This document discusses research on predicting the rankings of financial analysts. The goals are to accurately predict analyst rankings and identify variables that can discriminate between rankings. The research uses historical quarterly earnings per share forecasts and market/accounting data to initially rank analysts based on forecast accuracy. State variables are then used to predict these rankings using a Naive Bayes label ranking algorithm. Results show the predictions outperform default and naive rankings for many stocks, and certain variables like total accruals have higher discriminative power between rankings. The research aims to contribute new methods for analyzing and predicting financial analyst performance rankings.
This document discusses predicting the rankings of financial analysts based on their forecast accuracy and performance. It presents a research design to: 1) Create target rankings of analysts based on past forecast error; 2) Define state variables like market conditions and stock characteristics; 3) Use these variables to predict analyst rankings using a naive Bayes label ranking algorithm; and 4) Evaluate the predictive accuracy of the model versus baseline methods. The results show the model can predict rankings better than baselines in most sectors, with the consensus forecast being the most predictive variable.
This document provides an overview of key numerical measures used to describe data, including measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation). It defines each measure, provides examples of calculating them, and discusses their characteristics, uses, and advantages/disadvantages. The document also covers weighted means, geometric means, Chebyshev's theorem, and calculating measures for grouped data.
Bba 3274 qm week 6 part 1 regression modelsStephen Ong
This document provides an overview and outline of regression models and forecasting techniques. It discusses simple and multiple linear regression analysis, how to measure the fit of regression models, assumptions of regression models, and testing models for significance. The goals are to help students understand relationships between variables, predict variable values, develop regression equations from sample data, and properly apply and interpret regression analysis.
This document outlines the course content for Business Mathematics GMB 105 taught by Victor Gumbo from 28 August to 6 September 2014. It covers topics such as descriptive and inferential statistics, sampling methods, data accuracy and bias, frequency distributions, histograms, measures of central tendency and dispersion. Additional topics include compound interest, discounting, annuities, regression, correlation and dealing with multicollinearity in regression models. Examples are provided to illustrate key statistical and mathematical concepts.
The document analyzes the relationship between stock market performance and economic growth in the U.S. from 1980-2011. It finds a strong positive correlation between changes in the Dow Jones Industrial Average and nominal GDP. Regression analysis shows stock market fluctuations explained about 87% of the variation in GDP. The results suggest stock prices can influence economic activity by affecting business confidence, financing, and household wealth. Therefore, large declines in stock prices may precede and prolong economic downturns.
This document provides an overview of forecasting techniques. It begins with the objectives of the chapter, which are to understand various forecasting models and compare methods such as moving averages, exponential smoothing, and time-series models. It also covers qualitatively measuring forecast accuracy. The document then describes different forecasting techniques including qualitative models, time-series models, and causal models. It provides examples of moving averages, weighted moving averages, and exponential smoothing techniques. It concludes with examples of how to implement forecasting models in Excel.
This document provides teaching suggestions for regression models:
1) It suggests emphasizing the difference between independent and dependent variables in a regression model using examples.
2) It notes that correlation does not necessarily imply causation and gives an example of variables that are correlated but changing one does not affect the other.
3) It recommends having students manually draw regression lines through data points to appreciate the least squares criterion.
4) It advises selecting random data values to generate a regression line in Excel to demonstrate determining the coefficient of determination and F-test.
5) It suggests discussing the full and shortcut regression formulas to provide a better understanding of the concepts.
This chapter discusses regression models, including simple and multiple linear regression. It covers developing regression equations from sample data, measuring the fit of regression models, and assumptions of regression analysis. Key aspects covered include using scatter plots to examine relationships between variables, calculating the slope, intercept, coefficient of determination, and correlation coefficient, and performing hypothesis tests to determine if regression models are statistically significant. The chapter objectives are to help students understand and appropriately apply simple, multiple, and nonlinear regression techniques.
This document discusses predicting the rankings of financial analysts based on their forecast accuracy and performance. It presents a research design to: 1) Create target rankings of analysts based on past forecast error; 2) Define state variables like market conditions and stock characteristics; 3) Use these variables to predict analyst rankings using a naive Bayes label ranking algorithm; and 4) Evaluate the predictive accuracy of the model versus baseline methods. The results show the model can predict rankings better than baselines in most sectors, with the consensus forecast being the most predictive variable.
This document provides an overview of key numerical measures used to describe data, including measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation). It defines each measure, provides examples of calculating them, and discusses their characteristics, uses, and advantages/disadvantages. The document also covers weighted means, geometric means, Chebyshev's theorem, and calculating measures for grouped data.
Bba 3274 qm week 6 part 1 regression modelsStephen Ong
This document provides an overview and outline of regression models and forecasting techniques. It discusses simple and multiple linear regression analysis, how to measure the fit of regression models, assumptions of regression models, and testing models for significance. The goals are to help students understand relationships between variables, predict variable values, develop regression equations from sample data, and properly apply and interpret regression analysis.
This document outlines the course content for Business Mathematics GMB 105 taught by Victor Gumbo from 28 August to 6 September 2014. It covers topics such as descriptive and inferential statistics, sampling methods, data accuracy and bias, frequency distributions, histograms, measures of central tendency and dispersion. Additional topics include compound interest, discounting, annuities, regression, correlation and dealing with multicollinearity in regression models. Examples are provided to illustrate key statistical and mathematical concepts.
