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There has been growing attention to forecasting market trends among researchers, industrialists, economists, as well as the common people as the market serves a big part of their investments. Stock exchanges are financial entities that permit diverse assets to be given across stockbroker components. With a trading volume of money, it piques the public's interest in generating more money.
Investors have always known the basic principle of the stock market i.e. to buy in minimum cost and put up for sale at maximum price (Kesgin et al., 2019; Yang & Cogill, 2013; Neves et al.,2011; Su& Yi,2022; Cao, 2021; Adlakha et al., 2021; Tamrizal et al., 2021; Zhang &. Zuo, 2021), but this statement does not provide them with the insights which can help them in making the correct investment decisions. Before investing in stocks, any investor must need to know how the market acts. Investing at a bad time may lead to terrible results irrespective of the fact that the stock in which the money was invested was good. On the other hand, investing at the right time in a mediocre stock can be a very fruitful decision. So, Investors struggle with this problem of not knowing when, and which stock to buy or sell to maximize their profits. It necessitates the usage of modern approaches using newer technologies. The rapid fluctuations in the price of stocks make it extremely hard to predict the market on a day-to-day basis as compared to predicting the stock value over a larger frame of time.
The main problems faced by an investor investing in the stock market are:
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Trying to figure out a profitable investment decision on the overabundant nature of available stocks in the market.
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Reducing the gigantic amount of unreliability which contains a greater amount of risk on the nature of returns and hence making the decision-making part very difficult due to the monetary in security associated with it.
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The nature of returns and hence making the decision-making part very difficult due to the monetary in security associated with it.
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Trying to strike the right balance between expected return and associated risk,so, that the returns for the investor can be maximized and the risk can be minimized to the lowest possible denominator.
To anticipate the financial exchange precisely, various prediction models and algorithms have been proposed by researchers in several fields. Hence, this paper develops the system to predict the future value of the financial stocks of an organization and provide assistance to the investor on whether to buy the stock or reinvest his/her earnings in another stock. The proposed system makes use of machine learning techniques as well as various factors affecting the stock market to predict its behavior accurately and make it easier for people to earn optimum profits. Table 1 shows a list of abbreviations used in the paper with its full form.
Table 1. | Abbreviation | Full form |
| BB | Bollinger Bands |
| MACD | Moving Average Convergence/Divergence |
| MFI | Money Flow Index |
| RSI | Relative Strength Index |
| SVM | Support vector machine |
| LSSVM | Least squares support vector machine |
| SAR | Stop and reversal system |
| ML | Machine Learning |
| GARCH | Generalized AutoRegressive Conditional Heteroskedasticity |
| NPV | Net Present Value |
| INR | Indian National Rupee |
| ROE | Return on Equity |