A Statistical Analysis of Chinese Stock Indices Returns From Approach of Parametric Distributions Fitting

Si, Yuancheng; Nadarajah, Saralees

doi:10.1007/s40745-022-00421-9

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…), or segmentation based on business understanding of customer value related indicators; the second category is based on a pure data-and algorithm-driven customer segmentation models, such as association rule mining or clustering of customers based on their demographic characteristics' segmentation, such as age, gender, education, income status, business attributes, etc. [7][8] The third category is a hybrid form of the first two types of models, such as incorporating relevant data-driven [9] theories and methods in the feature extraction and weight determination sessions. In general, there is no unified paradigm or process for customer segmentation, and the current customer segmentation methods are not only broader in scope, but also introduce many cutting-edge mathematical, statistical and visualization tools in the segmentation techniques In this paper, the RFM model is chosen as a specific case study of customer segmentation methods for commercial banks, which takes into account the validity and simplicity of the model, as well as the availability of data at the data collection level for commercial banks as information collectors.…”

Section: Methodsmentioning

confidence: 99%

Enhancing customer segmentation in Chinese city commercial banks: A machine learning approach

Si,

2024

ACE

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This paper examines the progression and implementation of customer segmentation theory in banking, focusing particularly on an improved RFM (Recency, Frequency, Monetary) model. It highlights the essential role of identifying high-value customers for the sustained growth and success of commercial banks. The study elucidates the pivotal role of Customer Relationship Management (CRM) and detailed management in the realm of retail banking as crucial for achieving success. By analyzing real-case scenarios from the Chinese banking sector, this paper illuminates the evolving nature of customer segmentation theory and its varied applications. The objective is to provide insightful and practical recommendations for commercial banks aiming to develop or enhance their customer segmentation frameworks. This research contributes both to the theoretical understanding of customer segmentation and offers a pragmatic guide for banks seeking to improve their customer management approaches in a market that is becoming increasingly competitive.

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Section: Methodsmentioning

confidence: 99%

Enhancing customer segmentation in Chinese city commercial banks: A machine learning approach

Si,

2024

ACE

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“…They only require knowledge of the historical data of a single variable to make predictions. This makes the model easy to understand and interpret, especially in the financial field, where stock prices are often believed to be influenced by past prices [ 19 , 20 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling opening price spread of Shanghai Composite Index based on ARIMA-GRU/LSTM hybrid model

Si,

Nadarajah,

Zhang

et al. 2024

PLoS ONE

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In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index’s opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model’s proficiency in linear trend analysis and the deep learning models’ capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index’s opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.

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Price Gap Anomaly: Empirical Study of Opening Price Gaps and Price Disparities in Chinese Stock Indices

Si,

Nadarajah

2024

Asia-Pac Financ Markets

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A Statistical Analysis of Chinese Stock Indices Returns From Approach of Parametric Distributions Fitting

Cited by 4 publications

References 20 publications

Enhancing customer segmentation in Chinese city commercial banks: A machine learning approach

Enhancing customer segmentation in Chinese city commercial banks: A machine learning approach

Modeling opening price spread of Shanghai Composite Index based on ARIMA-GRU/LSTM hybrid model

Price Gap Anomaly: Empirical Study of Opening Price Gaps and Price Disparities in Chinese Stock Indices

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