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Letteratura scientifica selezionata sul tema "Stocks Rate of return Mathematical models"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 19 febbraio 2022

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Articoli di riviste sul tema "Stocks Rate of return Mathematical models"

1

Oygur, Tunc, e Gazanfer Unal. "Evidence of Large Fluctuations of Stock Return and Financial Crises from Turkey: Using Wavelet Coherency and Varma Modeling to Forecast Stock Return". Fluctuation and Noise Letters 16, n. 02 (25 maggio 2017): 1750020. http://dx.doi.org/10.1142/s0219477517500201.

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Shocks, jumps, booms and busts are typical large fluctuation markers which appear in crisis. Models and leading indicators vary according to crisis type in spite of the fact that there are a lot of different models and leading indicators in literature to determine structure of crisis. In this paper, we investigate structure of dynamic correlation of stock return, interest rate, exchange rate and trade balance differences in crisis periods in Turkey over the period between October 1990 and March 2015 by applying wavelet coherency methodologies to determine nature of crises. The time period includes the Turkeys currency and banking crises; US sub-prime mortgage crisis and the European sovereign debt crisis occurred in 1994, 2001, 2008 and 2009, respectively. Empirical results showed that stock return, interest rate, exchange rate and trade balance differences are significantly linked during the financial crises in Turkey. The cross wavelet power, the wavelet coherency, the multiple wavelet coherency and the quadruple wavelet coherency methodologies have been used to examine structure of dynamic correlation. Moreover, in consequence of quadruple and multiple wavelet coherence, strongly correlated large scales indicate linear behavior and, hence VARMA (vector autoregressive moving average) gives better fitting and forecasting performance. In addition, increasing the dimensions of the model for strongly correlated scales leads to more accurate results compared to scalar counterparts.

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2

Lioui, Abraham, e Paulo Maio. "Interest Rate Risk and the Cross Section of Stock Returns". Journal of Financial and Quantitative Analysis 49, n. 2 (10 marzo 2014): 483–511. http://dx.doi.org/10.1017/s0022109014000131.

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AbstractWe derive a macroeconomic asset pricing model in which the key factor is the opportunity cost of money. The model explains well the cross section of stock returns in addition to the excess market return. The interest rate factor is priced and seems to drive most of the explanatory power of the model. In this model, both value stocks and past long-term losers enjoy higher average (excess) returns because they have higher interest rate risk than growth/past winner stocks. The model significantly outperforms the nested models (capital asset pricing model (CAPM) and consumption CAPM (CCAPM)) and compares favorably with alternative macroeconomic models.

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3

Škrinjarić, Tihana, e Boško Šego. "Using Grey Incidence Analysis Approach in Portfolio Selection". International Journal of Financial Studies 7, n. 1 (23 dicembre 2018): 1. http://dx.doi.org/10.3390/ijfs7010001.

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Due to the development of financial markets, products, financial and mathematical models, portfolio selection today represents a comprehensive set of activities. Investors take into consideration many different factors, such as the market factors, return distribution characteristics and financial statements information. This research applies a Grey Relational Analysis (GRA) approach to evaluate the performance on a sample of stocks by taking those different factors into consideration. The results based upon a sample of 55 stocks for the trading year 2017 on the Croatian capital market show that using GRA approach in portfolio selection provides useful guidance for investors when making investment decisions, and better portfolio results in terms of risk and return are reachable compared to an equally weighted portfolio benchmark.

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4

Lee, Cheng-Wen, e Dolgion Gankhuyag. "Portfolio Optimization in Post Financial Crisis of 2008-2009 in the Mongolian Stock Exchange". Jurnal METRIS 21, n. 01 (1 giugno 2020): 47–58. http://dx.doi.org/10.25170/metris.v21i01.2432.

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In this study, we present the Mongolian stock market’s performance post phenomenal financial crisis of 2008-2009, opportunities to invest and the risks problems. For analysis of the study, we used financial portfolio optimization models with restricted structure, mathematical statistic methods and financial methods. First, we considered about portfolio optimization in the Mongolian Stock Exchange using Markowitz’s modern portfolio theory and Telser’s safety first model. We used MSE weekly trading data chosen 50 most traded stocks out of 237 stocks listed at the MSE between 2009 and 2013. We generated 50 weeks mean-variance portfolio and safety first portfolio for 2014 and discussed. We considered weekly investment in the MSE using mean-variance portfolio andsafety first portfolio. The mean-variance portfolio has the best performance of weekly portfolio return with average weekly return and cumulative return. We found stable portfolio against investing risk and did back-test the result. For prospect investors in the MSE, we suggest invest and earn high return in the MSE.

