Similar Search Results

Enhancing Time Series Momentum Strategies Using Deep Neural Networks

Lim, Bryan - 2020

While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this paper, we introduce Deep Momentum Networks -- a hybrid approach which injects deep learning based trading rules into...

Persistent link: https://www.econbiz.de/10012849594

Stock Price Forecasting and Hypothesis Testing Using Neural Networks

Varaku, Kerda - 2020

In this work we use Recurrent Neural Networks and Multilayer Perceptrons, to predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment with different architectures and compare data normalization techniques. Then, we leverage those findings to question the efficient-market...

Persistent link: https://www.econbiz.de/10012834485

Application of Feed-Forward Neural Networks Smoothing Transition Autoregressive Models in Stock Returns Forecasting

Giovanis, Eleftherios - 2010

In this paper we propose and examine new approaches in smoothing transition autoregressive (STAR) models. Firstly, a new STAR function is proposed, which is the hyperbolic tangent sigmoid function. Secondly, we propose Feed-Forward Neural Networks Smoothing Transition Autoregressive (FFNN-STAR)...

Persistent link: https://www.econbiz.de/10013138095

Forecasting detrended volatility risk and financial price Series using LSTM neural networks and XGBoost regressor

Raudys, Aistis; Goldstein, Edvinas - In: Journal of risk and financial management : JRFM 15 (2022) 12, pp. 1-12

It is common practice to employ returns, price differences or log returns for financial risk estimation and time series forecasting. In De Prado’s 2018 book, it was argued that by using returns we lose memory of time series. In order to verify this statement, we examined the differences...

Persistent link: https://www.econbiz.de/10014284192

Dynamic and context-dependent stock price prediction using attention modules and news sentiment

Königstein, Nicole - In: Digital finance : smart data analytics, investment … 5 (2023) 3/4, pp. 449-481

Persistent link: https://www.econbiz.de/10014451865

Topological tail dependence : evidence from forecasting realized volatility

Souto, Hugo Gobato - In: The Journal of finance and data science : JFDS 9 (2023), pp. 1-26

This paper proposes a novel theory, coined as Topological Tail Dependence Theory, that links the mathematical theory … behind Persistent Homology (PH) and the financial stock market theory. This study also proposes a novel algorithm to measure … of this study provide evidence that the predictions drawn from the Topological Tail Dependence Theory are correct and …

Persistent link: https://www.econbiz.de/10014514075

Quantum-enhanced forecasting : leveraging quantum gramian angular field and CNNs for stock return predictions

Xu, Zhengmeng; Wang, Yujie; Feng, Xiaotong; Wang, Yilin; … - In: Finance research letters 67 (2024) 1, pp. 1-10

Persistent link: https://www.econbiz.de/10015062172

Statistical evaluation of deep learning models for stock return forecasting

Yilmaz, Firat Melih; Yildiztepe, Engin - In: Computational economics 63 (2024) 1, pp. 221-244

Persistent link: https://www.econbiz.de/10014472083

Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

Bildirici, Melike - 2013

The study proposes and a family of regime switching GARCH neural network models to model volatility. The proposed MS-ARMA-GARCH-NN models allow MS type regime switching in both the conditional mean and conditional variance for time series and further augmented with artificial neural networks to...

Persistent link: https://www.econbiz.de/10013090501

Forecasting Nigerian stock market returns using ARIMA and artificial neural network models

Isenahd, Godknows M.; Olubusoye, Olusanya E. - In: CBN journal of applied statistics 5 (2014) 2, pp. 25-48

The study reports empirical evidence that artificial neural network based models are applicable to forecasting of stock market returns. The Nigerian stock market logarithmic returns time series was tested for the presence of memory using the Hurst coefficient before the models were trained. The...

Persistent link: https://www.econbiz.de/10011488820