Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model

Li, Xiafei; Liang, Chao; Ma, Feng

doi:10.1007/s10479-022-04716-1

Cited by 11 publications

(4 citation statements)

References 99 publications

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“…We use this kind of approach to use more relevant data in order to obtain different accuracy scores in a specific range. The function f w : X ← Y is used as a self-stepping function for prediction with Φ(v i , λ) , where the age is given by λ [45]. With hyperparameter tuning, as the number of estimators is increased, the model reaches a better accuracy score, as shown in the training of their metrics.…”

Section: Key Research Findings and Recent Approaches For Cryptocurren...mentioning

confidence: 99%

TRX Cryptocurrency Profit and Transaction Success Rate Prediction Using Whale Optimization-Based Ensemble Learning Framework

2023

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TRON is a decentralized digital platform that provides a reliable way to transact in cryptocurrencies within a decentralized ecosystem. Thanks to its success, TRON’s native token, TRX, has been widely adopted by a large audience. To facilitate easy management of digital assets with TRON Wallet, users can securely store and manage their digital assets with ease. Our goal is first to develop a methodology to predict the future price using regression and then move on to build an effective classifier to predict whether a profit or loss is made the next day and then make a prediction of the transaction success rate. Our framework is capable of predicting whether there will be a profit in the future based on price prediction and forecasting results using regressors such as XGBoost, LightGBM, and CatBoost with R2 values of 0.9820, 0.9825 and 0.9858, respectively. In this work, an ensemble-based stacking classifier with the Whale optimization approach has been proposed which achieves the highest accuracy of 89.05 percent to predict if there will be a profit or loss the next day and an accuracy of 98.88 percent of TRX transaction success rate prediction which is higher than accuracies obtained by standard machine learning models. An effective framework will be useful for better decision-making and management of risks in a cryptocurrency.

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Section: Key Research Findings and Recent Approaches For Cryptocurren...mentioning

confidence: 99%

TRX Cryptocurrency Profit and Transaction Success Rate Prediction Using Whale Optimization-Based Ensemble Learning Framework

2023

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“…Большое количество работ российских и зарубежных авторов посвящено математическому моделированию волатильности и прогнозированию фондовых рынков [9][10][11][12]. Причем многие исследователи применяют модели группы GARCH, ARFIMA и HAR, все чаще появляются исследования, доказывающие преимущества гибридных моделей с использованием нейросетей.…”

Section: Introductionunclassified

Forecasting the Volatility of the Russian Stock Market in the Context of International Economic Sanctions

Glebova,

Kovaleva

2024

jour

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The article is devoted to the study of trends in the development of the Russian stock market in the context of international economic sanctions. The purpose of the study is to make forecasts of the volatility of the Russian stock market using a scenario approach. Statistical data of the Moscow Stock Exchange were used for calculations. The authors have made a forecast of the volatility of the stock exchange market of the Russian Federation. The basis of the forecast calculations is the dynamics of the Moscow Exchange Index (IMOEX), taken as a key indicator of the Russian organized securities market, for the period from June 2013 to July 2022. Based on the basic historical dynamics of the Moscow Stock Exchange Index, negative (international economic sanctions are being tightened) and positive (implies the easing and/or lifting of some sanctions) scenarios for the development of the stock market of the Russian Federation are compiled. The scientific novelty is the authors’ assessment of the convergence of the volatility forecast under negative and positive scenarios to a certain level of volatility in 2023. The results of the calculations showed that under different scenarios of the situation, volatility tends to the same value at different assumed values of the Moscow Exchange Index, which allowed us to draw a new and practically significant conclusion that over time the economy of the Russian Federation stabilizes regardless of the tightening or easing of international economic sanctions — this may be due to the implementation of the country has a policy of import substitution, the formation of national production in most areas of the economy and the development of the domestic market. The work carried out by the authors contributes to the development of theoretical and applied economics in terms of making forecasts for the development of the stock market and using the results of forecasting to make economically sound decisions.

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“…As such, these models hold promise for improving the accuracy and generalizability of multivariable predictions of fear extinction. Ridge Regression, Lasso Regression, and Elastic Net Regression (ENR) are three commonly used penalized regression models that have advanced the clinical research in other domains [ 65 , 66 , 67 ]. In a study of depression treatment outcomes, each was found to be more accurate than traditional Ordinary Least Squares (OLS) regression in both a training sample and separate holdout sample [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting Fear Extinction in Posttraumatic Stress Disorder

Lewis,

Webb,

Kuhn

et al. 2023

Brain Sciences

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Fear extinction is the basis of exposure therapies for posttraumatic stress disorder (PTSD), but half of patients do not improve. Predicting fear extinction in individuals with PTSD may inform personalized exposure therapy development. The participants were 125 trauma-exposed adults (96 female) with a range of PTSD symptoms. Electromyography, electrocardiogram, and skin conductance were recorded at baseline, during dark-enhanced startle, and during fear conditioning and extinction. Using a cross-validated, hold-out sample prediction approach, three penalized regressions and conventional ordinary least squares were trained to predict fear-potentiated startle during extinction using 50 predictor variables (5 clinical, 24 self-reported, and 21 physiological). The predictors, selected by penalized regression algorithms, were included in multivariable regression analyses, while univariate regressions assessed individual predictors. All the penalized regressions outperformed OLS in prediction accuracy and generalizability, as indexed by the lower mean squared error in the training and holdout subsamples. During early extinction, the consistent predictors across all the modeling approaches included dark-enhanced startle, the depersonalization and derealization subscale of the dissociative experiences scale, and the PTSD hyperarousal symptom score. These findings offer novel insights into the modeling approaches and patient characteristics that may reliably predict fear extinction in PTSD. Penalized regression shows promise for identifying symptom-related variables to enhance the predictive modeling accuracy in clinical research.

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Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model

Cited by 11 publications

References 99 publications

TRX Cryptocurrency Profit and Transaction Success Rate Prediction Using Whale Optimization-Based Ensemble Learning Framework

TRX Cryptocurrency Profit and Transaction Success Rate Prediction Using Whale Optimization-Based Ensemble Learning Framework

Forecasting the Volatility of the Russian Stock Market in the Context of International Economic Sanctions

Predicting Fear Extinction in Posttraumatic Stress Disorder

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