Authors: Jay Patel, Siddhanth Jain, U. M. Prakash
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Stock market forecasts have always attracted the attention of many analysts and researchers. Popular theory suggests that stock markets are inherently straightforward, and trying to predict them is a trivial matter. It is a difficult problem in itself. The market behaves like a voting machine in the short term, but in the long term it behaves like a pair of scales. Therefore, there is room to predict market movements over a longer period of time. Applying machine learning techniques and other algorithms to stock price analysis and forecasting is a promising area. This white paper begins with a brief overview of the stock market and a taxonomy of stock market forecasting methods. We then highlight some research findings on resource analysis and forecasting. We discuss technical, fundamental, short- term and long-term approaches to equity analysis. Finally, we present some challenges and research opportunities in this area.
Stock market forecasting and analysis is an attempt to determine the future value of an exchange-traded company's stock or other financial instrument. The stock market is an important part of the country's economy and plays an important role in the growth of the country's industry and trade which ultimately affects the country's economy. Both investors and industry are involved in the stock market and want to know whether stocks will rise or fall over a period of time. The stock market is the primary source for companies to raise funds to expand their operations. It is based on the concept of supply and demand. If demand for a company's shares is high, the company's share price will rise, and if demand for the company's shares is low, the company's share price will fall.
National Stock Exchange of India Limited (NSE) is India's leading stock exchange based in Mumbai. NSE was founded in 1992 as the first demutualized electronic exchange in the country. NSE is the first stock exchange in the country to offer a fully automated, modern, screen-based electronic trading system that offers easy trading opportunities to investors across the country.
NIFTY 50 Index is the National Stock Exchange of India's broad benchmark equity market index for the Indian equity market. It represents a weighted average of 50 Indian corporate stocks across 12 sectors and is one of two major stock indices used in India, the other being the BSE Sensex contains very large datasets that are difficult to extract information from and manually analyze work development as they involve many industries and companies. The application developed in this project not only helps predict the future movement of stocks in the market, but also automates data search, trend analysis, predictive analysis and stock insight generation at the click of a button. Stock market analysis and forecasting reveals market patterns and predicts when to buy stocks. Correctly predicting the future price of stocks can yield big profits.
II. LITERATURE SURVEY
The first step in this process is collecting financial data and identifying crashes. We've been looking for daily price information from major stock markets with low correlation. Low cross-correlation is important for effective cross- validation and model testing. To avoid overfitting, we avoided including two datasets with a cross-correlation greater than 0.5 in the collection. Overfitting occurs when a machine learning model tries to cover all data points or exceeds the required data points for a given dataset. Because of this, the model starts caching noise and inaccurate values in the dataset, all of which reduce the efficiency and accuracy of the model. To identify clashes in each dataset, we first calculate the markdown. A drawdown is a continuous drop in price over several consecutive days from the last high to the next low. Then use Emily Jacobson's methodology. In this methodology, a crash in any market he defines as a drawdown at the 99.5% quantile. A quantile defines a specific portion of a data set. H. The quantile determines whether the values in the distribution are above or below certain limits.
We found that returns are based on duration and drawdown and are not dependent on the index\'s daily returns. To identify the decline in each dataset, we first calculated the price decline. A drawdown is a continuous drop in price for several days in a row from the last high to the next low. Simple price patterns defined by long-term price movements and volatility changes seem to occur regularly before crashes. The best models were able to learn these patterns and predict crashes much better than comparable random models. For example, the best regression model for 3-month crash prediction achieved an accuracy of 0.15 and a recall of 0.59 on the test set, whereas an equivalent random model with no predictive power achieved an accuracy of 0.04 and a recall of 0.16. Achieved. The results for 1-month and 6-month crash predictions are similar, with the highest F-beta scores for 6-month prediction and the worst for 1-month prediction. Whether these results are sufficient to optimize investment strategies is debatable. Looking at the test data during the crashes and the price index chart of the crash predictor indicator, some crashes were detected very well, while others occurred with little or no warning from the crash predictor.
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Copyright © 2023 Jay Patel, Siddhanth Jain, U. M. Prakash. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.