Abstract
Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information. However, predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high volatility and nonlinearity. This paper proposes a novel condensed polynomial neural network (CPNN) for the task of forecasting stock closing price indices. We developed a model that uses partial descriptions (PDs) and is limited to only two layers for the PNN architecture. The outputs of these PDs along with the original features are fed to a single output neuron, and the synaptic weight values and biases of the CPNN are optimized by a genetic algorithm. The proposed model was evaluated by predicting the next day’s closing price of five fast-growing stock indices: the BSE, DJIA, NASDAQ, FTSE, and TAIEX. In comparative testing, the proposed model proved its ability to provide closing price predictions with superior accuracy. Further, the Deibold-Mariano test justified the statistical significance of the model, establishing that this approach can be adopted as a competent financial forecasting tool.
Highlights
Stock index forecasting is the process of making predictions about the future performance of a stock market index based on existing stock market behavior
We developed an efficient model for stock market forecasting that proposes a condensed polynomial neural network (PNN) architecture for predicting stock index closing prices
The model employs the Genetic Algorithm (GA) to find the optimal weight and bias vectors, as it is capable of searching a Experimental results and analysis This section explains the experimental portion of our work, including the preparation of input data, the simulated parameters for the two forecasting models, and the results from the models
Summary
Stock index forecasting is the process of making predictions about the future performance of a stock market index based on existing stock market behavior. Over the last few decades, stock index modeling and forecasting has been an important and challenging task for researchers in both financial engineering and mathematical economics. Stock market behavior is very much like a random walk process, and the serial correlations are economically and statistically insignificant. Nayak and Misra Financial Innovation (2018) 4:21 global economic situations. All these factors have proven to be important elements influencing the markets. An effective and more accurate forecasting model is needed to predict stock market behavior. If the direction of the market can be predicted successfully, investors may find better guidance, and the financial rewards could be substantial
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