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Thematische Bibliographien / Stocks

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Auswahl der wissenschaftlichen Literatur zum Thema „Stocks“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 9. Juni 2025

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Zeitschriftenartikel zum Thema "Stocks"

1

Sapari, Fransissco Nicolas, and Agus Zainul Arifin. "Studi Perbandingan Nilai Value at Risk Antara Saham Berbasis Syariah Dengan Saham Non Syariah Periode 2010-2012." Jurnal Dinamika Akuntansi dan Bisnis 3, no. 1 (July 24, 2016): 26–36. http://dx.doi.org/10.24815/jdab.v3i1.4394.

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This study aimed to empirically compare the risk between sharia and non-sharia based stock investment. The Sharia stocks are refereed to stocks that issued by companies listed in LQ-45, whereas the non-sharia stocks are defined as stocks that are issued by companies listed in Jakarta Indonesia Index (JII) between 2011 and 2012. In total, there were 25 companies listed in LQ-45 and 15 companies listed in JII which were involved in this study. This study used GARCH model to estimate the risk of every individual stock. The result showed that there was a difference in risk between sharia and non-sharia based stock. This study also documented that non-Sharia based stocks were more risky than Sharia-based stodcks. Finally, this study provides information on risk characteristic in Indonesia Capital Market.

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Eltahir, Yassin Ibrahim, Osama Azmi Sallam, Hussien Omer Osman, and Fethi Klabi. "Does Volatility Generate Major and Minor Stocks in Saudi Stocks Market?" Integrated Journal of Business and Economics 4, no. 1 (January 15, 2020): 14. http://dx.doi.org/10.33019/ijbe.v4i1.239.

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This study attempts to answer the main question: are there reciprocal effects between the variances of the stock returns in the Saudi market, also the answer to a sub-question. What are the leading stocks in the Saudi market?. Study selected a sample of five stocks representing the basic materials, banking, services, food and transport sectors (SABIC, Al Rajhi, Etisalat, Almarai and Al Bahri respectively). The data sample for the period from 2011 to 2016 is taken, which represents the lifespan of the five-year plan. Daily stock returns were calculated during this period. Study applies the M GARCH-VEC methodology to estimate stock return variances and then perform a multiple regression of five equations using the ARCH Heteroscedasticity estimator. Results of the analysis show a positive effect between stock return variances as well as a positive automatic variance of all stocks returns variances. Finally, the results of the regression analysis of the various equations show that the returns variances of SABIC and Al Rajhi stocks have a dominant impact on the rest of the stock's returns. So they are considered as leading stocks in the market. While the variances returns of Etisalat, Almarai and Al Bahri have a limited impact on the rest of the stocks variances returns, so they are considered as minor stocks

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Liu, Mark H. "Analysts’ Incentives to Produce Industry-Level versus Firm-Specific Information." Journal of Financial and Quantitative Analysis 46, no. 3 (February 15, 2011): 757–84. http://dx.doi.org/10.1017/s0022109011000056.

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AbstractUsing stock returns around recommendation changes to measure the information produced by analysts, I find that analysts produce more firm-specific than industry-level information. Analysts produce more firm-specific information on stocks with higher idiosyncratic return volatilities. The amount of industry information produced by analysts increases with the absolute value of the stock’s industry beta and decreases with the stock’s idiosyncratic volatility. Other stocks in the industry also respond to the recommendation change, and the magnitude of the response increases with the absolute value of the industry beta of the recommended stock and that of other stocks in the industry. I also offer results on how investors may use analyst research more effectively and potentially improve their investment performance.

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Kreidl, Felix. "Stock-Market Behavior on Ex-Dates: New Insights from German Stocks with Tax-Free Dividend." International Journal of Financial Studies 8, no. 3 (September 21, 2020): 58. http://dx.doi.org/10.3390/ijfs8030058.

