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Reference: Nachi Gupta, Raphael Hauser and Neil F. Johnson, (2005). Forecasting Financial Time Series using Artificial Market Models. Unspecified.Citable link to this page:


Forecasting Financial Time Series using Artificial Market Models

Abstract: We discuss the theoretical machinery involved in predicting financial market movements using an artificial market model which has been trained on real financial data. This approach to market prediction - in particular, forecasting financial time-series by training a third-party or 'black box' game on the financial data itself - was discussed by Johnson et al in [10] and [13] and was based on some encouraging prelimary investigations of the dollar-yen exchange rate, various individual stocks, and stock market indices (see[12] for more details also). However, the initial attempts lacked a clear formal methodology. Here we present a detailed methodology, using optimization techniques to build an estimate of the strategy distribution across the multi-trader population. In contrast to earlier attempts, we are able to present a systematic method for identifying 'pockets of predictability' in real-world markets. We find that as each pocket closes up, the black-box system needs to be 'reset'- which is equivalent to saying that the current probability estimates of the strategy allocation across the multi-trader population are no longer accurate. Instead, new probability estimates need to be obtained by iterative updating, until a new 'pocket of predictability' emerges and reliable prediction can resume.

Bibliographic Details

Issue Date: 2005-06Identifiers

Urn: uuid:e66e58ac-9f6a-4228-8c6a-750bdcc35018 Item Description

Type: Technical Report;


Author: Nachi Gupta - - - Raphael Hauser - - - Neil F. Johnson - - - - Bibliographic Details Issue Date: 2005-06 - Identifiers Urn: uuid:



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