Improving Stock Return Forecasting by Deep Learning Algorithm
Journal Title: Advances in Mathematical Finance and Applications - Year 2019, Vol 4, Issue 3
Abstract
Improving return forecasting is very important for both investors and researchers in financial markets. In this study we try to aim this object by two new methods. First, instead of using traditional variable, gold prices have been used as predictor and compare the results with Goyal's variables. Second, unlike previous researches new machine learning algorithm called Deep learning (DP) has been used to improve return forecasting and then compare the results with historical average methods as bench mark model and use Diebold and Mariano’s and West’s statistic (DMW) for statistical evaluation. Results indicate that the applied DP model has higher accuracy compared to historical average model. It also indicates that out of sample prediction improvement does not always depend on high input variables numbers. On the other hand when using gold price as input variables, it is possible to improve this forecasting capability. Result also indicate that gold price has better accuracy than Goyal's variable to predicting out of sample return.
Authors and Affiliations
Zahra Farshadfar, Marcel Prokopczuk
The Relationship Between Non-Transparent Financial Reporting and Risk Stock Futures Fall Due to the Size and Performance
The purpose of this study was to investigate the relationship between stock futures fall risk with non-transparent financial reporting at three levels of size, efficiency and return on equity, in the period 2010 to 2014...
Free Cash Flow, Institutional Ownership and Long-Term Performance
Performance appraisal is a process which help shareholders make informed and optimal investment decisions. In recent decades, a long stream of research has devoted particular attention to the importance and impact of fin...
Investigating the Effect of Management Entrenchment on Speed of Cash Holding Adjustment in Companies Listed in Tehran Stock Exchange
In this study, the effect of management entrenchments on the speed of cash holding adjustment in Iran has been investigated. After designing the management entrenchment evaluation indicators, the transaction information...
A Long-term Casual Nexus between Stock Price and Dividends: Empirical Evidence from the Accepted Firms in Tehran Stock Exchange
this world; though all the discussions are focused on the causal relationships in all the scientific arguments. One of the methods to study the designed causal relationships objectively is Granger causality test. This pa...
Effect of Information Delay on Joint Investment Fund's Performance
The aim of this study is to evaluate the effect of information delay on the performance of joint investment funds. In order to achieve the aim of this study sample consisted of twenty funds in the Tehran Stock Exchange f...