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We examine stock prices and the number of stocks traded around ex-dividend dates of German stocks with tax-free dividend. Tax-free dividends are temporarily tax-exempt, as they reduce the initial purchasing price of a stock. With our analysis of this particular group of German stocks, we can make clear predictions regarding ex-date prices and analyze the number of stocks traded around ex-dates, doing so without the systematic bias of cum-ex trades over time. For XETRA, our empirical results indicate that ex-date prices decline, on average, by the amount of the dividend. We do not find a significant relationship between a stock’s price-drop ratio and dividend yield. Further, the empirical analysis suggests that there is no significant correlation between an abnormal number of a stock being traded and its dividend yield. These results are most consistent with tax-motivated reasoning. However, our volume analysis reveals no consistency regarding the abnormal number of stocks traded for multilateral trading facilities.

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Färber, Leonie, Rob van Gemert, Øystein Langangen, Joël M. Durant, and Ken H. Andersen. "Population variability under stressors is dependent on body mass growth and asymptotic body size." Royal Society Open Science 7, no. 2 (February 2020): 192011. http://dx.doi.org/10.1098/rsos.192011.

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The recruitment and biomass of a fish stock are influenced by their environmental conditions and anthropogenic pressures such as fishing. The variability in the environment often translates into fluctuations in recruitment, which then propagate throughout the stock biomass. In order to manage fish stocks sustainably, it is necessary to understand their dynamics. Here, we systematically explore the dynamics and sensitivity of fish stock recruitment and biomass to environmental noise. Using an age-structured and trait-based model, we explore random noise (white noise) and autocorrelated noise (red noise) in combination with low to high levels of harvesting. We determine the vital rates of stocks covering a wide range of possible body mass (size) growth rates and asymptotic size parameter combinations. Our study indicates that the variability of stock recruitment and biomass are probably correlated with the stock's asymptotic size and growth rate. We find that fast-growing and large-sized fish stocks are likely to be less vulnerable to disturbances than slow-growing and small-sized fish stocks. We show how the natural variability in fish stocks is amplified by fishing, not just for one stock but for a broad range of fish life histories.

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Du, Sha, and Hailong Shen. "A Stock Prediction Method Based on Deep Reinforcement Learning and Sentiment Analysis." Applied Sciences 14, no. 19 (September 27, 2024): 8747. http://dx.doi.org/10.3390/app14198747.

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Most previous stock investing methods were unable to predict newly listed stocks because they did not have historical data on newly listed stocks. In this paper, we use the Q-learning algorithm based on a convolutional neural network and add sentiment analysis to establish a prediction method for Chinese stock investment tasks. There are 118 companies that are ranked in the Chinese top 150 list for two consecutive years in both 2022 and 2023. We collected all comments under the stock bar of these 118 stocks for each day from 1 January 2022 to 1 July 2024, totaling nearly 10 million comments. There are 90 stocks left after the preprocessing of 118 stocks. We use these 90 stocks as the dataset. The stock’s closing price, volume, and comment text data are fed together to the agent, and the trained agent outputs investment behaviors that maximize future returns. We apply the trained model to two test sets that are completely different from the training set and compare it to several other methods. Our proposed method called SADQN-S obtains results of 1.1229 and 1.1054 on the two test sets. SADQN-S obtained higher final total assets than the other methods on both test sets. This shows that our model can help stock investors earn high returns on newly listed stocks.

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Michielsens, Catherine G. J., Samu Mäntyniemi, and Pekka J. Vuorinen. "Estimation of annual mortality rates caused by early mortality syndromes (EMS) and their impact on salmonid stockrecruit relationships." Canadian Journal of Fisheries and Aquatic Sciences 63, no. 9 (September 1, 2006): 1968–81. http://dx.doi.org/10.1139/f06-095.