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5

Hatem, Ben Said. "How Can We Measure Stock Market Returns? An International Comparison". International Business Research 10, n. 5 (24 aprile 2017): 121. http://dx.doi.org/10.5539/ibr.v10n5p121.

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The aim of our empirical work is to identify how we can measure stock returns. Stocks returns are approximated as the growth rate of market share price. We use two measures of stocks returns; return on assets, ROA, and return on equity, ROE. As a control variable, we use firm age. Our samples consists of 186 firms from United Kingdom and 186 firms from Ukraine studied over a period of 4 years from 2007 to 2010. To this end, we estimate three models. Using the data panels methodology, we conclude that return on equity approximates better socks returns for United kingdom and Ukraine. We could not however find evidence on a significant association between return on assets and stock returns.

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6

Yuan, Man. "Mathematical Analysis Method for Stock Market Using MA and KDJ Indicator". Asian Business Research 4, n. 2 (6 giugno 2019): 21. http://dx.doi.org/10.20849/abr.v4i2.618.

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With the rapid development of Economic Globalization as well as international trade and capital transaction, stock market take a more and more important position in the finance analysis.In this thesis, I combined the MA the KDJ, MA for long term trend analysis and KDJ for short term analysis. First I introduced MA and KDJ separately, their strength and weakness. Then I try to put them together, adjust the parameters to make them suitable for Shanghai Stock Exchange Composite Index.Then I use my model to simulate transaction in real world, estimate the rate of return and comparing with the stocks’ holding rate and inflation rate. The result is pleasant. At last, I give a conclusion and a further advice to this model.

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7

Shin, Dong Hoon. "Optimal Pairs Trading Strategy under Geometric Brownian Motion and its Application to the US stocks". International Journal for Innovation Education and Research 9, n. 5 (1 maggio 2021): 550–60. http://dx.doi.org/10.31686/ijier.vol9.iss5.3125.

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This study is a study on pair trading, a representative market-neutral investment strategy. A general pair trading strategy uses econometric techniques to select a pair of stocks and calculates the trading price level depending on a single variable called the variance of stock returns without any theoretical background. This study applies the optimal pair trading strategy proposed by Liu et al. (2020) to the top US market cap stocks and examines its performance. This strategy proposes a mathematical background for optimally calculating the trading price level. Since the statistical method for pair selection can be omitted, a pair can be formed only with good stocks with guaranteed liquidity. In addition, strategic risk management is possible because the stop loss set according to the market situation is performed. As the top 10 market cap stocks traded on the US exchange, daily closing price data for 10 years from 2011 to 2020 were applied to optimal pair trading. It was confirmed that the rate of return may differ depending on the adjustment of various parameters including the level of stop loss. In this study, an applicated strategy that properly managed pairs trading and stocks together earned the minimum annual average return 17.88% and the Sharpe ratio reached 1.81. These numbers can be better with the adjustment of the parameters.

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8

Neilson, E. T., D. A. MacLean, P. A. Arp, F. R. Meng, C. P.-A. Bourque e J. S. Bhatti. "Modeling carbon sequestration with CO2Fix and a timber supply model for use in forest management planning". Canadian Journal of Soil Science 86, Special Issue (1 marzo 2006): 219–33. http://dx.doi.org/10.4141/s05-081.

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Carbon (C) dynamics and forest management have become integrated in recent years, largely due to the Kyoto Protocol stipulating that forest C changes may be accountable in an emissions framework. A C stock modeling framework for forest managers is introduced in this paper. Empirical growth and yield models are used to develop sustainable timber supply for forest companies. These models use linear programming to solve the complex mathematical problem of timing and allocation of forest harvest and silviculture interventions. In this paper, we evaluated the effects of “business as usual”forest management versus management objectives to maximize C sequestration. Goal programming was used to minimize the deviation of two goals for C forest management: maximizing C in the forest, and maximizing the return on investment (net present value of forest timber products). Species-specific wood-to-C content conversion factors were used to parameterize the amount of C in forest stands on Canadian Forces Base Gagetown forest lands in New Brunswick, Canada. Goal programming reduced the loss of revenue associated with increasing C stocks in the forest. Partial harvesting and high valued end-products tended to increase C stocks and provided a higher return on investment in the simulations. Key words: Carbon stock modeling framework, forest, goal programming, partial harvesting, timber supply

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9

Setyawati, Ni Putu Eka Cahya, e Gede Merta Sudiartha. "PEMBENTUKAN PORTOFOLIO OPTIMAL MENGGUNAKAN MODEL MARKOWITZ". E-Jurnal Manajemen Universitas Udayana 8, n. 7 (10 marzo 2019): 4213. http://dx.doi.org/10.24843/ejmunud.2019.v08.i07.p08.

