Relevant bibliographies by topics / Long Short-Term Memory and Financial Stability

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Long Short-Term Memory and Financial Stability'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Long Short-Term Memory and Financial Stability"

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Poernomo, Ayu. "Rupiah Exchange Rate Prediction with Long Short-Term Memory Algorithm." Syntax Literate ; Jurnal Ilmiah Indonesia 10, no. 1 (2025): 122–30. https://doi.org/10.36418/syntax-literate.v10i1.55824.

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The fluctuation of the Rupiah exchange rate against foreign currencies in Asia presents a significant challenge in maintaining Indonesia’s economic stability. This study aims to forecast Rupiah exchange rates using the Long Short-Term Memory (LSTM) algorithm. Weekly exchange rate data from 2020 to 2024 were analyzed using a machine learning approach. The process involved data normalization, model training, and evaluation using Mean Absolute Per- centage Error (MAPE) and R-Squared. The results indicate that the LSTM model effectively captures non-linear patterns in time series data with high ac
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Malaikah, Hunida, and Jawaher Faisal Alabdali. "Analysis of Noise on Ordinary and Fractional-Order Financial Systems." Fractal and Fractional 9, no. 5 (2025): 316. https://doi.org/10.3390/fractalfract9050316.

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This study investigated the influence of stochastic fluctuations on financial system stability by analyzing both ordinary and fractional-order financial models under noise. The ordinary financial system experiences perturbations due to bounded random disturbances, whereas the fractional-order counterpart models memory-dependent behaviors by incorporating fractional Gaussian noise (FGN) characterized by a Hurst parameter that governs long-term correlations. This study used data generated through MATLAB simulations based on standard financial models from the literature. Numerical simulations com
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Kumari, Sweta, Naveen Kumar V., Rakshi Gupta, and Pankhuri Agarwal. "An innovative machine learning algorithm-based approach to financial forecasting for business management." Multidisciplinary Science Journal 6 (July 3, 2024): 2024ss0405. http://dx.doi.org/10.31893/multiscience.2024ss0405.

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A crucial component of company management is financial forecasting, which enables firms to deploy resources wisely and make educated decisions. Traditional forecasting techniques frequently rely on statistical models and historical data, which could not adequately account for the complexity of contemporary financial markets. This study uses cutting-edge machine learning algorithms to provide a novel method for financial forecasting. This study proposes a strategy for improving the precision and stability of financial forecasts by harnessing the power of machine learning and financial data. We
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Wang, Shihui. "A Study of Crude Oil Price Forecasting Based on Long Short-Term Memory Model." Advances in Economics, Management and Political Sciences 99, no. 1 (2024): 93–97. http://dx.doi.org/10.54254/2754-1169/99/2024ox0207.

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In industrial production, crude oil prices play a pivotal role, influencing economic stability due to their propensity to induce fluctuations. Predicting these prices accurately is thus a crucial task in economics. This study addresses this challenge by employing a Long Short-Term Memory (LSTM) model, trained on data spanning January 2005 to January 2019, to forecast crude oil prices. Compared against expectations from economists, financial markets, and policymakers, the LSTM model demonstrates robust fitting and reliable predictive capability across various time frames. Notably, it outperform
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Olaniyan, Julius, Deborah Olaniyan, Ibidun Christiana Obagbuwa, Bukohwo Michael Esiefarienrhe, Ayodele A. Adebiyi, and Olorunfemi Paul Bernard. "Intelligent Financial Forecasting with Granger Causality and Correlation Analysis Using Bayesian Optimization and Long Short-Term Memory." Electronics 13, no. 22 (2024): 4408. http://dx.doi.org/10.3390/electronics13224408.

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Financial forecasting plays a critical role in decision-making across various economic sectors, aiming to predict market dynamics and economic indicators through the analysis of historical data. This study addresses the challenges posed by traditional forecasting methods, which often struggle to capture the complexities of financial data, leading to suboptimal predictions. To overcome these limitations, this research proposes a hybrid forecasting model that integrates Bayesian optimization with Long Short-Term Memory (LSTM) networks. The primary objective is to enhance the accuracy of market t
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Tang, Qi, Ruchen Shi, Tongmei Fan, Yidan Ma, and Jingyan Huang. "Prediction of Financial Time Series Based on LSTM Using Wavelet Transform and Singular Spectrum Analysis." Mathematical Problems in Engineering 2021 (June 8, 2021): 1–13. http://dx.doi.org/10.1155/2021/9942410.