The document analyzes the relationship between stock market performance and economic growth in the U.S. from 1980-2011. It finds a strong positive correlation between changes in the Dow Jones Industrial Average and nominal GDP. Regression analysis shows stock market fluctuations explained about 87% of the variation in GDP. The results suggest stock prices can influence economic activity by affecting business confidence, financing, and household wealth. Therefore, large declines in stock prices may precede and prolong economic downturns.
This document provides an overview of forecasting techniques. It begins with the objectives of the chapter, which are to understand various forecasting models and compare methods such as moving averages, exponential smoothing, and time-series models. It also covers qualitatively measuring forecast accuracy. The document then describes different forecasting techniques including qualitative models, time-series models, and causal models. It provides examples of moving averages, weighted moving averages, and exponential smoothing techniques. It concludes with examples of how to implement forecasting models in Excel.
This document provides teaching suggestions for regression models:
1) It suggests emphasizing the difference between independent and dependent variables in a regression model using examples.
2) It notes that correlation does not necessarily imply causation and gives an example of variables that are correlated but changing one does not affect the other.
3) It recommends having students manually draw regression lines through data points to appreciate the least squares criterion.
4) It advises selecting random data values to generate a regression line in Excel to demonstrate determining the coefficient of determination and F-test.
5) It suggests discussing the full and shortcut regression formulas to provide a better understanding of the concepts.
This chapter discusses regression models, including simple and multiple linear regression. It covers developing regression equations from sample data, measuring the fit of regression models, and assumptions of regression analysis. Key aspects covered include using scatter plots to examine relationships between variables, calculating the slope, intercept, coefficient of determination, and correlation coefficient, and performing hypothesis tests to determine if regression models are statistically significant. The chapter objectives are to help students understand and appropriately apply simple, multiple, and nonlinear regression techniques.
The document describes a multiple regression model that was constructed to best explain sales using data from a file. The model's formal equation shows sales as a function of several independent variables including publicity, number of employees in administration and production, expenditure on research and development, income, seniority, number of products, training start time, and training period. The results of the analysis show that publicity, number of employees in production, expenditure on R&D, and training period are significant predictors of sales in the model.
This document discusses analysis of variance (ANOVA) techniques. It defines the F-distribution and its characteristics. It then covers testing for equal variances between two populations and comparing means of two or more populations using one-way and two-way ANOVA. Examples are provided to illustrate hypothesis testing using the F-statistic to compare variances and population means. Finally, it discusses developing confidence intervals for differences in treatment means and using ANOVA in Excel.
This overview discusses the predictive analytical technique known as Random Forest Regression, a method of analysis that creates a set of Decision Trees from a randomly selected subset of the training set, and aggregates by averaging values from different decision trees to decide the final target value. This technique is useful to determine which predictors have a significant impact on the target values, e.g., the impact of average rainfall, city location, parking availability, distance from hospital, and distance from shopping on the price of a house, or the impact of years of experience, position and productive hours on employee salary. Random Forest Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable. Random Forest Regression is just one of the numerous predictive analytical techniques and algorithms included in the Assisted Predictive Modeling module of the Smarten augmented analytics solution. This solution is designed to serve business users with sophisticated tools that are easy to use and require no data science or technical skills. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.
Modeling+pricing+strategies+using+game+theory+and+support+vector+machinesMuhammad Akbar Khan
This document discusses modeling pricing strategies in the credit industry using game theory and support vector machines (SVMs). It proposes a model where pricing is determined through a game-theoretic framework modeling competition between companies. Demand is estimated at the customer-level using SVMs to incorporate a large number of variables. This detailed demand estimation is then used in a market-level game theoretic model to understand the strategic interactions between competitors. The model allows companies to gain insights into customer behavior and competitor strategies to inform their own pricing decisions.
This document discusses the concepts of risk and return as they relate to investment portfolio diversification. It provides an example to illustrate how combining investments with imperfect correlations can reduce overall portfolio risk compared to holding individual assets. Specifically:
- It analyzes potential returns and risks of investing in bus and taxi companies under different economic scenarios.
- It then shows how a 50% allocation to each lowers total risk compared to the individual investments, from 10.68% portfolio standard deviation versus 26.42% and 34.13% for each individually.
- This demonstrates how diversification across imperfectly correlated assets reduces risk for the same expected return compared to holding single assets.
Holt-Winters forecasting allows users to smooth a time series and use data to forecast selected areas. Exponential smoothing assigns decreasing weights and values against historical data to decrease the value of the weight for the older data, so more recent historical data is assigned more weight in forecasting than older results. The right augmented analytics provides user-friendly application of this method and allow business users to leverage this powerful tool.
The document describes experimental designs and statistical tests used to analyze data from experiments with multiple groups. It discusses paired t-tests, independent t-tests, and analysis of variance (ANOVA). For ANOVA, it provides an example to calculate sum of squares for treatment (SST), sum of squares for error (SSE), and the F-statistic. The example shows applying a one-way ANOVA to compare average incomes of accounting, marketing and finance majors. It finds no significant difference between the groups. A randomized block design is then proposed to account for variability from GPA levels.
