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Multivariate variance ratio statistics
Hong, Seok Young; Linton, Oliver; Zhang, Hui Jun - 2014
We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the Efficient Market Hypothesis). We do not...
Persistent link: https://www.econbiz.de/10010365211
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An investigation into multivariate variance ratio statistics and their application to stock market predictability
Hong, Seok Young; Linton, Oliver; Zhang, Hui Jun - 2015
We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the weak form Efficient Market Hypothesis). We do...
Persistent link: https://www.econbiz.de/10010496122
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Detecting and Forecasting Large Deviations and Bubbles in a Near-Explosive Random Coefficient Model
Banerjee, Anurag Narayan - 2013
the fundamental asset value and the recurrent presence of autonomous deviations or bubbles. Such a process can be … theory to analyze ordinary least-squares (OLS) estimation. One important theoretical observation is that the estimator … common procedure in the presence of localizing parameters. This methodology allows to detect the presence of bubbles and …
Persistent link: https://www.econbiz.de/10013076483
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Detecting and Forecasting Large Deviations and Bubbles in a Near-Explosive Random Coe fficient Model
Banerjee, Anurag Narayan - 2017
the fundamental asset value and the recurrent presence of autonomous deviations or bubbles. Such a process can be … theory to analyze ordinary least-squares (OLS) estimation. One important theoretical observation is that the estimator … common procedure in the presence of localizing parameters. This methodology allows to detect the presence of bubbles and …
Persistent link: https://www.econbiz.de/10012973901
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A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing
Mehtab, Sidra; Sen, Jaydip - 2020
Long and Short-Term Memory (LSTM)-based deep learning network for predicting the closing price of the stocks and compare … the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by …
Persistent link: https://www.econbiz.de/10014094821
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Forecasting Indian Stock Market Index Using Singular Spectrum Analysis : A Comparison with ARIMA and Artificial Neural Network
Shukla, Suwarna - 2017
In this paper we focus on analyzing the predictive accuracy of three different types of forecasting techniques, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and Singular Spectral Analysis (SSA), used for predicting chaotic time series data. These techniques...
Persistent link: https://www.econbiz.de/10012947889
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How and When are High-Frequency Stock Returns Predictable?
Ait-Sahalia, Yacine; Fan, Jianqing; Xue, Lirong; Zhou, … - 2022
This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...
Persistent link: https://www.econbiz.de/10013290620
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How and When are High-Frequency Stock Returns Predictable?
Aït-Sahalia, Yacine; Fan, Jianqing; Xue, Lirong; Zhou, … - National Bureau of Economic Research - 2022
This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...
Persistent link: https://www.econbiz.de/10013362020
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Can Deep Neural Networks Outperform Fama-MacBeth Regression and Other Supervised Learning Approaches in Stock Returns Prediction With Asset-Pricing Factors?
Teng, Huei-Wen; Li, Yu-Hsien - 2022
In asset pricing, most studies focus on finding new factors such as macroeconomic factors or firm characteristics to explain risk premium. Investigating whether these factors are useful in forecasting stock returns remains active research in the field of finance and computer science. This paper...
Persistent link: https://www.econbiz.de/10014235825
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Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?
Teng, Huei-Wen; Li, Yu-Hsien - In: Digital finance : smart data analytics, investment … 5 (2023) 1, pp. 149-182
Persistent link: https://www.econbiz.de/10014251571