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In this paper, we demonstrate how information from broodstocks can be combined with lab information on alevins to obtain annual stock-specific mortality estimates from early mortality syndromes (EMS) using a probabilistic approach, how a hierarchical model structure can be used to predict these mortality rates for related, partly sampled, or unsampled stocks, and why these estimates should be used to remove the effect of this mortality on stockrecruit estimates. The approach has been illustrated for Atlantic salmon (Salmo salar) stocks in the Baltic Sea affected by the M74 syndrome. Results indicate that data on the proportion of M74-affected females, commonly used to approximate M74 mortality, overestimate actual M74-related mortality because of a declining trend in mortality among offspring of these females. The stock-specific M74 mortality estimates are used to account for nonstationarity in the stockrecruitment relationship caused by this fluctuating mortality. Because hierarchical meta-analyses assume exchangeability, the effect of M74 mortality is removed before including these stocks within hierarchical stockrecruit analyses of Atlantic salmon stocks, which are commonly unaffected by M74 mortality. Failure to remove the effect of M74 mortality on the stockrecruit data results in underestimation of the stock's productivity and resilience to exploitation, especially in the case of stocks with steep stockrecruit curves.

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Rasul, Dr Md Serajur. "Performance of Value and Growth Stocks: Returns of Stocks on Dhaka Stock Exchange." Indian Journal of Applied Research 3, no. 2 (October 1, 2011): 205–8. http://dx.doi.org/10.15373/2249555x/feb2013/71.

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Agarwal, Mehul. "Does Investment in Defensive Stocks Act as a Buffer during Market Downturns?" INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 10, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem30553.

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The study examines the performance of defensive stocks during market downturns in the Indian stock market. The research focuses on the period from January 2000 to December 2023. In this study selected stocks from the Fast-Moving Consumer Goods (FMCG) sector (HUL, ITC, Britannia Industries) and Pharma sector (Sun Pharmaceuticals Industries, Dr Reddy Laboratories and Cipla) have been taken into consideration. Five key metrics are covered to assess the stock’s performance: Stock return, Correlation, Beta Compound Annual Growth Rate (CAGR), and Dividend yield. For stock return a comparison is made between stock market return and selected stock return, also the average return of each stock is calculated and compared with the average market return during market downturns. Correlation has been used to understand if there is a relationship between stock returns and nifty returns. Beta has been used to understand the sensitivity of the stock in relation to the market and Compound Annual Growth Rate has been used to analyze the long-term stability of each stock. The average dividend yield is calculated to understand the extra return that an investor can get on his investments on top of capital gain. The study findings revealed that incorporating stocks from the Fast-Moving Consumer Goods (FMCG) and Pharmaceutical (Pharma) sectors protects investors against market downturns, Additionally, the research highlights the long-term stability of returns associated with defensive stocks, making them a valuable component for investors seeking portfolio diversification and a buffer against market fluctuations. Keywords: Defensive Stocks, Market Downturns, Stock Market Return, Beta, CAGR, FMCG, Pharma.

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Hui, Eddie C. M., Sheung-Chi Phillip Yam, and Si-Wei Chen. "SHIRYAEV-ZHOU INDEX – A NOBLE APPROACH TO BENCHMARKING AND ANALYSIS OF REAL ESTATE STOCKS." International Journal of Strategic Property Management 16, no. 2 (June 19, 2012): 158–72. http://dx.doi.org/10.3846/1648715x.2011.638946.

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Real estate markets and real estate stocks are interrelated and are important not only to the investors, but also to the academics. Real estate stocks are, in a sense, good measures of performance of the physical real estate market. The objective of this paper is to provide a preliminary study on gauging the performances of real estate stocks in Hong Kong using the Shiryaev-Zhou index. Evidence shows that the Shiryaev-Zhou index can gauge a real estate stock's performance, good or bad, according to the sign of the Shiryaev-Zhou index. Thus a trading strategy can be formulated as follows: buy a stock if its Shiryaev-Zhou index changes from negative to positive, then hold it until its Shiryaev-Zhou index turns negative, when it is time to sell the stock. We examine the Shiryaev-Zhou indices of the real estate stocks in Hong Kong, and from this we deduce the latest best selling dates of the stocks during the period of our study. The Shiryaev-Zhou index could be an indicator of whether the market is bullish or bearish and consequently tells an investor to hold a stock or not, and it naturally leads to an optimal selling strategy that maximize the average ratio of the selling price to the maximum stock price when the underlying coefficients are assumed to be constant over a definite period of time.