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In order to further overcome the difficulties of the existing models in dealing with the nonstationary and nonlinear characteristics of high-frequency financial time series data, especially their weak generalization ability, this paper proposes an ensemble method based on data denoising methods, including the wavelet transform (WT) and singular spectrum analysis (SSA), and long-term short-term memory neural network (LSTM) to build a data prediction model. The financial time series is decomposed and reconstructed by WT and SSA to denoise. Under the condition of denoising, the smooth sequence wi
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Ez-zaiym, Mustapha, Yassine Senhaji, Meriem Rachid, Karim El Moutaouakil, and Vasile Palade. "Fractional Optimizers for LSTM Networks in Financial Time Series Forecasting." Mathematics 13, no. 13 (2025): 2068. https://doi.org/10.3390/math13132068.

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This study investigates the theoretical foundations and practical advantages of fractional-order optimization in computational machine learning, with a particular focus on stock price forecasting using long short-term memory (LSTM) networks. We extend several widely used optimization algorithms—including Adam, RMSprop, SGD, Adadelta, FTRL, Adamax, and Adagrad—by incorporating fractional derivatives into their update rules. This novel approach leverages the memory-retentive properties of fractional calculus to improve convergence behavior and model efficiency. Our experimental analysis evaluate
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Hudzaifa, Ashilla Maula, Valerie Vincent Yang, and Defi Yusti Faidah. "THE IMPACT OF THE PRESIDENTIAL ELECTION ON IDX COMPOSITE PREDICTIONS USING LONG SHORT TERM MEMORY." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 4 (2024): 2397–412. http://dx.doi.org/10.30598/barekengvol18iss4pp2397-2412.

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An analysis of the performance of Indonesia's capital market, or Indonesia Stock Exchange (IDX), shows significant growth in recent years, with market capitalization increasing dramatically from IDR 679.95 trillion in 2004 to IDR 11,674.06 trillion by 2023. The IDX plays an important role in the Indonesian economy by facilitating capital formation and providing opportunities for investors to diversify their portfolios. However, the capital market is vulnerable to political events, such as presidential elections, which can affect national stability and economic performance. An analysis of the s
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Liu, Yezhen, Xilong Yu, Yanhua Wu, and Shuhong Song. "Forecasting Variation Trends of Stocks via Multiscale Feature Fusion and Long Short-Term Memory Learning." Scientific Programming 2021 (September 21, 2021): 1–9. http://dx.doi.org/10.1155/2021/5113151.

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Forecasting stock price trends accurately appears a huge challenge because the environment of stock markets is extremely stochastic and complicated. This challenge persistently motivates us to seek reliable pathways to guide stock trading. While the Long Short-Term Memory (LSTM) network has the dedicated gate structure quite suitable for the prediction based on contextual features, we propose a novel LSTM-based model. Also, we devise a multiscale convolutional feature fusion mechanism for the model to extensively exploit the contextual relationships hidden in consecutive time steps. The signif
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Bouslimi, Jihen, Sahbi Boubaker, and Kais Tissaoui. "Forecasting of Cryptocurrency Price and Financial Stability: Fresh Insights based on Big Data Analytics and Deep Learning Artificial Intelligence Techniques." Engineering, Technology & Applied Science Research 14, no. 3 (2024): 14162–69. http://dx.doi.org/10.48084/etasr.7096.

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This paper evaluates the performance of the Long Short-Term Memory (LSTM) deep learning algorithm in forecasting Bitcoin and Ethereum prices during the COVID-19 epidemic, using their high-frequency price information, ranging from December 31, 2019, to December 31, 2020. Deep learning (DL) techniques, which can withstand stylized facts, such as non-linearity and long-term memory in high-frequency data, were utilized in this paper. The LSTM algorithm was employed due to its ability to perform well with time series data by reducing fading gradients and reliance over time. The obtained empirical r
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Dissertations / Theses on the topic "Long Short-Term Memory and Financial Stability"

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Stark, Love. "Outlier detection with ensembled LSTM auto-encoders on PCA transformed financial data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296161.

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Financial institutions today generate a large amount of data, data that can contain interesting information to investigate to further the economic growth of said institution. There exists an interest in analyzing these points of information, especially if they are anomalous from the normal day-to-day work. However, to find these outliers is not an easy task and not possible to do manually due to the massive amounts of data being generated daily. Previous work to solve this has explored the usage of machine learning to find outliers in these financial datasets. Previous studies have shown that
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Bertani, Federico. "Deep Learning methods for Portfolio Optimization." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24245/.