This document provides an overview of mathematical and statistical foundations relevant to econometrics. It defines functions and their linear and nonlinear forms. It discusses straight lines, their slopes and intercepts. It also covers quadratic functions, their roots and shapes. Additionally, it introduces exponential functions, logarithms, and their properties. It describes summation and differentiation notation used in calculus. The overall summary is an introduction to functions, lines, and other mathematical concepts important for understanding econometrics.
An ARIMAX model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. It is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity. ARIMAX provides forecasted values of the target variables for user-specified time periods to illustrate results for planning, production, sales and other factors.
This document provides an introduction to statistics and key statistical concepts. It discusses variables, scales of measurement, frequency tables, different types of graphs including bar graphs, pie charts, line graphs and scatter plots. It also covers measures of central tendency including the arithmetic mean, median and mode. It provides examples of calculating and comparing the mean, median and mode. Additionally, it discusses the geometric mean, harmonic mean, and the relationship between the different averages.
Este documento presenta el Programa Zuzendu, un programa del Gobierno Vasco para fomentar la autocrítica en relación con las vulneraciones de derechos humanos en el pasado. El programa tiene como objetivo crear condiciones para que se produzcan procesos y testimonios de autocrítica que ayuden a la convivencia. Se argumenta que la autocrítica puede facilitar la reconciliación, generar confianza y acuerdos políticos. El programa no impone la autocrítica sino que la sugiere de forma voluntaria, y se cent
SWOOP offers two community service programs, SWOOPin' Saturday and Ramp It Up!. This is a short presentation of recent SWOOP projects. To learn more about swoop visit our website at www.swoop4u.org. @swoop4u, facebook.com/swoopvolunteers
Alicia vino a la clase para contarles a los estudiantes sobre su hijo Alex. Trajo fotos para que los estudiantes pudieran conocer mejor a Alex y su familia, incluyendo a su hermano pequeño a quien cuida bien, un perro que tenía cuando era niño, y una foto de él disfrazado con su padre en carnaval.
La Unión Europea ha propuesto un nuevo paquete de sanciones contra Rusia que incluye un embargo al petróleo. El embargo prohibiría las importaciones de petróleo ruso por vía marítima, pero permitiría el tránsito a través de oleoductos durante unos meses más para algunos países muy dependientes del crudo ruso. Este sexto paquete de sanciones de la UE pretende aumentar la presión sobre Moscú para que ponga fin a su invasión de Ucrania.
This document contains certification information for Balvinder Mander of London, UK. It lists his active certifications including Microsoft Certified Solutions Associate (Windows 7) from 2012 and Microsoft Certified IT Professional (Enterprise Desktop Support Technician on Windows 7) from 2011. It also lists older, legacy certifications and exams he has passed.
1) O documento discute as noções de direito ao longo da história, como direito, moral e religião costumavam ser indistintos em sociedades antigas. 2) Somente nos tempos modernos com a separação teórica entre esses campos é que foi possível entender o direito de forma específica. 3) Hoje, o direito é visto como um conjunto de regras obrigatórias que garantem a ordem social, mas há dificuldades em explicá-lo para leigos.
O documento discute os princípios e atos do processo executivo civil no Brasil. Resume que o processo executivo é regido por princípios como autonomia, título, patrimonialidade, resultado, disponibilidade e contraditório. Apresenta também a classificação dos atos executivos e os poderes do juiz nesse processo.
The document describes a multiple regression model that was constructed to best explain sales using data from a file. The model's formal equation shows sales as a function of several independent variables including publicity, number of employees in administration and production, expenditure on research and development, income, seniority, number of products, training start time, and training period. The results of the analysis show that publicity, number of employees in production, expenditure on R&D, and training period are significant predictors of sales in the model.
This document discusses analysis of variance (ANOVA) techniques. It defines the F-distribution and its characteristics. It then covers testing for equal variances between two populations and comparing means of two or more populations using one-way and two-way ANOVA. Examples are provided to illustrate hypothesis testing using the F-statistic to compare variances and population means. Finally, it discusses developing confidence intervals for differences in treatment means and using ANOVA in Excel.
This overview discusses the predictive analytical technique known as Random Forest Regression, a method of analysis that creates a set of Decision Trees from a randomly selected subset of the training set, and aggregates by averaging values from different decision trees to decide the final target value. This technique is useful to determine which predictors have a significant impact on the target values, e.g., the impact of average rainfall, city location, parking availability, distance from hospital, and distance from shopping on the price of a house, or the impact of years of experience, position and productive hours on employee salary. Random Forest Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable. Random Forest Regression is just one of the numerous predictive analytical techniques and algorithms included in the Assisted Predictive Modeling module of the Smarten augmented analytics solution. This solution is designed to serve business users with sophisticated tools that are easy to use and require no data science or technical skills. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.