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Dissertationen zum Thema "Stocks"

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Wong, Sau-shing Pierre. "A study of the correlation of share price movements of Taiwan listed companies with cross holdings /." Hong Kong : University of Hong Kong, 1997. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18836288.

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Abadiga, Gidi A., and Marcel Neibig. "Value vs Growth Stocks : Do Value Stocks Outperform Growth Stocks? Stockholm Stock Markets, 1995-2009." Thesis, Södertörns högskola, Institutionen för ekonomi och företagande, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-16720.

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Denna studie undersöker om en investering i värdeaktier kan generera en bättre avkastning jämfört med en investering i tillväxtaktier. Historisk data för aktier som handlats på Stockholmsbörsen har sammanställts från diverse källor. Till exempel Börsguide och från databasen Thomson Reuters Ecowin Pro. Med hjälp av denna och övrig relevant historisk sekundärdata har aktier grupperats in i värde- och tillväxtportföljer beroende på deras P/E-tal i fem portföljer med olika köp- och innehavstider som sträcker sig från 12 upp till 60 månader mellan åren 1996 och 2009. Inom varje innehavstid för de olika portföljerna har antalet av värde- och tillväxtaktier varierat. Från, till exempel, 11 aktier under period ett till 20 aktier under period fem. Aktier har ”köpts” och hållits kvar med en inledande investeringar på 20000 SEK i början av varje portföljs innehavstid med hänsyn till studiens syfte. Avkastningen för dessa investeringar beräknas med tre olika genomsnittliga avkastningsberäkningar. Årliga medelprisavkastningar, innehavsavkastningar och riskjusterade avkastningar. Beräkningar har gjorts för årlig innehavsperiod, för hela innehavsperioden och för alla portföljers innehavsperioder tillsammans. Utifrån resultaten för dessa beräkningar har utvecklingen för värde- och tillväxtaktier analyserats. När all fem portföljer jämförs tillsammans och den årliga medelvärdesavkastningen beräknats, så genererar värdeaktierna i genomsnitt 15,1 % högre avkastning än tillväxtaktier gällande en årlig genomsnittlig riskjusterad avkastning. Resultatet för innehavsavkastning är i genomsnitt 5,6 % högre än för tillväxtaktierna. De här resultaten tyder på att en investering i värdeaktier, genom att använda historisk fundamental information, kan generera en bättre avkastning jämfört med tillväxtaktier. Följaktligen kan man försiktigt hävda att Stockholmsbörsen tycks uppvisa egenskaper gällande en semi-stark form av den effektiva marknadshypotesen.<br>This study tries to examine if investment in value stocks (poor performing stocks) can generate superior returns over investment in growth stocks. Historical stock data for stocks traded in Stockholmstock markets are collected from various sources such as Börsguide and Reuters Thomson Ecowin Pro database. Using these and other relevant secondary historical data, stocks were grouped into value and growth portfolios depending on their P/E-multiples for five buy and hold periods which range from twelve months up to sixty months between investment periods 1996 and 2009. In each portfolio holding period, different numbers of value and growth stocks, ranging from, for example eleven stocks in period one, to twenty stocks in period five are purchased and held for an initial investment of 20000 SEK at the beginning of each portfolio holding period for the purpose of the study. The returns to these investments are computed for three different average return measurements. These are annual Mean Price Returns, Holding Period Returns and Risk-Adjusted Returns for each of the portfolio holding year, for the entire holding periods as well as for the entire portfolio holding periods combined together. Using the spread between these measures, the performances of both value and growth stocks are analyzed. When all the five portfolios are combined together and the mean annual rate of returns are computed, value stocks outperform growth stocks by an average of 15.1 % mean annual Risk -Adjusted Return Rate. The result for Holding Period Return is an average of 5.6 % higher than the growth stocks. These results indicate that investment made in value stocks identified using historical fundamental data can generate superior returns than growth stocks. Consequently, it can cautiously be argued thatStockholmstock markets appear to exhibit the characteristics of the semi-strong form of the Efficient Market Hypothesis.

