Research in Financial Time Series Forecasting with SVM: Contributions from Literature

Forecasting of financial time series is an intensive working area for researchers and practitioners. In this study, we analyze 59 articles and discuss the progress in financial time series analysis using support vector machines. Our main conclusions are: (a) forecasting is doing in a daily basis and studies in other time scales are scarce; (b) most of works are devoted to improve the parameter estimation process or preprocessing the time series; (c) most of the work is concerned to forecast market financial indexes; (d) in general, is commonly accepted than support vector machines are more accurate than classical time series forecasting methods and other types of neural networks; collected evidence in this work, shows that modified versions of support vector machines are more accurate that the traditional version; however, it is not possible to conclude about what improvements are better because used experimental cases in each study are different and it is no possible to draw general conclusions.