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Investment can be related to investing some funds in financial assets or real assets such as land, gold, shares, deposits, bonds and other forms. As a party who is make an investment, investors will be faced with a variety of options in investing that has a rate of return and risk-appropriate expectations. The ways that usually used by investors is to diversify through the creation of a portfolio. The aim of this research is to know the stocks that can be inserted into the optimal portofolio as well as the proportions of each of the stocks, that the model established by Markowitz. This research was conducted on the IDX30 index from January 2017 to January 2018, especially in the mining sector and consumer goods. The results showed, from 14 stock, 7 stock was selected as candidate of portfolio optimal Markowitz models. Stocks that are worth being a member of the optimal portfolio by a proportion of the allocation of each fund i.e. stocks ADRO (0.55%), ASII (0.15%), GGRM (17.61%), ICBP (9.46%), MEDC (5.275), UNVR (41.11%), and UNTR (25.86%), it gives the expected portfolio return of 3.2% and with the level of risk of 3.3%. Keywords: optimal portfolio, Markowitz model, mining sector and consumer goods

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Qudratullah, Mohammad Farhan. "Treynor Ratio to Measure Islamic Stock Performance in Indonesia". Jurnal Fourier 8, n. 1 (30 aprile 2019): 1–13. http://dx.doi.org/10.14421/fourier.2019.81.1-13.

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Treynor Ratio merupakan model pioner inovatif ukuran kinerja saham yang dikemukakan Jack Treynor pada tahun 1965 yang terdiri atas 3 (tiga) komponen, yaitu return saham, return bebas risiko, dan beta saham. Banyak penelitian mendekati return bebas risiko dengan suku bunga termasuk saat mengukur kinerja saham syariah, sedangkan suku bunga dilarang dalam konsep keuangan islam. Tulisan ini membahas variabel alternatif untuk mendekati return bebas risiko selain dengan suku bunga (BI-Rate), yaitu dengan 4 (empat) pendekatan, yaitu: menghilangkan suku bunga, mengganti dengan zakat rate, mengganti dengan inflasi, dan mengganti dengan gross domestic produc (GDP) pada model Treynor Ratio yang diimplementasikan pada pasar modal syariah di Indonesia periode Januari 2011-Juli 2018. Hasil yang diperoleh adalah terdapat kesesuaian yang sangat tinggi hasil pengukuran model Treynor Ratio dengan suku bunga dengan keempat model lainnya. Namun, model-model tersebut tidak menjamin bahwa saham yang memilki kinerja terbaik pada saat ini akan memilki kinerja terbaik dimasa yang akan datang atau sebaliknya. Dilihat dari kedekatan hasil pengukuran kinerjanya, kelima model Treynor Ratio tersebut dapat dikelompokan jadi 2 (dua), yaitu model dengan suku bunga, model dengan inflasi, dan model dengan GDP sebagai kelompok pertama, sedangkan model tanpa suku bunga dan model dengan zakat-rate sebagai kelompok kedua. [Treynor Ratio is an innovative pioneer model the size of stock performance proposed by Jack Treynor in 1965 which consists of 3 (three) components, namely stock returns, risk free returns, and stock beta. Many studies approach risk-free returns with interest rates, including when measuring the performance of Islamic stocks, while interest rates are prohibited in the concept of Islamic finance. This paper discusses alternative variables to approach risk-free returns other than interest rates (BI-Rate), namely with 4 (four) approaches, namely: eliminating interest rates, changing zakat rates, changing inflation, and substituting gross domestic products (GDP) in the Treynor Ratio model that is implemented in the Islamic capital market in Indonesia for the period January 2011 - July 2018. The results obtained are very high conformity in the measurement results of the Treynor Ratio model with interest rates with the other four models. However, these models do not guarantee that stocks that have the best performance at this time will have the best performance in the future or vice versa. Judging from the closeness of the results of performance measurement, the five Treynor Ratio models can be grouped into 2 (two), namely models with interest rates, models with inflation, and models with GDP as the first group, while models without interest rates and models with zakat-rate as second group.]

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Tesi sul tema "Stocks Rate of return Mathematical models"

1

Luo, Yan, e 罗妍. "Three essays on noise and institutional trading". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44549246.

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Emeny, Matthew. "The book-to-market effect and the behaviour of stock returns in the Australian equity market". Title page, contents and abstract only, 1998. http://web4.library.adelaide.edu.au/theses/09ECM/09ecme533.pdf.

