Technical Reports - Query Results
Your query term was 'number = 2000-22'1 report found
- ÖFAI-TR-2000-22 (
89kB g-zipped PostScript file, 
214kB PDF file)Temporal Pattern Recognition in Noisy Non-stationary Time Series Based on Quantization into Symbolic Streams: Lessons Learned from Financial Volatility Trading
- Peter Tino, Christian Schittenkopf, Georg Dorffner
- In this paper 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. The main argument
is that careful quantization can reduce the noise in the time series to make
model estimation more amenable given limited numbers of samples that can be
drawn due to the non-stationarity in the time series. As a main application
area we study the use of such an analysis 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
on a daily basis, based on predictions of the daily volatility differences in
the underlying indexes. We propose a parametric, data-driven quantization
scheme which transforms temporal patterns in the series of daily volatility
changes into grammatical and statistical patterns in the corresponding
symbolic streams. As symbolic predictors operating on the quantized streams
we use the classical fixed-order Markov models, variable memory length Markov
models and a novel variation of fractal-based predictors introduced in its
original form in (Tino, 2000b). The fractal-based predictors are designed to
efficiently use deep memory. We compare the symbolic models with continuous
techniques such as time-delay neural networks with continuous and categorical
outputs, and GARCH models. Our experiments strongly suggest that the robust
information reduction achieved by quantizing the real-valued time series is
highly beneficial. To deal with non-stationarity in financial daily time
series, we propose two techniques that combine ``sophisticated'' models fitted
on the training data with a fixed set of simple-minded symbolic predictors not
using older (and potentially misleading) data in the training set.
Experimental results show that by quantizing the volatility differences and
then using symbolic predictive models, market makers can generate a
statistically significant excess profit. However, with respect to our
prediction and trading techniques, the option market on the DAX does seem to
be efficient for traders and non-members of the stock exchange. There is a
potential for traders to make an excess profit on the FTSE 100. We also
mention some interesting observations regarding the memory structure in the
studied series of daily volatility differences.
Keywords: Markov models, prediction suffix trees, iterative function systems, fractal machines, volatility, straddles, options
- In this paper 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. The main argument
is that careful quantization can reduce the noise in the time series to make
model estimation more amenable given limited numbers of samples that can be
drawn due to the non-stationarity in the time series. As a main application
area we study the use of such an analysis 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
on a daily basis, based on predictions of the daily volatility differences in
the underlying indexes. We propose a parametric, data-driven quantization
scheme which transforms temporal patterns in the series of daily volatility
changes into grammatical and statistical patterns in the corresponding
symbolic streams. As symbolic predictors operating on the quantized streams
we use the classical fixed-order Markov models, variable memory length Markov
models and a novel variation of fractal-based predictors introduced in its
original form in (Tino, 2000b). The fractal-based predictors are designed to
efficiently use deep memory. We compare the symbolic models with continuous
techniques such as time-delay neural networks with continuous and categorical
outputs, and GARCH models. Our experiments strongly suggest that the robust
information reduction achieved by quantizing the real-valued time series is
highly beneficial. To deal with non-stationarity in financial daily time
series, we propose two techniques that combine ``sophisticated'' models fitted
on the training data with a fixed set of simple-minded symbolic predictors not
using older (and potentially misleading) data in the training set.
Experimental results show that by quantizing the volatility differences and
then using symbolic predictive models, market makers can generate a
statistically significant excess profit. However, with respect to our
prediction and trading techniques, the option market on the DAX does seem to
be efficient for traders and non-members of the stock exchange. There is a
potential for traders to make an excess profit on the FTSE 100. We also
mention some interesting observations regarding the memory structure in the
studied series of daily volatility differences.