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Wang, Hanfeng. "Essays on stock trading volume, volatility and information." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38826185.

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Cheung, Ping-wing Ricky. "Relative strength trading rules and efficiency of the Hong Kong market /." [Hong Kong : University of Hong Kong], 1985. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12316866.

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Polte, Marcel. "Aktiengattungen : eine rechtsvergleichende Untersuchung zum deutschen, US-amerikanischen und englischen Recht /." Frankfurt am Main ; New York : Lang, 2005. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=014612988&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Pang, Siu-kei. "Red-chips' (China-affiliated companies' shares) profitability, attractiveness and its implication to Hong Kong stock market." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19873815.

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Chu, Kut-leung. "The CEV model : estimation and option pricing /." Click to view the E-thesis via HKUTO, 1999. http://sunzi.lib.hku.hk/hkuto/record/B4257500X.

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Yiu, Fan-lai. "Applicability of various option pricing models in Hong Kong warrants market /." [Hong Kong : University of Hong Kong], 1993. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13570493.

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Ko, Chi-keung Anthony. "A preliminary study of Hong Kong warrants using the Black-Scholesoption pricing model /." [Hong Kong] : University of Hong Kong, 1985. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12316726.

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Yeh, Ho-leung Patrick. "The impact of new issues of derivative securities and the underlying blue chip securities /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19872446.

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Bücher zum Thema "Stocks"

1

Merrill, John F. Beyond stocks. Houston: Tanglewood Pub., 1997.

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Inc, Market Facts, ed. OTC stocks. Chicago, Ill. (676 N. St. Clair St., Chicago 60611): Market Facts, 1987.

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1915-, Samuelson Paul Anthony, ed. Growth stocks. New York: Chelsea House, 1988.

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Giangregorio, Dominic P. The penny stock millionaire: Fortunes in mini-stocks. [United States]: Xlibris, 2008.

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Gerald, Krefetz. The basics of stocks. Chicago, Ill: Dearborn Financial Pub., 1992.

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Matras, Kevin, ed. Finding #1 Stocks. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119201649.

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Faerber, Esme. All About Stocks. New York: McGraw-Hill, 2007.

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Program, Oregon Investor Information, ed. Stocks and bonds. Salem, Or: Oregon Division of Finance & Corporate Securities, Investor Information Program, 1995.

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Corrigan, Arnold. Understanding common stocks. Stamford, Conn: Longmeadow Press, 1987.

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Group, Mintel International, ed. Stocks and shares. London: Mintel International Group, 1999.

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Buchteile zum Thema "Stocks"

1

Kakushadze, Zura, and Juan Andrés Serur. "Stocks." In 151 Trading Strategies, 41–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02792-6_3.

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Poncet, Patrice, and Roland Portait. "Stocks, Stock Markets, and Stock Indices." In Springer Texts in Business and Economics, 255–308. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84600-8_8.

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Khanchandani, Khushi, Neha Patil, and Vineeta Bhujle. "Virtual Stocks: Stock Market Simulator." In Information and Communication Technology for Competitive Strategies (ICTCS 2021), 253–65. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0098-3_26.

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Paterson, Ron. "Consignment Stocks." In Off Balance Sheet Finance, 73–77. London: Palgrave Macmillan UK, 1993. http://dx.doi.org/10.1007/978-1-349-12613-2_9.

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Cort, José Luis, and Pablo Abaunza. "Two Stocks." In SpringerBriefs in Biology, 19–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11545-6_3.

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Thomsett, Michael C. "Energy Stocks." In Investing in Energy, 83–108. New York: Palgrave Macmillan US, 2014. http://dx.doi.org/10.1057/9781137358479_7.

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Blagden, Cyprian. "The Stocks." In The Stationers' Company, 92–109. London: Routledge, 2024. http://dx.doi.org/10.4324/9781003558583-6.

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Kanbur, Ravi. "Buffer Stocks." In The New Palgrave Dictionary of Economics, 1143–45. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_59.