Modeling+pricing+strategies+using+game+theory+and+support+vector+machinesMuhammad Akbar Khan
This document discusses modeling pricing strategies in the credit industry using game theory and support vector machines (SVMs). It proposes a model where pricing is determined through a game-theoretic framework modeling competition between companies. Demand is estimated at the customer-level using SVMs to incorporate a large number of variables. This detailed demand estimation is then used in a market-level game theoretic model to understand the strategic interactions between competitors. The model allows companies to gain insights into customer behavior and competitor strategies to inform their own pricing decisions.
This document discusses the concepts of risk and return as they relate to investment portfolio diversification. It provides an example to illustrate how combining investments with imperfect correlations can reduce overall portfolio risk compared to holding individual assets. Specifically:
- It analyzes potential returns and risks of investing in bus and taxi companies under different economic scenarios.
- It then shows how a 50% allocation to each lowers total risk compared to the individual investments, from 10.68% portfolio standard deviation versus 26.42% and 34.13% for each individually.
- This demonstrates how diversification across imperfectly correlated assets reduces risk for the same expected return compared to holding single assets.
Holt-Winters forecasting allows users to smooth a time series and use data to forecast selected areas. Exponential smoothing assigns decreasing weights and values against historical data to decrease the value of the weight for the older data, so more recent historical data is assigned more weight in forecasting than older results. The right augmented analytics provides user-friendly application of this method and allow business users to leverage this powerful tool.
The document describes experimental designs and statistical tests used to analyze data from experiments with multiple groups. It discusses paired t-tests, independent t-tests, and analysis of variance (ANOVA). For ANOVA, it provides an example to calculate sum of squares for treatment (SST), sum of squares for error (SSE), and the F-statistic. The example shows applying a one-way ANOVA to compare average incomes of accounting, marketing and finance majors. It finds no significant difference between the groups. A randomized block design is then proposed to account for variability from GPA levels.
This document provides an overview of mathematical and statistical foundations relevant to econometrics. It defines functions and their linear and nonlinear forms. It discusses straight lines, their slopes and intercepts. It also covers quadratic functions, their roots and shapes. Additionally, it introduces exponential functions, logarithms, and their properties. It describes summation and differentiation notation used in calculus. The overall summary is an introduction to functions, lines, and other mathematical concepts important for understanding econometrics.
An ARIMAX model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. It is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity. ARIMAX provides forecasted values of the target variables for user-specified time periods to illustrate results for planning, production, sales and other factors.
This document provides an introduction to statistics and key statistical concepts. It discusses variables, scales of measurement, frequency tables, different types of graphs including bar graphs, pie charts, line graphs and scatter plots. It also covers measures of central tendency including the arithmetic mean, median and mode. It provides examples of calculating and comparing the mean, median and mode. Additionally, it discusses the geometric mean, harmonic mean, and the relationship between the different averages.
Este documento presenta el Programa Zuzendu, un programa del Gobierno Vasco para fomentar la autocrítica en relación con las vulneraciones de derechos humanos en el pasado. El programa tiene como objetivo crear condiciones para que se produzcan procesos y testimonios de autocrítica que ayuden a la convivencia. Se argumenta que la autocrítica puede facilitar la reconciliación, generar confianza y acuerdos políticos. El programa no impone la autocrítica sino que la sugiere de forma voluntaria, y se cent
SWOOP offers two community service programs, SWOOPin' Saturday and Ramp It Up!. This is a short presentation of recent SWOOP projects. To learn more about swoop visit our website at www.swoop4u.org. @swoop4u, facebook.com/swoopvolunteers
Alicia vino a la clase para contarles a los estudiantes sobre su hijo Alex. Trajo fotos para que los estudiantes pudieran conocer mejor a Alex y su familia, incluyendo a su hermano pequeño a quien cuida bien, un perro que tenía cuando era niño, y una foto de él disfrazado con su padre en carnaval.
La Unión Europea ha propuesto un nuevo paquete de sanciones contra Rusia que incluye un embargo al petróleo. El embargo prohibiría las importaciones de petróleo ruso por vía marítima, pero permitiría el tránsito a través de oleoductos durante unos meses más para algunos países muy dependientes del crudo ruso. Este sexto paquete de sanciones de la UE pretende aumentar la presión sobre Moscú para que ponga fin a su invasión de Ucrania.
This document contains certification information for Balvinder Mander of London, UK. It lists his active certifications including Microsoft Certified Solutions Associate (Windows 7) from 2012 and Microsoft Certified IT Professional (Enterprise Desktop Support Technician on Windows 7) from 2011. It also lists older, legacy certifications and exams he has passed.
1) O documento discute as noções de direito ao longo da história, como direito, moral e religião costumavam ser indistintos em sociedades antigas. 2) Somente nos tempos modernos com a separação teórica entre esses campos é que foi possível entender o direito de forma específica. 3) Hoje, o direito é visto como um conjunto de regras obrigatórias que garantem a ordem social, mas há dificuldades em explicá-lo para leigos.
O documento discute os princípios e atos do processo executivo civil no Brasil. Resume que o processo executivo é regido por princípios como autonomia, título, patrimonialidade, resultado, disponibilidade e contraditório. Apresenta também a classificação dos atos executivos e os poderes do juiz nesse processo.
