Design interpretable experience of dynamical feed forward machine learning model for forecasting NASDAQ

• URL: http://arxiv.org/abs/2212.12044v1
• Date: Thu, 22 Dec 2022 21:27:40 GMT
• Title: Design interpretable experience of dynamical feed forward machine learning model for forecasting NASDAQ
• Authors: Pouriya Khalilian, Sara Azizi, Mohammad Hossein Amiri, and Javad T. Firouzjaee
• Abstract summary: The volatility of the stock market and the influence of economic indicators such as crude oil, gold, and the dollar in the stock market, and NASDAQ shares are also affected. We have examined the effect of oil, dollar, gold, and the volatility of the stock market in the economic market. Using PCA and Linear Regression algorithm, we have designed an optimal dynamic learning experience for modeling these stocks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: National Association of Securities Dealers Automated Quotations(NASDAQ) is an American stock exchange based. It is one of the most valuable stock economic indices in the world and is located in New York City \cite{pagano2008quality}. The volatility of the stock market and the influence of economic indicators such as crude oil, gold, and the dollar in the stock market, and NASDAQ shares are also affected and have a volatile and chaotic nature \cite{firouzjaee2022lstm}.In this article, we have examined the effect of oil, dollar, gold, and the volatility of the stock market in the economic market, and then we have also examined the effect of these indicators on NASDAQ stocks. Then we started to analyze the impact of the feedback on the past prices of NASDAQ stocks and its impact on the current price. Using PCA and Linear Regression algorithm, we have designed an optimal dynamic learning experience for modeling these stocks. The results obtained from the quantitative analysis are consistent with the results of the qualitative analysis of economic studies, and the modeling done with the optimal dynamic experience of machine learning justifies the current price of NASDAQ shares.

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