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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: International Journal of Finance and Managerial Accounting
- نوع مقاله: Journal Article
- کلمات کلیدی: deep learning,historical average model,nonlinear model,ANN,Oil Price
- چکیده:
- چکیده انگلیسی: Improving out-of-sample forecasting is one of the main issues in financial research. Previous studies have achieved this objective by increasing the number of input variables or changing the kind of input variables. Changing the forecasting model is another possible approach to improve out-of-sample forecasting. Most researches have focused on linear models, while few have studied nonlinear models. In the present study, we have reduced the number of variables and at the same time applied a nonlinear forecasting model. Oil prices have been used as predictors to predict return by application of a new artificial neural network nonlinear model named Deep Learning and its comparison with OLS and ANN methods. Results indicate that the applied non-linear model has higher accuracy compared to historical average model, OLS and ANN. It also indicates that out-of-sample prediction improvement does not always depend on high input variables numbers. On the other hand when using a smaller number of input variables, it is possible to improve this forecasting capability by changing the model and applying nonlinear models.
- انتشار مقاله: 21-05-1397
- نویسندگان: Zahra Farshadfar,Marcel Prokopczuk
- مشاهده
- جایگاه : پژوهشی
- مجله: Advances in Mathematical Finance and Application
- نوع مقاله: Journal Article
- کلمات کلیدی: deep learning,historical average model,nonlinear model,gold price
- چکیده:
- چکیده انگلیسی: 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.
- انتشار مقاله: 14-11-1397
- نویسندگان: Zahra Farshadfar,Marcel Prokopczuk
- مشاهده