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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

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

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

Effects of Macroeconomic Uncertainty on the Stock and Bond Markets

Asgharian, Hossein - 2015

In this paper we show that the long-run stock and bond volatility and the long-run stock-bond correlation depend on … macroeconomic uncertainty. We use the mixed data sampling (MIDAS) econometric approach. The findings are in accordance with the … flight-to-quality phenomenon when macroeconomic uncertainty is high …

Persistent link: https://www.econbiz.de/10013025703

Do the FAMA and FRENCH five-factor model forecast well using ANN?

Jan, Muhammad Naveed; Ayub, Usman - In: Journal of business economics and management 20 (2019) 1, pp. 168-191

-month ahead returns. Our findings reinforce the investment concept that the markets compensate the high-risk portfolios more than …

Persistent link: https://www.econbiz.de/10012175006

"Good" inflation, "bad" inflation : implications for risky asset prices

Bonelli, Diego; Palazzo, Berardino; Yamarthy, Ram S. - 2025

Persistent link: https://www.econbiz.de/10015271346

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

Good inflation, bad inflation : implications for risky asset prices

Bonelli, Diego; Palazzo, Berardino; Yamarthy, Ram - 2025

Persistent link: https://www.econbiz.de/10015407096

Weak-form market efficiency and corruption : a cross-country comparative analysis

İcan, Özgür; Çelik, Taha Buğra - In: Journal of capital markets studies 7 (2023) 1, pp. 72-90

relationship between transparency and market efficiency. Design/methodology/approach - Correlation analysis has been conducted … intermediate negative correlation has been found between CPI scores and predictability levels of stock indices. Considering the …

Persistent link: https://www.econbiz.de/10014318195

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