NSTL国家科技图书文献中心

1. International evidence on the financial performance of biodiversity investing NSTL国家科技图书文献中心

Francesco Paolo Appi... | Mohammed Benlemlih ... - 《Journal of Environmental Management》 - 2025,377(Mar.) - 124640.1～124640.13 - 共13页

摘要： performance of 10 biodiversity stock* indices in contrast to* | have recently developed equity indices* that track the* | their parent indices* from 2022 to 2024. Our findings* | The biodiversity depletion caused by human | activities stands out prominently. Companies are urged to

关键词： Biodiversity | Climate change | Stock indices | Financial performance | Green finance

2. Are machine learning models effective in predicting emerging markets? Investigating the accuracy of predictions in emerging stock market indices NSTL国家科技图书文献中心

Namitha,Yeldho | Dany,Thomas ... - 《Quality & Quantity》 - 2025,59(1) - 839～904 - 共66页

摘要： market indices. We investigated the stock* prediction* | Abstract The Indian stock* market is an* | ) models using Python programming in the emerging stock | market indices* like NIFTY 50 and NIFTY 100. A recurrent* | employed to predict the stock* price movements using data*

关键词： Stock prediction | Machine learning | Deep learning | Emerging market | Bidirectional Long short-term memory | BiLSTM

3. Climate risk and the nexus of clean energy and technology stocks NSTL国家科技图书文献中心

Elie Bouri | Tom L. Dudda ... - 《Annals of operations research》 - 2025,347(1) - 445～469 - 共25页

摘要： energy and technology stock* indices, whereas the effect* | sample periods and alternative stock* indices. On the* | We examine the impact of climate risks on the | nexus of clean energy and technology stocks using a | time-varying correlation model. We find that physical

关键词： Climate risk | Clean energy | Technology stocks | Energy transition

4. Assessing the Financial Impact of Natural Disasters on Global Stock Markets: Evidence from the Five Large Economies NSTL国家科技图书文献中心

Paravee Maneejuk | Yanagorn Panyagad ... - 《Integrated Uncertainty in Knowledge Modelling and Decision Making,Part II》 - International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making - 2025, - 234～242 - 共9页

摘要：, on the stock* indices of the world's five large* | average decrease of 0.0745% in stock* indices. In* | stock markets: the United States, China, Japan | from 2004 to 2022 across major indices, including the | significantly negatively affect stock* markets, leading to an*

关键词： Large stock markets | Natural disasters | Prais-Winsten regression | Stock markets

5. An intelligent framework based on optimized variational mode decomposition and temporal convolutional network: Applications to stock index multi-step forecasting NSTL国家科技图书文献中心

Yuanyuan Yu | Dongsheng Dai ... - 《Expert Systems with Application》 - 2025,268(Apr.) - 126222.1～126222.27 - 共27页

摘要： significant characteristics of stock* indices such as high* | stock indices has become a challenging task, thus | multi-scale features of stock* indices and accurately* | The stock* market is often subjected to* | daily closing prices of the Shanghai Stock* Exchange*

关键词： Stock index multi-step forecasting | Genetic algorithm | Variational mode decomposition | Temporal convolutional network

6. Aboveground biomass estimation in a grassland ecosystem using Sentinel‑2 satellite imagery and machine learning algorithms NSTL国家科技图书文献中心

Andisani Netsianda | Paidamwoyo Mhangara - 《Environmental Monitoring and Assessment》 - 2025,197(2) - 138.1～138.16 - 共16页

摘要： expands the carbon stock* in terrestrial ecosystems* | between Sentinel-2-derived vegetation indices* and* | The grassland ecosystem forms a critical part | of the natural ecosystem, covering up to 15-26% of | the Earths land surface. Grassland significantly

关键词： Carbon stock | Leaf area index | Vegetation indices | Carbon cycle | Carbon sink

7. Machine learning vs deep learning in stock market investment: an international evidence NSTL国家科技图书文献中心

Jing Hao | Feng He ... - 《Annals of operations research》 - 2025,348(1) - 93～115 - 共23页

摘要： stock indices constituting stocks in these markets | forecast stock* prices and construct statistical arbitrage* | strategies in five stock* markets, including mainland China* | Chinese stock* market. We find that DNN models generally* | Machine learning and deep learning are

关键词： Machine learning | Deep learning | Quantitative investment | Arbitrage

8. The dynamic connectedness between oil price shocks and emerging market economies stock markets: Evidence from new approaches NSTL国家科技图书文献中心

Tiwari A.K. | Dam M.M.... - 《Energy economics》 - 2025,141(Jan.) - 1.1～1.31 - 共31页 - 被引量：1

摘要： stock market indices of Brazil, Argentina and Mexico | correlation in all stock* indices, especially in the weekly* | risk shocks that Ready (2018) developed and the stock | considerable degree of dynamic connectedness among the stock | stock markets of Brazil, Mexico, and Argentina have

关键词： Dynamic connectedness | Emerging market economies | Oil price shocks | Spillover | TVP-VAR | Wavelet quantile correlation

