Journal articles on the topic 'Market activity predictions'
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1
Abinzano, Isabel, Luis Muga, and Rafael Santamaria. "Hidden Power of Trading Activity: The FLB in Tennis Betting Exchanges." Journal of Sports Economics 20, no. 2 (September 22, 2017): 261–85. http://dx.doi.org/10.1177/1527002517731875.
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This article examines the impact of trading activity on the favorite–long shot bias (FLB) in tennis betting exchanges, using direct measures such as betting volume, average bet, and standard deviation of the odds. According to predictions based on disagreement models, odds mispricing is positively associated with trading volume but negatively associated with the presence of institutional bettors. The FLB is also positively related to the degree of uncertainty in the market. The existence of two simultaneous markets (a “main” and an “alternative” market) in this specific sports-betting environment has enabled us to observe that the relative amount of attention given to the favorite versus that given to the long shot is positively associated with the FLB. Finally, information is more rapidly incorporated into the odds in the market that receives more attention from bettors, an effect that is intensified by the arbitrage and hedging that occurs between the two markets.
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Jaiswal, Rupashi, Kunal Mahato, Pankaj Kapoor, and Sudipta Basu Pal. "A Comparative Analysis on Stock Price Prediction Model using DEEP LEARNING Technology." American Journal of Electronics & Communication 2, no. 3 (January 3, 2022): 12–19. http://dx.doi.org/10.15864/ajec.2303.
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In today's world, Artificial Intelligence and Deep Learning are getting popular regularly. The various applications areas of artificial intelligence are related to human activity. One of the general application areas of neural networks and artificial intelligence is prediction analysis. In this paper, the authors also have performed one comparative study based on artificial intelligence. Authors have performed stock market predictions using different models. In reality, stock markets are entirely volatile, so there is very much a requirement of good prediction analysis for judging the stocks prices and their ups and downs with time. The stock prices can easily be predicted using machine learning algorithms on data available in financial news, as this data can also change investors' interests. However, traditional prediction methods have become obsolete and do not provide accurate predictions over non-stationary time series data. This paper proposes a stock price prediction method that gives accurate results with the advancements in deep learning technologies.
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Dixit, Nandan, Chirag Patel, Mansi Bhavsar, Saumya Patel, Rakesh Rawal, and Hitesh Solanki. "QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIP (QSAR) STUDY OF LIVER TOXIC DRUGS." International Association of Biologicals and Computational Digest 1, no. 1 (May 2, 2022): 63–71. http://dx.doi.org/10.56588/iabcd.v1i1.17.
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Drug-induced liver injury (DILI) is one of the most severe adverse effects (AEs) causing life-threatening conditions, such as acute liver failure. t has also been recognized as the single most common cause of safety- related post-market withdrawals or warnings Due to the nature and idiosyncrasy of clinical forms of DILI, attempts to develop new predictive approaches to evaluate the risk of a medication being a hepatotoxicant have been difficult. The FDA Adverse Event Reporting System (AERS) provides post-market data illustrating AE morbidity. A quantitative structure –activity relationship (QSAR) model for DILI prediction with satisfactory output is urgently needed. In this study, we documented a high-quality QSAR model for predicting the hepatotoxicity risk of DILI by integrating the use of eight effective classifiers and molecular descriptors given by the VlifeMds program. For the present QSAR study, data set of 99 compounds (withdrawn and approved drugs) collected from different databases were taken. Multiple linear regression and partial least square analysis methods had developed two dimensional QSAR models, and then validated for internal and external predictions. The 2D QSAR model developed was statistically important, and was highly predictive. The validation methods presented essential statistical parameters that proved the model's predictive ability. The developed 2D QSAR model revealed the significance of SsssCE-index, SsOHcount, SsssNcount and SdssPcount descriptors. These findings will prove to be an important guide for furtherdesigning and developing new hepatotoxicity activity.
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Padilla, Washington, Jesús García, and José Molina. "Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets." Sensors 19, no. 2 (January 12, 2019): 286. http://dx.doi.org/10.3390/s19020286.
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In this paper, a monitoring system of agricultural production is modeled as a Data Fusion System (data from local fairs and meteorological data). The proposal considers the particular information of sales in agricultural markets for knowledge extraction about the associations among them. This association knowledge is employed to improve predictions of sales using a spatial prediction technique, as shown with data collected from local markets of the Andean region of Ecuador. The commercial activity in these markets uses Alternative Marketing Circuits (CIALCO). This market platform establishes a direct relationship between producer and consumer prices and promotes direct commercial interaction among family groups. The problem is presented first as a general fusion problem with a network of spatially distributed heterogeneous data sources, and is then applied to the prediction of products sales based on association rules mined in available sales data. First, transactional data is used as the base to extract the best association rules between products sold in different local markets, knowledge that allows the system to gain a significant improvement in prediction accuracy in the spatial region considered.
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TOKMAK, Mahmut. "Stock Price Prediction Using Long-Short-Term Memory Network." Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi 6, no. 2 (September 29, 2022): 309–22. http://dx.doi.org/10.31200/makuubd.1164099.
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One of the most important transactions of the financial system is stock trading. Stock price data is handle as a financial time series. Stock price predictions using time series analysis are the activity of determining the future value of stocks listed on the stock market. Predicting the price of the stock correctly reduces the risk factor in the decisions to be taken by the investors. Therefore, it is an important issue for the investor. However, because there are many variables that affect the stock price, it is a very complex process to predict. Machine learning methods, especially deep learning algorithms, are frequently used in prediction in the field of finance, as in many other fields. In this study, stock price prediction was made using Long-Short-Term Memory networks, which is one of the deep learning methods. Four stocks within the scope of Borsa İstanbul Technology Index were determined and a 2578-day data set was created between 2012 and 2022, and training and testing was carried out with the established model. As a result of the test process, consistent and realistic predictions were obtained.
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Fainmesser, Itay P., and Andrea Galeotti. "The Market for Online Influence." American Economic Journal: Microeconomics 13, no. 4 (November 1, 2021): 332–72. http://dx.doi.org/10.1257/mic.20200050.
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Recent developments in social media have morphed the age-old practice of paying influential individuals for product endorsements into a multibillion dollar industry, extending well beyond celebrity sponsorships. We develop a parsimonious model in which influencers trade off the increased revenue they obtain from paid endorsements with the negative impact that these have on their followers’ engagement and, therefore, on the price influencers receive from marketers. The model provides testable predictions that match suggestive evidence on pricing of paid endorsements, reveals a novel type of inefficiency that emerges in this market, and clarifies the role of search technology and advice transparency in shaping market activity. In particular, we show that recent policies that make paid endorsements more transparent can backfire, whereas an increase in the effectiveness of the search technology that matches followers to influencers has both direct and strategic positive welfare effects. (JEL D83, L82, L86, M31.)
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Quintero, Luis Eduardo, and Paula Restrepo. "Market Access and the Concentration of Economic Activity in a System of Declining Cities." REGION 5, no. 3 (December 28, 2018): 97–109. http://dx.doi.org/10.18335/region.v5i3.223.
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Market access has been widely used as a measure of agglomeration spillovers in models that seek to explain productivity, economic or population growth at the city level. Most results have shown that having higher market access is beneficial to these outcomes. These results, both theoretical and empirical, have been obtained in a context of population growth. This article examines the impact that market access has on a system of cities that has suffered a negative population shock. An extended version of the Brezis and Krugman (1997) model of life cycle of cities predicts that a system of cities experiencing population loss will see a relative reorganization of its population from small to larger cities, and that higher market potential will make this movement stronger. We test these predictions with a comprehensive sample of cities in Eastern Europe and Central Asia. We find that having higher market access - when operating in an environment of population decline - is detrimental to city population growth. This result is robust to different measures of market access that use population. Alternative measures that use economic size rather population are tested, and the result weaker. A possible explanation is that using NLs restricts the sample to only using larger cities.
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Fu, Xianshu, Xiaoping Yu, Zihong Ye, and Haifeng Cui. "Analysis of Antioxidant Activity of Chinese Brown Rice by Fourier-Transformed Near Infrared Spectroscopy and Chemometrics." Journal of Chemistry 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/379327.
