Abstract
This paper proposes a multivariate and online prediction of stock prices via the paradigm of kernel adaptive filtering (KAF). The prediction of stock prices in traditional classification and regression problems needs independent and batch-oriented nature of training. In this article, we challenge this existing notion of the literature and propose an online kernel adaptive filtering-based approach to predict stock prices. We experiment with ten different KAF algorithms to analyze stocks' performance and show the efficacy of the work presented here. In addition to this, and in contrast to the current literature, we look at granular level data. The experiments are performed with quotes gathered at the window of one minute, five minutes, ten minutes, fifteen minutes, twenty minutes, thirty minutes, one hour, and one day. These time windows represent some of the common windows frequently used by traders. The proposed framework is tested on 50 different stocks making up the Indian stock index: Nifty-50. The experimental results show that online learning and KAF is not only a good option, but practically speaking, they can be deployed in high-frequency trading as well.
Highlights
Prediction has applications in a multitude of areas such as economics [1], business planning and production [2], and weather forecasting [3]
In light of the challenges and the potential solution specified we propose the paradigm of online kernel adaptive filtering (KAF) for stock price prediction. us, this study aims to predict stock movements in an online manner
In order to describe the contribution of this paper, the following points summarize the essence of the article in brief: (i) We propose the use of online-KAF techniques for stock price prediction
Summary
Prediction has applications in a multitude of areas such as economics [1], business planning and production [2], and weather forecasting [3]. We focus our attention on financial time-series prediction and its application to stock price forecasting. Research clearly specifies that prediction of stocks, especially the nonlinear and nonstationary financial time-series forecasting, is still challenging [9]. In this regard, several models have been developed; for instance, studies focused on volatility [6, 10], option pricing [11], classification of stock movements [12], predicting prices [13], and so on. We follow the same line of thought and take the existing methods [17, 19, 20] as the foundation of the proposed work to propose a KAF-based approach for close-price prediction
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