![]() ![]() The study also proves that this knowledge can be used to make arbitrage profits in times of economic stability. The results of this study are positive, Google Trends can predict future returns of individual companies and it seems that increases in Google Trends for a company on aggregate predict future negative returns. Using econometric models and trading strategies, the predictive power of Google Trends is investigated both if it can predict future stock returns but also if this knowledge can be used to make arbitrage profits above the market. (More) The purpose of this thesis is to use social data in the form of Google Trends for companies listed on the S&P 100 to see if they contain information that allows us to predict future returns in the stock market. ![]() ![]() The study also proves that this knowledge can be used to make arbitrage profits in times of. Once you get an insight into the market direction you can then use your own stock trading. The Google trends stock prediction analysis can be used more to give you a market bias. These Google trends trading strategies will not help you time the market, but it can be an additional tool to help you gauge the market sentiment before prices start changing direction. The results of this study are positive, Google Trends can predict future returns of individual companies and it seems that increases in Google Trends for a company on aggregate predict future negative returns. With the Google trends stock prediction tool we can access the retail market sentiment and make profits in the stock market. Our results show that Google Trends can help in predicting the direction of the stock market index.Department of Economics Abstract The purpose of this thesis is to use social data in the form of Google Trends for companies listed on the S&P 100 to see if they contain information that allows us to predict future returns in the stock market. The hit ratios for ISCA-BPNN with Google Trends reach 86.81% for the S&P 500 Index, and 88.98% for the Dow Jones Industrial Average Index. The experimental results indicate that ISCA–BPNN outperforms BPNN, GWO-BPNN, PSO-BPNN, WOA-BPNN and SCA-BPNN in terms of predicting the direction of the opening price for both types and significantly for Type II. The predictability of stock price direction is verified by using the hybrid ISCA-BPNN model. We analyze two types of prediction: Type I is the prediction without Google Trends and Type II is the prediction with Google Trends. In addition, Google Trends data are taken into consideration for improving stock prediction. Thus, ISCA and BPNN are combined to create a new network, ISCA-BPNN, for predicting the directions of the opening stock prices for the S&P 500 and Dow Jones Industrial Average Indices, respectively. In this paper, we present an improved sine cosine algorithm (ISCA), which introduces an additional parameter into the sine cosine algorithm (SCA), to optimize the weights and basis of back propagation neural networks (BPNN). Many researchers focus on stock market analysis using advanced knowledge of mathematics, computer sciences, economics and many other disciplines. Can internet search queries help to predict stock market volatility Eur. Predicting the direction of stock markets movement has been one of the most widely investigated and challenging problems for investors and researchers as well. The stock market is affected by many factors, such as political events, general economic conditions, and traders’ expectations. ![]()
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