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"August 1998" Bibliography: leaves 74-78. The relationship between the returns to a stock, and ratio of book equity to market equity of the firm, are tested for the Australian stock market, and statistically significant evidence is found in support if the :book to market effect". Several tests are performed to determine whether this return premium is the result of additional risk or market inefficiency. No evidence is found to suggest that high book-to-market stocks are associated with additional risk, and only weak evidence is found to suggest that return premium is a result of investor over-reaction. An alternative explanation IS offered, relying on the dynamic behavior of firms and the process by which investors value the stocks of these firms.

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3

Wong, Po-shing. "Some mixture models for the joint distribution of stock's return and trading volume /". [Hong Kong] : University of Hong Kong, 1991. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13009485.

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4

Van, Wyk Tyrone. "The relationships between the price-earnings ratio and selected risk and return and valuation models". Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/53156.

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Assignment (MAcc )--University of Stellenbosch, 2002.
ENGLISH ABSTRACT: The price-earnings ratio is one of a series of benchmarks developed after the Great Depression, to measure the fair value of shares on a relative basis. It originated from the idea that investors buy the earnings of a company and that the price-earnings ratio provides a consensus indication of the future growth potential of a company. Therefore, the price-earnings ratio is a rating of a company's future profitability. The price-earnings ratio developed, over the years, firstly, into an indicator of the relative risk associated with a company as the market anomalies associated with the ratio were investigated and clarified, and the theoretical background of the ratio integrated with the portfolio theory. It is now clear that the price-earnings ratio can be a useful indicator of the risk associated with an investment and the uncertainty associated with the duration of the growth phase of a company. Secondly, the price-earnings ratio is also a growth and valuation model with a theoretical background that can be linked to popular dividend discount models and the growth opportunities approach to investment valuation. With the use of the price-earnings ratio it is easy to visualise the relative profitability and the total investment required to raise a company's rating of future profitability. This simplicity allows one the opportunity to evaluate the reasonableness and likelihood of the investment reaching its projected potential profit targets. Lastly, as a result of accounting changes and the different accounting rules in force today, the price-earnings ratio also assists in the identification and elimination of the effects of accounting on investment decisions. It is apparent that the price-earnings ratio possesses the capabilities to assist investors significantly with the analysis of investment opportunities.
AFRIKAANSE OPSOMMING: Die prys-verdienste verhouding is een van 'n reeks relatiewe maatstawwe ontwikkel na die Groot Depressie om die redelike waarde van aandele te bepaal. Dit is gebaseer op die idee dat beleggers die winste van 'n maatskappy koop en dat die prys-verdienste verhouding 'n konsensus aanduiding verskaf van die toekomstige groeipotensiaal van 'n maatskappy. As gevolg hiervan is die prys-verdienste verhouding 'n aanduiding van die relatiewe toekomstige winsgewendheid van 'n maatskappy. Die prys-verdienste verhouding het oor die jare ontwikkel, eerstens as 'n aanwyser van die relatiewe risiko verbonde aan 'n maatskappy soos abnormaliteite wat daaraan verwant is ondersoek en verklaar is, en die teorieë onderliggend aan die verhouding ontwikkel het saam met die portefeulje teorie. Dit is nou duidelik dat die prys-verdienste verhouding 'n bruikbare aanduider is van die risiko wat geassosieer word met 'n belegging en die onsekerheid wat gepaard gaan met die duur van die groeifase van 'n maatskappy. Tweedens is die prys-verdienste verhouding ook 'n waardasie- en groeimodel met 'n teoretiese agtergrond wat verband hou met die populêre dividend verdiskonteringsmodelle en die groeigeleenthede-benadering tot waardasie. Met die gebruik van die prys-verdienste verhouding is dit maklik om die relatiewe winsgewendheid en die totale belegging wat benodig word om die waarde van die relatiewe winsgewendheid van 'n maatskappy te verhoog, tevisualiseer. Hierdie eenvoud verskaf die geleentheid om die redelikheid en die waarskynlikheid van 'n belegging om sy voorsiene winsgewendheidsdoelwitte te bereik, te evalueer. Laastens, as 'n resultaat van die rekeningkundige veranderinge, en die verskillende rekeningundige reëls huidiglik van toepassing in die wêreld, help die prys-verdienste verhouding ook met die identifikasie en die eliminasie van rekeningkundige komplikasies op beleggingsbesluite. Dit is duidelik dat die prys-verdienste verhouding die vermoë het om die belegger by te staan met die ontleding van beleggingsgeleenthede.

