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Forecasting the market risk premium with artificial neural networks

Oikonomikou, Leoni Eleni - 2016 - Revised version: November 2015

This paper aims to forecast the Market Risk premium (MRP) in the US stock market by applying machine learning techniques, namely the Multilayer Perceptron Network (MLP), the Elman Network (EN) and the Higher Order Neural Network (HONN). Furthermore, Univariate ARMA and Exponential Smoothing...

Persistent link: https://www.econbiz.de/10011454074

Comparing the market risk premia forecasts in JSE and NYSE equity markets

Oikonomikou, Leoni Eleni - 2016 - Corrected version: March 2016

This paper examines the evidence regarding predictability in the market risk premium using artificial neural networks (ANNs), namely the Elman Network (EN) and the Higher Order Neural network (HONN), univariate ARMA and exponential smoothing techniques, such as Single Exponential Smoothing (SES)...

Persistent link: https://www.econbiz.de/10011454082

China Markets and Global Stock Return Predictability

Sun, Yulong - 2020

We have seen China's growing role in the past decades, and the world economy has become more exposed to the influence … of China. This paper explores emerging China's impact on the global equity market through the lens of asset pricing. We … study the predictive properties of the lagged China returns for global stock returns and find that the lagged China returns …

Persistent link: https://www.econbiz.de/10012824300

Economically-Linked Economies and Forecasting Chinese Stock Returns

Jordan, Steven J. - 2012

indices from countries that China net exports from can forecast the Chinese aggregate market return at the weekly time horizon …. Countries that China net exports to have no consistently significant OOS predictability.The economic intuition for our results … follows from the fact that China has positioned itself as a low-cost provider competing on price. As a low-cost provider China …

Persistent link: https://www.econbiz.de/10013098289

Forecasting returns with machine learning and optimizing global portfolios : evidence from the Korean and U.S. stock markets

Chun, Dohyun; Kang, Jongho; Kim, Jihun - In: Financial innovation : FIN 10 (2024), pp. 1-30

This study employs a variety of machine learning models and a wide range of economic and financial variables to enhance the forecasting accuracy of the Korean won-U.S. dollar (KRW/USD) exchange rate and the U.S. and Korean stock market returns. We construct international asset allocation...

Persistent link: https://www.econbiz.de/10015359391

Does the U.S. extreme indicator matter in stock markets? : international evidence

Jing, Xiaozhen; Xu, Dezhong; Li, Bin; Singh, Tarlok - In: Financial innovation : FIN 10 (2024), pp. 1-27

We propose a new predictor - the innovation in the daily return minimum in the U.S. stock market () - for predicting international stock market returns. Using monthly data for a wide range of 17 MSCI international stock markets during the period spanning over half a century from January 1972 to...

Persistent link: https://www.econbiz.de/10015361591

Forecasting Realized Intra-Day Volatility and Value at Risk : Evidence From a Fractional Integrated Asymmetric Power ARCH Skewed-T Model

Degiannakis, Stavros Antonios - 2018

Predicting the one-step-ahead volatility is of great importance in measuring and managing investment risk more accurately. Taking into consideration the main characteristics of the conditional volatility of asset returns, I estimate an asymmetric Autoregressive Conditional Heteroscedasticity...

Persistent link: https://www.econbiz.de/10012910129

Short-Term Predictability of Stock Market Indexes Following Large Drawdowns and Drawups

Ratton Brandi, Vinicius - 2018

The Efficient Market Hypothesis is one of the most popular subjects in the empirical finance literature. Previous studies in the stock markets, which are mostly based on fixed time price variations, do not provide conclusive findings, in which evidence of short-term predictability varies...

Persistent link: https://www.econbiz.de/10012914355

Does Investor Sentiment Predict Future Mexican Stock Market Returns?

Perez-Liston, Daniel - 2017

This paper studies whether investor sentiment can predict future Mexican stock market returns. Furthermore, we examine the dynamic correlation between sentiment and returns. Lastly, we examine whether sentiment innovations influence unexpected returns. We find that sentiment has significant...

Persistent link: https://www.econbiz.de/10012948714

Time-Disaggregated Dividend-Price Ratio and Dividend Growth Predictability in Large Equity Markets

Asimakopoulos, Panagiotis - 2016

Even in large equity markets, the dividend-price ratio is significantly related with the growth of future dividends. In order to uncover this relationship, we use monthly dividends and a mixed data sampling technique which allows us to cope with within-year seasonality. We reduce the effect of...

Persistent link: https://www.econbiz.de/10013006710