The Australasian Fruit Culturist; by David Alexander Crichton (1893)FalXda
The Australasian Fruit Culturist; by David Alexander Crichton (1893) >>>>Containing full and complete information as to the history, traditions, uses, propagation and culture of such fruits as are suitable to Victoria, New South Wales, South Australia, Queensland, Western Australia, Tasmania and New Zealand. Also Descriptive Lists
El documento describe diferentes enfoques para la secuencia de enseñanza-aprendizaje. Explica que al diseñar una secuencia se debe considerar la interacción entre docente-alumno y alumno-alumno, así como el contexto del alumno. Describe dos modelos de secuencia didáctica: el circuito didáctico dogmático de 4 fases y la propuesta de "investigación del medio" de 7 fases. A continuación, presenta 4 unidades didácticas como ejemplo.
Slides delle lezioni del corso di Strumenti e applicazioni del Web per il corso di laurea magistrale in Teoria e tecnologia della comunicazione - Università di Milano Bicocca - Prof.R.Polillo (a.a.2014-15) - Lezione del 1 aprile 2015
This document discusses various linking words used to connect ideas in writing. It provides examples of linking words for addition, comparison, contrast, result, and other categories. Some common linking words listed are and, but, because, however, although, though, as well as, both, in addition, also, too, furthermore, moreover, apart from, in addition to, besides, so, now, so that, in order that, then, the result is, because of, in spite of, despite, in case, unless, as, like. The document also provides rules and examples for the use of some specific linking words.
Example 33.2 Principal Factor Analysis This example uses t.docxSANSKAR20
Example 33.2 Principal Factor Analysis
This example uses the data presented in Example 33.1 and performs a principal factor analysis
with squared multiple correlations for the prior communality estimates. Unlike Example 33.1,
which analyzes the principal components (with default PRIORS=ONE), the current analysis is
based on a common factor model. To use a common factor model, you specify PRIORS=SMC in
the PROC FACTOR statement, as shown in the following:
ods graphics on;
proc factor data=SocioEconomics
priors=smc msa residual
rotate=promax reorder
outstat=fact_all
plots=(scree initloadings preloadings loadings);run;
ods graphics off;
In the PROC FACTOR statement, you include several other options to help you analyze the
results. To help determine whether the common factor model is appropriate, you request the
Kaiser’s measure of sampling adequacy with the MSA option. You specify the RESIDUALS
option to compute the residual correlations and partial correlations.
The ROTATE= and REORDER options are specified to enhance factor interpretability. The
ROTATE=PROMAX option produces an orthogonal varimax prerotation (default) followed by
an oblique Procrustes rotation, and the REORDER option reorders the variables according to
their largest factor loadings. An OUTSTAT= data set is created by PROC FACTOR and
displayed in Output 33.2.15.
PROC FACTOR can produce high-quality graphs that are very useful for interpreting the factor
solutions. To request these graphs, you must first enable ODS Graphics by specifying the ODS
GRAPHICS ON statement, as shown in the preceding statements. All ODS graphs in PROC
FACTOR are requested with the PLOTS= option. In this example, you request a scree plot
(SCREE) and loading plots for the factor matrix during the following three stages: initial
unrotated solution (INITLOADINGS), prerotated (varimax) solution (PRELOADINGS), and
promax-rotated solution (LOADINGS). The scree plot helps you determine the number of
factors, and the loading plots help you visualize the patterns of factor loadings during various
stages of analyses.
Principal Factor Analysis: Kaiser’s MSA and Factor Extraction Results
Output 33.2.1 displays the results of the partial correlations and Kaiser’s measure of sampling
adequacy.
Output 33.2.1 Principal Factor Analysis: Partial Correlations and Kaiser’s MSA
Partial Correlations Controlling all other Variables
Population School Employment Services HouseValue
http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/statug_factor_sect028.htm
http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/statug_factor_sect028.htm
http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/statug_factor_sect006.htm#statug.factor.factorpriorsop
http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/statug_factor_sect006.htm#statug.factor.factorpriorsop
http://support. ...
This document provides an overview of demand forecasting methods. It discusses qualitative and quantitative forecasting models, including time series analysis techniques like moving averages, exponential smoothing, and adjusting for trends and seasonality. It also covers causal models using linear regression. Key steps in forecasting like selecting a model, measuring accuracy, and choosing software are outlined. The homework assigns practicing examples on least squares, moving averages, and exponential smoothing from a textbook.
The document presents the portfolio theory of information retrieval. It draws an analogy between ranking documents and selecting a portfolio of stocks, where the relevance scores of documents are uncertain and correlated. The portfolio theory models a ranked list as having an expected relevance and variance, and aims to optimize this by maximizing expected relevance while minimizing variance. Experiments show the portfolio theory approach outperforms probability ranking and diversity-based reranking on standard evaluation metrics.
This document summarizes a paper that adapts naive Bayes for label ranking problems. It applies this to algorithm recommendation and ranking financial analysts. It modifies naive Bayes to calculate prior and conditional probabilities based on similarity rather than classification. This outperforms baselines in experiments recommending algorithms and ranking financial analysts based on past performance data. Future work includes handling missing data and adapting it for continuous variables.
