Category Archives: Commodities

Productivity and Deflation

What happens when productivity grows faster than production?

We produce more with less work and that means unemployment, right? Initially, the answer is yes, but looking at history we can see that the answer is more encouraging than that. Productivity growth eventually transitions into falling prices. And these days, it should happen even faster. Here’s why:

Competition and consumer choice, especially now that information flows so freely, has led to much more efficient markets in terms of pricing. Improving productivity is rarely unique to a particular company… in other words, if one company benefits from a new technology, then others follow, competition drives prices down, and consumers ultimately benefit.

Are falling prices always good?

Falling prices is called deflation, and deflation is an ugly beast; it exaggerates the disparity in the distribution of wealth and creates an artificial investment hurdle. Deflation increases the buying power of wealth. It makes money more powerful. Those who have money can buy more with it, and people in debt fall deeper in debt. This makes the “real” distribution of wealth even more concentrated. Similarly, deflation means that your cash grows in value; if your cash grows in value, then your investments will have to appear very strong before you will be willing to make them.

The solution to these problems is a low stable inflation rate. Low stable inflation helps to maintain investment by discouraging holding cash, slowly eroding stagnant concentrations of old wealth unless it is invested.

In order to achieve a low stable inflation rate, the deflationary pressure of productivity growth should be balanced by growth in the money supply and a low FED Funds rate. The faster productivity grows (and it appears to be accelerating over the decades), the more aggressive the Federal Reserve may have to be in order to avoid deflation.

AI and Program Trading

NewScientist published a good article describing neural network program trading systems.

An extension allows for a large number of competing signalling systems. One such signalling system may be an improvement on traditional cointegration techniques.

Cointegration improvements

Cointegration typically uses the price information for two related securities, and provides relative value signals. As with traditional technical trading strategies, changes to the fundamentals create a risk of bad relative value signals. With 2-security cointegration, this risk is doubled because changes to the fundamentals of either company can skew the relative value signal. However, this problem can be cut in half by creating baskets (portfolios) of securities and running the cointegration analysis with each security against the basket. This effectively generates signals which are skewed only by changes to the fundamentals of the individual security. Additionally, the required correlation matrix for the permuted set of security combinations can be replaced by a single vector of correlations – greatly improving calculation efficiency and extending the analysis processing potential.

To improve upon normalizing data to % changes, factors typically associated with beta may also provide better signalling data. For example, as the size of a company grows over the course of a few years, its price volitility may fall. Similarly, as market cap grows, the price change correlations may increase relative to larger cap baskets and decrease relative to smaller cap baskets.

By backtesting, optimal trigger strengths and bet sizes can be measured, however, given the correlation coefficients, volitilities, number of positions, and risk preferences, probabalistically optimal bet sizes may provide better results.

Forecast Changes in Asset Values based on Measurable Cultural influences

The sum total of the media contributed to the internet approximates the attention of society during that period. Then analysis of this media indicates trends in attention and preferences. These trends create signals about the directions of values for securities and other assets. Frequency and trend directions of keyword usage, volume of content in certain classifications, and level and type of contribution of media files vs. sector and industry pricing trends are recommended starting points for analytical comparison.

Financial Markets Evolve

Arbitrages – even of very small marginal size – will be eliminated based on a large number of artificially intelligent program trading systems that will mine the historical and currently released information identifying and exploiting trends. The process of the elimination of arbitrage opportunities will create vast concentrations of wealth within the companies that embrace the tools that automate this process. As new information sources become available for analysis, new arbitrages may be identified with increasing complexity. The abstraction of trading systems to automaically test and integrate new data sources will mark the last decades of financially advantageous investment in hedge funds. After that time, return will be a stochastic function of expected risk.