Stock Trading with Random Forests Trend Detection



This paper proposes an expert system that uses novel machine learning techniques to predict the price return over these seasonal events, and then uses these predictions to develop a profitable trading strategy. However, it is getting more and more common for people to pay too much for bonds now. What is your project address? Prospectus MELT Daily Gold Miners Index Bear 1X Shares. Daily Silver Miners Index Bear 2X Shares.




We Ttend the effectiveness of various regression techniques and methods for expert weighting. Seasonality effects and empirical regularities in financial data have been well documented in the financial economics literature for over seven decades. This paper proposes an expert system that uses novel machine learning techniques to predict the price return over these seasonal events, and then uses these predictions to develop a profitable trading strategy.

While simple approaches to trading these regularities can prove profitable, such trading leads to potential large drawdowns peak-to-trough decline of an investment measured as a percentage between the peak and the trough in profit. In this paper, we introduce an automated trading system based on performance weighted ensembles of random forests that improves the profitability and stability of trading seasonality events.

Forestx analysis of various regression techniques is performed as well as an exploration of the merits of various techniques for expert weighting. The performance of the models is analysed using a large sample of stocks from the DAX. The results show that recency-weighted ensembles of random forests produce superior results in terms of both profitability and prediction accuracy compared with other ensemble techniques. It is also found that Detecction seasonality effects produces superior results than not having them modelled explicitly.

Screen reader users, click here to load entire article This page uses JavaScript to progressively load the article content as a user scrolls. Screen reader users, click the load entire article button to bypass dynamically loaded article content. Please note that Internet Explorer Trens 8. Please refer to this blog post for more information. ScienceDirect Journals Books Register Sign in Sign in using your ScienceDirect credentials Username Password Remember me Forgotten username or password?

Sign in via your institution OpenAthens Other institution Journals Books Register Sign in Help close Sign in using your Trading signals forex academy credentials Username Password Remember me Forgotten username or password? Sign in via your institution OpenAthens Other institution. JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page.

This page uses JavaScript to progressively load the article content as a user scrolls. Click the View full text link to bypass dynamically loaded article content. Expert Systems with Applications. Abstract Seasonality effects and empirical regularities in financial data have been well documented in the financial economics literature for over seven decades.

Elsevier About ScienceDirect Remote access Shopping cart Contact Stock Trading with Random Forests Trend Detection support Terms and conditions Privacy policy Cookies are used by this site. For more information, visit the cookies page. This article has not been cited.




Detecting A Trend Change In The Markets.


Stock Trading with Random Forests, Trend Detection Tests and Force Index Stock Trading with Random Forests, Trend Detection Tests and Force Index Volume. 30 Day Trial for ALL Markets, S&C Trading System Awards. Get Up to $2, and Trade Free for 90 Days When You Open an Account!.

Add a comment

Your e-mail will not be published. Required fields are marked *