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Portfolio optimization is one of the most studied fields that have been researched with machine learning approaches because of its inherent demand for forecasting future market properties. In this thesis, it is shown how one can use deep neural networks with historical returns to do risk adjusted asset allocation. Unlike previous studies which set as target variable asset prices, the variable to predict here is represented by the best asset allocation strategy. Experiments performed on a time period of seven years show that temporal convolutional networks are superior to long short term memory
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Dametto, Ronaldo César. "Estudo da aplicação de redes neurais artificiais para predição de séries temporais financeiras." Universidade Estadual Paulista (UNESP), 2018. http://hdl.handle.net/11449/157058.

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Submitted by Ronaldo Cesar Dametto (rdametto@uol.com.br) on 2018-09-18T19:17:34Z No. of bitstreams: 1 Dissertação_Completa_Final.pdf: 2885777 bytes, checksum: 05b2d5417efbec72f927cf8a62eef3fb (MD5)<br>Approved for entry into archive by Lucilene Cordeiro da Silva Messias null (lubiblio@bauru.unesp.br) on 2018-09-20T12:19:07Z (GMT) No. of bitstreams: 1 dametto_rc_me_bauru.pdf: 2877027 bytes, checksum: cee33d724090a01372e1292109af2ce9 (MD5)<br>Made available in DSpace on 2018-09-20T12:19:07Z (GMT). No. of bitstreams: 1 dametto_rc_me_bauru.pdf: 2877027 bytes, checksum: cee33d724090a01372e12921
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Huang, Kuo-Chuan, and 黃國銓. "Bidirectional Long Short-Term Memory Semantic Judgment Model of Taiwan Financial News." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/phjm4r.

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碩士<br>輔仁大學<br>統計資訊學系應用統計碩士班<br>106<br>Financial news is an important reference source for Taiwan stock investors. With the development of technology, the Intertnet has become one of the main ways for people to obtain information. And there are a lot of information presented in text on the Internet. All investors concern about how to quickly and effectively understand the information of the financial news on the internet. The characteristics of bidirectional long short-term memory language model is that it can provide additional context to the network and results in fuller learning on sequenc
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LIU, CHIAO-JOU, and 劉巧柔. "Information Content of Financial Analysts’ Earnings Forecasts - Using Long Short-Term Memory Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/u7m3j5.

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碩士<br>輔仁大學<br>會計學系碩士班<br>107<br>This study aims to provide empirical evidence on the information content of analysts’ earnings forecasts from the perspective of big data analysis. When in-ves-tigating the determinants of future earnings, traditional empirical studies usually select the predictors on the basis of the findings of previous studies. The findings of previous studies provide solid foundations for adopting some specific items as determinants. However, a concern for omitted variables exists in tradi-tional re-search design because there are many financial variables reported in financi
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Liao, Liang-Wei, and 廖亮瑋. "Exchange Rate Forecasting using Long Short Term Memory Networks — Considering Economic Variables and Financial News." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/3ev9a6.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>107<br>With the collapse of fixed exchange rate system, the power of the government intervention to control the exchange rate diminished and the financial market has become a free market. Taiwan also plays an important role in the liberalization of international trade. Since frequent trading activities and the investment risk usually come hand in hand, the government and the enterprises will suffer severe capital loss if they are not able to forecast exchange rate so as to make a bad investment decision. Therefore, exchange rate forecasting has become an important re
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Cardoso, Luís Gil Miguéns. "Financial time series forecasting using artificial neural networks." Master's thesis, 2020. http://hdl.handle.net/10071/21560.

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This study builds an artificial neural network framework with the use of stacked autoencoders (SAE) to extract deep denoised features, and long short-term memory (LSTM) to generate forecasts for the next-day adjusted closing price of S&P500. Data for seven different stock indices, technical indicators, and macroeconomic variables is used to train three different models: a 'price model' which predicts the next-day price, a 'change model' which predicts the relative change in price, and a ’binary model’ which predicts the probability of a price increase. The models were judged based on predi
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Vieira, Tiago Alexandre Rodrigues de Sousa. "Forecasting sovereign bonds markets using machine learning: forecasting the portuguese government bond using machine learning approach." Master's thesis, 2021. http://hdl.handle.net/10362/112036.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management<br>Financial markets, due to their non-linear, volatile and complex nature turn any type of forecasting into a difficult task, as the classical statistical methods are no longer adequate. Many factors exist that can influence the government bonds yields and how these bonds behave. The consequence of the behaviour of these bonds are extended over geographies and individuals. As the financial markets grow bigger, more inve
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Books on the topic "Long Short-Term Memory and Financial Stability"

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Lampert, Jay. Philosophy of the Short Term. Bloomsbury Publishing Plc, 2023. http://dx.doi.org/10.5040/9781350347991.