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Xu, Jin, e 徐瑾. "Distress risk and value premium: evidence from Japan". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40203682.

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Lin, Gang. "Nesting regime-switching GARCH models and stock market volatility, returns and the business cycle /". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1998. http://wwwlib.umi.com/cr/ucsd/fullcit?p9906497.

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7

King, Daniel Jonathan. "Modelling stock return volatility dynamics in selected African markets". Thesis, Rhodes University, 2013. http://hdl.handle.net/10962/d1006452.

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Stock return volatility has been shown to occasionally exhibit discrete structural shifts. These shifts are particularly evident in the transition from ‘normal’ to crisis periods, and tend to be more pronounced in developing markets. This study aims to establish whether accounting for structural changes in the conditional variance process, through the use of Markov-switching models, improves estimates and forecasts of stock return volatility over those of the more conventional single-state (G)ARCH models, within and across selected African markets for the period 2002-2012. In the univariate portion of the study, the performances of various Markov-switching models are tested against a single-state benchmark model through the use of in-sample goodness-of-fit and predictive ability measures. In the multivariate context, the single-state and Markov-switching models are comparatively assessed according to their usefulness in constructing optimal stock portfolios. It is found that, even after accounting for structural breaks in the conditional variance process, conventional GARCH effects remain important to capturing the heteroscedasticity evident in the data. However, those univariate models which include a GARCH term are shown to perform comparatively poorly when used for forecasting purposes. Additionally, in the multivariate study, the use of Markov-switching variance-covariance estimates improves risk-adjusted portfolio returns when compared to portfolios that are constructed using the more conventional single-state models. While there is evidence that the use of some Markov-switching models can result in better forecasts and higher risk-adjusted returns than those models which include GARCH effects, the inability of the simpler Markov-switching models to fully capture the heteroscedasticity in the data remains problematic.

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Fratus, Brian J. "Rational asset pricing : book-to-market equity as a proxy for risk in utility stocks /". Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-11242009-020322/.

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Wong, Po-shing, e 黃寶誠. "Some mixture models for the joint distribution of stock's return and trading volume". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31210065.

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Wagenaar, Elmien. "A mathematical approach to financial allocation strategies". Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/52648.

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Libri sul tema "Stocks Rate of return Mathematical models"

1

Flood, Robert P. Estimating the expected marginal rate of substitution: Exploiting idiosyncratic risk. Cambridge, MA: National Bureau of Economic Research, 2004.

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2

Flood, Robert P. Estimating the expected marginal rate of substitution: Exploiting idiosyncratic risk. Cambridge, Mass: National Bureau of Economic Research, 2004.

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Kandel, Shmuel. Portfolio inefficiency and the cross-section of expected returns. Cambridge, MA: National Bureau of Economic Research, 1994.

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Campbell, John Y. Understanding risk and return. Cambridge, MA: National Bureau of Economic Research, 1993.

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Kandel, Shmuel. On the predictability of stock returns: An asset-allocation perspective. Cambridge, MA: National Bureau of Economic Research, 1995.

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Cochrane, John H. Volatility tests and efficient markets: A review essay. Cambridge, MA: National Bureau of Economic Research, 1991.

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Cecchetti, Stephen G. The equity premium and the risk free rate: Matching the moments. Cambridge, MA: National Bureau of Economic Research, 1991.

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Ferson, Wayne E. Weak and semi-strong form stock return predictability revisited. Cambridge, Mass: National Bureau of Economic Research, 2005.

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Ferson, Wayne E. Weak and semi-strong form stock return predictability, revisited. Cambridge, MA: National Bureau of Economic Research, 2004.

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Ferson, Wayne E. Weak and semi-strong form stock return predictability, revisited. Cambridge, Mass: National Bureau of Economic Research, 2004.

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Capitoli di libri sul tema "Stocks Rate of return Mathematical models"

1

Melicherčík, Igor, e Daniel Ševčovič. "Dynamic model of pension savings management with stochastic interest rates and stock returns". In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 295–303. Milano: Springer Milan, 2012. http://dx.doi.org/10.1007/978-88-470-2342-0_35.

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Nauss, Robert M. "Incorporating the Concept of Internal Rate of Return in Linear and Integer Programming Models". In Algorithms and Model Formulations in Mathematical Programming, 169. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-83724-1_23.

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3

Chen, Hong-Yi, Cheng Few Lee e Tzu Tai. "The Joint Determinants of Capital Structure and Stock Rate of Return: A LISREL Model Approach". In Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning, 1345–97. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811202391_0035.

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