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This paper develops a rapid method using near infrared (NIR) spectroscopy for analyzing the antioxidant activity of brown rice as total phenol content (TPC) and radical scavenging activity by DPPH (2,2-diphenyl-2-picrylhydrazyl) expressed as gallic acid equivalent (GAE). Brown rice (n=121) collected from five producing areas was analyzed for TPC and DPPH by reference methods. The NIR reflectance spectra were measured with compact powders of samples and no treatment was used. Full-spectrum partial least squares (FS-PLS) and interval PLS (iPLS) were used as the regression methods to relate the antioxidant activity values to the NIR data. The spectral range of 4800–5600 cm−1plus 6000–6400 cm−1has the best correlation with TPC, while the range of 4400–5200 cm−1plus 6000–6400 cm−1is the most suitable for predicting DPPH. With standard normal variate (SNV) transformation and the selected wavelength ranges, the root mean squared error of prediction (RMSEP) is 0.062 mg GAE g−1for TPC and 0.141 mg GAE g−1for DPPH radical, respectively. The multiple correlation coefficients of predictions for TPC and DPPH are 0.962 and 0.974, respectively. The developed NIR method might have a potential application to quality control of brown rice in the domestic market.
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Godard, John. "Strikes as Collective Voice: A Behavioral Analysis of Strike Activity." ILR Review 46, no. 1 (October 1992): 161–75. http://dx.doi.org/10.1177/001979399204600112.
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This paper outlines a “collective voice” approach for examining the behavioral determinants of variation in strike activity at the organizational level. The author argues that strikes should be viewed primarily as expressions of worker discontent rather than a result of imperfect or asymmetrical information. An analysis of survey data collected from 112 Canadian firms in 1980–81 indicates that managerial practices, operations size and technology, product market structure and conditions, union politics, and various other factors that influence the behavioral context of negotiations are significantly related to days lost due to strike activity. These findings are generally consistent with predictions from the collective voice approach.
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Wu, Ke, Spencer Wheatley, and Didier Sornette. "Classification of cryptocurrency coins and tokens by the dynamics of their market capitalizations." Royal Society Open Science 5, no. 9 (September 2018): 180381. http://dx.doi.org/10.1098/rsos.180381.
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We empirically verify that the market capitalizations of coins and tokens in the cryptocurrency universe follow power-law distributions with significantly different values for the tail exponent falling between 0.5 and 0.7 for coins, and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a simple proportional growth with birth and death model previously employed to describe the size distribution of firms, cities, webpages, etc. We empirically validate the model and its main predictions, in terms of proportional growth (Gibrat's Law) of the coins and tokens. Estimating the main parameters of the model, the theoretical predictions for the power-law exponents of coin and token distributions are in remarkable agreement with the empirical estimations, given the simplicity of the model. Our results clearly characterize coins as being ‘entrenched incumbents’ and tokens as an ‘explosive immature ecosystem’, largely due to massive and exuberant Initial Coin Offering activity in the token space. The theory predicts that the exponent for tokens should converge to 1 in the future, reflecting a more reasonable rate of new entrants associated with genuine technological innovations.
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Miliou, Ioanna, Xinyue Xiong, Salvatore Rinzivillo, Qian Zhang, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi, and Alessandro Vespignani. "Predicting seasonal influenza using supermarket retail records." PLOS Computational Biology 17, no. 7 (July 12, 2021): e1009087. http://dx.doi.org/10.1371/journal.pcbi.1009087.
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Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.
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Sukmaningrum, Puji Sucia, Muhammad Madyan, and Achsania Hendratmi. "REAKSI PASAR SAHAM YANG TERDAFTAR DALAM JAKARTA ISLAMIC INDEX (JII) TERHADAP PENGUMUMAN PENETAPAN GUBERNUR DKI JAKARTA TAHUN 2017." Jurnal Ekonomi dan Bisnis Islam (Journal of Islamic Economics and Business) 5, no. 1 (June 30, 2019): 1. http://dx.doi.org/10.20473/jebis.v5i1.10087.
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The purpose of this research is to analyze the reaction of investors before and after the announcement of the determination of the Governor of DKI Jakarta in 2017 against abnormal return and trading volume of activity. These studies use quantitative methods of event study. Estimation period is 60 days and research period is 10 days before and 10 days after the announcement. the sample of this research is 30 stocks listed on the Jakarta Islamic Index (JII). The results showed no significant difference against AAR and ATVA before and after the announcement. Investor it is possible already to react before the official announcement of the Election Commission (KPU). Investors could do predictions the election results of the Survey or the quick count.Keywords: Market Reaction, Event Study, Abnormal Return, Trading Volume Activity, Islamic Capital Market.
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Jones Ritten, Chian, Christopher Bastian, and Owen Phillips. "The relative effectiveness of law enforcement policies aimed at reducing illegal trade: Evidence from laboratory markets." PLOS ONE 16, no. 11 (November 2, 2021): e0259254. http://dx.doi.org/10.1371/journal.pone.0259254.
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Despite recent emphasis and implementation of national and international anti-money laundering policies, illegal product markets, and their associated illicit profit remain a global problem. In addition to law enforcement aimed at reducing money-laundering, enforcement also takes place during (1) the production (e.g. crop eradication) and (2) sale (e.g. seizure of products during transportation that interrupts buyer and seller transactions) of the illegal product. Since funds for enforcement come from limited budgets, understanding where in this production-trade-laundering cycle law enforcement is most impactful becomes a global question. Using laboratory experimental markets and a seizure rate of 20%, we find that law enforcement focused on seizing laundered profits does little to reduce illegal market activity when compared to no law enforcement, suggesting that focusing law enforcement on money laundering will likely be ineffective at reducing crime. Results further show the amount of illicit trade is nearly 32% lower when law enforcement is focused at the point of sale, and there may be additional economic incentives that reduce illicit trade in the long run when compared to no law enforcement. Enforcement at the point of production also reduces market activity, but not as effectively as enforcement at the point of sale. Lastly, the empirical findings deviate from equilibrium predictions, suggesting law enforcement policy based on theory alone may lead to inefficient allocation of limited law enforcement resources.
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Cobas, Jose A. "Paths to Self-Employment among Immigrants." Sociological Perspectives 29, no. 1 (January 1986): 101–20. http://dx.doi.org/10.2307/1388944.
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Using data collected in a sample of Cuban exiles in Puerto Rico, this article tests hypotheses derived from four explanations of immigrant entrepreneurship, each of which emphasizes one of the following: Business background, labor market disadvantages, sojourning, and participation in the ethnic subeconomy. In addition, the article proposes an extension of the explanations, to wit, that their predictions will vary according to the type of entrepreneurial activity under consideration. Data support three of the interpretations—business background, labor market disadvantages, and participation in the ethnic subeconomy—as well as the extension. However, they reject the sojourning explanation. The implications of these findings and suggestions for further research are discussed.
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Kamal, Imam Mustafa, Hyerim Bae, Sim Sunghyun, and Heesung Yun. "DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index." Applied Sciences 10, no. 4 (February 22, 2020): 1504. http://dx.doi.org/10.3390/app10041504.
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The Baltic Dry Index (BDI) is a commonly utilized indicator of global shipping and trade activity. It influences stakeholders’ and ship-owners’ decisions respecting investments, chartering, operational plans, and export and import activities. Accurate prediction of the BDI is very challenging due to its volatility, non-stationarity, and complexity. To help stakeholders and ship-owners make sound short- and long-term maritime business decisions and avoid market risk, we performed short- and long-term predictions of BDI using an ensemble deep-learning approach. In this study, we propose to apply recurrent neural network models for BDI prediction. The state-of-the-art of sequential deep-learning models such as RNN, LSTM, and GRU are employed to predict one- and multi-step-ahead BDI values. In order to increase the accuracy, we assemble the models. In experiments, we compared our results with those of traditional methods such as ARIMA and MLP. The results showed that our proposed method outperforms ARIMA, MLP, RNN, LSTM, and GRU in both short- and long-term prediction of BDI.
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Canes-Wrone, Brandice, and Jee-Kwang Park. "Elections, Uncertainty and Irreversible Investment." British Journal of Political Science 44, no. 1 (October 31, 2012): 83–106. http://dx.doi.org/10.1017/s000712341200049x.
