Borsa Istanbul Review最新文献

英文中文

The good, the better and the challenging: Insights into predicting high-growth firms using machine learning

IF 6.3 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2024-12-01 DOI: 10.1016/j.bir.2024.12.001

Sermet Pekin, Aykut Şengül

This study aims to classify high-growth firms using several machine learning algorithms, including K-Nearest Neighbors, Logistic Regression with L1 (Lasso) and L2 (Ridge) Regularization, XGBoost, Gradient Descent, Naive Bayes and Random Forest. Leveraging a dataset composed of financial metrics and firm characteristics between 2009 and 2022 with 1,318,799 unique firms (averaging 554,178 annually), we evaluate the performance of each model using metrics such as MCC, ROC AUC, accuracy, precision, recall and F1-score. In our study, ROC AUC values ranged from 0.53 to 0.87 for employee-high growth and from 0.53 to 0.91 for turnover-high growth, depending on the method used. Our findings indicate that XGBoost achieves the highest performance, followed by Random Forest and Logistic Regression, demonstrating their effectiveness in distinguishing between high-growth and non-high-growth firms. Conversely, KNN and Naive Bayes yield lower accuracy. Furthermore, our findings reveal that growth opportunity emerges as the most significant factor in our study. This research contributes valuable insights to financial analysts and investors in identifying high-growth firms and underscores the potential of machine learning in economic prediction.

{"title":"The good, the better and the challenging: Insights into predicting high-growth firms using machine learning","authors":"Sermet Pekin, Aykut Şengül","doi":"10.1016/j.bir.2024.12.001","DOIUrl":"10.1016/j.bir.2024.12.001","url":null,"abstract":"<div><div>This study aims to classify high-growth firms using several machine learning algorithms, including K-Nearest Neighbors, Logistic Regression with L1 (Lasso) and L2 (Ridge) Regularization, XGBoost, Gradient Descent, Naive Bayes and Random Forest. Leveraging a dataset composed of financial metrics and firm characteristics between 2009 and 2022 with 1,318,799 unique firms (averaging 554,178 annually), we evaluate the performance of each model using metrics such as MCC, ROC AUC, accuracy, precision, recall and F1-score. In our study, ROC AUC values ranged from 0.53 to 0.87 for employee-high growth and from 0.53 to 0.91 for turnover-high growth, depending on the method used. Our findings indicate that XGBoost achieves the highest performance, followed by Random Forest and Logistic Regression, demonstrating their effectiveness in distinguishing between high-growth and non-high-growth firms. Conversely, KNN and Naive Bayes yield lower accuracy. Furthermore, our findings reveal that growth opportunity emerges as the most significant factor in our study. This research contributes valuable insights to financial analysts and investors in identifying high-growth firms and underscores the potential of machine learning in economic prediction.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"24 ","pages":"Pages 47-60"},"PeriodicalIF":6.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143347779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network

IF 6.3 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2024-12-01 DOI: 10.1016/j.bir.2024.12.002

Burak Gülmez

Stock price prediction remains a complex challenge in financial markets. This study introduces a novel Long Short-Term Memory (LSTM) model optimized by Sand Cat Swarm Optimization (SCSO) for stock price prediction. The research evaluates multiple algorithms including ANN, LSTM variants, Auto-ARIMA, Gradient Boosted Trees, DeepAR, N-BEATS, N-HITS, and the proposed LSTM-SCSO using DAX index data from 2018 to 2023. Model performance was assessed through Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and out-of-sample R2 metrics. Statistical significance was validated using Model Confidence Set analysis with 5000 bootstrap replications. Results demonstrate LSTM-SCSO's superior performance across all evaluation metrics. The model achieved an annualized return of 66.25% compared to the DAX index's 47.45%, with a Sharpe ratio of 2.9091. The integration of technical indicators and macroeconomic variables enhanced the model's predictive capabilities. These findings establish LSTM-SCSO as an effective tool for stock price prediction, offering practical value for investment decision-making.

