Using Inertia to Offset Loss Aversion and the Status Quo

In our Foundations of Integrative Thinking (FIT) class, we were reading an interesting case about savings programs. Particularly, that countries with a high marginal propensity to consume (MPC) naturally exhibit low savings (low marginal propensity to save, MPS = 1 - MPC). Coupled with hyperbolic discounting (the idea that our myopic views cause us to over value short term events at the expense of long term events) and the stickiness of the status quo, inertia and resistance to change. In finance terms, the maturity premium on our discount rates are underweighted.

Using these tools, the question was asked: "Is it possible to build a program which encourages people to save?" An ingenious solution was the SMarT program (Save More Tomorrow Program), which tried to affect the various levers to obtain the desired result (increased savings).

A simple proposed solution was simply: "Can you make them aware of their situation and ask them to save more?" A logical model assuming 'rational' players, however, loss aversion and myopic views would prevent people from taking the 'medicine' they needed. Often many workers were making 'just enough to get by'.

The next attempt to reduce loss aversion would be to ask them to save more when they receive a promotion. However, myopic views and discounting still have an strong enough negative effect that will prevent significant savings program acceptance.

However, the SMarT program mixes a knowledge of all the concepts listed above in a fairly unique manner. Rather than take any of the traditional and 'logical' methods based on simple models in order to solve the problem, they proposed a different solution:

At a given point T=0 (Now) propose that when an employee gets their next
pay raise, that a they increase their savings at that point.

Key points to note:

• By making the decision now to save later, this discounts the future 'loss' associated with having to save more in the future (and consuming less now).
• By introducing the idea of making the decision in the future also results in inertia working in the benefit of savers by biasing them towards not changing their intended course as it relates to saving (sticking to the program).
• Since the trigger is a pay increase, the loss as described by decreased consumption usually associated with saving is offset by an increase which reduces the loss aversion further.

In reading the results of the article, it was shown that this methodology of getting people to increase their savings was significantly more successful than previous methods. The success of the program is primarily attributed to the methodology which considers not only the logical conclusions, but also how the mind works and precieves the benefits of different solutions based on how they are framed. After all, the goal in each of the programs is the same, a modification of saving behaviour. However, the approach in any 'behavioural problem' is critical to obtaining the desired outcome.

Fixing "Problems" We Don't "Understand"

We were discussing statistics in our class and how to understand numbers you are presented with respect to their distribution. Particularly, we were focusing on prediction metics and describing how a residual value (calculated as actual minus prediction) is defined as biased if the mean value is not equal to zero (or FAR from zero, our heuristic is if the mean is approximately 20% of the standard deviation).

I proposed a solution that we could "correct" the bias (even if we didn't understand it) by making a permanent subtraction from our prediction model by the mean of the residual.

My teammate, Matt Clare, and the professor further refined this model to say it would work IF the residual pattern exhibits constant variability (the variation and volitility isn't changing).

While I am loath to use solutions which insufficently describe the underlying causes, this seems to be one of the FEW examples where math can actually be used to solve a problem without a keen knowledge of what is happening (assuming an intimate knowledge of the mechanics of why patterns occur, rather just a knowledge that they exist and are stable).

For residuals that increase with the size of the actual, I think that considering errors as a fraction of residual over actual (in the same way we normalize variation for residual based on actual size) can understand variation in the same way.

Negative LIFO reserve?

I had previously posted about LIFO reserve and how to approach questions on the CFA exam, but I had a a great question about if it was possible to get negative LIFO reserve. While it is mathematically possible, I suggested that there might be some sort of accounting rule that prevents it (I thought it would look really awkward on financial statements).

When I asked my professor if it was possible, however, he was quick to point out that it was (both in theory and practice). When asked what real world scenario would this actually happen, he brought up the example of computer companies such as Dell and Cisco, whose technology products depreciate in cost (rapidly). It becomes apparent that they would:

1. Prefer FIFO accounting over LIFO
2. Probably have small, just-in-time (JIT) inventory management

Revenue Recognition for Large Projects

I apologize for not really discussing Financial Accounting. At the risk of sounding a bit arrogant, a lot of this material (but certainly not all of it - liability recognition percentages for GAAP (80%) versus IFRS (50%) ) has been covered in the CFA level I (for the question that most people ask, should I do an MBA or CFA, this is part of the answer... I should post more on this sometime and why I decided to do both). Plus, as my colleague Megan so eloquently puts it:

"... it is an important and necessary subject, so I may as well learn to love it
because I can't escape it."

Currently, we are discussing different revenue (and cost and profit) recognition techniques for larger projects who span more than one reporting period. The question is how should we recognize these items in financial statements? There are two methods we are looking at: the percentage of completion method and the completed contract method.

The completed contract method is quite simple, where (intuitively) all the revenues are accounted for once the contract is completed. It is more appropriate in scenarios where the contract involves extremely high risks (and may not be completed).

Another method not yet mentioned is the cost recovery method, where revenues match costs until the last period where gross profits are recognized all at once.

[Case] Same as the case in the percentage of completion post:

Assume a construction project with the following construction cost structure:

Year 1: $5M
Year 2: $15M
Year 3: $10M

The overall contract price is $46M.How much profit should be recognized in each of the given years under different methods?

