Relevant bibliographies by topics / Market price prediction opportunities

Contents

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

Academic literature on the topic 'Market price prediction opportunities'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Market price prediction opportunities"

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Adithya, Vishnu. "Stock Market Analysis Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47888.

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Abstract— The stock market price prediction and classification is Stock market price prediction is a common problem in finance that involves using historical data to forecast future prices of stocks or other financial instruments. The goal of stock market price prediction is to identify profitable trading opportunities and make informed investment decisions, to resolve this issues different machine learning algorithm is implemented for predict the model accuracy of stock market price level The problem statement for stock market price prediction involves identifying the factors that influence s
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Purnama, Panji Satria Taqwa Putra. "Optimizing Bitcoin Price Prediction with LSTM: A Comprehensive Study on Feature Engineering and the April 2024 Halving Impact." Elinvo (Electronics, Informatics, and Vocational Education) 9, no. 1 (2024): 165–77. http://dx.doi.org/10.21831/elinvo.v9i1.72518.

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This research aims to develop a Bitcoin price prediction model using machine learning techniques, with a specific focus on Long Short-Term Memory (LSTM) neural networks. The Bitcoin market is characterized by unique features such as high volatility and the influence of various external factors, which differ significantly from traditional financial markets. As such, precise feature engineering is crucial for accurately modelling Bitcoin prices. Utilizing historical Bitcoin price data from 2014 to 2023, this study extensively evaluates LSTM models. The results indicate that LSTM models provide h
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Li, Jingyao. "Comparison of Different Machine Learning Approaches for Forecasting Stock Prices." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 17–23. http://dx.doi.org/10.54097/2re5n809.

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Predicting stock prices is a crucial task that has significant implications for investment decisions, business strategies, and financial market stability. Accurate predictions can help investors make informed decisions, capture opportunities, and minimize risks. Understanding financial markets, economic data, company-specific elements, and a variety of statistical and analytical approaches are all necessary for making accurate stock price predictions. This paper employs three machine learning methods to forecast stock price data from a three-year time series dataset. Stock prediction plays a f
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Shah, Dev, Haruna Isah, and Farhana Zulkernine. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques." International Journal of Financial Studies 7, no. 2 (2019): 26. http://dx.doi.org/10.3390/ijfs7020026.

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Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them. Predicting stock prices is a challenging problem in itself because of the number of variables which are involved. In the short term, the market behaves like a voting machine but in the longer term, it acts like a weighing machine and hence there is scope for predicting the market movements for a longer timeframe. Application of machine learning techniques and other algorithms for stoc
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Et. al., Ai Rosita ,. "Market Price Signal Prediction Based On Deep Learning Algorithm." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 11 (2021): 1051–57. http://dx.doi.org/10.17762/turcomat.v12i11.5995.

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Nowadays, many people are venturing into market trading and investment, thus producing many new traders and investors worldwide. The main goal is to gain profit and prevent loss. Most of them are researching global investment opportunities to learn about the market, especially to predict market prices in the future. However, it will become challenging as the financial indicators are very complicated, and it will require a lot of experience and knowledge. The price movement in the price chart also is hard to be predicted when using a fractal indicator. Recently, machine learning and deep learni
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Plastun, Alex, and Serhii Bashlai. "Volatility explosions and price prediction: case of oil market." Journal of Governance and Regulation 6, no. 2 (2017): 48–60. http://dx.doi.org/10.22495/jgr_v6_i2_p5.

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This paper explores behavior of oil market after volatility explosions (days with abnormally high price volatility). It examines possible price patterns and whether they create exploitable profit opportunities from trading. A number of statistical tests both parametrical (t-test, ANOVA, regression analysis with dummy variables) and non-parametrical (Mann–Whitney U test) confirm presence of price patterns after volatility explosions: the next day price changes in both directions are bigger than after “normal” days. Oil prices (case of Brent) for the period from January 2000 till the end of 2016
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Islam, Md Zahidul, Md Shahidul Islam, Md Abdullah Al Montaser, et al. "EVALUATING THE EFFECTIVENESS OF MACHINE LEARNING ALGORITHMS IN PREDICTING CRYPTOCURRENCY PRICES UNDER MARKET VOLATILITY: A STUDY BASED ON THE USA FINANCIAL MARKET." American Journal of Management and Economics Innovations 06, no. 12 (2024): 15–38. https://doi.org/10.37547/tajmei/volume06issue12-03.

