Forecasting stock market prices using advanced tools of machine learning

Abstract

Stock market, a very unpredictable sector of nance, involves a large number of investors,buyers and sellers. Stock market prediction is the act of attempting to see the long run price of an organization stock or di erent money instrument listed on a monetary exchange. People invest in stock market supported some prediction. For predicting, the stock exchange prices people search such ways and tools which can increase their pro ts, whereas minimize their risks. Prediction plays a awfully necessary role available market business that is sophisticated and di cult method. A part of our thesis will be directed at assembling the required tools to aggregate nancial data from various sources. Here, we proposed a model by applying machinelearning to technical analysis. Technical analysis reviews the past direction of prices using stock charts to anticipate the probable future direction of that security's price. In alternative words, technical analysis uses open, close, high and low prices, still as its volume information to construct stock chart to work out that direction the protection ought to take, supported its past information. An arti cial trader can use the ensuing forecasting models to trade on any given stock market. The performance of the research is assessed using Dhaka Stock Exchange data.