Trends In Financial Time Series Research Articles

Towards efficient similarity embedded temporal Transformers via extended timeframe analysis

AbstractPrice prediction remains a crucial aspect of financial market research as it forms the basis for various trading strategies and portfolio management techniques. However, traditional models such as ARIMA are not effective for multi-horizon forecasting, and current deep learning approaches do not take into account the conditional heteroscedasticity of financial market time series. In this work, we introduce the similarity embedded temporal Transformer (SeTT) algorithms, which extend the state-of-the-art temporal Transformer architecture. These algorithms utilise historical trends in financial time series, as well as statistical principles, to enhance forecasting performance. We conducted a thorough analysis of various hyperparameters including learning rate, local window size, and the choice of similarity function in this extension of the study in a bid to get optimal model performance. We also experimented over an extended timeframe, which allowed us to more accurately assess the performance of the models in different market conditions and across different lengths of time. Overall, our results show that SeTT provides improved performance for financial market prediction, as it outperforms both classical financial models and state-of-the-art deep learning methods, across volatile and non-volatile extrapolation periods, with varying effects of historical volatility on the extrapolation. Despite the availability of a substantial amount of data spanning up to 13 years, optimal results were primarily attained through a historical window of 1–3 years for the extrapolation period under examination.

Predictive Quantization and Symbolic Dynamics

Capturing long-term statistics of signals and time series is important for modeling recurrent phenomena, especially when such recurrences are a-periodic and can be characterized by the approximate repetition of variable length motifs, such as patterns in human gestures and trends in financial time series or musical melodies. Regressive and auto-regressive models that are common in such problems, both analytically derived and neural network-based, often suffer from limited memory or tend to accumulate errors, making them sensitive during training. Moreover, such models often assume stationary signal statistics, which makes it difficult to deal with switching regimes or conditional signal dynamics. In this paper, we describe a method for time series modeling that is based on adaptive symbolization that maximizes the predictive information of the resulting sequence. Using approximate string-matching methods, the initial vectorized sequence is quantized into a discrete representation with a variable quantization threshold. Finding an optimal signal embedding is formulated in terms of a predictive bottleneck problem that takes into account the trade-off between representation and prediction accuracy. Several downstream applications based on discrete representation are described in this paper, which includes an analysis of the symbolic dynamics of recurrence statistics, motif extraction, segmentation, query matching, and the estimation of transfer entropy between parallel signals.

Predição de tendências em séries financeiras utilizando metaclassificadores

Neste trabalho foi desenvolvido um metaclassificador baseado em métodos de inteligência computacional para prever tendências em séries temporais financeiras. O kernel do metaclassificador foi baseado na ferramenta (Weka). Sete classificadores foram combinados para realizar a metaclassificação. Testes foram realizados com nove ativos da Bolsa de Valores de São Paulo. Os resultados iniciais foram promissores, com boa acurácia na classificação e ganhos de até 100% do valor de capital inicialmente investido no período de um ano.

A Model-Free Approach to Delta Hedging

We utilize the existence of trends for financial time series (Fliess M., Join C.: A Mathematical Proof of the Existence of Trends in Financial Time Series, http://ssrn.com/abstract=1459662) in order to propose a model-free setting for delta hedging. It avoids most of the shortcomings encountered with the now classic Black-Scholes framework. Several convincing computer simulations are presented. Some of them are dealing with abrupt changes, i.e., jumps.

Systematic Risk Analysis: First Steps Towards a New Definition of Beta

We suggest a new model-free definition of the beta coefficient, which plays an important role in systematic risk management. This setting, which is based on the existence of trends for financial time series via nonstandard analysis (Fliess M., Join C.: A Mathematical Proof of the Existence of Trends in Financial Time Series) leads to convincing computer experiments which are easily implementable.

Towards New Technical Indicators for Trading Systems and Risk Management

We derive two new technical indicators for trading systems and risk management. They stem from trends in time series, the existence of which has been recently mathematically demonstrated by the same authors (A mathematical proof of the existence of trends in financial time series, Proc. Int. Conf. Systems Theory: Modelling, Analysis and Control, Fes, 2009), and from higher order quantities which replace the familiar statistical tools. Recent fast estimation techniques of algebraic flavor are utilized. The first indicator tells us if the future price will be above or below the forecasted trendline. The second one predicts abrupt changes. Several promising numerical experiments are detailed and commented.

DuPONT'S MESSAGE AT THE END OF ITS SECOND CENTURY: TiO 2 IS MORE AFFORDABLE THAN EVER

We derive two new technical indicators for trading systems and risk management. They stem from trends in time series, the existence of which has been recently mathematically demonstrated by the same authors (A mathematical proof of the existence of trends in financial time series, Proc. Int. Conf. Systems Theory: Modelling, Analysis and Control, Fes, 2009), and from higher order quantities which replace the familiar statistical tools. Recent fast estimation techniques of algebraic flavor are utilized. The first indicator tells us if the future price will be above or below the forecasted trendline. The second one predicts abrupt changes. Several promising numerical experiments are detailed and commented.