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The cryptocurrency market is one of the most dynamic and volatile markets in the world's financial ecosystem, and investment landscapes in the US financial market have changed so much. In slightly over a decade, cryptocurrencies have moved from niche digital assets to mainstream investment opportunities such as Bitcoin, Ethereum, and many others. The prime objective of this research project was to investigate the effectiveness of various machine learning algorithms in the prediction of cryptocurrency prices within the volatile US financial market. This research pinpointed which Machine Learnin
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Chen, Weihang. "Application of Market Cycle Analysis and LSTM in Prediction of Stock Price Movements." BCP Business & Management 38 (March 2, 2023): 856–61. http://dx.doi.org/10.54691/bcpbm.v38i.3787.

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The stock market prediction has been carried out by several ways in data science using deep learning approaches to capture profitable trading opportunities and making the trading plans. However, it is widely believed there are two main issues involved in it, i.e., efficient market hypothesis and low information noise ratio. Therefore, a prediction based model will be affected by noises thus hard to produce a prediction. In this paper, two methods will be presented for forecasting stock future performance. To be specific, LSTM (long-short time memory) and cycle analysis are implemented to predi
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Swamy, J. Kumara, and Navya V K. "Deep Learning Based Bitcoin Price Forecasing Using LSTM." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1094–99. http://dx.doi.org/10.22214/ijraset.2023.49601.

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Abstract: Bitcoin is one of the most popular and valuable cryptocurrency in the current financial market, attracting traders for investment and thereby opening new research opportunities for researchers. Countless research works have been performed on Bitcoin price prediction with different machine learning prediction algorithms. For the research: relevant features are taken from the dataset having strong correlation with Bitcoin prices and random data chunks are then selected to train and test the model. The random data which has been selected for model training, may cause unfitting outcomes
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Mao, Jiayi, and Zhiyong Wang. "Deep Learning-Based Stock Price Prediction Using LSTM Model." Proceedings of Business and Economic Studies 7, no. 5 (2024): 176–85. http://dx.doi.org/10.26689/pbes.v7i5.8611.

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The stock market is a vital component of the broader financial system, with its dynamics closely linked to economic growth. The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets. By examining historical transaction data, latent opportunities for profit can be uncovered, providing valuable insights for both institutional and individual investors to make more informed decisions. This study focuses on analyzing historical transaction data from four banks to predict closing price trends. Various models, including decision tree
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Dissertations / Theses on the topic "Market price prediction opportunities"

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Li, Heng S. M. Massachusetts Institute of Technology. "A price prediction method In real estate market." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/103843.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 71-75).<br>Current housing price prediction usually employs hedonic or repeat-sales models. The objective is to build a statistical model which is more focused on statistic methods. Neither ordinary nor regularized regression model haven been applied to the field of real estate, even though they are rather well-known statistical procedures. This thesis concludes lots of ordinary and regularized regre
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Karlsson, Nils. "Comparison of linear regression and neural networks for stock price prediction." Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445237.

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Stock market prediction has been a hot topic lately due to advances in computer technology and economics. One economic theory, called Efficient Market Hypothesis (EMH), states that all known information is already factored into the prices which makes it impossible to predict the stock market. Despite the EMH, many researchers have been successful in predicting the stock market using neural networks on historical data. This thesis investigates stock prediction using both linear regression and neural networks (NN), with a twist. The inputs to the proposed methods are a number of profit predictio
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Le, Minxian. "Prediction of large price changes in the energy market using extreme value statistics." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14146.

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In this project we have first and foremost been comparing the performance of the ACER method with the POT method in the prediction of extreme values from the heavy tailed distributions; especially for data from the energy markets. The energy market is an exciting dynamic market where small singularities can make large differences in the price. Therefore it is very important and challenging to analyse and make predictions in this market. We have also analysed a dataset which is not from the energy market, to compare and see the main differences between the two markets. We have also taken in con
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Wilkens, Carl. "Auri sacra fames : interest rates : prediction, jumps and the market price of risk /." Stockholm : Department of Economics, Stockholm University, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-710.

