Nonlinearity in Forecasting of High-Frequency Stock Returns

Reboredo, Juan C.; Matías, J. M.; García‐Rubio, Raquel

doi:10.1007/s10614-011-9288-5

Cited by 38 publications

(15 citation statements)

References 54 publications

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“…Unlike [189,225], our research demonstrates, in most cases, the market has gone back to efficiency and thus is unpredictable after a one-minute timespan. Furthermore, while we agree that predictability exists in the tick-by-tick market, we again 4 Continuation is a term used by [170] and refers to the pattern where the signs of at least two non-zero consecutive changes are in the same direction.…”

Section: Introductionmentioning

confidence: 82%

“…A paper by Reboredo et al [189] found profitability over a benchmark for 5, 10, 30, and 60 minute intervals of intra-day data using Markov switching, artificial neural networks and support vector machine regression models. Additionally Wang and Yang [225] found intra-day market inefficiency in the energy markets using 30 minute intra-day prices.…”

Section: Existing Research Demonstrating Predictabilitymentioning

confidence: 99%

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Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction

Rechenthin¹

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This thesis explores predictability in the market and then designs a decision support framework that can be used by traders to provide suggested indications of future stock price direction along with an associated probability of making that move.Markets do not remain stable and approaches that are highly predictive at one moment may cease to be so as more traders spot the patterns and adjust their trading techniques. Ideally, if these "concept drifts" could be anticipated, then the trader could store models to use with each specific market condition (or concept) and later apply those models to incoming data. The assumption however is that the future is uncertain, therefore future concepts are still undecided.Maintaining a model with only the most up-to-date price data is not necessarily the most ideal choice since the market may stabilize and old knowledge may become useful again. Additionally, decreasing training times to enable modified classifiers to work with streaming high-frequency stock data may result in decreases in performance (e.g. accuracy or AUC) due to insufficient learning times. Our framework takes a different approach to learning with drifting concepts, which is to assume that concept drift occurs and builds this into the model. The framework adapts to these market changes by building thousands of traditional base classifiers (SVMs, Decision Trees, and Neural Networks), using random subsets of past data, and covering similar (sector) stocks and heuristically combining the best of these base classifiers.This "ensemble", or pool of multiple models selected to achieve better predictive periii formance, is then used to predict future market direction. As the market moves, the base classifiers in the ensemble adapt to stay relevant and keep a high level of model performance. Our approach outperforms existing published algorithms.This thesis also addresses problems specific to learning with stock data streams, specifically class imbalance, attribute creation (e.g. technical and sentiment analysis), dimensionality reduction, and model performance due to release of news and time of

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Section: Introductionmentioning

confidence: 82%

Section: Existing Research Demonstrating Predictabilitymentioning

confidence: 99%

Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction

Rechenthin¹

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“…(2012) [13] and Reboredo et al (2012) [14], who model the behaviour of high frequency returns on the S&P 500 index using intra-day data. Gradojevic and Yang (2006) [15] compare high frequency US dollar Canadian dollar exchange rate behaviour and report that ANN models outperform linear time series models.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies

Allen

McAleer

Peiris

et al. 2016

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Abstract:This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models, logistic smooth transition regressions models, threshold autoregressive models, nonlinear autoregressive models, and additive nonlinear autoregressive models, plus Neural Network models. The models are evaluated on the basis of error metrics for twenty day out-of-sample forecasts using the mean average percentage errors (MAPE). The results suggest that there is no dominating class of time series models, and the different currency pairs relationships with the US dollar are captured best by neural net regression models, over the ten year sample of daily exchange rate returns data,

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“…al. [8] employed a ANN system to forecast the high-frequency S&P500 data, where many lead times were applied to test the performance of their propose and many other nonlinear models. Lee [9] employs two computational intelligence algorithms, ANN and genetic Programming, for forecasting the volatility of high-frequency TAIEX financial data, where his conclusion was that the used algorithms are powerful for modeling the volatility of highfrequency intraday financial data.…”

Section: Introductionmentioning

confidence: 99%

Applying a general hybrid intelligent system for ultra-high-frequency stock market forecasting

Neto

Ferreira

2016

2016 International Joint Conference on Neural Networks (IJCNN)

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The stock market is the most important institution for global investments all around the world. Among the possibles analysis, the study and forecasting of ultra-high-frequency time series is an interesting and great challenge to econometric modeling and statistical analysis due its complex behaviour. This work proposes a hybrid intelligent system to forecast ultrahigh-frequency stock prices. The intelligent system is composed of a genetic algorithm (GA) that seeks the best parameters (the number of nodes in the input and hidden layers, and the training algorithm) of an artificial neural network (ANN) of type multiLayer perceptron (MLP). The proposed method called Time-delay Added Evolutionary Forecasting (TAEF) is a data-driven approach, that performs a pos-processing, where the objective is to reduce the difference between the forecasting and the actual series. The experimental study is performed using ultra-high frequency time series (Amazon, APPLE, Google and Intel stock prices) and shows that the proposed approach overcomes classical techniques of the computational intelligence and statistics in light of six relevant evaluation measures.

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Nonlinearity in Forecasting of High-Frequency Stock Returns

Cited by 38 publications

References 54 publications

Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction

Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction

Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies

Applying a general hybrid intelligent system for ultra-high-frequency stock market forecasting

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