Volatility trading via temporal pattern recognition in quantized nancial time series Export

@misc{citeulike:1477459, abstract = {We investigate the potential of the analysis of noisy non-stationary time series by quantizing it into streams of discrete symbols and applying finite-memory symbolic predictors. Careful quantization can reduce the noise in the time series to make model estimation more amenable. We apply the quantization strategy in a realistic setting involving financial forecasting and trading. In particular, using historical data, we simulate the trading of straddles on the financial indexes DAX and FTSE 100 ...}, author = {Tino, P. and Schittenkopf, C. and Dorffner, G.}, citeulike-article-id = {1477459}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.4350}, keywords = {timeseries}, posted-at = {2007-07-24 21:10:14}, priority = {2}, title = {Volatility trading via temporal pattern recognition in quantized nancial time series}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.4350}, year = {2001} }

TY - GEN ID - citeulike:1477459 L3 - citeulike-article-id:1477459 N2 - We investigate the potential of the analysis of noisy non-stationary time series by quantizing it into streams of discrete symbols and applying finite-memory symbolic predictors. Careful quantization can reduce the noise in the time series to make model estimation more amenable. We apply the quantization strategy in a realistic setting involving financial forecasting and trading. In particular, using historical data, we simulate the trading of straddles on the financial indexes DAX and FTSE 100 ... TI - Volatility trading via temporal pattern recognition in quantized nancial time series KW - timeseries AU - Tino, P AU - Schittenkopf, C AU - Dorffner, G PY - 2001/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.4350 ER -