nep-for New Economics Papers
on Forecasting
Issue of 2019‒03‒04
three papers chosen by
Rob J Hyndman
Monash University

  1. Working Paper: Improved Stock Price Forecasting Algorithm based on Feature-weighed Support Vector Regression by using Grey Correlation Degree By Quanxi Wang
  2. Asian Development Outlook Forecast Accuracy 2007–2016 By Ferrarini, Benno
  3. Financial series prediction using Attention LSTM By Sangyeon Kim; Myungjoo Kang

  1. By: Quanxi Wang
    Abstract: With the widespread engineering applications ranging from artificial intelligence and big data decision-making, originally a lot of tedious financial data processing, processing and analysis have become more and more convenient and effective. This paper aims to improve the accuracy of stock price forecasting. It improves the support vector machine regression algorithm by using grey correlation analysis (GCA) and improves the accuracy of stock prediction. This article first divides the factors affecting the stock price movement into behavioral factors and technical factors. The behavioral factors mainly include weather indicators and emotional indicators. The technical factors mainly include the daily closing data and the HS 300 Index, and then measure relation through the method of grey correlation analysis. The relationship between the stock price and its impact factors during the trading day, and this relationship is transformed into the characteristic weight of each impact factor. The weight of the impact factors of all trading days is weighted by the feature weight, and finally the support vector regression (SVR) is used. The forecast of the revised stock trading data was compared based on the forecast results of technical indicators (MSE, MAE, SCC, and DS) and unmodified transaction data, and it was found that the forecast results were significantly improved.
    Date: 2019–02
  2. By: Ferrarini, Benno (Asian Development Bank)
    Abstract: This paper assesses the accuracy of Asian Development Outlook growth and inflation forecasts for 43 Asian economies from 2007 to 2016, against the benchmark of World Economic Outlook projections by the International Monetary Fund. They are found to overlap quite closely, notwithstanding much heterogeneity across countries and years. Forecast accuracy sharpens over time as additional data and evidence become available and get incorporated during quarterly revisions. However, errors widen during crisis years as forecasters struggle to reflect such events in their projections.
    Keywords: Asian Development Bank; Asian Development Outlook; International Monetary Fund; macroeconomic forecasts; World Economic Outlook
    JEL: E17 E37
    Date: 2019–01–15
  3. By: Sangyeon Kim; Myungjoo Kang
    Abstract: Financial time series prediction, especially with machine learning techniques, is an extensive field of study. In recent times, deep learning methods (especially time series analysis) have performed outstandingly for various industrial problems, with better prediction than machine learning methods. Moreover, many researchers have used deep learning methods to predict financial time series with various models in recent years. In this paper, we will compare various deep learning models, such as multilayer perceptron (MLP), one-dimensional convolutional neural networks (1D CNN), stacked long short-term memory (stacked LSTM), attention networks, and weighted attention networks for financial time series prediction. In particular, attention LSTM is not only used for prediction, but also for visualizing intermediate outputs to analyze the reason of prediction; therefore, we will show an example for understanding the model prediction intuitively with attention vectors. In addition, we focus on time and factors, which lead to an easy understanding of why certain trends are predicted when accessing a given time series table. We also modify the loss functions of the attention models with weighted categorical cross entropy; our proposed model produces a 0.76 hit ratio, which is superior to those of other methods for predicting the trends of the KOSPI 200.
    Date: 2019–02

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