This document provides instructions for how to perform Analytic Hierarchy Process (AHP) analysis in Excel. It discusses developing a hierarchy of decision criteria and alternatives, making pairwise comparisons between elements, normalizing the comparisons to generate weights, and checking consistency. The basic steps are outlined as developing pairwise comparison matrices, normalizing the matrices to obtain weights, and calculating a consistency ratio to validate the judgments.
International journal of applied sciences and innovation vol 2015 - no 2 - ...sophiabelthome
This document describes using a simulation model to determine the optimal order quantity for a wholesale supplier. Regression analysis was used to forecast quarterly sales for 2007. A simulation model was built in Excel to express the company's sales and inventory schedule. By varying order quantities and simulating demand, profit distributions were found. The order quantities that minimized risk and showed relatively high profit for each quarter were determined to be the optimal order quantities. These were 310,000m for Q1, 270,000m for Q2, 250,000m for Q3, and 440,000m for Q4.
- The document describes new functions implemented in the R package factorAnalytics for fitting and analyzing fundamental factor models (FFMs).
- It provides examples using stock return data sets and fitted FFMs to illustrate functions for examining model fit, computing risk exposures, and decomposing portfolio returns.
- Key functions demonstrated include fitting a FFM (fitFfm), assessing model fit over time via R-squared and t-statistics (ffmRsq, ffmTstats), computing portfolio exposures to factors (repExposures), and decomposing portfolio returns (repReturn).
The document discusses basic descriptive quantitative data analysis techniques such as tables, graphs, and summary statistics. It covers topics like frequency distributions, contingency tables, bar graphs, pie charts, and measures of central tendency and variation. The objectives are to learn how to perform these analyses in Excel and how they are useful for understanding complex quantitative data and communicating findings to others. Employers value these types of quantitative and data visualization skills.
This document discusses descriptive statistics and numerical measures used to describe data sets. It introduces measures of central tendency including the mean, median, and mode. The mean is the average value calculated by summing all values and dividing by the number of values. The median is the middle value when values are arranged in order. The mode is the most frequently occurring value. The document also discusses measures of dispersion like range and standard deviation which describe how spread out the data is. Examples are provided to demonstrate calculating the mean, median and other descriptive statistics.
This document analyzes the gross domestic product (GDP) of the United States, United Kingdom, and Australia using time series analysis. It finds that while the GDP of all three countries increases exponentially over time, there are also deviations from the trend. The document fits multivariate and univariate autoregressive models to the differenced GDP data to remove nonstationarity. It uses the models to make predictions for GDP over the next five years and compares the results across three statistical programs: ITSM, R, and SAS.
Marketing Research Approaches to Demand EstimationConsumer Surveysdata from survey questionsObservational Researchdata from observed behaviorConsumer Clinicsdata from laboratory experimentsMarket Experimentsdata from real market tests
Regression Analysis
Scatter Diagram
Regression AnalysisRegression Line: Line of Best Fit
Regression Line: Minimizes the sum of the squared vertical deviations (et) of each point from the regression line.
Ordinary Least Squares (OLS) Method
Ordinary Least Squares (OLS)
Model:
Ordinary Least Squares (OLS)
Objective: Determine the slope and intercept that minimize the sum of the squared errors.
Ordinary Least Squares (OLS)
Estimation Procedure
Ordinary Least Squares (OLS)
Estimation Example
Ordinary Least Squares (OLS)
Estimation Example
Tests of Significance
Standard Error of the Slope Estimate
Tests of Significance
Example Calculation
Tests of Significance
Example Calculation
Tests of Significance
Calculation of the t Statistic
Degrees of Freedom = (n-k) = (10-2) = 8
Critical Value at 5% level =2.306
Tests of Significance
Decomposition of Sum of Squares
Total Variation = Explained Variation + Unexplained Variation
Tests of Significance
Coefficient of Determination
Tests of Significance
Coefficient of Correlation
Multiple Regression Analysis
Model:
Multiple Regression Analysis
Adjusted Coefficient of Determination
Multiple Regression Analysis
Analysis of Variance and F Statistic
Problems in Regression AnalysisMulticollinearity: Two or more explanatory variables are highly correlated.Heteroskedasticity: Variance of error term is not independent of the Y variable.Autocorrelation: Consecutive error terms are correlated.
Durbin-Watson Statistic
Test for Autocorrelation
If d = 2, autocorrelation is absent.
Steps in Demand EstimationModel Specification: Identify VariablesCollect DataSpecify Functional FormEstimate FunctionTest the Results
Functional Form Specifications
Linear Function:
Power Function:
Estimation Format:
Chapter 5 Appendix
Getting StartedInstall the Analysis ToolPak add-in from the Excel installation media if it has not already been installedAttach the Analysis ToolPak add-inFrom the menu, select Tools and then Add-Ins...When the Add-Ins dialog appears, select Analysis ToolPak and then click OK.
Entering DataData on each variable must be entered in a separate columnLabel the top of each column with a symbol or brief description to identify the variableMultiple regression analysis requires that all data on independent variables be in adjacent columns
Example Data
Running the RegressionSelect the Regression tool from the Analysis ToolPak dialogFrom the menu, select Tools and then Data Analysis...On the Data Anal.