[Solution]
Case 1 - Completed contract method revenue:
Year 1: $0
Year 2: $0
Year 3: $46M

Profit:
Year 1: $0
Year 2: $0
Year 3: $46M - $30M = $16M

Case 2 - Cost recovery method PROFIT:
Year 1: $0M
Year 2: $0M
Year 3: $16M

(Although I don't know what the cost schedule looks like, the revenue recognition will be such that revenues will match and equal costs, until the last year). Note that while the profit schedules are identical in both methods, the revenue and cost recognition schedules are different in these two methods.

Continuing the class after the break, we discussed COGS and Inventory as an extention of the above concepts. Our professor is starting to do a primer (and enter into) the topic of LIFO versus FIFO, one of my top (most popular) posts. And as mentioned before, there are interesting concerns when it comes to accurately measuring performance versus position. Another major issue we are discussing is accounting for inventory costs when they haven't yet been sold, but their value moves with what happens in the market and LOCOM, the lower of cost or market as an accounting standard for both US GAAP and IFRS.

Stackelberg and First Mover Advantage

We have to be careful here, as first mover response has a very specific definition and I believe there are some inherent assumptions here. Most of us would associate first mover response (used colloquially) as when a company creates a new product and is the first to market. It seems as if the theory we learned to describe Stackelberg (essentially a Cournot model where one firm has the opportunity to move first) has a good description for a very specific definition of "first mover advantage".

Intuitively, you can predict that you can exert a form of control over your competitors ('the market') by making a first move. How this differs from Cournot is that Cournot assumes that both firms move together at the same time, and their decisions are unverifyable by the other company. In Stackelberg, by knowing the response functions of both companies (and understanding that the more one firm creates, the less another firm will be incented to create) and confirming quantities early, it affects what the other company will be able to benefit.

The important factor is that the ambiguity and uncertainty of what the other firm produces is removed. The Cournot model is heavily affected by the anticipation of what the other firm will do, rather than the certainty. The result is that optimal quantities in both models change (as demonstrated by the practical and general solving of the formulas in each case). In the Stackelberg case, rather than having to solve an algebraic equation where Q1 is 'floating' (uncertain and expressed as a formula related to what firm 1 produces), Q2 is simply expressed as a formula where Q1 is a known quantity. In that way, firm 1 can 'force' firm 2 to produce their 'optimal' quantity given that firm 1 has put a stake in the ground by actually creating a certain number of units and making sure firm 2 knows.

Bertrand Equilibrium

Continuing with other different models for firm interaction, our professor introduced the idea of choosing price versus choosing quantity as in the Cournot model, known as the Bertrand model. This focuses more on price competition. A key consideration is that in price competition (for homogeneous products) if firm 1 dropped it's price, it could capture the whole market. However, firm 2 is then incented to undercut the new price to recapture the market and the price cutting continues.

Where does it stop? It starts to move towards perfect competition immediately which is the crux of the model. Bertrand proposed that you could have a model which describes perfect competition with only two firms whereas Cournot required that perfect competition would occur where there were more firms in the market.

This highlights the importance of basing decisions on quatity versus price.

Cournot Duopoly - Oligopoly and Foundations of Game Theory

In all my previous undergrad economics courses, we've always discussed oligopolies with OPEC and oil being great examples. However, in our managerial economics class, we've actually started applying some basic math to the model. It's simply expressed as:

P = a - b(Q1 + Q2)
MR1 = a - bQ2 - 2bQ2
MR2 = a - bQ1 - 2bQ2

An interesting point raise by a student was: "Why do we learn these models if we can't necessarily always apply them quantitatively in real life?" and our professor came up with a brilliant answer: "I would hope that especially with your FIT class, you should be able to understand that models are intended simply as a simplification and can help you understand relationships even if you don't always use hard numbers."

Another interesting new way to think we've been introduced to is the Q1 v Q2 graph (explicity not to be confused with a downward sloping demand curve) and is described as a graph of Firm 1's Best-Response Function. Something which I tripped on just now in class was understanding the x intercept (Q1 m - what firm 1 would produce if Q2 produced nothing, a monoploy like scenario) and y intercept (what quantity Q2 would have to produce to kick Q1 out of the market).

I guessed that the y-intercept was the same as the x-intercept which was wrong, because I made some gross assumptions (cost curves of both companies are the same). Whereas, the truth is that firm 2 would have to have a much better MC curve by completely pushing out firm 1 at Q2m. If the cost curves were the same, there would probably be a more balanced production quantitiy for both.

It turns out the y-intercept is the (a-c1)/b. Superimposing the Q2 response function, the reverse appears, the y-intercept is Q2m and the x-intercept is (a-c2)/b, where c1 and c2 is the marginal cost for the respective firms.

The prof then goes to show how, with perfect knowledge ("collusion", or assuming each firm knows both response functions), the two firms will naturally return to equilibrium, even if they try to push (or start) to produce any quantity outside of equilibrium. It is very similar to decreasing osciliations about the mean until the signal stabilizes.

He also discusses collusion, monopolies and "cheating" in collusion and how profit seeking entities will look to predictibly optimize their profits with respect to their competitor's (the market's) production decisions.

How does this relate to game theory? If you can predict what your competitor (in the duopoly model... or any model with N players where N is greater than 1) you can make the appropriate arrangements to maximize your profitability in the market. Suddenly, your mutual decisions are "forecastable" according to game theory.

You can even start to understand the case where the firms would actually prefer to merge and perform as a monoploy. And this is the reason and primary concern for anti-trust with the DOJ or Competition Bureau.