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This article argues that the policy uncertainty generated by elections encourages private actors to delay investments that entail high costs of reversal, creating pre-election declines in the associated sectors. Moreover, this incentive depends on the competitiveness of the race and the policy differences between the major parties/candidates. These arguments are tested using new survey and housing market data from the United States. The survey analysis assesses whether respondents’ perceptions of presidential candidates’ policy differences increased the likelihood that they would delay certain purchases and actions. The housing market analysis examines whether elections are associated with a pre-election decline in economic activity, and whether any such decline depends on electoral competitiveness. The results support the predictions and cannot be explained by existing theories.
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Dong, Xinyu, Yining He, and Muyi Lei. "Investment Planning Model Based on Quantitative Trading Strategies." BCP Business & Management 19 (May 31, 2022): 227–35. http://dx.doi.org/10.54691/bcpbm.v19i.808.
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Investment, as a financial activity for the general public, has a wide influence on the development of national enterprises. In the research, the main object is Bitcoin and gold, and the investment prediction of short-term investment is made according to the real financial market, and the investment decision prediction model is established by using XGBoost, BP neural network model, entropy value method, coefficient of variation method, linear programming, and value-at-risk model to solve. The topic provides historical data related to bitcoin and gold. First, for the amount of gold and bitcoin increase, with the price deviation rate for data processing. For the amount and increase problem, which is difficult to judge the accuracy, both XGBoost and BP neural network models are used to predict and compare the results, and finally XGBoost, which has higher accuracy, is chosen as the result. After that, the market sentiment is judged according to the obtained data results, and the entropy value method is applied to calculate the weight of relevant indicators to establish the bull and bear market prediction model. Next, the value-at-risk model is applied to establish a risk prediction model. Finally, the predictions for price, market, and risk are combined, and the coefficient of variation method is used to determine the weights of each component to obtain the objective function and design the law of investment behavior as the result of the final prediction model. The initial amount is brought into the model for calculation, and the final return is found to be about $240,000. To assess the sensitivity of the model, three scenarios are discussed and analyzed in terms of changes in transaction costs, gold-only purchases, and bitcoin-only purchases. The three extreme cases are solved by dynamic programming in turn, and it is found that the decision model is very sensitive to changes in transaction costs. And for the case of only enough to buy gold, versus only buying bitcoin, the conclusion finds that the sensitivity is still high, and it can be judged that bitcoin has a high investment potential as an emerging investment product.
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Ivanov, Asen, Dan Levin, and James Peck. "Hindsight, Foresight, and Insight: An Experimental Study of a Small-Market Investment Game with Common and Private Values." American Economic Review 99, no. 4 (August 1, 2009): 1484–507. http://dx.doi.org/10.1257/aer.99.4.1484.
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We experimentally test an endogenous-timing investment model in which subjects privately observe their cost of investing and a signal correlated with the common investment return. Subjects overinvest, relative to Nash. We separately consider whether subjects draw inferences, in hindsight, and use foresight to delay profitable investment and learn from market activity. In contrast to Nash, cursed equilibrium, and level-k predictions, behavior hardly changes across our experimental treatments. Maximum likelihood estimates are inconsistent with belief-based theories. We offer an explanation in terms of boundedly rational rules of thumb, based on insights about the game, which provides a better fit than quantal response equilibrium. (JEL C72, D82, D83, G11)
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Meurers, Martin, and Johannes Moenius. "Market Potential and Fiscal Incentives Influence Firms’ Location Decisions: Evidence From U.S. Counties." Economic Development Quarterly 34, no. 2 (April 29, 2020): 126–39. http://dx.doi.org/10.1177/0891242420919805.
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How important are market potential and fiscal incentives for firms’ location decisions? We estimate the influence of subsidies and tax breaks on the decisions of firms to relocate or to remain in a certain U.S. county using a structural economic geography model developed in Meurers and Moenius (2018). In a panel data set from 1990 to 2016 for almost 3,000 U.S. counties, the authors find a strong and robust impact of economic geography on firms’ location decisions: The closer a county is to market demand and to the supply of inputs, the more firms locate there. As the model predicts, public investment attracts firms while the local tax burden disincentivizes economic activity, although to a lesser extent. Furthermore, in counties that are closer to economic centers, firms respond less to public investment and tax changes than firms in counties far away from centers. These data, therefore, confirm the predictions of the model regarding the potential effectiveness of regional development policies, in particular for investment tax credits, job creation, and training.
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Ahmed, Kamran, Muhammad Nurul Houqe, John Hillier, and Steven Crockett. "Properties of analysts’ consensus cash flow forecasts for Australian firms." Accounting Research Journal 33, no. 1 (January 2, 2020): 128–47. http://dx.doi.org/10.1108/arj-11-2017-0197.
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Purpose The purpose of this paper is to determine the properties of analysts’ cash flows from operations (CFO) forecast generated for Australian listed firms as a productive activity, within the wider processes of financial disclosure in Australia. Design/methodology/approach Two categories of criteria are adopted: first, basic predictive statistical performance relative to a benchmark model and earnings forecasts; and second, relevance for equity pricing, as indicated by the market reaction to cash flow or forecast error reactions. The final sample comprised 2,138 observations between 2001 and 2016 and several regression models are estimated to determine the relative performance and market reaction. Findings Analysts’ consensus cash flow forecasts demonstrate poor predictive performance relative to earnings forecasts. Cash flow forecasts are typically naïve extensions of earnings forecasts. Furthermore, cash flow forecasts appear to be of minimal use for equity market participants in complementing earnings forecasts’ role in informing firms’ equity pricing. Practical implications While analysts’ earnings forecasts are useful for making predictions, the role of analysts’ cash flow forecasts in capital market functional efficiency appears quite limited. Originality/value This study is one of few that examines comparative usefulness of analysts’ earnings and cash flow forecasts and their predictive power using the Australian setting. Additionally, it enriches the sparse international literature on such forecasts.
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Silva, Raí, João Poiani, Ryan Ramos, Josivan Costa, Carlos Silva, Davi Brasil, and Cleydson Santos. "Ligand- and structure-based virtual screening from 16-((diisobutylamino)methyl)-6α-hydroxyivouacapane-7β,17β-lactone a compound with potential anti-prostate cancer activity." Journal of the Serbian Chemical Society 84, no. 2 (2019): 153–74. http://dx.doi.org/10.2298/jsc180129047s.
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Prostate cancer is one of the leading causes of disease and death on the planet. The probable bioactive pose of 16-((diisobutylamino)methyl)- -6?-hydroxyvouacapane-7?,17?-lactone (N,N-DHL), a pivot compound with prostatic anti-cancer activity, was investigated via a semi-empirical method (PM3) and refined with the base set 6-31+G(d,p) calculated in the DFT method at the B3LYP level of theory. This structure was used in ligand-based virtual screening for five commercial compound bases using the software ROCS and EON that selected 2000 per base and another that resulted in 100 per base, respectively. This set was used for pharmacokinetic and toxicological predictions. The molecular overlap index at 50 % steric/electrostatic provided 68 structures that were used for a molecular docking study. The results showed that of 238,922 structures, only eight, 7 (?10.9 kcal mol-1) as the best in the series and 1 (?8.1 kcal mol-1) as less favorable, with others in this range (?2.8 kcal mol-1) with their respective binding affinity: 8 (?8.2 kcal mol-1), 5 (?8.2 kcal mol-1), 4 (?8.3 kcal mol-1), 2 (?8.5 kcal mol-1), 3 (?8.6 kcal mol-1) and 6 (?8.8 kcal mol-1) remaining in the final selection. The predictions for 21 pharmacokinetic properties were within the recommended range, similar to 95 % of the drugs available on the market, with no toxicity warning. The structures showed similarity greater than 75 % to the pivot based on binding affinity and predictions but only the structures 6 and 7 were considered more promising for their potential anti-prostate cancer activity (PC-3).
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Santos-Pinto, Luis. "Human Capital Accumulation and the Evolution of Overconfidence." Games 11, no. 4 (October 22, 2020): 46. http://dx.doi.org/10.3390/g11040046.
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This paper studies the evolution of overconfidence over a cohort’s working life. To do this, the paper incorporates subjective assessments into a continuous time human capital accumulation model with a finite horizon. The main finding is that the processes of human capital accumulation, skill depreciation, and subjective assessments imply that overconfidence first increases and then decreases over the cohort’s working life. In the absence of skill depreciation, overconfidence monotonically increases over the cohort’s working life. The model generates four additional testable predictions. First, everything else equal, overconfidence peaks earlier in activities where skill depreciation is higher. Second, overconfidence is lower in activities where the distribution of income is more dispersed. Third, for a minority of individuals, overconfidence decreases over their working life. Fourth, overconfidence is lower with a higher market discount rate. The paper provides two applications of the model. It shows the model can help make sense of field data on overconfidence, experience, and trading activity in financial markets. The model can also explain experimental data on the evolution of overconfidence among poker and chess players.
