Over the last twenty years, researchers and practitioners have attempted in many ways to effectively predict market trends. Till date, however, no satisfactory solution has been found. Many approaches have been applied to predict market trends, from technical analysis to fundamental analysis passing through sentiment analysis. A promising research direction is to exploit market technical indicators together with market sentiments extracted from social media for predicting market directional movements. In this paper, we propose a new approach that leverages technical analysis to predict market directional movements. In particular, we aim to predict the directional movement of the NASDAQ's most capitalized stocks by solving a classification problem. The results on real-world data show that our proposal achieves interesting performance when predicting the market directional movements. This work focuses on forecasting a portfolio of different stocks, instead of concentrating on a single stock which most of the works in this field do. Furthermore, the proposed model is able to solve the issue of skewed classes through the use of appropriate data balancing techniques.
|Titolo:||Ensemble of Technical Analysis and Machine Learning for Market Trend Prediction|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||04.01 - Contributo in atti di convegno|