引用次数: 0

More than just sentiment: Using social, cognitive, and behavioral information of social media to predict stock markets with artificial intelligence and big data

IF 6.3 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2024-12-01 DOI: 10.1016/j.bir.2024.12.003

Yunus Emre Akdogan , Adem Anbar

Digital transformation offers unprecedented opportunities to access data on hard-to-measure social aspects. In this digital era, social media platforms have become critical data sources for the social sciences. This study moves beyond traditional finance assumptions of “perfect information,” “rational humans,” and “isolated individuals” by analyzing retail investor behavior using Twitter data. It adopts a human model characterized by incomplete information, bounded rationality, and the influence of social and emotional factors. Tweets shared between January 1, 2012, and February 28, 2020, were collected. A GRU-based context classifier achieved 98% accuracy in identifying tweets related to Borsa Istanbul (BIST). Sentiment classification using a BERT model achieved 91% accuracy for positive and negative classes. Relationships between Twitter-obtained features and BIST indices were analyzed using machine learning methods such as linear regression, Lasso regression, random forest, and XGBoost. The analysis revealed that 91% of the change in the opening value, 63% of the change in trading volume, and 67% in volatility of the BIST 100 index could be attributed to cognitive, behavioral, and social features gleaned from tweets.

{"title":"More than just sentiment: Using social, cognitive, and behavioral information of social media to predict stock markets with artificial intelligence and big data","authors":"Yunus Emre Akdogan , Adem Anbar","doi":"10.1016/j.bir.2024.12.003","DOIUrl":"10.1016/j.bir.2024.12.003","url":null,"abstract":"<div><div>Digital transformation offers unprecedented opportunities to access data on hard-to-measure social aspects. In this digital era, social media platforms have become critical data sources for the social sciences. This study moves beyond traditional finance assumptions of “perfect information,” “rational humans,” and “isolated individuals” by analyzing retail investor behavior using Twitter data. It adopts a human model characterized by incomplete information, bounded rationality, and the influence of social and emotional factors. Tweets shared between January 1, 2012, and February 28, 2020, were collected. A GRU-based context classifier achieved 98% accuracy in identifying tweets related to Borsa Istanbul (BIST). Sentiment classification using a BERT model achieved 91% accuracy for positive and negative classes. Relationships between Twitter-obtained features and BIST indices were analyzed using machine learning methods such as linear regression, Lasso regression, random forest, and XGBoost. The analysis revealed that 91% of the change in the opening value, 63% of the change in trading volume, and 67% in volatility of the BIST 100 index could be attributed to cognitive, behavioral, and social features gleaned from tweets.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"24 ","pages":"Pages 61-82"},"PeriodicalIF":6.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143347781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond polarity: How ESG sentiment influences idiosyncratic volatility in the Turkish stock market

IF 6.3 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2024-12-01 DOI: 10.1016/j.bir.2024.11.003

Alev Atak

This study investigates the influence of Environmental, Social, and Governance (ESG) sentiment in corporate disclosures on idiosyncratic volatility (IVOL) in the Turkish stock market. Using FinBERT-ESG, a language model specifically designed for financial and ESG-related texts, we construct four novel indices: the Positive ESG Index (PESGIN), capturing positive ESG sentiment; the Negative ESG Index (NESGIN), representing adverse ESG sentiment; the Balanced Polarity Index (BPI), measuring the overall balance between positive and negative sentiment; and the Amplified Negative Polarity Index (ANPI), which emphasizes the intensity of negative sentiment. By employing a system-GMM approach, which effectively addresses endogeneity concerns common in finance, we find that PESGI is negatively associated with IVOL, suggesting that transparent and optimistic ESG communication reduces firm-specific risk. Conversely, ANPI positively correlates with IVOL, supporting the overreaction hypothesis and highlighting elevated investor sensitivity to adverse ESG disclosures. These findings underscore the complex interplay between ESG sentiment and investor behaviour, offering valuable insights for enhancing risk assessment and guiding investment strategies.

