Multiple - output support vector regression with a firefly algorithm for 1 interval - valued stock price index forecasting

نویسندگان

  • Tao Xiong
  • Yukun Bao
  • Zhongyi Hu
چکیده

6 Highly accurate interval forecasting of a stock price index is fundamental to 7 successfully making a profit when making investment decisions, by providing a range 8 of values rather than a point estimate. In this study, we investigate the possibility of 9 forecasting an interval-valued stock price index series over short and long horizons 10 using multi-output support vector regression (MSVR). Furthermore, this study 11 proposes a firefly algorithm (FA)-based approach, built on the established MSVR, for 12 determining the parameters of MSVR (abbreviated as FA-MSVR). Three globally 13 traded broad market indices are used to compare the performance of the proposed 14 FA-MSVR method with selected counterparts. The quantitative and comprehensive 15 assessments are performed on the basis of statistical criteria, economic criteria, and 16 computational cost. In terms of statistical criteria, we compare the out-of-sample 17 forecasting using goodness-of-forecast measures and testing approaches. In terms of 18 economic criteria, we assess the relative forecast performance with a simple trading 19 strategy. The results obtained in this study indicate that the proposed FA-MSVR 20 method is a promising alternative for forecasting interval-valued financial time series. 21

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting

Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short and long horizons using multi-output support vector regression (MSVR). Furtherm...

متن کامل

Support vector regression with chaos-based firefly algorithm for stock market price forecasting

Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box–Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is propo...

متن کامل

Predict the Stock price crash risk by using firefly algorithm and comparison with regression

Stock price crash risk is a phenomenon in which stock prices are subject to severe negative and sudden adjustments. So far, different approaches have been proposed to model and predict  the  stock price crash risk, which in most cases have been the main emphasis on the factors affecting it, and often traditional methods have been used for prediction. On the other hand, using  Meta Heuristic Alg...

متن کامل

Predicting stock prices on the Tehran Stock Exchange by a new hybridization of Fuzzy Inference System and Fuzzy Imperialist Competitive Algorithm

Investing on the stock exchange, as one of the financial resources, has always been a favorite among many investors. Today, one of the areas, where the prediction is its particular importance issue, is financial area, especially stock exchanges. The main objective of the markets is the future trend prices prediction in order to adopt a suitable strategy for buying or selling. In general, an inv...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013