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Marszalski, Mariusz. "Speculations on the Future of Economic Models in the Wake of Trans/Posthuman Sentient Evolution in Charles Stross’s SF Novel “Accelerando”." Anglica Wratislaviensia 59 (December 28, 2021): 11–19. http://dx.doi.org/10.19195/0301-7966.59.1.
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Economy, understood as a domain of production, distribution and consumption of goods and services, has been unquestionably comprehended as a social activity, the purpose of which is to satisfy first of all vital material, but also immaterial, needs of the biological natural human being. Whatever the underlying ideology—whether protectionist mercantilism, the physiocrats’ laissez-faire policy, Adam Smith’s free-market capitalism, Karl Marx’s socialist economics, Keynesian state interventionism, or present day neoliberalism—economic considerations have been invariably driven by the fundamental problem of scarcity. The objective of the proposed paper is to present Charles Stross’s speculative predictions, made in his SF novel Accelerando, about the future of economic models in light of trans/posthuman evolution hailed by, among others, Ray Kurzweil, Max More, and Hans Moravec.
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Nesheim, Sveinung, Kjell Arne Malo, and Nathalie Labonnote. "Competitiveness of Timber Floor Elements: An Assessment of Structural Properties, Production, Costs, and Carbon Emissions." Forest Products Journal 71, no. 2 (March 1, 2021): 111–23. http://dx.doi.org/10.13073/fpj-d-20-00067.
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Abstract As long-spanning timber floor elements attempt to achieve a meaningful market share, proof of serviceability continues to be a demanding task as international consensus remains unsettled. Initiatives to improve vibration levels are achievable, but a lack of confidence in the market is resulting in increases in margins for both manufacturers and contractors. State-of-the-art concrete alternatives are offered at less than half the price, and even though timber floors offer reduced completion costs and low carbon emissions, the market is continuously reserved. Cost reductions for timber floor elements to competitive levels must be pursued throughout the product details and in the stages of manufacturing. As new wood products are introduced to the market, solution space is increased to levels that demand computerized optimization models, which require accurate expenditure predictions. To meet this challenge, a method called item-driven activity-based consumption (IDABC) has been developed and presented in this study. The method establishes an accurate relationship between product specifications and overall resource consumption linked to finished manufactured products. In addition to production time, method outcomes include cost distributions, including labor costs, and carbon emissions for both accrued materials and production-line activities. A novel approach to resource estimation linked to assembly friendliness is also presented. IDABC has been applied to a timber component and assembly line operated by a major manufacturer in Norway and demonstrates good agreement with empirical data.
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Czub, Natalia, Adam Pacławski, Jakub Szlęk, and Aleksander Mendyk. "Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor." Pharmaceutics 13, no. 10 (October 16, 2021): 1711. http://dx.doi.org/10.3390/pharmaceutics13101711.
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Introduction of a new drug to the market is a challenging and resource-consuming process. Predictive models developed with the use of artificial intelligence could be the solution to the growing need for an efficient tool which brings practical and knowledge benefits, but requires a large amount of high-quality data. The aim of our project was to develop quantitative structure–activity relationship (QSAR) model predicting serotonergic activity toward the 5-HT1A receptor on the basis of a created database. The dataset was obtained using ZINC and ChEMBL databases. It contained 9440 unique compounds, yielding the largest available database of 5-HT1A ligands with specified pKi value to date. Furthermore, the predictive model was developed using automated machine learning (AutoML) methods. According to the 10-fold cross-validation (10-CV) testing procedure, the root-mean-squared error (RMSE) was 0.5437, and the coefficient of determination (R2) was 0.74. Moreover, the Shapley Additive Explanations method (SHAP) was applied to assess a more in-depth understanding of the influence of variables on the model’s predictions. According to to the problem definition, the developed model can efficiently predict the affinity value for new molecules toward the 5-HT1A receptor on the basis of their structure encoded in the form of molecular descriptors. Usage of this model in screening processes can significantly improve the process of discovery of new drugs in the field of mental diseases and anticancer therapy.
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Miloud, Tarek. "Offer price, target ownership structure and post-listing liquidity of newly listed firms." Managerial Finance 40, no. 9 (September 2, 2014): 928–50. http://dx.doi.org/10.1108/mf-06-2013-0127.
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Purpose – Initial public offerings (IPOs) underpricing is a world-wide phenomenon in the stock market. It is generally explained with asymmetric information and risk. The purpose of this paper is to complement these traditional explanations with a theory where investors also worry about the after-market illiquidity that may result from asymmetric information after the IPO. Design/methodology/approach – The model blends such liquidity concerns with adverse selection and risk as motives for underpricing and liquidity. The model's predictions are supported by evidence for 798 French IPOs realized between 1995 and 2008. Using various measures of liquidity, the author finds that expected after-market liquidity and liquidity risk are important determinants of IPO underpricing. Findings – The author finds evidence that less liquid the aftermarket is expected to be, and the less predictable its liquidity, the larger will be the IPO underpricing. Practical implications – The study provides empirical evidence that shares outstanding and author IPO characteristics play a vital role on post-IPO liquidity. According to the results obtained, three IPO characteristics, that is, relative size, blockholder and underpricing of offering have an explanatory for the liquidity and trading activity of the shares outstanding. It should be noted that this explanatory power is much greater before isolating the market effect. Nevertheless, given the evidence to show that these operations are executed during upmarket periods when trading volume is high, the non-exclusion of the market effect may attribute these variables with more explanatory power than they actually possess. Be that as it may, even after eliminating the market effect, their explanatory capacity is still considerable. Originality/value – The author has found that underpricing is negatively related to the breadth of shareholders but positively related to institutional shareholders after the IPO. When a company is underpriced, it is likely, on average, to have a higher breadth of shareholder base and lower concentration of large outside investors.
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Shah, Zinnia, Umar Farooq Gohar, Iffat Jamshed, Aamir Mushtaq, Hamid Mukhtar, Muhammad Zia-UI-Haq, Sebastian Ionut Toma, Rosana Manea, Marius Moga, and Bianca Popovici. "Podophyllotoxin: History, Recent Advances and Future Prospects." Biomolecules 11, no. 4 (April 19, 2021): 603. http://dx.doi.org/10.3390/biom11040603.
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Podophyllotoxin, along with its various derivatives and congeners are widely recognized as broad-spectrum pharmacologically active compounds. Etoposide, for instance, is the frontline chemotherapeutic drug used against various cancers due to its superior anticancer activity. It has recently been redeveloped for the purpose of treating cytokine storm in COVID-19 patients. Podophyllotoxin and its naturally occurring congeners have low bioavailability and almost all these initially discovered compounds cause systemic toxicity and development of drug resistance. Moreover, the production of synthetic derivatives that could suffice for the clinical limitations of these naturally occurring compounds is not economically feasible. These challenges demanded continuous devotions towards improving the druggability of these drugs and continue to seek structure-optimization strategies. The discovery of renewable sources including microbial origin for podophyllotoxin is another possible approach. This review focuses on the exigency of innovation and research required in the global R&D and pharmaceutical industry for podophyllotoxin and related compounds based on recent scientific findings and market predictions.
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Arkolakis, Costas. "A Unified Theory of Firm Selection and Growth *." Quarterly Journal of Economics 131, no. 1 (February 1, 2016): 89–155. http://dx.doi.org/10.1093/qje/qjv039.
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Abstract This article develops an analytical framework to study firm and exporter growth and provides a dynamic foundation for a standard general equilibrium trade model. Firm-level growth is the result of idiosyncratic productivity improvements with a continuous arrival of new potential producers. A firm enters a market if it is profitable to incur the marginal cost to reach the first consumer and pays an increasing marketing cost to reach additional consumers. I calibrate the model using data on the cross section of firm sales and bilateral trade, as well as the rate of incumbent firm exit. The calibrated model predicts that a firm’s growth is inversely related to its initial size, and that the distribution of growth rates of small firms is heavily skewed to the right. These predictions are confirmed by looking at the growth of sales of U.S. firms and Brazilian exporters to the United States. I use this model to study the impact of cross-firm reallocations on economic activity and measured productivity.