{"title":"Beyond polarity: How ESG sentiment influences idiosyncratic volatility in the Turkish stock market","authors":"Alev Atak","doi":"10.1016/j.bir.2024.11.003","DOIUrl":"10.1016/j.bir.2024.11.003","url":null,"abstract":"<div><div>This study investigates the influence of Environmental, Social, and Governance (ESG) sentiment in corporate disclosures on idiosyncratic volatility (IVOL) in the Turkish stock market. Using FinBERT-ESG, a language model specifically designed for financial and ESG-related texts, we construct four novel indices: the Positive ESG Index (PESGIN), capturing positive ESG sentiment; the Negative ESG Index (NESGIN), representing adverse ESG sentiment; the Balanced Polarity Index (BPI), measuring the overall balance between positive and negative sentiment; and the Amplified Negative Polarity Index (ANPI), which emphasizes the intensity of negative sentiment. By employing a system-GMM approach, which effectively addresses endogeneity concerns common in finance, we find that PESGI is negatively associated with IVOL, suggesting that transparent and optimistic ESG communication reduces firm-specific risk. Conversely, ANPI positively correlates with IVOL, supporting the overreaction hypothesis and highlighting elevated investor sensitivity to adverse ESG disclosures. These findings underscore the complex interplay between ESG sentiment and investor behaviour, offering valuable insights for enhancing risk assessment and guiding investment strategies.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"24 ","pages":"Pages 10-21"},"PeriodicalIF":6.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143347782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Powering perception, echoing green voices: The interplay of Cryptocurrency's energy footprint and environmental discourse in steering the direction of the market

IF 6.3 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2024-12-01 DOI: 10.1016/j.bir.2024.12.020

Iheb Ghazouani, Ines Ghazouani, Abdelwahed Omri

This study examines the influence of cryptocurrency's environmental footprint on market behavior through an analysis of 66,582 Reddit posts about Bitcoin and 23,231 about Ethereum. Using a vector autoregression (VAR) model, it explores the relationship between social media discussions on environmental issues, electricity use, and cryptocurrencies' market dynamics. We find a negative correlation between environmental discussions and Bitcoin volatility. Moreover, real electricity use has a more pronounced impact than social media discussions on both Bitcoin and Ethereum volatility. This indicates that crypto market investors prioritize real-world indicators over information from social media discussions. The study also reveals a bidirectional relationship between Bitcoin volatility and environmental posts, highlighting the complex interplay between market behavior and public discourse on environmental matters in the cryptocurrency domain. These results suggest the need for policies that limit energy consumption due to mining, promote renewable energy, and enhance investor education on environmental impacts to support sustainable practices in the cryptocurrency market.

{"title":"Powering perception, echoing green voices: The interplay of Cryptocurrency's energy footprint and environmental discourse in steering the direction of the market","authors":"Iheb Ghazouani, Ines Ghazouani, Abdelwahed Omri","doi":"10.1016/j.bir.2024.12.020","DOIUrl":"10.1016/j.bir.2024.12.020","url":null,"abstract":"<div><div>This study examines the influence of cryptocurrency's environmental footprint on market behavior through an analysis of 66,582 Reddit posts about Bitcoin and 23,231 about Ethereum. Using a vector autoregression (VAR) model, it explores the relationship between social media discussions on environmental issues, electricity use, and cryptocurrencies' market dynamics. We find a negative correlation between environmental discussions and Bitcoin volatility. Moreover, real electricity use has a more pronounced impact than social media discussions on both Bitcoin and Ethereum volatility. This indicates that crypto market investors prioritize real-world indicators over information from social media discussions. The study also reveals a bidirectional relationship between Bitcoin volatility and environmental posts, highlighting the complex interplay between market behavior and public discourse on environmental matters in the cryptocurrency domain. These results suggest the need for policies that limit energy consumption due to mining, promote renewable energy, and enhance investor education on environmental impacts to support sustainable practices in the cryptocurrency market.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"24 ","pages":"Pages 91-101"},"PeriodicalIF":6.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143347785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital technology development and systemic financial risks: Evidence from 22 countries 数字技术发展与系统性金融风险：来自 22 个国家的证据