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Fong, Wai Mun. "Growth Stocks." In The Lottery Mindset: Investors, Gambling and the Stock Market, 77–100. London: Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137381736_4.

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Knott, Geoffrey. "Controlling Stocks." In Financial Management, 243–49. London: Macmillan Education UK, 1998. http://dx.doi.org/10.1007/978-1-349-14766-3_19.

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Konferenzberichte zum Thema "Stocks"

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Havidz, Shinta Amalina Hazrati, Umair Saeed Bhutta, Sheren Susanto, Nicholas Lai Wijaya, and Najaf Iqbal. "Big Data Analytics of Green Stocks vs. Islamic Stocks: Are These Safe Havens?" In 2025 International Conference on Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), 41–44. IEEE, 2025. https://doi.org/10.1109/icocseti63724.2025.11020223.

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Zhang, Yanting, Lei Yang, Shipei Du, Yu Sun, and Yaxin Hou. "Predicting financial and manufacturing stocks in China's stock market with random forest." In Second International Conference on Big Data, Computational Intelligence and Applications (BDCIA 2024), edited by Sos S. Agaian, 107. SPIE, 2025. https://doi.org/10.1117/12.3059615.

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Aravindhan, M., and D. Manikavelan. "Improving Stock Market Close Price Prediction: A Generalized Linear Model Approach for NSE Banking Stocks." In 2024 5th IEEE Global Conference for Advancement in Technology (GCAT), 1–6. IEEE, 2024. https://doi.org/10.1109/gcat62922.2024.10923875.

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Kaburuan, Emil R., Amiya Bhaumik, Oyyappan Duraipandi, and Alfi Aziz. "Prediction of Stock Prices Using a Combination of LSTM and GRU Algorithms on LQ45 Indexed Stocks." In 2024 International Conference on Orange Technology (ICOT), 1–8. IEEE, 2024. https://doi.org/10.1109/icot64290.2024.10936928.

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Sharma, Akshat, Ashtha Goyal, Durgaprasad Gangodkar, and Yogesh Lohumi. "Quantitative Analysis for Stocks and Cryptocurrencies using Python." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739272.

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Irsyad, Akhmad, Anton Prafanto, Muhammad Bambang Firdaus, Hario Jati Setiyadi, Putut Pamilih Widagdo, and Muhammad Abdillah Rahmat. "Forecasting Stocks Prices with GRU and Attention Mechanism." In 2024 International Conference on Electrical Engineering and Informatics (ICELTICs), 114–19. IEEE, 2024. https://doi.org/10.1109/iceltics62730.2024.10776108.

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Paraschiv, Daniel, and Srinivas Raghavendra. "Stocks scanner evaluator for stocks or options." In 2009 IEEE Symposium on Computational Intelligence for Financial Engineering (CIFEr). IEEE, 2009. http://dx.doi.org/10.1109/cifer.2009.4937499.

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Lynch, John, and Brad Cannon. "Does Cross-Sectional Return Extrapolation Explain Anomalies?" In 5th World Conference on Business, Management, Finance, Economics, and Marketing, 31. Eurasia Conferences, 2024. http://dx.doi.org/10.62422/978-81-968539-6-9-010.

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We provide evidence that dividend-paying stocks are less exposed to return extrapolation than non-dividend-paying stocks (capital-gain stocks). In particular, social media sentiment and analyst price targets of capital-gain stocks are each significantly more sensitive to past returns. Consistent with models of return extrapolation, capital-gain stocks earn higher momentum and long-term reversal returns. The significant difference in returns is not explained by factors nor stock characteristics related to dividend status. The value premium, however, is similar among both groups. Collectively, our findings suggest that return extrapolation may be an important source of some anomaly returns.

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Liu, Fang, XiangXia Li, and Lin Wang. "Exploring Cluster Stocks based on deep learning for Stock Prediction." In 2019 12th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2019. http://dx.doi.org/10.1109/iscid.2019.10107.

