Abstract
Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from the users. In this paper, a novel fuzzy document-based information retrieval scheme (FDIRS) is proposed for the purpose of Stock Market Index forecasting. The novelty of the proposed approach is the use of a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions: (1) In the proposed system, the simple daily time series data are converted to an enriched fuzzy linguistic time series with a unique approach of incorporating information about the manner in which the OHLC (open, high, low, and close) price formation took place at every instance of the time series, and (2) A unique approach is followed while modeling the information retrieval (IR) system which converts a simple IR system into a forecasting system. The modified IR system provides us with a trend forecast and after which a crisp value is generated that becomes the forecast value that can be achieved in next few trading sessions. From the performance comparison of FDIRS with standard benchmark models, it can be affirmed that the proposed model has a potential of becoming a good forecasting model. Transaction data of CNX NIFTY-50 index of National Stock Exchange of India are used to experiment and validate the proposed model.
Highlights
Prediction or forecasting is both an art as well as science
The contribution of this paper has two dimensions: (1) In the proposed system, the simple daily time series is converted to an enriched fuzzy linguistic time series with a unique approach of incorporating information about the manner in which the OHLC price formation took place at every instance of the time series, and (2) A unique approach is followed while modeling the information retrieval (IR) system which converts a simple IR system into a forecasting system
The root mean squared error (RMSE) is a measure frequently used to calculate the difference between values predicted by a model and the values observed from the environment from where the model is created
Summary
Prediction or forecasting is both an art as well as science. The process and outcome of forecasting have long been a matter of research and still are in its childhood state. According to Bagheri et al (2014), the investors and traders in the stock markets use two types of tools for forecasting; one is the fundamental analysis and second is technical analysis. Fundamental analysis uses information gathered from business and economic structure of the company and its related markets, to predict the future stock prices of the company. Technical analysis uses the information present in the stock prices from the past to predict the future. Zhang and Wu (2009) proposed a novel approach of combining back-propagation neural network with an improved Bacterial Chemo-taxis Optimization (IBCO) for stock market data forecasting. Wang et al (2013) proposed fuzzy time series for stock market prediction where the data are fuzzified to the cluster centers. The fuzzy rules are created by gathering experiences of various traders and investors. The rules are formed on the basis of fundamental analysis done by the actual traders and investors
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