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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

GARCH - Monte-Carlo Simulation Models with Wavelets Decomposition Algorithm for Stock Returns Prediction

Giovanis, Eleftherios - 2011

In this paper we examine four different approaches in trading rules for stock returns. More specifically we examine the popular procedures in technical analysis, which are the moving average and the Moving Average Convergence-Divergence (MACD) oscillator. The third approach is the simple random...

Persistent link: https://www.econbiz.de/10013126948

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

Firm Characteristics and Expected Stock Returns

Han, Yufeng - 2020

We analyze the joint out-of-sample predictive ability of a comprehensive set of 299 firm characteristics for cross-sectional stock returns. We develop a cross-sectional out-of-sample R2 statistic that provides an informative measure of the accuracy of cross-sectional return forecasts in terms of...

Persistent link: https://www.econbiz.de/10012852228

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

Stock Returns Forecasting via Machine Learning with Average Windows Forecasts

Ho, Tsung-Wu; LIN, Ya-chi - 2022

Stock returns predictability has been a long-standing topic in the literature on financial economics. Developments in prediction technology have facilitated the wide use of machine learning techniques, which motivates our study of whether stock returns predictability can be improved using...

Persistent link: https://www.econbiz.de/10013313206

Improving the Prediction of Asset Returns With Machine Learning by Using a Custom Loss Function

Dessain, Jean - 2021

The loss function in supervised deep learning is a key element for training AI algorithms. For models aiming at predicting asset returns, not all prediction errors are equal in terms of impact on the efficiency of the algorithm. Indeed, some errors result in poor investment decisions while other...

Persistent link: https://www.econbiz.de/10013312657

Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns

Chen, Shiyi - 2017

In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a...

Persistent link: https://www.econbiz.de/10012966267

Machine learning for predicting stock return volatility

Filipović, Damir; Khalilzadeh, Amir - 2021

We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as...

Persistent link: https://www.econbiz.de/10012800743

Diverging roads: theory-based vs. machine learning-implied stock risk premia

Grammig, Joachim; Hanenberg, Constantin; Schlag, Christian - 2020

We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the … preferable to rely on a theory-based approach instead of engaging in the computerintensive hyper-parameter tuning of statistical … models. The theory-based approach also delivers a solid performance at the one year horizon, at which only one machine …

Persistent link: https://www.econbiz.de/10012163064