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Chaos based pediction models' accuracy
A research was conducted in order to know whether the observed forecasting capability of our model has statistical significance or not.
It must be remarked that proving that a system has a certain predictability power is a somewhat tedious process since it cannot be proven in a closed form, but by way of accumulating evidence that supports the hypothesis under scrutiny.
Normally the statisticians design several tests to be evaluated over the forecasted data. Those tests must be passed to accumulate favorable evidence. Just one negative outcome to those tests invalidates completely the hypothesis.
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After years of research on market modeling, market prediction and forecasting science we have built a large knowledge base of books and white papers.
As we understand that the trading community is continuously studying and looking for new techniques, new applications and theories, we would like to share with you some of our knowledge.
If you would like to get more involved in the fundamentals and the science behind our systems we invite you visit these links, or get the recommended books listed here.
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Market efficiency and the long-memory of supply and demand: Is price impact variable and permanent or fixed and temporary? - J. Doyne Farmer, Austin Gerig, Fabrizio Lillo, Szabolcs Mike
Abstract: In this comment we discuss the problem of reconciling the linear efficiency of price returns with the long-memory of supply and demand. We present new evidence that shows that efficiency is maintained by a liquidity imbalance that co-moves with the imbalance of buyer vs. seller initiated transactions.
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J. Doyne . Farmer is the co-founder of Prediction Company, now part of the UBS. See www.predict.com
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The Theory of Money - Martin Shubik
Abstract: The basic role of fiat money in a dynamic economy is considered. Its role as a virtual
asset whose store of value properties are the outcome of the dynamics is explored and the role of
the limits on the money supply and the bankruptcy laws in bounding prices are considered. The
actions of the government may serve to bound individual expectations.
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Massively Parallel Architectures and Algorithms for Time Series Analysis - Kurt Thearling
Abstract: With the recent development of massively parallel computing, extremely large amounts of processing power and memory capacity are available for the analysis of complex data sets. At the same time, the complexity and size of these data sets has been increasing. Both of these trends are expected to continue for the foreseeable future. This paper will provide a general overview of massively parallel architectures and algorithms for the analysis of time series data. Two distinct approaches to this problem, computational and memory-based, will be described.
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