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Yaddarabullah, Yaddarabullah, and Egie Hermawan. "Implementation Internet of Things for Feeding Catfish Water Quality Analysis Using Linear Regression and K-Nearest Neighbor." JISA(Jurnal Informatika dan Sains) 5, no. 2 (December 27, 2022): 159–64. http://dx.doi.org/10.31326/jisa.v5i2.1431.
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Cultivation of catfish (Clarias Gariepinus) is a promising business field and also a very productive activity because public interest in catfish is high. This factor is observed by market demand for catfish which is increasing from year to year. In catfish farming, you must pay attention to the acidity of the water (pH), temperature, and oxygen levels, which can change if too much feed is given. This can cause catfish seedlings to die and affect the catfish harvest. Catfish farmers often provide excessive food which causes many catfish seeds to die. This research will conduct a study on an Internet Of Things technology that can be used to monitor the acidity level in water pH, temperature, and oxygen levels as well as feed fish. The Internet of Things is very influential for monitoring the quality of catfish ponds by distributing information data resulting from sensor monitoring. The data obtained will be predicted for water quality in the pond by implementing a Linear Regression method. Furthermore, the acquisition of data from the predictions that have been carried out will be processed again to go to the next phase, namely classifying with the K-Nearest Neighbor algorithm method to carry out the identification phase of water types based on the nearest neighbors. This prediction is used to anticipate and notify catfish farmers through applications if there is a water acidity level (pH), temperature, and oxygen and feed levels that have run out
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Meng, Dan, Jiali Xie, Yihao Li, Ruoyu Li, Hui Zhou, and Ping Deng. "Structure-Guide Design and Optimization of Potential Druglikeness Inhibitors for TGFβRⅠ with the Pyrrolopyrimidine Scaffold." Pharmaceuticals 15, no. 10 (October 13, 2022): 1264. http://dx.doi.org/10.3390/ph15101264.
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Among all types of TGFβ signal blockers, small molecule kinase inhibitors (SMKIs) have attracted wide attention due to their economical production, obvious stability, and ease of oral administration. Nevertheless, SMKIs of TGFβRⅠtypically have low druggability so there are none on the market. In this study, structure-based drug design (SBDD) was performed focusing on the pyrrolopyrimidin scaffold of BMS22 to find TGFβRⅠinhibitors with excellent medical potential. The binding mode, druggability, and target affinity were assessed by molecular docking, ADMET predictions, and molecular dynamics (MD) simulations for the designed TGFβRⅠinhibitors. Finally, the highly druggable compound W8 was discovered and then synthesized, which inhibited TGFβRⅠwith an IC50 value of about 10 μM. In addition, the binding free energies (ΔGbind) of W8 (−42.330 ± 3.341 kcal/mol) and BMS22 (−30.560 ± 6.076 kcal/mol) indicate that the high binding affinity is not necessarily accompanied by high inhibitory activity. Last but not least, the per-residue interaction analysis revealed that the contribution energy of ASP351 to binding was the most significant difference between BMS22 and W8, −2.195 kcal/mol and 1.707 kcal/mol, respectively. As a result, increasing the affinity between SMKIs and ASP351 of TGFβRⅠmay effectively improve the inhibitory activity. The insights gained from this study could help with structure-guided optimization in searching for better SMKIs of TGFβRⅠ.
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Andreev, Boris, Georgios Sermpinis, and Charalampos Stasinakis. "Modelling Financial Markets during Times of Extreme Volatility: Evidence from the GameStop Short Squeeze." Forecasting 4, no. 3 (July 19, 2022): 654–73. http://dx.doi.org/10.3390/forecast4030035.
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Ever since the start of the coronavirus pandemic, lockdowns to curb the spread of the virus have resulted in an increased interest of retail investors in the stock market, due to more free time, capital, and commission-free trading brokerages. This interest culminated in the January 2021 short squeeze wave, caused in no small part due to the coordinated trading moves of the r/WallStreetBets subreddit, which has rapidly grown in user base since the event. In this paper, we attempt to discover if coordinated trading by retail investors can make them a market moving force and attempt to identify proactive signals of such movements in the post activity of the forum, to be used as a part of a trading strategy. Data about the most mentioned stocks is collected, aggregated, combined with price data for the respective stock and analysed. Additionally, we utilise predictive modelling to be able to better classify trading signals. It is discovered that despite the considerable capital that retail investors can direct by coordinating their trading moves, additional factors, such as very high short interest, need to be present to achieve the volatility seen in the short squeeze wave. Furthermore, we find that autoregressive models are better suited to identifying signals correctly, with best results achieved by a Random Forest classifier. However, it became apparent that even the best performing model in our experimentation cannot make accurate predictions in extreme volatility, evidenced by the negative returns shown by conducted back-tests.
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Ho, Anson T. Y., Philip M. Polgreen, and Tim Prendergast. "Prediction Market for Disease Surveillance: A Case Study of Influenza Activity." Journal of Prediction Markets 10, no. 1 (September 20, 2016): 68–82. http://dx.doi.org/10.5750/jpm.v10i1.1162.
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We conducted a pilot study on the use of prediction markets to aggregate private information for disease surveillance. Influenza activity in Iowa, North Carolina, and Nebraska between 2008-2010 was forecast through prediction markets operated on the Iowa Electronic health Markets (IEhM). We found that prediction markets were well utilized by participants, and they achieved high level of forecasting accuracy as far as 4 weeks before actual influenza-level outcomes were announced. Trading activities indicate that new information continuously flowed into the markets during the trading window, which further improved prediction accuracy as contracts drew down to expiry. This project demonstrates that a prediction market is a practical infectious disease surveillance mechanism that provides low-cost useful information for public health administration in a timely manner.
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Cousins, Henry C., Clara C. Cousins, Alon Harris, and Louis R. Pasquale. "Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns." Journal of Medical Internet Research 22, no. 7 (July 30, 2020): e19483. http://dx.doi.org/10.2196/19483.
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Background Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. Objective We investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States. Methods We used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels. Results Predictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05. Conclusions Identifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity.
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Gupta, Sumeet. "FUNDAMENTAL & TECHNICAL ANAYSIS OF CRUDE OIL PRICES." Journal of Global Economy 17, no. 1 (April 17, 2021): 3–20. http://dx.doi.org/10.1956/jge.v17i1.617.
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The human mind is not as good at processing large amounts of information as we might like. Psychologists have shown that human beings are only able to juggle small numbers of related and often conflicting pieces of information without making judgment errors. As a result, individuals faced with the vast amounts of information available to support investment decisions often find themselves swamped by the enormity of the task; unable to see the wood from the trees. Technical analysis is a field of financial markets research that works to address the above problem by focusing on a single, commonly available, data source that reflects all known information and activity relating to all monetary securities- Price history. Technical analysts argue that as markets are efficient, prices reflect all known information and that they move over time as participants react to new information and changing needs.
As a result, the technical analysis of these price changes can provide real insight into the market dynamics and be used to develop trade strategies that exhibit superior risk/reward characteristics. While technical analysis approaches have developed significantly over the past few decades, some techniques are far more ancient. While their real origins are anonymous, Japanese candlestick charts have been recorded as being employed in the rice markets as far back as the 1600s. What is particularly interesting is that various of these ancient approaches continue to provide highly effective trading signals when applied to modern markets and securities.
Crude oil price volatility is in the midst of the largest business risk that oil and gas companies face. This is followed by unstable policy regime, managing costs and risks emerging from technological advancements. The high levels and rapid fluctuations of petroleum prices have become a great concern to individual consumers, firms, policy makers and society. Technical Analysis is the forecasting of future financial price movements based on an examination of past price movements. Like weather forecasting, technical analysis does not result in absolute predictions about the future. Instead, technical analysis can help investors anticipate what is "likely" to happen to prices over time. Technical analysis uses a wide variety of charts that show price over time. Hence, to mitigate the negative impacts of price volatility and to predict about the future price movement of crude oil and natural gas we can use technical analysis.
Technical analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting price trends. The term “market action” includes the three principal source of action available to the technician-price, volume and open interest. This research paper highlights fundamental factor which affects the Brent price and analysed the factor which are highly correlated with Brent price and on the basis of the results forecasted the Brent price for next five years. Fundamental analysis of Brent oil, price pattern & movement of crude oil has also been carried out using candlestick technical tool.
