High Precision, Low Volatility Asset Management


In the near future, precision computing methods will help predict stock prices more accurately than any other techniques ever employed in the past. How? New computing techniques are under development in the hard sciences. Some of these computing techniques will be applied to the world of asset management. Why? Precision helps us see more details in the data. The local variations in the data give us clues what we should be watching.

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High Precision Examples

For example, JPL/NASA used a special satellite to make a map of the gravity field for Earth with more precision than any previous instrument ever used. The gravity variations are shown in colors from blue to red. The image shows us that red often occurs wherever we see mountains (but not always), and blue often occurs at low spots in the topography (but not always).

Thus, if we see a low spot, and it is red, then we know that location is unusual. The local relationships help direct our attention to the little things that might be interesting.

Another example is a startup named Zapata. The founders are using advanced computing to design prescription drugs – using direct computations – to model and predict the interactions between the molecules. Up until right now, such computations have been intractable with conventional computing. In other words, impossible. Solving the atomic equations may take years off of the development time for creating life-saving drugs.

Lastly, precision computation is advancing in the high performance computing space to investigate major issues on huge datasets, such as global warming (and what actually drives it). New custom chip architectures have been designed to perform these computations at 10X speeds. The U.S. Department of Energy is building huge supercomputers based on that special chip architecture to help solve some of the hardest problems in computational science.

Inevitably, advanced computing methods will move toward the world of asset management.

Precision to Battle Volatility

Traditionally, in the past, volatility has always been a major difficulty. True, some portfolio managers have become famous for their ability to increase portfolio growth, in spite of the volatility. Yet, so far, no theory or mathematical technique has been consistently successful for achieving steady portfolio returns.

One published paper points out why it is so difficult to predict future stock prices over the short term:

“The difficulty is that there are too many different factors that affect the amplitude and frequency of the rise and fall of stocks at the same time.”

This description also reveals why “overfitting” is so common in statistical models – the energy content of the time series is literally changing over time.

In the section under Methodology, the authors also point out:

“The most common techniques used for stock forecasting are statistics algorithms and economics models. However, statistical algorithms and economics models cannot capture the stock movement’s patterns.”

Precision - It's the Future

Inevitably, the asset management space will see the arrival of a completely new breakthrough algorithmic process. Something that addresses the spirit of the random walk in full, while also possessing robust adaptive anticipation of upcoming changes.

Probably, the new math for asset management will focus on accurate short term prediction for trading, to obtain steady portfolio returns. Probably, the best asset managers will employ fundamental analysis in conjunction with their short term trading strategies. Probably, vertical companies will provide prediction services, and dramatic cost reductions, to the benefit of all participants.

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