9. Enhancing stock market predictions using hybrid machine learning approach with XGBOOST-LSTM and XGBOOST-GRU models NSTL国家科技图书文献中心

Manoranjan Dash | Christo Aditya Bikra...... - 《International Journal of Applied Systemic Studies》 - 2025,12(1) - 73～87 - 共15页

摘要： the performance of stock* indices remain a challenge* | . Also, the stock* market periodically faces some of the* | explore the hidden variations in stock* index movements* | stock market forecast. It utilises XGBOOST to select | In the ever uncertain and volatile global

关键词： Deep learning | DL | COVID-19 | Machine learning | ML | Hybrid model | Stock market

10. Leveraging large language models to examine the interaction between investor sentiment and stock performance NSTL国家科技图书文献中心

Yong Zhuang | Feilong Wang ... - 《Engineering Applications of Artificial Intelligence》 - 2025,150(Jun.) - 110602.1～110602.12 - 共12页

摘要： single stock* indices, overlooking the impact of brand* | investor sentiment and stock* performance is crucial in* | focus on financial news and stock* prices, while* | investor sentiment and stock* performance, using Chinese* | text data from stock* forums. By employing in-context*

摘要： performance of 10 biodiversity stock* indices in contrast to* | have recently developed equity indices* that track the* | their parent indices* from 2022 to 2024. Our findings* | The biodiversity depletion caused by human | activities stands out prominently. Companies are urged to

关键词： Biodiversity | Climate change | Stock indices | Financial performance | Green finance

摘要： market indices. We investigated the stock* prediction* | Abstract The Indian stock* market is an* | ) models using Python programming in the emerging stock | market indices* like NIFTY 50 and NIFTY 100. A recurrent* | employed to predict the stock* price movements using data*

关键词： Stock prediction | Machine learning | Deep learning | Emerging market | Bidirectional Long short-term memory | BiLSTM

摘要： energy and technology stock* indices, whereas the effect* | sample periods and alternative stock* indices. On the* | We examine the impact of climate risks on the | nexus of clean energy and technology stocks using a | time-varying correlation model. We find that physical

关键词： Climate risk | Clean energy | Technology stocks | Energy transition

摘要：, on the stock* indices of the world's five large* | average decrease of 0.0745% in stock* indices. In* | stock markets: the United States, China, Japan | from 2004 to 2022 across major indices, including the | significantly negatively affect stock* markets, leading to an*

关键词： Large stock markets | Natural disasters | Prais-Winsten regression | Stock markets

摘要： significant characteristics of stock* indices such as high* | stock indices has become a challenging task, thus | multi-scale features of stock* indices and accurately* | The stock* market is often subjected to* | daily closing prices of the Shanghai Stock* Exchange*

关键词： Stock index multi-step forecasting | Genetic algorithm | Variational mode decomposition | Temporal convolutional network

摘要： expands the carbon stock* in terrestrial ecosystems* | between Sentinel-2-derived vegetation indices* and* | The grassland ecosystem forms a critical part | of the natural ecosystem, covering up to 15-26% of | the Earths land surface. Grassland significantly

关键词： Carbon stock | Leaf area index | Vegetation indices | Carbon cycle | Carbon sink

摘要： stock indices constituting stocks in these markets | forecast stock* prices and construct statistical arbitrage* | strategies in five stock* markets, including mainland China* | Chinese stock* market. We find that DNN models generally* | Machine learning and deep learning are

关键词： Machine learning | Deep learning | Quantitative investment | Arbitrage

摘要： stock market indices of Brazil, Argentina and Mexico | correlation in all stock* indices, especially in the weekly* | risk shocks that Ready (2018) developed and the stock | considerable degree of dynamic connectedness among the stock | stock markets of Brazil, Mexico, and Argentina have

关键词： Dynamic connectedness | Emerging market economies | Oil price shocks | Spillover | TVP-VAR | Wavelet quantile correlation

摘要： the performance of stock* indices remain a challenge* | . Also, the stock* market periodically faces some of the* | explore the hidden variations in stock* index movements* | stock market forecast. It utilises XGBOOST to select | In the ever uncertain and volatile global

关键词： Deep learning | DL | COVID-19 | Machine learning | ML | Hybrid model | Stock market

摘要： single stock* indices, overlooking the impact of brand* | investor sentiment and stock* performance is crucial in* | focus on financial news and stock* prices, while* | investor sentiment and stock* performance, using Chinese* | text data from stock* forums. By employing in-context*

关键词： Large language model | Sentiment analysis | Investor sentiment indicator | Vector autoregression model

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： performance of 10 biodiversity stock indices in contrast to | have recently developed equity indices that track the | their parent indices from 2022 to 2024. Our findings | The biodiversity depletion caused by human | activities stands out prominently. Companies are urged to

关键词： Biodiversity | Climate change | Stock indices | Financial performance | Green finance

摘要： market indices. We investigated the stock prediction | Abstract The Indian stock market is an | ) models using Python programming in the emerging stock | market indices like NIFTY 50 and NIFTY 100. A recurrent | employed to predict the stock price movements using data