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Pan, Xin, Xuanjin Chen, and Lutao Ning. "Exploitative technological diversification, environmental contexts, and firm performance." Management Decision 56, no. 7 (July 9, 2018): 1613–29. http://dx.doi.org/10.1108/md-03-2017-0228.
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PurposeAlthough technological diversification is often understood as an explorative activity, the authors argue that it can also be explained as exploitation. The purpose of this paper is to examine how exploitative technological diversification (ETD) affects firm performance and what factors may moderate this relationship.Design/methodology/approachThe sample consists of 1,569 Chinese listed firms with 7,555 observations from 2003 to 2014. Patent data were collected from the State Intellectual Property Office, while financial information was collected from the China Stock Market and Accounting Research database. The system generalised method of moments model was used for testing the hypotheses.FindingsThe empirical findings indicate that the relationship between ETD and firm performance is inversely U-shaped. Moreover, this relationship is negatively moderated by environmental munificence, which refers to the availability of resources in the environment where the firm operates, and positively moderated by environmental dynamism, which refers to the extent of volatility and unpredictable change in firms’ external environments.Originality/valueOverlooking ETD limits applications of diversification logic and the precision of their predictions. This paper tries to fill this gap by empirically testing the relationship between ETD and financial performance.
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Penisa, Xaviery N., Michael T. Castro, Jethro Daniel A. Pascasio, Eugene A. Esparcia, Oliver Schmidt, and Joey D. Ocon. "Projecting the Price of Lithium-Ion NMC Battery Packs Using a Multifactor Learning Curve Model." Energies 13, no. 20 (October 11, 2020): 5276. http://dx.doi.org/10.3390/en13205276.
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Renewable energy (RE) utilization is expected to increase in the coming years due to its decreasing costs and the mounting socio-political pressure to decarbonize the world’s energy systems. On the other hand, lithium-ion (Li-ion) batteries are on track to hit the target 100 USD/kWh price in the next decade due to economy of scale and manufacturing process improvements, evident in the rise in Li-ion gigafactories. The forecast of RE and Li-ion technology costs is important for planning RE integration into existing energy systems. Previous cost predictions on Li-ion batteries were conducted using conventional learning curve models based on a single factor, such as either installed capacity or innovation activity. A two-stage learning curve model was recently investigated wherein mineral costs were taken as a factor for material cost to set the floor price, and material cost was a major factor for the battery pack price. However, these models resulted in the overestimation of future prices. In this work, the future prices of Li-ion nickel manganese cobalt oxide (NMC) battery packs - a battery chemistry of choice in the electric vehicle and stationary grid storage markets - were projected up to year 2025 using multi-factor learning curve models. Among the generated models, the two-factor learning curve model has the most realistic and statistically sound results having learning rates of 21.18% for battery demand and 3.0% for innovation. By year 2024, the projected price would fall below the 100 USD/kWh industry benchmark battery pack price, consistent with most market research predictions. Techno-economic case studies on the microgrid applications of the forecasted prices of Li-ion NMC batteries were conducted. Results showed that the decrease in future prices of Li-ion NMC batteries would make 2020 and 2023 the best years to start investing in an optimum (solar photovoltaic + wind + diesel generator + Li-ion NMC) and 100% RE (solar photovoltaic + wind + Li-ion NMC) off-grid energy system, respectively. A hybrid grid-tied (solar photovoltaic + grid + Li-ion NMC) configuration is the best grid-tied energy system under the current net metering policy, with 2020 being the best year to deploy the investment.
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Gómez-Gallego, C., S. Pohl, S. Salminen, W. M. De Vos, and W. Kneifel. "Akkermansia muciniphila: a novel functional microbe with probiotic properties." Beneficial Microbes 7, no. 4 (September 1, 2016): 571–84. http://dx.doi.org/10.3920/bm2016.0009.
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Akkermansia muciniphila is an intestinal anaerobe which has been proposed as a new functional microbe with probiotic properties. However, the species is not included in the European Union qualified presumption of safety (QPS) list and has not yet been assessed. Moreover, products containing A. muciniphila are not on the market and are thus controlled by the Novel Foods Regulation, which requires extensive safety assessment. This review addresses the safety aspects of the use of A. muciniphila based on published information on its functions in humans and predictions based on its activity in model animals. Further, comprehensive studies related to A. muciniphila and its safety properties have gradually appeared and are summarised here. Many of the criteria required for novel food safety assessment in Europe can thus be fulfilled. However, studies focusing on the toxicological properties of A. muciniphila, including long-term and reproduction studies, have not so far been reported and are discussed in the light of the observation that most, if not all, healthy subjects are known to carry this intestinal anaerobe. As this also applies to other beneficial bacteria found in the human intestinal tract, the A. muciniphila case can be seen as a model for the comprehensive safety evaluations required by the European authorities.
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Miljenović, Dejan, and Bono Beriša. "Pandemics trends in E-commerce." Pomorstvo 36, no. 1 (June 30, 2022): 31–43. http://dx.doi.org/10.31217/p.36.1.4.
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Drop shipping represents a delivery business model based on e-commerce logistics, which is very relevant in the global pandemic crisis. In this model, buyers order products and services directly from the manufacturer over the Internet through the intermediary of a drop shipper who ensures the easiest and fastest delivery. Drop shipper is a type of e-commerce entrepreneur that offers goods using online logistics infrastructure to ensure direct physical delivery from the manufacturer to the retailer or customer. This includes drop shipping as a business model to Industry 4.0 (i.e. the digital economy). This paper elaborates how a particular e-commerce model affects the development and improvement of global supply chains. Aim of this paper is to research how entrepreneur can lower its costs along drop shipping supply chain by managing inventory ratios or cost of stock. This enables drop shipping to greatly facilitate market access for cash-strapped entrepreneurs. We also conducted a simulation of optimized drop shipping process to show that there is a constant adaptability for entrepreneurs using drop shipping, especially among small and medium enterprises (SMEs). The timeliness of drop shipping is that it has facilitated as a global delivery solution in relation to the coronavirus disease pandemic (COVID-19). Moreover, drop shipping is based on a Activity-Based Costing (ABC model) whose quantitative efficiency has been proven by scientific analysis and research results. Finally, the legal aspects of drop shipping are given importance in the situation of COVID-19 which had a crucial impact on international trade in 2020, especially in predicting short-term trade trends. Due to this within this paper we also examined the effects of the ˝coronavirus˝ on global e-commerce. Global drop shipping data ensures qualitative predictions considering pandemic environment, social distancing issues and internet trade curves which are more than useful in today’s changing business conditions.
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Lysenko, Denis Vladimirovich, and Leyla Akgün. "ANALYSIS OF THE MAIN INDICATORS OF ECONOMIC ACTIVITY OF ENTERPRISES PRODUCING TRUCKS ACCORDING TO ACCOUNTING DATA." Chronos: economy sciences 6, no. 3(31) (November 3, 2021): 4–9. http://dx.doi.org/10.52013/2712-9713-31-1-1.
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The modern system of accounting and management accounting includes a set of methods for analysing and evaluating the analysis of the financial condition of an enterprise. A comprehensive analysis of economic activity is a system of special knowledge related to the study of economic processes that develop under the influence of objective economic laws and factors of an objective order. Comprehensive analysis is of great importance in strengthening the financial condition, increasing the liquidity of assets, their solvency, searching for reserves for economic growth, efficiency in the use of resources, and in general business processes. The transformation of accounting, which is being carried out as part of the restructuring of the economy to market track, has once again brought to life such an important element of analytical work as financial analysis. The effectiveness of enterprise management is largely determined by the degree of its organization and the quality of information support. Analysis of financial statements in the framework of a comprehensive analysis of economic activity consists in the application of analytical tools and methods to the indicators of financial documents in order to identify significant relationships and characteristics necessary for making any decision. It serves to transform data so numerous and varied in our computer age into necessary and always scarce information. The business analysis process is described differently depending on the task at hand. It can be used as a preliminary screening tool when choosing a direction for investment or possible options for a merger of enterprises. It can also act as a forecasting tool for future financial conditions and results. Comprehensive analysis is also applicable to identify problems in the management of production activities. It can serve to assess the performance of the company’s management. And most importantly, financial analysis allows you to rely less on guesses, premonitions and intuition, to reduce the inevitable uncertainty that is present in any decision-making process. Financial analysis does not eliminate the need for business sense but provides a solid and systematic foundation for its rational application. Reporting analysis in the process of a comprehensive analysis of the economic activity of an enterprise is a process that aims to assess the current and past financial condition and results of the enterprise, while the primary goal is to determine estimates and predictions regarding the future conditions and activities of the enterprise.