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Forman, Marcus R., and John M. Wear. "Putting a Price on Strategy: Implementing a Prediction Market in a Modern Military Unit." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/7341.

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Prediction markets are speculative markets created for aggregating relevant information on some measurable future event. Simply put, prediction markets ask participants to trade ideas as stocks. The “market price” of a particular idea or contract can then be interpreted as the probability that an event will occur, or as a feedback mechanism regarding how well some course of action is working. The application and utility of prediction markets to military strategy and decision-making has yet to be adequately tested in any real or empirical way. This thesis seeks to understand the conditions unde
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Burgard, Andrew. "Can Business News Provide Insight into a Stock’s Future Price Performance?" Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1673.

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Mutual funds and money managers have recently come under fire for their inability to beat market level returns since the Great Recession. With the recent trend towards passive money management through ETFs and other market-based securities, many investors have come to doubt whether above market returns are realizable in today’s economic climate. This paper examines whether business news has any predictable impact on stock price. Specifically, the paper explores the impact of analyst reports, mergers & acquisition news, legal affairs, insider buying and selling and changes to executive leadersh
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Candela, Garza Eduardo. "Revenue optimization for a hotel property with different market segments : demand prediction, price selection and capacity allocation." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113433.

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Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 53-55).<br>We present our work with a hotel company as an example of how machine learning techniques can be used to improve the demand predictions of a hotel property, as well as its pricing and capacity allocation decisions. Firs
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Yeoh, Daniel Ghee Chong, and danielyeoh@cimb com my. "An Empirical Examination of Physical Asset Expenditure Announcements in Australia: Growth Opportunities, Free Cash Flow and Capital Market Monitoring." The Australian National University. Commerce, 2001. http://thesis.anu.edu.au./public/adt-ANU20010702.160428.

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This thesis examines the stock market price variations associated with physical asset expenditure announcements in Australia. With the exception of the study of Chen and Ho (1997) in Singapore, most capital expenditure studies in other markets investigate the announcement effects associated with changes in budgeted capital expenditures. The fact that there is almost never any firm level capital budget announcement in Australia presents a unique opportunity to examine individual physical asset expenditure announcements. ¶ Three primary hypotheses pertaining to growth opportunities, free cash
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Li, Qi. "Application of Improved Feature Selection Algorithm in SVM Based Market Trend Prediction Model." Thesis, Portland State University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10979352.

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<p> In this study, a <b>Prediction Accuracy Based Hill Climbing Feature Selection Algorithm</b> <b>(AHCFS)</b> is created and compared with an <b>Error Rate Based Sequential Feature Selection Algorithm</b> <b> (ERFS)</b> which is an existing Matlab algorithm. The goal of the study is to create a new piece of an algorithm that has potential to outperform the existing Matlab sequential feature selection algorithm in predicting the movement of S&amp;P 500 (</p><p>GSPC) prices under certain circumstances. The twoalgorithms are tested based on historical data of </p><p>GSPC, and <b>SupportVector Ma
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Rosenius, Niklas, and Gustav Sjöholm. "Arbitrage opportunities on the OMXS : How to capitalize on the ex-dividend effect." Thesis, Umeå universitet, Företagsekonomi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-81173.

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Investors are continuously looking to increase the return on their investments. In an ideal world investors want to increase there return and outperform the market. Theory states that it is impossible to do so without increasing your risk. Arbitrage is a concept where investors are able to generate risk-free returns exceeding the market. Dividend is a common tool for publicly listed firms when rewarding their shareholders. On ex- dividend day, the day after the dividend payout, the stock price should according to theory decrease in order for the valuation of the stock to be held constant. In o
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Books on the topic "Market price prediction opportunities"

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Vrontamitis, Michael G. International price discrimination in the market for new cars: Do the opportunities exist between France, Germany, the UK and the US? typescript, 1995.

