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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
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Risk and policy uncertainty on stock-bond return correlations : evidence from the US markets
Chiang, Thomas C. - In: Risks : open access journal 8 (2020) 2/58, pp. 1-16
positively correlated with economic policy uncertainty, however, are negatively correlated with the monetary policy and fiscal … policy uncertainties. Correlation coefficients between stock and bond returns are positively related to total policy … uncertainty for returns of the Dow-Jones Industrial Average (DJIA) and the S&P 500 Value stock index (VALUE), but negatively …
Persistent link: https://www.econbiz.de/10012292914
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PREDICTING STOCK RETURN AND VOLATILITY WITH MACHINE LEARNING AND ECONOMETRIC MODELS : A COMPARATIVE CASE STUDY OF THE BALTIC STOCK MARKET
Nõu, Anders; Lapitskaya, Darya; Eratalay, Mustafa Hakan; … - 2022
For stock market predictions, the essence of the problem is usually predicting the magnitude and direction of the stock price movement as accurately as possible. There are different approaches (e.g., econometrics and machine learning) for predicting stock returns. However, it is non-trivial to...
Persistent link: https://www.econbiz.de/10013305881
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Identifying time variability in stock and interest rate dependence
Stein, Michael; Islami, Mevlud; Lindemann, Jens - 2012
The correlation between stock markets and interest rates has been discussed in numerous studies in the past, with … which allow for time-variability and regime changes in correlation. All estimated models allowing for timevarying … correlation complement each other in identifying time-varying patterns found in the (co-)movement between the variables …
Persistent link: https://www.econbiz.de/10009625556
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A bibliometric analysis of machine learning econometrics in asset pricing
Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of risk and financial management : JRFM 15 (2022) 11, pp. 1-17
nonlinearity, and improved out-of-sample prediction. This article conducted a comprehensive, objective, and quantitative …
Persistent link: https://www.econbiz.de/10013475217
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Dynamic stock-bond return correlations and financial market uncertainty
Chiang, Thomas C.; Li, Jiandong; Yang, Sheng-Yung - In: Review of quantitative finance and accounting 45 (2015) 1, pp. 59-88
Persistent link: https://www.econbiz.de/10011333137
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Effects of macroeconomic uncertainty on the stock and bond markets
Asgharian, Hossein; Christiansen, Charlotte; Hou, Ai Jun - In: Finance research letters 13 (2015), pp. 10-16
Persistent link: https://www.econbiz.de/10011552317
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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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A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing
Mehtab, Sidra; Sen, Jaydip - 2020
Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy...
Persistent link: https://www.econbiz.de/10014094821
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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