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fma.ny.presentation
1. Understanding rankings of financial analysts
Artur Aiguzhinov1
Carlos Soares1
Ana Paula Serra2
1
LIAAD-INESC Porto LA & Faculdade de Economia da Universidade do Porto
2
Faculdade de Economia da Universidade do Porto & CEFUP - Centro de Economia e Finan¸cas
da Universidade do Porto
October 23rd, 2010
FMA Annual Meeting, New York
1 of 23
2. Motivation: the value of the recommendations
Efficient Market Hypothesis (Fama, 1970);
High information costs provide possibilities for abnormal returns
(Grossman & Stiglitz, 1980);
On average, recommendations bring value to investors (Womack, 1996);
Analysts’ accuracy in forecasts is valuable (Brown & Mohammad, 2003);
2 of 23
3. Motivation: rankings of the analysts
StarMine R
issues annual analyst rankings:
Ranks the analysts based on recommendation performance and EPS
forecast accuracy;
Why not to predict stock prices directly?
Analysts’ relative performance (rankings) is more predictable than the stock
prices.
Is it possible to predict these rankings?:
If yes, can we use those predictions in profitable strategy?;
3 of 23
4. The Goals of the Research
Accurately predict the rankings of financial analysts;
4 of 23
5. Research contributions
Interdisciplinary approach to an interesting research topic;
Financial Economics contributions:
analysis of the financial analysts based on state variables concerning market
conditions and stock characteristics;
first methodology to predict the rankings;
verify if there is a ranking consistency over time;
identify variables that discriminate the rankings;
5 of 23
6. Research design: an overview
Initial rankings of the analysts (target rankings):
Analysts evaluation models (Clement, 1999; Brown, 2001; Creamer &
Stolfo, 2009);
Predict rankings of the analysts (label ranking):
Evaluate the ranking accuracy;
Identify discriminative independent variables;
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7. Database to use
ThomsonOne I/B/E/S Detailed History:
Quarterly EPS forecasts (1989Q1-2009Q4);
ThomsonOne DataStream:
Accounting data;
Market data;
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8. Description of the data
Table: Summary of the data
Sector # analysts # stocks mean forecasts mean stocks
per stock per quarter per analysts per quarter
Energy 135 34 7.57 1.91
Industrials 208 66 3.66 1.05
Materials 147 30 5.14 1.16
IT 301 106 7.85 2.76
Total 791 236 6.06 1.72
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9. Average forecasts
Figure: Average issued forecasts per quarter
0 20 40 60 80
010203040506070
Quarters
Averagenumberofforecastsperquarter
Sectors
Energy
Industrials
IT
Materials
9 of 23
10. Creating target rankings: indexing of the analysts
Based on previous research, we use EPS mean adjusted forecast error
(MAFE) as a measure of analysts predicting accuracy:
FEq,a,s = |ActEPSq,s − EPSq,a,s| (1)
FEq,s =
1
n
n
a=1
FEq,a,s (2)
MAFEq,a,s =
FEq,a,s
FEq,s
(3)
10 of 23
11. Context characterization: state variables
Variables that describe market conditions and stock characteristics
(Jegadeesh, Kim, Krische, & Lee, 2004):
Analysts variables:
Lagged forecasting error;
Change in consensus;
Earnings momentum:
Standardized Unexpected Earnings;
Growth indicators:
Sales growth;
Fundamentals:
Total accruals to total assets ratio;
Valuation multiples:
Earnings-to-price ratio;
Market volatility;
11 of 23
12. Label ranking algorithm
Table: Example of analysts rankings based on the observed variables x1 . . . x4
Quarters x1 x2 x3 x4 Ranks
Alex Brown Craig
1 High Low High Medium 1 2 3
2 High High High Low 2 3 1
3 Medium Medium High Low 1 2 3
4 Low Low Low High 1 3 2
5 Medium High High Medium 1 2 3
6 High Medium High Low 3 1 2
Naive Bayes algorithm for label ranking (Aiguzhinov, Soares, & Serra, 2010):
ˆπ = arg max
π∈ΠL
PLR (π)
m
a=1
PLR (xi,a|π) (4)
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13. Predicted vs. default ranking
Table: Summary of the results compared to default ranking
Sectors Outperformed Outperformed- # stocks with p-values
the default ranking to-total rate 1% 5% 10%
Energy 18 0.53 7 9 9
Industrials 31 0.47 16 18 23
Materials 12 0.40 5 7 8
IT 51 0.48 18 27 30
Total 112 0.47 46 61 70
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14. Predicted vs naive ranking