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Stevens, Leigh. Essential technical analysis: Tools and techniques to spot market trends. Wiley, 2002.

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United States. Congress. Senate. Committee on the Judiciary. Raising tobacco prices: New opportunities for the black market? : hearings before the Committee on the Judiciary, United States Senate, One Hundred Fifth Congress, second session, on examining the impact on the tobacco industry of proposed legislation relating to the increase in the price of tobacco products, focusing on whether new opportunities for the black market would be prevalent if tobacco prices rise, April 30, May 12, and May 13, 1998. U.S. G.P.O., 1999.

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MAHESHA, Dr C. R., Dr S. BASKARAN, Dr RAJU T N, and Smt SUPRABHA R. SALES AND DISTRIBUTION MANAGEMENT. KAAV PUBLICATIONS, DELHI, INDIA, 2022. http://dx.doi.org/10.52458/9789391842420.2022.tb.

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Business leaders know that accurate sales &amp; distribution management is a critical organizational capability. Proper sales management is predicting the future, and the list of what needs to be predicted to run a world-class organization and its supply chain is virtually endless. Accurate sales management and distribution system is essential for identifying new market opportunities, forecasting risks, events, supply chain disruptions, innovation, competition, market growth and trends. It also includes the ability to conduct 'what-if' analysis to understand the tradeoff implications of decisi
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Make A Fortune From The Biggest Market Opportunities In Us History A Guide To The 7 Greatest Bargains From Main Street To Wall Street. Avery Publishing Group, 2010.

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Stevens, Leigh. Essential Technical Analysis: Tools and Techniques to Spot Market Trends. Wiley & Sons, Incorporated, John, 2007.

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Essential technical analysis: Tools and techniques to spot market trends. Wiley, 2002.

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Essential Technical Analysis: Tools and Techniques to Spot Market Trends. Wiley & Sons, Incorporated, John, 2002.

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Stevens, Leigh. Essential Technical Analysis. Wiley & Sons, Incorporated, John, 2002.

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Battilossi, Stefano, Alfredo Gigliobianco, Giuseppe Marinelli, and With The Cooperation Of Sandra Natoli and Ivan Triglia. Resource Allocation by the Banking System. Edited by Gianni Toniolo. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199936694.013.0017.

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In Italy's bank-oriented financial system, bank credit is the most important source of external finance for firms. The allocative efficiency of banks is therefore a critical element underlying the overall performance of the economy. This chapter focuses on credit allocation across industrial sectors with different growth opportunities, as revealed by stock market data. We constructed a unique database which includes annual data on bank credit to different sectors and data on listed firms from 1948 to 2009. We assume that average sectoral price/earnings ratios are a proxy for growth opportuniti
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Book chapters on the topic "Market price prediction opportunities"

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Singh, Satish Kumar, and Sheeba Praveen. "Evaluation Metrices in Stock Market Price Prediction." In Challenges and Opportunities for Innovation in India. CRC Press, 2024. https://doi.org/10.1201/9781003606260-35.

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Khekare, Ganesh, Anil V. Turukmane, Chetan Dhule, Pooja Sharma, and Lokesh Kumar Bramhane. "Experimental Performance Analysis of Machine Learning Algorithms." 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_104.

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AbstractMachine Learning models and algorithms have become quite common these days. Deep Learning and Machine Learning algorithms are utilized in various projects, and now, it has opened the door to several opportunities in various fields of research and business. However, identifying the appropriate algorithm for a particular program has always been an enigma, and that necessitates to be solved ere the development of any machine learning system. Let’s take the example of the Stock Price Prediction system, it is used to identify the future asset prediction of a industry or other financial aspe
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Pardeshi, Karan, Sukhpal Singh Gill, and Ahmed M. Abdelmoniem. "Stock Market Price Prediction." In Applications of AI for Interdisciplinary Research. CRC Press, 2024. http://dx.doi.org/10.1201/9781003467199-11.

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Pawar, Kriti, Raj Srujan Jalem, and Vivek Tiwari. "Stock Market Price Prediction Using LSTM RNN." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2285-3_58.

