Cost-Sensitive Estimation of ARMA Models for Financial Asset Return Data

Kim, Minyoung

doi:10.1155/2015/232184

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Prior to the use of deep learning within the field of financial times series prediction, methods such as ARIMA and its modifications were mainly used. For instance, Minyoung Kim replaced the conventional maximum likelihood estimation with an asymmetric loss function owing to the asymmetric distribution of financial time series returns (M. Kim, 2015), and Adebiyi et al evaluated the ARIMA and artificial neural network prediction performance on NYSE stocks (e. g., stock price of Dell Incorporation) (Adebiyi, Adewumi, & Ayo, 2014). The empirical findings of these studies demonstrated the superiority of neural networks over ARIMA models.…”

Section: Related Workmentioning

confidence: 99%

Dynamic and context-dependent stock price prediction using attention modules and news sentiment

Königstein¹

2023

Digit Finance

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The growth of machine-readable data in finance, such as alternative data, requires new modeling techniques that can handle non-stationary and non-parametric data. Due to the underlying causal dependence and the size and complexity of the data, we propose a new modeling approach for financial time series data, the αt-RIM (recurrent independent mechanism). This architecture makes use of key-value attention to integrate top-down and bottom-up information in a context-dependent and dynamic way. To model the data in such a dynamic manner, the αt-RIM utilizes an exponentially smoothed recurrent neural network, which can model non-stationary times series data, combined with a modular and independent recurrent structure. We apply our approach to the closing prices of three selected stocks of the S&P 500 universe as well as their news sentiment score. The results suggest that the αt-RIM is capable of reflecting the causal structure between stock prices and news sentiment, as well as the seasonality and trends. Consequently, this modeling approach markedly improves the generalization performance, that is, the prediction of unseen data, and outperforms state-of-the-art networks such as long short-term memory models.

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Section: Related Workmentioning

confidence: 99%

Dynamic and context-dependent stock price prediction using attention modules and news sentiment

Königstein¹

2023

Digit Finance

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“…Chen-Xu and Jie-Sheng [8] used ARMA model to forecast the cash flow of a commercial bank. In order to create an ARMA model to forecast stock returns, Kim investigated the symmetric maximum likelihood (ML) loss function and developed an asymmetric loss function [9]. Similarly, numerous researchers studied ARIMA models for time series predicting [10,11].…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid fuzzy inference rules of descent method and wavelet function for volatility forecasting

et al. 2022

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This research employs the gradient descent learning (FIR.DM) approach as a learning process in a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) to improve volatility prediction of daily stock market prices using Saudi Arabia’s stock exchange (Tadawul) data. The MODWT comprises five mathematical functions and fuzzy inference rules. The inputs are the oil price (Loil) and repo rate (Repo) according to multiple regression correlation, and the Engle and Granger Causality test Engle RF, (1987). The logarithm of the stock market price (LSCS) in Tadawul reflects the output variable. The correlation matrix reveals that there is no collinearity between the input variables, and the causality test demonstrates that the input variables significantly influence the outcome variable. According to the multiple regression, there is a substantial negative influence between Loil and LSCS but a significant positive effect between Repo and output. For the 80% dataset under ME (0.000005), MAE (0.003214), and MAPE (0.064497), the MODWT-LA8 (ARIMA(1,1,0) with drift) for the LSCS variable performs better than other WT functions. In the novel hybrid model MODWT-FIR.DM, each function’s approximation coefficient (LSCS) is applied with input variables (Loil and Repo). We evaluate the performance of the proposed model (MODWT-LA8-FIR.DM) using different statistical measures (ME, RMSE, MAE, MPE) and compare it to two established models: the original FIR.DM and other MODWT-FIR.DM functions for forecasting 20% of datasets. The outcomes show that the MODWT-LA8-FIR.DM performs better than the traditional models based on lower ME (3.167586), RMSE (3.167638), MAE (3.167586), and MPE (80.860849). The proposed hybrid model may be a potential stock market forecasting model.

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“…Chen-Xu and Jie-Sheng [18] built a model based on ARMA to predict bank cash flow. Kim [19] discussed the symmetric maximum likelihood (ML) loss function and proposed asymmetric loss function to build ARMA model to forecast stock returns. Chen et al [17] applied an adaptive approach to build ARMA model by deriving the error based on the theory of minimum mean square error (MMSE).…”

Section: Literature Reviewmentioning

confidence: 99%

Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

2021

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Due to its complexity, financial time-series forecasting is regarded as one of the most challenging problems. During the past two decades, nonlinear modeling techniques, such as artificial neural networks, were commonly employed to solve a variety of time-series problems. Recently, however, deep neural network has been found to be more efficient than those in many application domains. In this article, we propose a model based on deep neural networks that improves the forecasting of stock prices. We investigate the impact of combining deep learning techniques with multiresolution analysis to improve the forecasting accuracy. Our proposed model is based on an empirical wavelet transform which is shown to outperform traditional stationary wavelet transform in capturing price fluctuations at different time scales. The proposed model is demonstrated to be substantially more effective than other models when evaluated on the S&P500 stock index and Mackey-Glass time series.

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Cost-Sensitive Estimation of ARMA Models for Financial Asset Return Data

Cited by 7 publications

References 19 publications

Dynamic and context-dependent stock price prediction using attention modules and news sentiment

Dynamic and context-dependent stock price prediction using attention modules and news sentiment

Hybrid fuzzy inference rules of descent method and wavelet function for volatility forecasting

Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

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