minimal, with all classes in society equally benefitting from economic growth (Kuznets’ theory).
Most people were expected to spend all of their savings during their retirement years (Modigliani’s life-cycle hypothesis). Macroeconomic effects of bequests to heirs were considered to be insignificant.

What the crisis did

The 2008 — 09 crisis brought massive destruction of wealth in housing markets and stock markets.This was followed by the loss of millions of jobs.
The attempt to stem these losses with highly unconventional monetary policy resulted in an economy that is struggling with sluggish growth and stagnant wages ever since, though job creation, after a long, slow recovery, has hit a decent pace in the last 3 years (dt. June 2016).

A matter of trust

The crisis showed the wealthy families, first hand, that one’s wealth could be destroyed suddenly and almost instantaneously. This has made them ask a lot of questions about the management of their wealth.
The string of stories in the media during the crisis, on corruption and malfeasance among white collar banking professionals, has made the wealthy families nervous about leaving their wealth totally under the management of external financial institutions.
Thus family offices are once again taking back more and more control in the management of the wealth of their families.

IX. The Family Office in the post 2008 — 09 crisis world

The psychology

The lingering memory of such wealth destruction in 2008 — 09 has led to a persistent psychology of panic and fear which gets activated at the first hint of a financial crisis, which in turn, has added great uncertainty to long-term wealth planning.

The math and the technology

The wealth advisors of the Baby Boomer parents are finding it difficult to connect with the inheriting heirs from the Millennial generation who are highly tech-savvy.
The old rules of thumb or ‘golden rules’ that wealth advisors asked the Baby Boomer parents to follow are not appealing to the Millennials.
The Millennials would prefer to have the latest apps that connect them 24-hours to their wealth accounting and address various contigencies. For example, they would like to plug in numbers to simulate the long-term evolution of their wealth, under various scenarios.

X. Is there value for family offices to invest in their own resources for modeling?

Value comparison

Say, a family office gives $200 MM to $300 MM of assets to a hedge fund for asset management. This would incur $4 MM to $6 MM upfront cost every year, in addition to a 20% charge on any profits made (under the 2-and-20 standard rate in the hedge fund industry). Moreover, for the next year’s management, these 2-and-20 fees would have to be paid out again, as if starting all over from scratch.
With the family office taking the approach of building its own modeling resources over the long-term, the model building provides benefits that are cumulative, and the total budget would be less than one-tenth of the fees paid to asset managers.

Value added

But the more important value is that having its own resources for economic modeling gives the family office the means of independently evaluating and monitoring its asset base, and pro-actively preparing for future contingencies based on the results simulated by the models.

XI. Having one’s own economic modeling resources

Technology building blocks

Important to implement next generation of technology, to avoid overheads with large global teams operating dozens of applications. Currently, each financial institution adopts its own Software Development Life Cycle (SDLC) process, which is only lightly coordinated with industry standards. Adopting uniform standards with Big Data and cost efficient cloud-based strategies are crucial for success.
Crucial to focus first on the model development process. After that, use Big Data resources to implement the model quickly, and operate it smoothly. Bottom up: requirements come from the modeling process, not the technological constraints.
Important to have a common framework for implementing new models.

XII. Summary

After the financial crisis of 2008 — 09, highly wealthy families have been finding an acute need for trust-worthy financial services that are customized to their personal circumstances, with particular attention to their younger generation.
Moreover, the cost-benefit tradeoff is extremely in favor, when compared to management fees paid to hedge funds and wealth management funds, as well as compared to benefits received from private banks.