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Kvach, Iryna. "The cost management approaches in trade industry." Economics ecology socium 3, no. 3 (September 30, 2019): 35–43. http://dx.doi.org/10.31520/2616-7107/2019.3.3-5.
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Introduction. The current state of financial and economic government institutions negatively affects Ukrainian budget execution, and the general trend of deterioration in the standard of living of the people, in general leads to low level of functioning of the enterprise and their competitiveness, especially in such industry as trade.
Aims and tasks. In the conditions of an unstable political and economic situation in commodity market and services to overcome disproportions between operating profit of some commodity groups of trade enterprise and its added value which indicates depreciation of the capital invested by owners not only doesn't provide compensation of investments, but also leads to losses because of inflationary processes therefore there is a need for the mutual integration of approaches of management of expenses for assessment.
Results. The practical value of application of a method of Activity-based costing (ABC) and Economic Value Added (EVA) in management of expenses not only in creation of a system of accounting of expenses, but also and predictions through new approaches for the analysis for identification of unproductive fields of activity in value creation of a product is proved, including positively influences the growth of business activity for trade enterprises.
In the field of innovative approaches the balanced system of indexes (BSI) and EVA methods harmoniously are integrated in processes of costs planning, management of them at the level of departments and in general are distributed among operation processes to responsible persons, which has a positive impact on maximizing capital cost of the enterprise.
Conclusions. Application of methods of cost management as uniform system provides chance to distribute expenses on commodity groups and to define goods which create added value gives the chance to settle the impact of minimum change of influence of a factor on commodity turnover level due to decrease of unit cost in life cycle of a product through the analysis of a point of profitability for increase in investment attractiveness.
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Georgiana, Noja Gratiela, and Moroc Andrei. "Labour Mobility Within the Eu: Major Effects and Implications for the Main Sending and Receiving Economies." European Journal of Economics and Business Studies 5, no. 1 (August 30, 2016): 87. http://dx.doi.org/10.26417/ejes.v5i1.p87-100.
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The paper aims to analyse the effects induced by labour mobility within the European Union, focusing both on emigration and immigration effects for major sending and host economies in terms of the overall economic activity, empowering the business enterprise sector and labour market, as well as on economic (labour force) and non-economic (humanitarian, asylum seekers) migration. Labour mobility within the European Union is an important coordinate of the economic integration process and one of the freedoms granted to the member states, with significant consequences upon their economies. Nevertheless, the international labour migration mainly resides from wage differentials, working conditions or opportunities between sending and host economies, thus proving to be an important symbol of global economic inequality. Taking into consideration all these aspects, our analysis is based on developing various double-log fixed (LSDV) and random (ECM) effects models, using a panel structure that covers five main EU destination countries and ten New EU Member States, respectively a complex set of indicators compiled during 2000-2014 and 2006-2015. The models are processed through OLS and GLS methods of estimation, as well as by using the correlated panels corrected standard errors (PCSE) method, being completed by in-sample and out-of-sample predictions. The results show that immigration flows have important economic consequences leading to significant changes in labour market performances both for natives and foreign population (decreases in employment rates and lowering wage levels). Still, one of the most important positive effects of immigration reflected by the results obtained is represented by an increase in the number of innovative enterprises in the host country, thus confirming the theories linking migration to innovation. In terms of labour emigration, there is evidence to attest that it generates positive effects on the main sending economies from Central and Eastern Europe on the GDP per capita, earnings and exports, especially through remittances, but the overall negative impact is predominant.
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Ayal, Adi, and Yaad Rotem. "THE FAILING FIRM DEFENSE—AN EQUITY-BASED APPROACH." Journal of Competition Law & Economics 15, no. 4 (December 2019): 468–99. http://dx.doi.org/10.1093/joclec/nhz020.
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Abstract The failing firm defense is used to allow a seemingly anti-competitive merger, when one of the parties is a financially distressed firm. The basic argument is that harm to competition will ensue regardless of the merger, while allowing it provides a preferable alternative, maintaining commercial activity and protecting employment. The problem with the failing firm doctrine lies in antitrust enforcement agencies making highly uncertain predictions regarding future states of competition and broadening their discretion to encompass non-competition-related goals. As a result, enforcement agencies are pushed to extremes, succumbing to either type I errors (enjoining mergers that should have been approved) or type II errors (approving mergers that should have been blocked). Legal history suggests that regulators are excessively risk-averse, rarely accepting the failing firm defense. This paper offers a novel approach which minimizes the costs of both types of errors. We argue that rather than merely approve of, or forbid, the merger, antitrust agencies should also consider a third possibility: approval of the merger under the condition that the Government receive a passive equity stake in the merged entity in proportion to the actual post-merger decrease in market competition. The decrease in competition and the subsequent dilution of incumbent equity holders is to be decided ex post facto by comparing the change in an agreed-upon measure before and after the merger, preferably over several time intervals. This approach transforms the ex ante complex and uncertain regulatory decision to a rather simple ex post measurement problem. The proposed solution also deters firms from post-merger exploitation of market power, while allowing the public to be compensated for any harm to competition caused by the merger. All this, while reducing the costs of administrating the regulatory process. Finally, our approach also allows consideration of various stakeholders’ interests beyond direct consumers, that is, employees and creditors of the financially distressed firm, as well as economy-wide interests and the interest of the public at large.
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BenDor, Todd K., Jordan Branham, Dylan Timmerman, and Becca Madsen. "Predicting the Existence and Prevalence of the US Water Quality Trading Markets." Water 13, no. 2 (January 14, 2021): 185. http://dx.doi.org/10.3390/w13020185.
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Water quality trading (WQT) programs aim to efficiently reduce pollution through market-based incentives. However, WQT performance is uneven; while several programs have found frequent use, many experience operational barriers and low trading activity. What factors are associated with WQT existence, prevalence, and operational stage? In this paper, we present and analyze the most complete database of WQT programs in the United States (147 programs/policies), detailing market designs, trading mechanisms, traded pollutants, and segmented geographies in 355 distinct markets. We use hurdle models (joint binary and count regressions) to evaluate markets in concert with demographic, political, and environmental covariates. We find that only one half of markets become operational, new market establishment has declined since 2013, and market existence and prevalence has nuanced relationships with local political ideology, urban infrastructure, waterway and waterbody extents, regulated environmental impacts, and historic waterway impairment. Our findings suggest opportunities for better projecting program need and targeting program funding.
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L, Dushyanth. "A SURVEY ON STOCK PRICE PREDICTION USING DEEP LEARNING." International Research Journal of Computer Science 9, no. 2 (February 28, 2022): 5–8. http://dx.doi.org/10.26562/irjcs.2022.v0902.002.
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Stock is a curve with a lot of unknowns. Stock market forecasting is fraught with complications and unpredictability. One of the most challenging and sophisticated methods of doing business is investing in the stock market. Stock forecasting is a difficult and time-consuming activity since the stock market is extremely volatile with stock prices fluctuating due to a variety of variables. Investors nowadays want quick and precise information to make informed decisions, thanks to the rapid growth of technology in stock price prediction. Understanding a company's stock price pattern and estimating its future development and financial growth will be quite advantageous. As the stock is made up of dynamic data, data is the critical source of efficiency. In the current trend of predicting stocks, deep learning is the most popular among the prediction of datasets. To forecast and automate operations, deep learning employs several prediction models and algorithms. The paper briefs about different algorithms and methods used for stock market prediction.
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Zimmer, Christoph, Sequoia I. Leuba, Reza Yaesoubi, and Ted Cohen. "Use of daily Internet search query data improves real-time projections of influenza epidemics." Journal of The Royal Society Interface 15, no. 147 (October 2018): 20180220. http://dx.doi.org/10.1098/rsif.2018.0220.
