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Risks
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Modern Statistical and Machine Learning Techniques for Financial Data
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Modern Statistical and Machine Learning Techniques for Financial Data

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3546

Special Issue Editor

Prof. Dr. Lei (Larry) Hua

E-Mail Website
Guest Editor
Department of Statistics and Actuarial Science, Northern Illinois University, DeKalb, IL 60115, USA
Interests: dependence modeling; copula; quantitative finance; quantitative risk management

Special Issue Information

Dear Colleagues,

Modern statistical and machine learning methods have provided powerful tools with which to tackle large amounts of financial data, either for financial risk management or for investment and trading strategies.

The Special Issue aims to collect research work on innovative applications of modern statistical and machine learning methods related to financial data, including, but not limited to, the following topics:

  1. Explanatory/interpretable machine learning methods for financial data.
  2. Tail risks, tail dependence, and extreme value modeling for financial data.
  3. Systemic risk, liquidity risk, anomaly detection, and financial stability.
  4. Behavioral finance, sentiment analysis, and news as well as social network analysis.
  5. Market microstructure analysis and high-frequency trading strategies.

Prof. Dr. Lei (Larry) Hua
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • high-frequency financial data
  • limit order book data
  • alternative data
  • explanatory machine learning/deep learning
  • multivariate non-Gaussian and extreme values
  • copula and dependence modeling
  • financial time series
  • volatility modeling
  • statistical arbitrage
  • futures, stocks, ETFs, forex, and cryptos

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

21 pages, 2179 KiB  
Article
Market Predictability Before the Closing Bell Rings
by Lu Zhang and Lei Hua
Risks 2024, 12(11), 180; https://doi.org/10.3390/risks12110180 - 13 Nov 2024
Viewed by 388
Abstract
This study examines the predictability of the last 30 min of intraday stock price movements within the US financial market. The analysis encompasses several potential explanatory variables, including returns from each 30 min intraday trading session, overnight returns, the federal reserve fund rate [...] Read more.
This study examines the predictability of the last 30 min of intraday stock price movements within the US financial market. The analysis encompasses several potential explanatory variables, including returns from each 30 min intraday trading session, overnight returns, the federal reserve fund rate decision days and the subsequent three days, the US dollar index, month effects, weekday effects, and market volatilities. Market-adaptive trading strategies are developed and backtested on the basis of the study’s insights. Unlike the commonly employed multiple linear regression methods with Gaussian errors, this research utilizes a Bayesian linear regression model with Student-t error terms to more accurately capture the heavy tails characteristic of financial returns. A comparative analysis of these two approaches is conducted and the limitations inherent in the traditionally used method are discussed. Our main findings are based on data from 2007 to 2018. We observed that well-studied factors such as overnight effects and intraday momentum have diminished over time. Some other new factors were significant, such as lunchtime returns during boring days and the tug-of-war effect over the days after a federal fund rate change decision. Ultimately, we incorporate findings derived from data spanning 2022 to 2024 to provide a contemporary perspective on the examined components, followed by a discussion of the study’s limitations. Full article
(This article belongs to the Special Issue Modern Statistical and Machine Learning Techniques for Financial Data)
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16 pages, 668 KiB  
Article
A Hybrid Model for Forecasting Realized Volatility Based on Heterogeneous Autoregressive Model and Support Vector Regression
by Yue Zhuo and Takayuki Morimoto
Risks 2024, 12(1), 12; https://doi.org/10.3390/risks12010012 - 16 Jan 2024
Cited by 1 | Viewed by 2715
Abstract
In this study, we proposed two types of hybrid models based on the heterogeneous autoregressive (HAR) model and support vector regression (SVR) model to forecast realized volatility (RV). The first model is a residual-type model, where the RV is first predicted using the [...] Read more.
In this study, we proposed two types of hybrid models based on the heterogeneous autoregressive (HAR) model and support vector regression (SVR) model to forecast realized volatility (RV). The first model is a residual-type model, where the RV is first predicted using the HAR model, and the residuals are used to train the SVR model. The residual component is then predicted using the SVR model, and the results from both the HAR and SVR models are combined to obtain the final prediction. The second model is a weight-based model, which is a combination of the HAR and SVR models and uses the same independent variables and dependent variables as the HAR model; we adjust the contribution of the two models to the predicted values by giving different weights to each model. In particular, four volatility models are used in RV forecasting as basic models. For empirical analysis, the RV of returns of the Tokyo stock price index and five individual stocks of TOPIX 30 is used as the dataset. The empirical results reveal that according to the model confidence set test, the weight-type model outperforms the HAR model and the residual-type HAR–SVR model. Full article
(This article belongs to the Special Issue Modern Statistical and Machine Learning Techniques for Financial Data)
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