关键词： Stock prediction | Machine learning | Deep learning | Emerging market | Bidirectional Long short-term memory | BiLSTM

摘要： energy and technology stock indices, whereas the effect | sample periods and alternative stock indices. On the | We examine the impact of climate risks on the | nexus of clean energy and technology stocks using a | time-varying correlation model. We find that physical

关键词： Climate risk | Clean energy | Technology stocks | Energy transition

摘要：, on the stock indices of the world's five large | average decrease of 0.0745% in stock indices. In | stock markets: the United States, China, Japan | from 2004 to 2022 across major indices, including the | significantly negatively affect stock markets, leading to an

关键词： Large stock markets | Natural disasters | Prais-Winsten regression | Stock markets

摘要： significant characteristics of stock indices such as high | stock indices has become a challenging task, thus | multi-scale features of stock indices and accurately | The stock market is often subjected to | daily closing prices of the Shanghai Stock Exchange

关键词： Stock index multi-step forecasting | Genetic algorithm | Variational mode decomposition | Temporal convolutional network

摘要： expands the carbon stock in terrestrial ecosystems | between Sentinel-2-derived vegetation indices and | The grassland ecosystem forms a critical part | of the natural ecosystem, covering up to 15-26% of | the Earths land surface. Grassland significantly

关键词： Carbon stock | Leaf area index | Vegetation indices | Carbon cycle | Carbon sink

摘要： stock indices constituting stocks in these markets | forecast stock prices and construct statistical arbitrage | strategies in five stock markets, including mainland China | Chinese stock market. We find that DNN models generally | Machine learning and deep learning are

关键词： Machine learning | Deep learning | Quantitative investment | Arbitrage

摘要： stock market indices of Brazil, Argentina and Mexico | correlation in all stock indices, especially in the weekly | risk shocks that Ready (2018) developed and the stock | considerable degree of dynamic connectedness among the stock | stock markets of Brazil, Mexico, and Argentina have

关键词： Dynamic connectedness | Emerging market economies | Oil price shocks | Spillover | TVP-VAR | Wavelet quantile correlation

摘要： the performance of stock indices remain a challenge | . Also, the stock market periodically faces some of the | explore the hidden variations in stock index movements | stock market forecast. It utilises XGBOOST to select | In the ever uncertain and volatile global

关键词： Deep learning | DL | COVID-19 | Machine learning | ML | Hybrid model | Stock market

摘要： single stock indices, overlooking the impact of brand | investor sentiment and stock performance is crucial in | focus on financial news and stock prices, while | investor sentiment and stock performance, using Chinese | text data from stock forums. By employing in-context

关键词： Large language model | Sentiment analysis | Investor sentiment indicator | Vector autoregression model

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： performance of 10 biodiversity stock* indices in contrast to* | have recently developed equity indices* that track the* | their parent indices* from 2022 to 2024. Our findings* | The biodiversity depletion caused by human | activities stands out prominently. Companies are urged to

摘要： market indices. We investigated the stock* prediction* | Abstract The Indian stock* market is an* | ) models using Python programming in the emerging stock | market indices* like NIFTY 50 and NIFTY 100. A recurrent* | employed to predict the stock* price movements using data*

摘要： energy and technology stock* indices, whereas the effect* | sample periods and alternative stock* indices. On the* | We examine the impact of climate risks on the | nexus of clean energy and technology stocks using a | time-varying correlation model. We find that physical

摘要：, on the stock* indices of the world's five large* | average decrease of 0.0745% in stock* indices. In* | stock markets: the United States, China, Japan | from 2004 to 2022 across major indices, including the | significantly negatively affect stock* markets, leading to an*

摘要： significant characteristics of stock* indices such as high* | stock indices has become a challenging task, thus | multi-scale features of stock* indices and accurately* | The stock* market is often subjected to* | daily closing prices of the Shanghai Stock* Exchange*

摘要： expands the carbon stock* in terrestrial ecosystems* | between Sentinel-2-derived vegetation indices* and* | The grassland ecosystem forms a critical part | of the natural ecosystem, covering up to 15-26% of | the Earths land surface. Grassland significantly

摘要： stock indices constituting stocks in these markets | forecast stock* prices and construct statistical arbitrage* | strategies in five stock* markets, including mainland China* | Chinese stock* market. We find that DNN models generally* | Machine learning and deep learning are

摘要： stock market indices of Brazil, Argentina and Mexico | correlation in all stock* indices, especially in the weekly* | risk shocks that Ready (2018) developed and the stock | considerable degree of dynamic connectedness among the stock | stock markets of Brazil, Mexico, and Argentina have

摘要： the performance of stock* indices remain a challenge* | . Also, the stock* market periodically faces some of the* | explore the hidden variations in stock* index movements* | stock market forecast. It utilises XGBOOST to select | In the ever uncertain and volatile global

摘要： single stock* indices, overlooking the impact of brand* | investor sentiment and stock* performance is crucial in* | focus on financial news and stock* prices, while* | investor sentiment and stock* performance, using Chinese* | text data from stock* forums. By employing in-context*