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Seasonal influenza causes millions of illnesses and tens of thousands of deaths per year in the USA alone. While the morbidity and mortality associated with influenza is substantial each year, the timing and magnitude of epidemics are highly variable which complicates efforts to anticipate demands on the healthcare system. Better methods to forecast influenza activity would help policymakers anticipate such stressors. The US Centers for Disease Control and Prevention (CDC) has recognized the importance of improving influenza forecasting and hosts an annual challenge for predicting influenza-like illness (ILI) activity in the USA. The CDC data serve as the reference for ILI in the USA, but this information is aggregated by epidemiological week and reported after a one-week delay (and may be subject to correction even after this reporting lag). Therefore, there has been substantial interest in whether real-time Internet search data, such as Google, Twitter or Wikipedia could be used to improve influenza forecasting. In this study, we combine a previously developed calibration and prediction framework with an established humidity-based transmission dynamic model to forecast influenza. We then compare predictions based on only CDC ILI data with predictions that leverage the earlier availability and finer temporal resolution of Wikipedia search data. We find that both the earlier availability and the finer temporal resolution are important for increasing forecasting performance. Using daily Wikipedia search data leads to a marked improvement in prediction performance compared to weekly data especially for a three- to four-week forecasting horizon.
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Alsarhan, Abdulwahab, Nayef Al-Shammari, and Mohammad Alenezi. "Testing the production efficiency of the investment sector in Kuwait using two-stage approach." Journal of Economic and Administrative Sciences 31, no. 2 (November 16, 2015): 109–23. http://dx.doi.org/10.1108/jeas-10-2014-0028.
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Purpose – Testing the efficiency in the economy has been highly pronounced since the financial crisis in 2008, as many countries have started to deregulate their economic sectors. The potential impact of testing efficiency is thus the key driver of world output and welfare. For this purpose, the main objective of the Capital Market Authority consists of more regulation of securities trading to boost economic efficiency. In particular, the purpose of this paper, is to examine the efficiency of 40 investment companies in Kuwait. In this study, the authors investigate the efficiency in the investment sector in Kuwait. Studying such a case is important for several reasons. First, the investment sector in Kuwait is affected by the World Trade Organization (WTO) conditions and regulations for more market liberalization. Second, most studies on efficiency have focussed on developed countries, such as those of Europe and the USA, with very few studies examining developing countries, such as Kuwait. Third, the study efficiency features is important in helping policy makers evaluate how the investment sector will be affected by increasing competition and then formulate policies that affect that sector and the economy as a whole. Design/methodology/approach – In this study, we use non-parametric data envelopment analysis (DEA) to estimate investment companies’ efficiency in Kuwait. The authors test predictions of the model using yearly data for 2006-2010. In the analysis, the authors follow the two-stage approach suggested by Coelli et al. (1998). In the literature on DEA efficiency score measurement, this two-stage approach is the most prominent. This approach uses the efficiency score, measured by the DEA model, as the dependent variable in a regression model with explanatory variables that are supposed to capture the impact of external factors (Hahn, 2007). In the second stage, the authors used a Tobit model to investigate factors affecting the efficiency in the Kuwaiti investment sector. Findings – The findings of the second stage suggest that 2008-2010 had a negative impact on firms’ efficiency in Kuwait. The results confirm the substantial influence of the 2008 global financial crisis on the investment sector in Kuwait. In addition, the results show that factors affecting production efficiency in the investment sector in Kuwait include the total revenues, total assets, government participation, and Islamic firm dummy. These second-stage results are confirmed using different specifications of a fixed effect model, a random effects model, and a logit model. Originality/value – The results may be utilized by both monetary authorities and policy makers in establishing the general economic policy in the country. A number of policy implications may be derived from the estimates obtained in the current paper. First, the results show that the investment sector in Kuwait faced a sharp drop in its efficiency in 2008 due to the global financial crisis. This result tells us that there was a spillover effect of the global financial crisis in the Kuwaiti investment market, as companies in this market are highly vulnerable to global shocks. As a result, the investment sector needs to be regulated by, for example, encouraging more company mergers and acquisitions. Second, to meet the appropriate regulations in the investment sector in Kuwait, monetary authority in Kuwait should take into consideration the WTO conditions for more openness in the economic sector. Therefore, companies in the investment sector should be more efficient to compete with foreign investment companies that decide to enter into Kuwaiti market. Therefore, the need for regulations in the Kuwaiti investment sector is more necessary than before. Third, the study of efficiency features is important to help policy makers evaluate how the investment sector will be affected by increasing competition and then formulate policies that affect that sector and the economy as a whole. Furthermore, monetary policy can play an important role in influencing the efficiency in the investment sector. Therefore, the Central Bank of Kuwait should take a leading role in regulating abnormal financial activity in the Kuwaiti market.
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Talati, Drashti, Dr Miral Patel, and Prof Bhargesh Patel. "Stock Market Prediction Using LSTM Technique." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1820–28. http://dx.doi.org/10.22214/ijraset.2022.43976.
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Abstract: One of the most intricate machine learning problems is the share value prediction. Stock market prediction is an activity in which investors need fast and accurate information to make effective decisions. Moreover, the behavior of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. Prices of stocks are depicted by time series data and neural networks are trained to learn the patterns from trends in the existing data. This system employed algorithm using LSTM to improve the accuracy of stock price prediction.
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Vávrová, E. "The Czech agricultural insurance market and a prediction of its development in the context of the European Union." Agricultural Economics (Zemědělská ekonomika) 51, No. 11 (February 21, 2012): 531–38. http://dx.doi.org/10.17221/5148-agricecon.
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In the market economy, agriculture ranks among the important political and economic issues. Risks associated with agricultural activity can be catastrophic. For farmers and farms, damages resulting from materialized risks represent significant and existence-threatening problems. For the state, damages in agriculture can endanger the food supply chain, cause fluctuation in employment or jeopardize the state’s foreign-policy position due to lack of self-sufficiency. This is why it is necessary to discuss the methods and ways to deal with the problem, to eliminate agricultural risks or to minimize their occurrence and materialization. One of the possible ways is insurance. With regard to these facts, the author attempts to make an analysis of the possible ways to eliminate risks that endanger agricultural production and, according to this analysis, to describe the basic approaches to minimizing or eliminating the materialization of risks associated with agricultural activity. Subsequently, the author focuses on agricultural insurance systems in the countries of the European Union, and on the present-day situation in the field of agricultural insurance in the Czech Republic.
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Agnello, Luca, Vitor Castro, and Ricardo M. Sousa. "ECONOMIC ACTIVITY, CREDIT MARKET CONDITIONS, AND THE HOUSING MARKET." Macroeconomic Dynamics 22, no. 7 (July 3, 2017): 1769–89. http://dx.doi.org/10.1017/s1365100516000869.
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In this paper, we assess the characteristics of the housing market and its main determinants. Using data for 20 industrial countries over the period 1970Q1–2012Q2 and a discrete-time Weibull duration model, we find that the likelihood of the end of a housing boom or a housing bust increases over time. Additionally, we show that the different phases of the housing market cycle are strongly dependent on the economic activity, but credit market conditions are particularly important in the case of housing booms. The empirical findings also indicate that although housing booms have similar lengths in European and non-European countries, housing busts are typically shorter in European countries. The use of a more flexible specification for the hazard function that is based on cubic splines suggests that it evolves in a nonlinear way. From a policy perspective, our study can be useful for predicting the timing and the length of housing boom–bust cycles. Moreover, it highlights the importance of monetary policy by influencing lending rates and affecting the likelihood of occurrence of housing booms.
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Ma'in, Masturah, Arifin Md. Salleh, and Abd Ghafar Ismail. "Stock market and real activity : an empirical study of several Asian countries." Social and Management Research Journal 4, no. 1 (June 1, 2007): 39. http://dx.doi.org/10.24191/smrj.v4i1.5124.
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The objective ofthis study is to investigate the performance ofthe stock market as an indicator to real activity. The evidence ofthis relationship will focus on the sample of data obtained from Malaysia, Japan, Australia, India and Pakistan. The ordinary least square (OLS) and ECM-causality are used to examine the cointegration relationship and causality effect through the sample of data frequency to the related countries. The results show that there is causal-link between stock returns and industrial production index. This particularly exists in Australia, Japan and Malaysia. However, in Pakistan and India, there are no effects traced Therefore, based on the empirical evidence, it clearly shows that the stock market does not predict the real activity in all Asian countries compared to the developed countries in which their stock markets play an important role in predicting the real activity.