IF 6.3 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2024-12-01 DOI: 10.1016/j.bir.2024.08.002

Xu Haoran, Miao Wenlong, Zhang Siyu

This study evaluates how digital technology development affects systemic financial risks in various countries. We employ cross-country sample data from over 5000 financial institutions in 22 countries from 2013 to 2021. The results reveal that the rapid growth of digital technology increases the systemic financial risks of various countries; this increase is related to disparities in the digital technology development stages and financial system structures. Furthermore, this study investigates the emotional contagion, complex financial linkage, and valuation inhibition effects on digital technology development's impact on systemic financial risks. Heterogeneity analysis shows that in countries with high levels of digital technology development and market-oriented financial systems, digital technology's effect on intensifying systemic financial risks is more significant.

本研究评估了数字技术发展如何影响各国的系统性金融风险。我们采用了 22 个国家 5000 多家金融机构 2013 年至 2021 年的跨国样本数据。研究结果表明，数字技术的快速发展增加了各国的系统性金融风险；这种增加与数字技术发展阶段和金融系统结构的差异有关。此外，本研究还探讨了数字技术发展对系统性金融风险影响的情绪传染、复杂金融联系和估值抑制效应。异质性分析表明，在数字技术发展水平高、金融体系市场化程度高的国家，数字技术对系统性金融风险的加剧效应更为显著。

引用次数: 0

Gauging the dynamic interlinkage among robotics, artificial intelligence, and green crypto investment: A quantile VAR approach

IF 6.3 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2024-12-01 DOI: 10.1016/j.bir.2024.11.006

Le Thanh Ha

A large amount of new green crypto investment is required to achieve the United Nations’ sustainable development goals. The development and application of artificial intelligence (AI) are essential for attracting this investment because it has the potential to increase the adoption of environmental innovation and individual environmental awareness. In our research, we use a DCC-GARCH copula model to examine time-varying spillover effects and demonstrate interconnections between the development of AI and green cryptocurrencies from January 1, 2018, to September 8, 2023. Our results show that when we consider the full data sample, the variables studied all have only weak connections. These results clearly demonstrate temporal variance in systemic connection caused by the COVID-19 pandemic, the Russia-Ukraine war, and bank failures. Robotics & AI ETF (BOTZ) is a net recipient of shocks across quantiles throughout the study, according to the total net directional connectivity across quantiles. Pairwise directional connectivity in an evolving net indicates that BOTZ consistently appears to be dominated by green cryptocurrencies in both the short and long term. Understanding the primary sources of spillovers between AI and green cryptocurrencies can help policymakers design the most effective strategies for mitigating these vulnerabilities and reducing market risk.

{"title":"Gauging the dynamic interlinkage among robotics, artificial intelligence, and green crypto investment: A quantile VAR approach","authors":"Le Thanh Ha","doi":"10.1016/j.bir.2024.11.006","DOIUrl":"10.1016/j.bir.2024.11.006","url":null,"abstract":"<div><div>A large amount of new green crypto investment is required to achieve the United Nations’ sustainable development goals. The development and application of artificial intelligence (AI) are essential for attracting this investment because it has the potential to increase the adoption of environmental innovation and individual environmental awareness. In our research, we use a DCC-GARCH copula model to examine time-varying spillover effects and demonstrate interconnections between the development of AI and green cryptocurrencies from January 1, 2018, to September 8, 2023. Our results show that when we consider the full data sample, the variables studied all have only weak connections. These results clearly demonstrate temporal variance in systemic connection caused by the COVID-19 pandemic, the Russia-Ukraine war, and bank failures. Robotics & AI ETF (BOTZ) is a net recipient of shocks across quantiles throughout the study, according to the total net directional connectivity across quantiles. Pairwise directional connectivity in an evolving net indicates that BOTZ consistently appears to be dominated by green cryptocurrencies in both the short and long term. Understanding the primary sources of spillovers between AI and green cryptocurrencies can help policymakers design the most effective strategies for mitigating these vulnerabilities and reducing market risk.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"24 ","pages":"Pages 22-31"},"PeriodicalIF":6.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143347783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of the optimal rebalancing frequency for pairs trading using machine learning techniques

