Analysis of High Frequency Markets: Global News Impact and Efficient Market Hypothesis Insights

Financial market analysis is a challenging task because of its stochastic behavior. But efficient knowledge of financial markets is required for making a good decision. Investors in a market invest after having estimated future conditions on the basis of which decision is made. Investors’ sentiments, public opinion and economic conditions may contribute in the price fluctuation of any financial market. In order to understand the impact of news on high frequency markets, five international stocks are considered including Amazon, Tesla, Netflix, Apple and Microsoft. S&P 500 index and NASDAQ composite are also studied to investigate if the complete market is more sensitive to the international news or the individual stocks. Random forest model is used to predict the future prices and it was found that the prediction of international stocks has comparatively lower value of RMSE as compared to that of international market concluding that stocks are more predictive and more sensitive to the news rather than a complete market. According to Efficient Market Hypothesis (EMH), it can be stated that the S&P 500 index and NASDAQ are efficient markets. In addition, it is also observed that among considered stock Apple Inc. is the most predictive market where as Netflix is he least predictive based on international news proving Apple as an inefficient market while Netflix can be considered comparatively efficient market as per EMH. Other machine learning models including SVM, linear regression, KNN have also been used to validate the result which supports our outcome.