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Sasi Kiran, J., P. Dhana Lakshmi, Naheed Sultana, G. Naga Rama Devi, Suwarna Gothane, and K. Reddy Madhavi. "Stock Market Price Prediction Using Sentiment Analysis." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0644-0_24.

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Barik, Rhada, Amine Baina, and Mostafa Bellafkih. "The Prediction Stock Market Price Using LSTM." In Lecture Notes on Data Engineering and Communications Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15191-0_42.

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Anubha, Kaustubh Tripathi, Kshitiz Kumar, and Gopesh Khandelwal. "Onion Price Prediction for the Market of Kayamkulam." In Data Analytics and Management. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8335-3_8.

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Agilandeeswari, L., R. Srikanth, R. Elamaran, and K. Muralibabu. "Stock Market Price Trend Prediction – A Comprehensive Review." In Intelligent Systems Design and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35501-1_48.

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Nuseir, Mohammed T., Iman Akour, Muhammad Turki Alshurideh, Barween Al Kurdi, Haitham M. Alzoubi, and Ahmad Qasim Mohammad AlHamad. "Stock Market Price Prediction Using Machine Learning Techniques." In Studies in Big Data. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-31801-6_20.

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Bhagirath, Neetu Mittal, and Sushil Kumar. "Online Resale Bike Price Prediction in Indian Market." In Innovations in Cyber Physical Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4149-7_13.

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Conference papers on the topic "Market price prediction opportunities"

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S, Rahul, Shrinivas V. N, Brindha G, and Karthikeyan M. V. "Stock Market Analysis and Price Prediction." In 2025 International Conference on Frontier Technologies and Solutions (ICFTS). IEEE, 2025. https://doi.org/10.1109/icfts62006.2025.11031782.

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Mallam, Madhavi, M. K. Linga Murthy, Tatikonda Sri Devi, Jami Venkata Suman, and Subba Rao Polamuri. "Stock Market Price Prediction Using Machine Learning." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690737.

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Abivarshini, R., and K. France. "Stock Market Price Prediction Using Deep Learning." In 2025 3rd International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2025. https://doi.org/10.1109/icici65870.2025.11069794.

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Modia, Vinni, Nandini Agrawal, Neetu Sardana, and Ishika Jain. "Tweeting the Market: Leveraging LSTM for Stock Price Prediction." In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). IEEE, 2024. https://doi.org/10.1109/icaiccit64383.2024.10912372.

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Saiyyad, Arshiya, Snehlata Wankhade, Apeksha Sakhare, Purva Kale, Grishma Yenchilwar, and Pranali Sharma. "Stock Price Prediction for Stock Market Forecasting using Machine Learning." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11070798.

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Phatangare, Sheetal, Ayush Laddha, Shreya Bambal, Bharati Borhade, and Prasanna Atram. "A Data-Driven Approach to Crop Yield and Market Price Prediction." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714807.

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K, Suresh, Aritra Sarkar, Anjali Rai, Apoorva Vasishtha, Tyrell Fernandes, and Cecil Donald A. "Analyzing Market Factors for Stock Price Prediction using Deep Learning Techniques." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714754.

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Govindasamy, P., G. V. Radhakrishnan, and Uma Shankar. "High-Frequency Stock Market Price Prediction Using Blockchain and Deep Learning." In 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT). IEEE, 2025. https://doi.org/10.1109/ce2ct64011.2025.10941286.

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Mishra, Nitin, Mohammad Anzar, Saksham Pandey, and Santosh Mishra. "A Review on Stock Market Trends and Stocks Price Prediction Using Sentiment Analysis and Market Data." In 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI). IEEE, 2025. https://doi.org/10.1109/iccsai64074.2025.11063888.

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Durga, G. Lakshmi, A. Dhana Lakshmi, S. Pavitra, T. T. N. Thanuja, and P. Leelavathi. "Deep Learning Approaches for High-Frequency Bitcoin Trading and Market Price Prediction." In 2025 Fourth International Conference on Smart Technologies, Communication and Robotics (STCR). IEEE, 2025. https://doi.org/10.1109/stcr62650.2025.11019215.

