To and From tables of connectedness are useful

 

A very useful way to think about connected has been developed by Diebold and Yilmaz in a set of papers that are connected through their book, Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring. The connectedness table is related to the variance decomposition that is often used with VAR models. The H-step forecast error d(i,j) is just the fraction of i's H-step forecast error due to shocks in variable j. The full set of these pairwise forecasts error relationships for the system can be seen in a connectedness table.

If you have a 4x4 connectedness table, we can look at the pairwise directional connectedness through the off-diagonal elements, d(i,j). If we look at element in the matrix (1,2), we will say that the pairwise directional connectedness is C( of j to i, or 2 to 1). There will be N-squared - N pairwise directional connectedness measures. The net pairwise directional connectedness will be C(1,2) = C(2,1) - C(1,2) which leads to (N-squared - N)/2 net pairwise direction connectedness measures.  

There will be 8 off-diagonal row and columns sums labeled "from" and "to" which are the total directional connectedness measures.  So, the column at the right will be the sum all of the off-diagonals and will say that x% of the variation comes from other markets. The sum of the columns for a give row i tells us the total variation from others to i. If we look at the column sums, this will tell us the total directional connectedness from j to others. We can look at the net total direction effects, through say the C(2) which is the "to" or column impact minus the "from" or row impact which provides the combination effect. The total combination is the sum of the "from" column and "to" row is the total system-wide connectedness. 

We can look at the net connectedness through time for the whole system or we can look at the to or from connection for each element in our matrix. This will tell us the amount of forecast error variance that is due to shocks from other markets. A generalized variance decomposition will be used to create the matrix. 

The "to" and "from tables can be converted into pairwise network graphs to show how market re connected visually.

Two types of forecasters - both are not very good

 

“There are two kinds of forecasters: those who don’t know, and those who don’t know they don’t know.”

― John Kenneth Galbraith

Accept that forecasting is hard, and most will get it wrong. We can explain only a small portion of the change in returns.  We get the large macro forecasts wrong.We often miss the big picture, yet our livelihood is based on our ability to have some view of the future. It can be simple as saying the returns and the economy will trend, or that using past information can help us say something about future. The question is whether we can form some likelihood about what will happen in the future. The is no certainty and we often cannot even get probability right, yet it is critical that we form some analysis about the future. Just don't put a lot of stock in the forecasts of the professionals. 

Money and intelligence - there may not be a connection

Galbraith says “the specious association of money and intelligence.” When people get rich, others take that to mean they’re smart. And when investors succeed, it’s often assumed their intelligence can lead to similarly good results in other fields. Further, successful investors often come to believe in the strength of their own intellect and opine about fields with no connection to investing.  - "The Folly of Certainty" Oaktree Howard Marks 

Another relevant quote is associated with D McCloskey, which she calls the American Question,"If you are so smart why aren't you smart, and the retort, "If you are so rich why aren't you smart." See "The Limits of Expertise".

There may not be an association between brains and financial success. There is a minimum amount of intelligence that is needed, but there are other skills more important like tolerance for risk, the ability to be disciplined, and the ability to sift through many facts, or perhaps the most important skill of being humble and accept that you may often be wrong. 

Financial memory - memory loss is a problem

 

“For practical purposes, the financial memory should be assumed to last, at a maximum, no more than 20 years. This is normally the time it takes for the recollection of one disaster to be erased and for some variant on previous dementia to come forward to capture the financial mind. It is also the time generally required for a new generation to enter the scene, impressed, as had been its predecessors, with its own innovative genius.”  - J K. Galbraith 

Financial memory is an often-overlooked important issue. Think of those who started their career after the GFC. If the professional started in finance at 22, he is now 37 and has never seen a pre-QE world. If you started your career at 25, you are now entering your 40's without ever having seen a large bear market. Many quants create back-tests that are much shorter than 15 years. It is no wonder that leverage is a king, and many investors want to continue holding risky assets and buying on dips.