توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Predict the trend of stock prices using machine learning techniques
اطلاعات انتشار: دومین کنفرانس بین المللی آینده پژوهی، مدیریت و توسعه اقتصادی، سال
تعداد صفحات: ۹
According to growing importance of the stock market in economic conditions of each country and since, stock prices are the most important factors influencing investment decisions for selecting stock, predicting the stock price movement is an integral part of the investment. This paper presented to forecast the movement of stock prices Tejarat bank of Iran with considerable precision. Accordingly, to predict the trend of stock prices using machine learning techniques and economic indicators have been considerd. About 18,000 different indicators are presented, both simple moving average (SMA), weighted moving average (WMA), relative strength indicator (RSI) and moving average convergence divergence (MACD) indicator those are widely used in the stock market of Iran, have been chosen. The output of those is input three clasifire, support vector machines, random forests and k–nearest neighbor. The outputs of the three clasifire will be compared with each other. The results in this paper show that, respectively, random forest classifier, support vector machine and the k– nearest neighbor have the best accuracy in categories.<\div>
نمایش نتایج ۱ تا ۱ از میان ۱ نتیجه