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Reports on the topic "Market price prediction opportunities"

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Dassanayake, Wajira, Chandimal Jayawardena, Iman Ardekani, and Hamid Sharifzadeh. Models Applied in Stock Market Prediction: A Literature Survey. Unitec ePress, 2019. http://dx.doi.org/10.34074/ocds.12019.

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Stock market prices are intrinsically dynamic, volatile, highly sensitive, nonparametric, nonlinear, and chaotic in nature, as they are influenced by a myriad of interrelated factors. As such, stock market time series prediction is complex and challenging. Many researchers have been attempting to predict stock market price movements using various techniques and different methodological approaches. Recent literature confirms that hybrid models, integrating linear and non-linear functions or statistical and learning models, are better suited for training, prediction, and generalisation performan
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Casado, Alejandro, and David Martínez-Miera. Local lending specialization and monetary policy. Banco de España, 2024. http://dx.doi.org/10.53479/37912.

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We provide evidence that bank loan supply reactions to monetary policy changes are market-specific, emphasizing the importance of banks’ local specialization. We analyze the U.S. mortgage market and find that when monetary policy eases, banks increase new mortgage lending growth more in markets in which they are geographically specialized relative to other markets and banks. This holds after controlling for local lending opportunities and (unobservable) bank differences. Further empirical findings, supported by a simple model, suggest that banks face market-specific differences in lending adva
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3

Derbentsev, V., A. Ganchuk, and Володимир Миколайович Соловйов. Cross correlations and multifractal properties of Ukraine stock market. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1117.

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Recently the statistical characterizations of financial markets based on physics concepts and methods attract considerable attentions. The correlation matrix formalism and concept of multifractality are used to study temporal aspects of the Ukraine Stock Market evolution. Random matrix theory (RMT) is carried out using daily returns of 431 stocks extracted from database time series of prices the First Stock Trade System index (www.kinto.com) for the ten-year period 1997-2006. We find that a majority of the eigenvalues of C fall within the RMT bounds for the eigenvalues of random correlation matr
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4

Rogers, Roger, Dillon Clarke, and Mark D. Wenner. Guyana's PetroCaribe Rice Compensation Scheme Has Ended: Assessment and Policy Implications. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0009276.

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The government of Guyana signed a rice compensation agreement with Venezuela in 2009 wherein Guyanese rice exports were accepted in partial payment for imports of Venezuelan oil. The agreement ended in November 2015 and was not renewed for 2016. The scheme had provided stimulus to the Guyanese rice sector, resulting in higher levels of investments in improved inputs and machinery, an expansion in area cultivated, higher levels of outputs, higher levels of exports, and increased employment. The main incentive was the payment of a market premium, averaging 20 percent greater than world price. De
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5

Lunn, Pete, Marek Bohacek, Jason Somerville, Áine Ní Choisdealbha, and Féidhlim McGowan. PRICE Lab: An Investigation of Consumers’ Capabilities with Complex Products. ESRI, 2016. https://doi.org/10.26504/bkmnext306.

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Executive Summary This report describes a series of experiments carried out by PRICE Lab, a research programme at the Economic and Social Research Institute (ESRI) jointly funded by the Central Bank of Ireland, the Commission for Energy Regulation, the Competition and Consumer Protection Commission and the Commission for Communications Regulation. The experiments were conducted with samples of Irish consumers aged 18-70 years and were designed to answer the following general research question: At what point do products become too complex for consumers to choose accurately between the good ones
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6

Dzebo, Adis, and Kevin M. Adams. The coffee supply chain illustrates transboundary climate risks: Insights on governance pathways. Stockholm Environment Institute, 2022. http://dx.doi.org/10.51414/sei2022.002.

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The interconnections between countries in a globalizing world continue to deepen and are central to the modern international economy. Yet, governance efforts to build resilience to the adverse risks and impacts of climate change are highly fragmented and have not sufficiently focused on these international dimensions. Relationships between people, ecosystems and economies across borders change the scope and nature of the climate adaptation challenge and generate climate risks that are transboundary (Challinor et al., 2017). Climate impacts in one country can create risks and opportunities – an
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