Table: Summary of the results compared to naive ranking
Sectors Outperformed Outperformed- # stocks with p-values
the naive ranking to-total rate 1% 5% 10%
Energy 11 0.32 5 7 7
Industrials 27 0.41 5 6 7
Materials 11 0.37 0 0 1
IT 36 0.34 1 1 1
Total 85 0.36 11 14 16
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19. Discriminative Value
X1 average Weights Weighted average
a1 vs. b1 0.00 1 0.00
a1 vs. c1 0.50 2 1.00
a1 vs. d1 0.25 3 0.75
b1 vs. c1 0.50 1 0.50
b1 vs. d1 0.75 2 1.50
c1 vs. d1 0.50 1 0.50
0.708
Discriminative Power : 1-0.708=0.292 The higher the discriminative power,
the more different are the rankings
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20. Discriminative power
Table: Discriminative power of independent variables
Sectors FELAG SUE consensus EP SG TA MKT
Energy 0.235 0.211 0.234 0.208 0.194 0.341 0.148
Industrials 0.248 0.230 0.279 0.233 0.263 0.282 0.089
Materials 0.180 0.197 0.278 0.232 0.156 0.378 0.051
IT 0.213 0.238 0.243 0.230 0.185 0.298 0.155
Total 0.219 0.219 0.258 0.226 0.199 0.325 0.111
20 of 23
21. References (1)
Aiguzhinov, A., Soares, C., & Serra, A. P. (2010). A similarity-based
adaptation of naive bayes for label ranking: Application to the
metalearning problem of algorithm recommendation. In B. Pfahringer,
G. Holmes, & A. Hoffmann (Eds.), Discovery science (Vol. 6332, pp.
16–26). Springer.
Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial
Analysts Journal, 48(5), 28–43.
Brazdil, P., Soares, C., & Costa, J. (2003). Ranking Learning Algorithms:
Using IBL and Meta-Learning on Accuracy and Time Results.
Machine Learning, 50(3), 251–277.
Brown, L. (2001). How Important is Past Analyst Earnings Forecast
Accuracy? Financial Analysts Journal, 57(6), 44–49.
Brown, L., & Mohammad, E. (2003). The Predictive Value of Analyst
Characteristics. Journal of Accounting, Auditing and Finance, 18(4).
21 of 23
22. References (2)
Clement, M. (1999). Analyst forecast accuracy: Do ability, resources, and
portfolio complexity matter? Journal of Accounting and Economics,
27(3), 285–303.
Creamer, G., & Stolfo, S. (2009). A link mining algorithm for earnings
forecast and trading. Data Mining and Knowledge Discovery, 18(3),
419–445.
Fama, E. (1970). Efficient Capital Markets: A Review of Empirical Work.
The Journal of Finance, 25, 383–417.
Grauer, R. (2008). Benchmarking measures of investment performance with
perfect-foresight and bankrupt asset allocation strategies. The Journal
of Portfolio Management, 34(4), 43–57.
Grossman, S., & Stiglitz, J. (1980). On the Impossibility of Informationally
Efficient Prices. American Economic Review, 70, 393–408.
H¨ullermeier, E., F¨urnkranz, J., Cheng, W., & Brinker, K. (2008). Label
ranking by learning pairwise preferences. Artificial Intelligence,
172(2008), 1897–1916.
22 of 23
23. References (3)
Jegadeesh, N., Kim, J., Krische, S., & Lee, C. (2004). Analyzing the
Analysts: When Do Recommendations Add Value? The Journal of
Finance, 59(3), 1083–1124.
Ljungqvist, A., Malloy, C., & Marston, F. (2009). Rewriting history. The
Journal of Finance, 64(4), 1935–1960.
Vembu, S., & G¨artner, T. (2010, October). Preference learning. Springer.
Vogt, M., Godden, J., & Bajorath, J. (2007). Bayesian interpretation of a
distance function for navigating high-dimensional descriptor spaces.
Journal of chemical information and modeling, 47(1), 39-46.
Womack, K. (1996). Do Brokerage Analysts’ Recommendations Have
Investment Value? The Journal of Finance, 51, 137–168.
23 of 23
24. Similarity-based Naive Bayes for Label Ranking: Prior
probability of label ranking
Table: Demonstration of the prior probability for label ranking
Quarters x1 x2 x3 x4 Ranks
Alex Brown Craig
1 High Low High Medium 1 2 3
2 High High High Low 2 3 1
3 Medium Medium High Low 1 2 3
4 Low Low Low High 1 3 2
...
...
...
...
...
...
...
...
14 Medium High High Medium 1 2 3
15 High Medium High Low 3 1 2
Maximizing the likelihood is equivalent to minimizing the distance (i.e.,
maximizing the similarity) in a Euclidean space (Vogt, Godden, & Bajorath,
2007)
25. Label ranking: formalization
Instance: X ⊆ {V1, . . . , Vm}
Labels: L = {λ1, . . . , λk }
Output: Y = ΠL
Training set: T = {xi , yi }i∈{1,...,n} ⊆ X × Y
Learn a mapping h : X → Y such that a loss function is minimized:
=
n
i=1 ρ(πi , ˆπi )
n
(5)
with ρ being a Spearman correlation coefficient:
ρ(π, ˆπ) = 1 −
6
k
j=1(πj − ˆπj )2
k3 − k
(6)
where π and ˆπ are, respectively, the target and predicted rankings for a
given instance.
26. Posterior probability of label ranking
Proir probability of label ranking:
PLR (π) =
n
i=1 ρ(π, πi )
n
(7)
Conditional probability of label ranking:
PLR (va,i |π) =
i:xi,a=va,i
ρ(π, πi )
|{i : xi,a = va,i }|
(8)
Estimated ranking:
ˆπ = arg max
π∈ΠL
PLR (π)
m
a=1
PLR (xi,a|π) (9)