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Widiputra, Harya, and Leo Christianto. "Indonesia stock exchange liquid stocks identification using self-organizing map." In 2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE). IEEE, 2012. http://dx.doi.org/10.1109/urke.2012.6319526.

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Berichte der Organisationen zum Thema "Stocks"

1

Cochrane, John. Stocks as Money: Convenience Yield and the Tech-Stock Bubble. Cambridge, MA: National Bureau of Economic Research, June 2002. http://dx.doi.org/10.3386/w8987.

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Ang, Andrew, Geert Bekaert, and Jun Liu. Why Stocks May Disappoint. Cambridge, MA: National Bureau of Economic Research, July 2000. http://dx.doi.org/10.3386/w7783.

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3

Woodall, Christopher W., John W. Coulston, Grant M. Domke, Brian F. Walters, David N. Wear, James E. Smith, Hans-Erik Andersen, et al. The U.S. forest carbon accounting framework: stocks and stock change, 1990-2016. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station, 2015. http://dx.doi.org/10.2737/nrs-gtr-154.

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Goetzmann, William, Akiko Watanabe, and Masahiro Watanabe. Procyclical Stocks Earn Higher Returns. Cambridge, MA: National Bureau of Economic Research, May 2024. http://dx.doi.org/10.3386/w32509.

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5

Naddafi, Rahmat, Göran Sundblad, Alfred Sandström, Lachlan Fetterplace, Jerker Vinterstare, Martin Ogonowski, and Nataliia Kulatska. Developing management goals and associated assessment methods for Sweden’s nationally managed fish stocks : a project synthesis. Department of Aquatic Resources, Swedish University of Agricultural Sciences, 2023. http://dx.doi.org/10.54612/a.31cfjep2i0.

