What does gold arbitrage tell us about globaliization

 

Gold quality is the same worldwide. There are differences in purity which can be accounted for in price, but an oz of gold in Shanghai, London, or New York should fetch the same price within a range. The price range difference should reflect the cost of transporting physical gold from one location to another. The tightness of price differences around the world is a measure of the fissure of globalization and free trade. If the world is in a free trade environment, gold price differences should be within the range of transport costs. If there are large differences in locational prices, trade is disrupted. 

A close look at price differences across major gold markets suggests an arbitrage breakdown due to tariff uncertainty in 2025. There has also been a disconnect in physical markets due to the desire of major buyers to hold gold in their own domiciles. As gold has become in short supply in some locations, there has been a disconnect that cannot be solved by the usual form of transportation arbitrage. This is a sign of a bubble, but also a sign that investors and physical users do not want to have geographical uncertainty.

Fiscal versus moentary dominance - the real battle

Janet Yellen, who served as both Treasury Secretary and Fed chairman, presented "The Future of the Fed: Central Bank Independence and Fiscal Dominance" at the AEA convention earlier this month. She does a thoughtful job of describing the differences between these two forms of dominance, yet she misses the mark in her description of the current environment.

We cannot continue independence and monetary dominance if there is a fiscal crisis. Fiscal policy saw periods of deficit and then a return to something normal; however, in the last decade, or since the Great Financial Crisis, there has been a change in government debt dynamics, so that fiscal policy has a more dominant role in monetary policy. Dominant does not mean controlling. In this example, fiscal dominance means the issues with fiscal policy have a more dramatic impact on the economy than monetary policy.

The current debt levels cannot be sustained with a growing amount of tax revenue used to pay interest on debt. The Fed has ignored this fiscal crisis. They have refused to comment on rising debt-to-GDP ratios to avoid being political. Yet the ongoing QE process, coupled with Fed high Treasury balances, shows that the Fed has lost its monetary dominance and must deal with a debt crisis. 

Trump's desire to lower interest rates is just an extreme manifestation of the fiscal dominance needed to sustain current government policy. Let's not forget that inflation is one way of getting out of a fiscal bind. If there were controlled deficits, there would not be a need to discuss lower interest rates. The fiscal excesses of the past have to be addressed. Yet, who wants to say we have a debt problem?  

Once again, our foecasting skill is poor

We try and try, but the results are always the same. We are not good forecasters. Should we stop trying to predict? That would be the obvious answer, yet anyone who invests needs to make assumptions, which are forecasts. The easy answer is to diversify, but there are assumptions about what may happen in the future. The classic 60/40 stock/bond mix will not last forever. 

Turn the loser's game into one where you can limit downside. Provide ranges and not point forecasts. The key is knowing your limitations when it comes to forecasting. If you are likely to be wrong, think in terms of probabilities. Think about the impact of being wrong and how much exposure you would like to have.

 

Deep learning and asset management - it is here but requires significant work

The use of deep learning techniques has exploded in finance, but few papers summarize what has been achieved. That has changed with a new paper,  see Deep Learning in Asset Management: Architectures, Applications, and Challenges. This work provides a good survey of how to apply deep learning within asset management. While the authors suggest that the use of deep learning is promising, they also note significant challenges, including low signal-to-noise ratios, non-stationarity in financial time series, and the market's adaptive nature, which can face regime changes and adapt to patterns. Along with outlining the problems with CNNs, RNNs, and transformers, there are many practical challenges in using deep learning models, from data usage to the usual overfitting problems. While deep learning has focused on quality predictions, there may be useful ways to integrate deep learning into holistic approaches to asset management