Revolutionising Financial Portfolio Management: The Non-Stationary Transformer’s Fusion of Macroeconomic Indicators and Sentiment Analysis in a Deep Reinforcement Learning Framewor... by Yuchen Liu, Daniil Mikriukov, Owen Christopher Tjahyadi, Gangmin Li, Terry R. Payne, Yong Yue, Kamran Siddique, Ka Lok Man

“…This model is designed to decode complex patterns in financial time-series data, enhancing portfolio management strategies with deeper insights and robustness. …”
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Assessing Time Series Reversibility through Permutation Patterns by Massimiliano Zanin, Alejandro Rodríguez-González, Ernestina Menasalvas Ruiz, David Papo

“…We apply this method to the study of financial time series, showing that stocks and indices present a rich irreversibility dynamics. …”
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Risk Factor Evolution for Counterparty Credit Risk under a Hidden Markov Model by Ioannis Anagnostou, Drona Kandhai

“…An important limitation of GBM is that, due to the assumption of constant drift and volatility, stylized facts of financial time-series, such as volatility clustering and heavy-tailedness in the returns distribution, cannot be captured. …”
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Robust estimation of time-dependent precision matrix with application to the cryptocurrency market by Paola Stolfi, Mauro Bernardi, Davide Vergni

“…In this paper, we present a robust divergence estimator for a time-varying precision matrix that can manage both the extreme events and time-dependency that affect financial time series. Furthermore, we provide an algorithm to handle parameter estimations that uses the “maximization–minimization” approach. …”
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Determinants of commercial banks profitability through analysis of financial performance indicators: evidence from Kosovo by Artor Nuhiu, Arbër Hoti, Mejdi Bektashi

“…The study identifies the main factors that affect the profitability of commercial banks through analysis of financial time series and panel data of the banking sector in Kosovo. …”
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Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting by Charalampos M. Liapis, Sotiris Kotsiantis

“…The use of deep learning in conjunction with models that extract emotion-related information from texts to predict financial time series is based on the assumption that what is said about a stock is correlated with the way that stock fluctuates. …”
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Utility of a Shuffled Differential Evolution algorithm in designing of a Pi-Sigma Neural Network based predictor model by Rajashree Dash, Rasmita Rautray, Rasmita Dash

“…Due to its distinguishing features such as generalization ability, robustness and strong ability to tackle nonlinear problems, it appears to be more popular in financial time series modeling and prediction. In this paper, a Pi-Sigma Neural Network is designed for foretelling the future currency exchange rates in different prediction horizon. …”
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Study on the problems that influence the success contractor in Kuantan by Suhaiza, Subri

“…The main problems that influence the success contractor in completing the construction are problem in financial, time, cost, material and problems occur during construction. …”
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Hybrid Model for Stock Market Volatility by Kofi Agyarko, Nana Kena Frempong, Eric Neebo Wiah

“…The proposed BSGARCH (1, 1) model was applied to simulated data and two real financial time series data (NASDAQ 100 and S&P 500). The outcome was then compared to the outcomes of the GARCH (1, 1), EGARCH (1, 1), GJR-GARCH (1, 1), and APARCH (1, 1) with different error distributions (ED) using the mean absolute percentage error (MAPE), the root mean square error (RMSE), Theil’s inequality coefficient (TIC) and QLIKE. …”
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Topological data analysis of Chinese stocks’ dynamic correlations under major public events by Hongfeng Guo, Ziwei Ming, Bing Xing

“…Additionally, it has also been gradually applied in financial time series analysis and proved effective in exploring the topological features of such data. …”
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An Agile Mortality Prediction Model: Hybrid Logarithm Least-Squares Support Vector Regression with Cautious Random Particle Swarm Optimization by Chien-Lung Chan, Chia-Li Chen, Hsien-Wei Ting, Dinh-Van Phan

“…Logarithm Least-Squares Support Vector Regression (LLS-SVR) has been applied in addressing forecasting problems in various fields, including bioinformatics, financial time series, electronics, plastic injection moulding, Chemistry and cost estimations. …”
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Stock price prediction using principal components. by Mahsa Ghorbani, Edwin K P Chong

“…To address the time-varying nature of financial time series, we assign exponential weights to the price data so that recent data points are weighted more heavily. …”
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Fractal and Entropy Analysis of the Dow Jones Index Using Multidimensional Scaling by José A. Tenreiro Machado

“…Financial time series have a fractal nature that poses challenges for their dynamical characterization. …”
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Performance of the Multifractal Model of Asset Returns (MMAR): Evidence from Emerging Stock Markets by Samet Günay

“…The MMAR, which takes into account stylized facts of financial time series, such as long memory, fat tails and trading time, was developed as an alternative to the ARCH family models. …”
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ANALISIS DATA INFLASI DI INDONESIA MENGGUNAKAN MODEL REGRESI SPLINE by Suparti Suparti

“…The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. …”
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Financial innovation and economic growth: evidence from Zimbabwe by Alex Bara, Calvin Mudzingiri

“…Using the Autoregressive Distributed Lag (ARDL) bounds tests and Granger causality tests on financial time series data of Zimbabwe for the period 1980-2013, the study finds that financial innovation has a relationship to economic growth that varies depending on the variable used to measure financial innovation. …”
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Inference for Levy-Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo by Jasra, A, Stephens, D, Doucet, A, Tsagaris, T

“…The driving gamma process can capture the stylized behaviour of many financial time series and a discretized version, fit in a Bayesian manner, has been found to be very useful for modelling equity data. …”

MSGNN: a spectral graph neural network based on a novel magnetic signed Laplacian by He, Y, Permultter, M, Reinert, G, Cucuringu, M

“…Additionally, we provide a novel synthetic network model, which we refer to as the Signed Directed Stochastic Block Model, and a number of novel real-world data sets based on lead-lag relationships in financial time series.…”

Study on the application of LSTM-LightGBM Model in stock rise and fall prediction by Guo Yuankai, Li Yangyang, Xu Yuan

“…This paper proposes a hybrid financial time series forecast model based on LSTM and LightGBM, namely LSTM_LightGBM model. …”
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Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations by Didit Budi Nugroho, Bambang Susanto, Kezia Natalia Putri Prasetia, Rebecca Rorimpandey

“…Our empirical findings conclude that GARCH(1,1) models under Tukey transformations should be considered in risk management decisions since the models are more appropriate than standard for describing returns and volatility of financial time series and its stylized facts including fat tails and mean reverting. …”
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