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This report summarizes and synthesizes results from the Swedish Agency of Marine and Water Management (SwAM, or HaV) funded project “Förvaltningsmål för nationella arter (Management goals for nationally managed species)”. The objectives of the project have been to promote the development of management goals and associated status assessment methods and indicators, as well as reference points, for some nationally managed fish stocks both in coastal as well as freshwater areas. The report focusses largely on species and stocks that can be defined as data-poor. Such stocks are characterised by marked limitations in data availability and/or resources allocated to detailed analytical stock projections. Data-poor stocks also often lack carefully formulated management goals and associated methods and indicators for assessing stock status. In this report, we provide an overview of potential assessment methods and indicators and try to synthesise how they work and what the strengths and weaknesses are by applying them to selected data poor stocks such as pikeperch, pike, whitefish, and vendace. We also discuss how they relate to different potential management goals and provide recommendations for their application. We grouped the indicators and assessment methods by the three categories that are now used in the yearly status assessment framework provided by SLU Aqua (Resursöversikten/Fiskbarometern) – i) mortality, ii) abundance/biomass and iii) size/age structure. The results are also described for these three main categories of assessment indicators. Included is also a status report from a size- and age-based population dynamics model (Stock Synthesis 3) that is being developed for pikeperch in Lake Hjälmaren. An important experience from the project is that to improve the assessment methods for Swedish national fish stocks, it is important that managers develop both general as well as more detailed quantitative goals for the individual stocks. This should ideally be conducted in various forms of collaboration with the main stakeholders and scientists involved with assessment as participatory processes foster legitimacy. Carefully articulated management goals, which are possible to translate into quantitative targets, will facilitate the development of various approaches and methods to monitor stock statuses. Given the strong and complex interactions of fish and their environments it is also important to consider other pressures than fisheries when developing indicators and assessment methods. Our synthesis highlights a number of areas where the assessment of data-poor stocks can be improved: 1. Apply precautionary principles for data-limited stocks, particularly ones that are known to be vulnerable to exploitation. 2. Tailor approaches to how fisheries are managed in Sweden. Swedish nationally managed fish stocks are not managed by quotas (with one exception, vendace in the Bothnian Bay) and do not aim for maximum sustainable yield. Instead, the coastal and inland fisheries are managed by regulating the effort in the small-scale commercial fisheries (number of fishers/licenses and amount of gear). Regulation of recreational and subsistence fisheries effort, in terms of licenses or number of fishers) is not applied, nor possible since the fisheries is lacking obligatory notification and reporting systems. All national fisheries, however, are regulated by various technical measures (closed areas, size-limits, bag-limits, gear restrictions etc). Thus, goals and assessment methods that result in harvest limits or quota recommendations expressed in e.g. biomass/numbers are difficult to use as basis for management. Instead, there is a need for alternative management goals and associated assessment methods. 3. Use best practice methods and indicators and adapt as scientific knowledge is developed. Data-limited methods are developing rapidly, and new methods/approaches are proposed in the scientific literature every year. It is thus important to be updated on the most recent developments. 4. Clearly describe limitations/assumptions of methods used. It is important to be aware of and critically evaluate the assumptions underlying the analyses, and to carefully communicate uncertainty together with the stock status assessment. 5. Be particularly careful with low sample numbers. Many indicators and methods can be applied also on small sample sizes, however, the accuracy and precision of the estimates risk being low in such cases. 6. Accept that there is no "gold standard" for fisheries assessment. Each case study is unique and needs to be balanced against data availability, local needs and other important factors. This also means that analysts need to be careful when using generic reference levels or “borrowing” data from other stocks. 7. If possible, use several different methods/indicators. Although several indicators aim to measure similar aspects of the stock, small methodological differences can support the overall interpretation of individual indicator values. It is particularly important to incorporate many aspects and indicators (size/age/abundance/mortality) in order to produce a balanced assessment. 8. Develop means of communication. Indicators and goals should be easy to understand. However, interpretation of results from multi-indicator frameworks can be challenging. There is thus a need for finding ways of communication that can convey complicated results in a simple-to-understand manner. 9. For details on additional improvements, we refer the reader to the sub-header “recommendations for the future” found under each chapter. The implementation of Stock Synthesis for pikeperch in Lake Hjälmaren showed that it is possible to develop a more ambitious and detailed stock assessment model for a relatively data-poor stock. The model results partly support earlier interpretations of the development of the stock and the importance of the changes in regulations in 2001 (increased minimum size, increased mesh size and reduced mortality of undersized pikeperch). Before the model can be implemented and used for practical management, a number of actions for improvement are needed, which are highlighted in the relevant chapter. The most important next step is establishing management goals and reference levels for this stock. We recommend that such a dialogue is initiated by managers. The fisheries management goals should consider both biomass, fisheries mortality and size-based targets. To conclude, we stress the importance of improving all ongoing aspects related to the assessments of data-poor Swedish stocks. Strong local stocks and sustainable fisheries are vital for a variety of fisheries-related businesses and practices, particularly in rural areas, providing economical and societal value. Fishes also have important roles in aquatic food-webs and it is important that ecological values are managed wisely in order to reach targets for water quality, ecosystem structure and diversity. Given the strong and complex interactions of fish and their environments it is also important to consider other pressures than fisheries when developing indicators and assessment methods.

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Jagannathan, Ravi. On Frequent Batch Auctions for Stocks. Cambridge, MA: National Bureau of Economic Research, October 2019. http://dx.doi.org/10.3386/w26341.

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7

Cieslak, Anna, and Hao Pang. Common Shocks in Stocks and Bonds. Cambridge, MA: National Bureau of Economic Research, December 2020. http://dx.doi.org/10.3386/w28184.

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8

Smith, James E., Linda S. Heath, and Michael C. Nichols. US forest carbon calculation tool: forest-land carbon stocks and net annual stock change. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station, 2007. http://dx.doi.org/10.2737/nrs-gtr-13.

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9

Guidolin, Massimo, and Giovanna Nicodano. Managing International Portfolios with Small Capitalization Stocks. Federal Reserve Bank of St. Louis, 2007. http://dx.doi.org/10.20955/wp.2007.030.

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Research Institute (IFPRI), International Food Policy. Food security stocks: Economic and operational issues. Washington, DC: International Food Policy Research Institute, 2017. http://dx.doi.org/10.2499/9780896292499_08.

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