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The concept of the short term involves a complex network of quantitative, qualitative, and operational ideas. It is essential everywhere from the ontology of time, to the science of memory, to the preservation of art, to emotional life, to the practice of ethics. But what does the idea of the short term mean? What makes a temporal term short? What makes a time segment terminate? Is the short term a quantitative idea, or a qualitative or functional idea? When is it a good idea to understand events as short term events, and when is it a good idea to make decisions based on the short term? What d
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Shengelia, Revaz. Modern Economics. Universal, Georgia, 2021. http://dx.doi.org/10.36962/rsme012021.

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Economy and mankind are inextricably interlinked. Just as the economy or the production of material wealth is unimaginable without a man, so human existence and development are impossible without the wealth created in the economy. Shortly, both the goal and the means of achieving and realization of the economy are still the human resources. People have long ago noticed that it was the economy that created livelihoods, and the delays in their production led to the catastrophic events such as hunger, poverty, civil wars, social upheavals, revolutions, moral degeneration, and more. Therefore, the
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Book chapters on the topic "Long Short-Term Memory and Financial Stability"

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López, María T., Antonio Fernández-Caballero, Miguel A. Fernández, and Ana E. Delgado. "Sensitivity from Short-Term Memory vs. Stability from Long-Term Memory in Visual Attention Method." In Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499305_46.

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Weng, Yexuan, Guanming Su, Hanyu Chen, and Rong Huang. "Long Short-Term Memory Neural Network for Different Regional Financial Time Series." In Advances in Intelligent Systems Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-742-7_19.

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Lin, Ruibin, Dabin Zhang, Liwen Ling, Junjie Huang, and Guotao Cai. "Transfer Learning Based Long Short-Term Memory Network for Financial Time Series Forecasting." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1645-0_1.

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Ordoñez-Ordoñez, Pablo F., Martha C. Suntaxi Sarango, Cristian Narváez, Maria del Cisne Ruilova Sánchez, and Mario Enrique Cueva-Hurtado. "Deep Learning Model for Forecasting Financial Sales Based on Long Short-Term Memory Networks." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32022-5_46.

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Khouangvichit, Chintana. "The relationship between macroeconomic factors and non-performing loans (NPLs) in Lao PDR." In Green and Digital Transitions. Szegedi Tudományegyetem, 2024. http://dx.doi.org/10.14232/gtk.gdtgiss.2024.10.

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The role of the banking sector in driving economic development cannot be understated. Its stability is a critical factor that sets the pace for economic progress. Among the various indicators of financial stability, non-performing loans (NPLs) held by banks hold particular significance as they reflect asset quality, credit risk, and the efficient allocation of resources to productive sectors. NPLs have indeed been a subject of concern for the banking sector, with their prominence intensifying, especially after the 2008 financial crisis. This study investigates the relationship between Macroeco
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Liu, Lei, Zheng Pei, Peng Chen, Zhisheng Gao, Zhihao Gan, and Kang Feng. "An Effective GAN-Based Multi-classification Approach for Financial Time Series." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_110.

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AbstractDeep learning has achieved significant success in various applications due to its powerful feature representations of complex data. Financial time series forecasting is no exception. In this work we leverage Generative Adversarial Nets (GAN), which has been extensively studied recently, for the end-to-end multi-classification of financial time series. An improved generative model based on Convolutional Long Short-Term Memory (ConvLSTM) and Multi-Layer Perceptron (MLP) is proposed to effectively capture temporal features and mine the data distribution of volatility trends (short, neutra
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Gu, Tingyun, Qihui Feng, Jianyang Zhu, Long Xiao, and Yan Zhang. "Transient Voltage Stability Assessment of Power System Based on Bidirectional Long Short-Term Memory Network and Attention Mechanism." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4787-3_67.

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Consoli, Sergio, Luca Tiozzo Pezzoli, and Elisa Tosetti. "Information Extraction From the GDELT Database to Analyse EU Sovereign Bond Markets." In Mining Data for Financial Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66981-2_5.

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AbstractIn this contribution we provide an overview of a currently on-going project related to the development of a methodology for building economic and financial indicators capturing investor’s emotions and topics popularity which are useful to analyse the sovereign bond markets of countries in the EU.These alternative indicators are obtained from the Global Data on Events, Location, and Tone (GDELT) database, which is a real-time, open-source, large-scale repository of global human society for open research which monitors worlds broadcast, print, and web news, creating a free open platform
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Yuan, Jianjun, Pengzi Chu, Chunye Huang, Zhe Shen, and Yi Yu. "Study of Health Degree Assessment and Prediction for Axle Counter Equipment in Urban Rail Transit." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-7887-4_89.

