Abstract
Stock market behavior is extremely volatile and complex in nature due to the randomness of the governing parameters. This makes attaining a high degree of accuracy in stock market forecasting extremely challenging. Several techniques have been explored to forecast stock market behavior while aiming to mitigate the effects of noisy data sets, nonlinearity and randomness in the data. In general, the inherent nature of the stock data to be noisy containing sudden fluctuations and spikes makes pattern recognition extremely challenging and prone to errors. In this paper, the complex values wavelet transform along with the gradient descent approach has been proposed for stock market forecasting. The wavelet transform has been employed as an iterative sampling filter to remove the effects of noise in the data. The gradient descent algorithm has been used to train a deep neural network using the filtered data. The neural network is fed with approximate and detailed coefficient values of the decomposed data so as to find the patterns in each of the levels of decomposition while leaving out the noisy effects at each level of decomposition. This multi-level input vector augments the capability of the gradient decent algorithm to evaluate the actual effects of noise in the data set. The day-wise data for shares has been considered with opening prices, closing prices, average price, and volume as the temporal parameters. It has been shown that the proposed approach is capable of analyzing noisy data samples and attains an accuracy higher than contemporary approaches, which makes it a promising technique for stock market forecasting.
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Dhakar, D.S., Shiwani, S. (2023). Stock Market Prediction Employing Discrete Wavelet Transform and Moving Average Gradient Descent. In: Kumar, A., Ghinea, G., Merugu, S., Hashimoto, T. (eds) Proceedings of the International Conference on Cognitive and Intelligent Computing. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-2358-6_56
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DOI: https://doi.org/10.1007/978-981-19-2358-6_56
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