IF 6.3 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2024-12-01 DOI: 10.1016/j.bir.2024.12.004

Mahmut Bağcı, Pınar Kaya Soylu

Selection of the optimal rebalancing frequency (ORF) is crucial for the pair trading algorithm (PTA) that periodically rebalances the allocation of two assets. This study proposes a machine learning (ML) approach to predict ORF ranges. To improve ML accuracy, pairs were categorized into three subgroups based on their correlation levels: positively, weakly, and negatively correlated. The statistical distribution of the ORF values is also presented. Accuracy scores show that random forest, logistic regression, and support vector classifiers perform competitively for the ORF range classification in both short- and long-term applications. The negatively correlated pairs showed the best classification performance, whereas the positively correlated pairs showed the lowest accuracy rate. Furthermore, the robustness of the proposed ML procedure is verified using a validation dataset, demonstrating the applicability of ORF range classification in practical exchange markets.

引用次数: 0

Do firms increase ESG activities during periods of geopolitical risk? Evidence from Korean business groups 在地缘政治风险时期，企业是否会增加 ESG 活动？来自韩国企业集团的证据

IF 6.3 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2024-11-01 DOI: 10.1016/j.bir.2024.11.002

Hongmin Chun , Boyoung Moon

This study examines the impact of the geopolitical risk (GPR) on the environmental, social, and governance (ESG) activities of South Korean business groups. Our empirical results indicate that GPR is positively associated with the ESG activities of South Korean firms, and this relationship is more pronounced among business groups. Furthermore, our results imply that South Korean business groups prioritizing their reputation or operating in a competitive market increase their ESG activities when GPR increases. Specifically, South Korean firms strategically increase their ESG activities during periods of significant GPR to enhance their reputation and build moral capital.

本研究探讨了地缘政治风险（GPR）对韩国企业集团的环境、社会和治理（ESG）活动的影响。我们的实证结果表明，地缘政治风险与韩国企业的环境、社会和治理活动呈正相关，而且这种关系在企业集团中更为明显。此外，我们的结果表明，当 GPR 增加时，优先考虑自身声誉或在竞争激烈的市场中运营的韩国企业集团会增加其环境、社会和治理活动。具体来说，韩国企业在 GPR 显著增长期间会战略性地增加其环境、社会和治理活动，以提高其声誉并建立道德资本。

引用次数: 0

Oil shocks and the transmission of higher-moment information in US industry: Evidence from an asymmetric puzzle 石油冲击与美国工业中高时刻信息的传播：来自不对称难题的证据

IF 6.3 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2024-11-01 DOI: 10.1016/j.bir.2024.07.005

Muhammad Abubakr Naeem , Raazia Gul , Ahmet Faruk Aysan , Umar Kayani

Using a cross-quantilogram approach, this study analyzes the transmission of higher-moment information across US industries with high-frequency (1-min) data. We investigate the effects of oil demand and supply shocks on this transmission, revealing that the impact is asymmetric. Specifically, negative oil price shocks amplify the asymmetric transmission of higher-moment information, whereas positive shocks have the opposite effect. The findings highlight the complexity in information transmission dynamics in response to oil price fluctuations, highlighting the need for policy makers and investors to account for these nuances when assessing risk and making decisions. The results emphasize the critical role of the direction and magnitude of oil prices in shaping the information landscape across industries.

本研究采用交叉量表法，利用高频（1 分钟）数据分析了高时刻信息在美国各行业间的传递。我们研究了石油供求冲击对这种传递的影响，发现这种影响是不对称的。具体来说，石油价格的负向冲击会放大高频信息的非对称传递，而正向冲击则会产生相反的影响。研究结果凸显了应对石油价格波动的信息传递动态的复杂性，强调决策者和投资者在评估风险和做出决策时需要考虑到这些细微差别。研究结果强调了石油价格的方向和幅度在形成各行业信息格局中的关键作用。

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Borsa Istanbul Review

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