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Abstract Axle counter equipment is a key component in urban rail transit signal systems, and its stability has a great impact on safety and operation efficiency. In this paper, we pay attention to the assessment and prediction methods of the health degree for axle counter equipment in urban rail transit and the corresponding maintenance suggestions. Mainly, two prediction methods, the GM (1, 1) algorithm and the long short-term memory (LSTM) network, are used to compare the prediction effect of health degree for axle counter equipment. Based on enough historical information, the LSTM network c
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Nguyen, An Pham Ngoc, Martin Crane, and Marija Bezbradica. "Cryptocurrency Volatility Index: An Efficient Way to Predict the Future CVI." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_28.

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AbstractThe Cryptocurrency Volatility Index (CVI index) has been introduced to estimate the 30-day future volatility of the cryptocurrency market. In this article, we introduce a new Deep Neural Network with an attention mechanism to forecast future values of this index. We then look at the stability and performance of our proposed model against the benchmark models widely used for time series prediction. The results show that our proposed model performs well when compared to popular methods such as traditional Long Short Term Memory, Temporal Convolution Network, and other statistical methods
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Conference papers on the topic "Long Short-Term Memory and Financial Stability"

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Meng, Qingxiao, and Qi Liu. "Corporation Financial Risk Prevention and Control using Long Short-Term Memory Networks." In 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2025. https://doi.org/10.1109/icdcece65353.2025.11035721.

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Kundu, Sourodeep, Nachiketa Tarasia, and Rabindra Kumar Barik. "QLSTM4FM: Quantum Assisted Long Short-Term Memory Framework for Financial Market Trend Forecasting." In 2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT). IEEE, 2024. https://doi.org/10.1109/ic-sit63503.2024.10862667.

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Bi, Chunhui, and Yunlai Wang. "Long Short Term Memory Network (LSTM) Model Based on Neural Networks in Financial Forecasting." In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON). IEEE, 2024. http://dx.doi.org/10.1109/nmitcon62075.2024.10699249.

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Yang, Qiuyu. "Financial Distress Prediction by using Long Short-Term Memory based Adaptive Whale Optimization Algorithm." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934466.

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AL-Attabi, Kassem, Kumar Rethik, Ranjusha J. P, N. Sindhuja, Sanjay Yadav, and B. Shivakalyan. "ML-Based Financial Forecasting in ERP: Improving Budgeting Accuracy with Long Short-Term Memory Networks." In 2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS). IEEE, 2024. http://dx.doi.org/10.1109/iicccs61609.2024.10763845.

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Patel, Ajaykumar, Vijay Ukani, and Priyank Thakkar. "Time Series Forecasting in Financial Market: Long Short-Term Memory (LSTM) Approach for Stock Price Prediction." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752158.

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Du, Chunyu. "Enterprise Financial Risk Prediction using a Hybrid Long Short-Term Memory-Gated Recurrent Unit Deep Learning Approach." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11071009.

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Rathi, Snehal, Vijayshri Khedkar, Kavya Naidu, Bhairavnath Hake, Suyash Phapale, and Vedant Kulkarni. "Predicting Stock Market Trends Using Long Short-Term Memory (LSTM) Networks: A Deep Learning Approach for Financial Time-Series Forecasting." In 2025 3rd International Conference on Smart Systems for applications in Electrical Sciences (ICSSES). IEEE, 2025. https://doi.org/10.1109/icsses64899.2025.11009670.

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Fjellstrom, Carmina. "Long Short-Term Memory Neural Network for Financial Time Series." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020784.

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Wang, Qilin, Chengzong Pang, and Hashim Alnami. "Transient Stability Prediction Based on Long Short-term Memory Network." In 2021 North American Power Symposium (NAPS). IEEE, 2021. http://dx.doi.org/10.1109/naps52732.2021.9654462.

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Reports on the topic "Long Short-Term Memory and Financial Stability"

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Financial Stability Report - Second Semester of 2021. Banco de la República, 2022. http://dx.doi.org/10.32468/rept-estab-fin.sem2.eng-2021.

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Banco de la República’s main objective is to preserve the purchasing power of the currency in coordination with the general economic policy that is intended to stabilize output and employment at long-term sustainable levels. Properly meeting the goal assigned to the Bank by the 1991 Constitution critically depends on preserving financial stability. This is understood to be a general condition in which the financial system assesses and manages the financial risks in a way that facilitates the economy’s performance and efficient allocation of resources while, at the same time, it is able to, on
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