Debt Crisis Looming? Yes, Corporate Debt Expanded but Don’t Panic Over the Prospect of Bbb Downgrades
In our sixth article from the (JACF Spring/Summer issue)Martin Fridson’s piece examines how the popular press often tends towards sensationalism and, unfortunately, the supposedly more sober financial press is not always better. It is true that many American companies have taken the opportunity to borrow large sums during recent years when interest rates were close to their all-time lows. This has also led some media commenters to predict a large number of marginally investment grade debt issues (e.g. BBB rated on the S&P rating scale) will be downgraded to less-than-investment-grade status–or to “junk”–as such bonds are commonly known.
Veteran fixed income analyst Martin Fridson takes stock of the situation in mid-2018. While emphasizing that a bear market is inevitable someday, he advises investors not to panic now. Despite the more apocalyptic scenarios offered by financial commentators making dubious connections between today’s corporate bond market and possible future high-yield events, the aggregate numbers do not add up to an end-of-civilization-as-we-know-it story. Some of the numbers mentioned in financial commentary are at least slightly misleading.
The present market lacks the sort of structural weaknesses likely to trigger a major bear cycle in fixed income securities, such as overleveraged buyouts and early-stage telecoms. While there are some questionable issuers in the market, these are isolated cases, rather than representatives of a vast segment of today’s high-yield universe.
Authored by Martin Fridson, Lehmann Livian Fridson Advisors.
“Big Data” Analysis: Putting the Data Cart before the Modeling Horse?
In our fifth article from the (JACF Spring/Summer issue) the authors discuss the statistical analysis of very large data sets, so-called Big Data or Data Analytics, and how they have become enormously popular in Statistical Analysis and Operations Research. In some cases, such as research into the buying habits of online consumers, the results have come quickly and been very significant. Analysis of other data sets, however, is questionable. For example, time-series based statistical analysis, often under the descriptive envelope of “neural networks” and “data mining,” of stock market and futures prices, sometimes in combination with historical accounting figures such as earnings and cash flows.
The appeal is understandable given the availability of share price data and cheap computer processing power. Nevertheless, the notion that historical data form some sort of repeatable pattern over time, and that complex time series or neural network techniques can be then be used to forecast future prices is hard to justify.
Economic modeling necessarily needs to factor in human behavior, unlike modeling in the pure sciences. The authors cite Lancaster University Professor Michael Pidd who summarizes six relevant principles:
1. Model simple, think complicated
2. Be parsimonious, start small and add
3. Divide and conquer, avoid mega models
4. Use metaphors, analogies and similarities
5. Do not fall in love with data
6. Model building may feel like muddling through.
Economic modeling must recognize three key components:
(i) the incorporation of human cognitive understanding and experience of the underlying systems,
(ii) the use of data to validate emerging models, and
(iii) the role of mathematics to ensure internal coherence and logic.
Decision-makers ought to be very skeptical of models which skimp on any one of these three components. The authors emphasize that, rather than Big Data adding value, per se, people add value by creating models that use it.
Authored by Graham D Barr, Theodor J. Stewart & Brian S. Kantor, University of Cape Town