Untapped riches

Building Real Estate Riches

... goals, real estate has consistently been one of the strongest long-term investments This book will give you the keys to a wealth -building strategy that will get you building your own real estate riches! ... allowing me to use some company resources and drawings Dennis Dahm, Real Estate Courses, Inc was helpful in providing information on real estate and financing Dawn BeVard created the illustrations A ... Building Real Estate Riches How to Invest in New Homes for Maximum Profit CHRIS CONDON McGraw-Hill New York...

Tài liệu Building Real Estate Riches How to Invest in New Homes for Maximum Profit ppt

... Building Real Estate Riches How to Invest in New Homes for Maximum Profit CHRIS CONDON McGraw-Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San ... resources and drawings Dennis Dahm, Real Estate Courses, Inc was helpful in providing information on real estate and financing Dawn BeVard created the illustrations A Field Guide to American Homes (McAlister, ... this information closely You will learn how to save money through innovative design and thorough planning You will also learn how to build a well-planned home without giving all of the savings to...

Báo cáo khoa học: "From RAGS to RICHES: exploiting the potential of a ﬂexible generation architecture" pot

... the same ideals In addition, we aim to test the claims that the RAGS data model approach supports the ﬂexible processing of information in an NLG setting The RAGS data model The starting ... representation language An O /A database consists of a collection of objects, each of which has a unique identiﬁer and a type, and arrows, each of which has a unique identiﬁer, a type, and source and ... Referring Expression Generation, commonly viewed as a single task, needs to have access to at least rhetorical and an implementation of the RAGS Object and Arrows (O /A) data representation, support...

make your fortune in real estate - book 2 - fortune without fear - real estate riches in an uncertain market - barry lenson

... simple approach to building a fortune in real estate by timing your investments against trends in the marketplace, in society, and in your life Fortune without Fear teaches another set of critical ... Welcome to Fortune without Fear, the second book in the Make Your Fortune in Real Estate self-education books on real estate from Trump University Catch the Wave, the ﬁrst book in the series, showed ... business Tenancy in Common What is Tenancy in Common? Chances are that you have not heard of its advantages for real estate investors In certain circumstances, Tenancy in Common offers smaller real...

New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_2 ppt

... (3.13) The linear scaling function for [−1, 1], transforming a variable xk into x∗∗ , has the following form: k x∗∗ = · k,t xk,t − min(xk ) −1 max(xk ) − min(xk ) (3.14) A nonlinear scaling method ... network link the input variables x to the encoding neurons C11 and C12, and to the nonlinear principal components The parameters also link the nonlinear principal components to the decoding neurons ... default in credit cards and in banking- sector fragility (Chapter 8) For dimensionality reduction, the race is between linear principal components and the neural net auto-associate mapping We show, in...

New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_3 doc

... point for quite some time during the training period Unfortunately, there is no silver bullet for avoiding the problems of local minima in nonlinear estimation There are only strategies involving ... They call this forecast a thick model forecast We can also use this method for obtaining intervals for our forecasts of the network Granger and Jeon have pointed out an intriguing result from ... discuss in Section 4.2.8, bootstrapping involves resampling the original training set with replacement, and then taking repeated forecasts Bagging is particularly useful if the data set exhibits instability...

New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_4 pptx

... estimation an expanding window The sample size, of course, becomes larger as we move forward in time An alternative to the expanding window is the moving window In this case, for the ﬁrst forecast we ... estimation for use in the test set For cross-section studies with large numbers of observations, withholding 25% of the data is reasonable In time-series forecasting, however, the main interest is in forecasting ... To evaluate the out-of-sample performance of a model, we begin by dividing the data into an in- sample estimation or training set for obtaining the coeﬃcients, and an out-of-sample or test set...

New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_5 doc

... linear and network out-of-sample errors appear in Figure 5.10 We see, not surprisingly, that both the linear and network models deliver about the same accuracy in out-of-sample forecasting Since ... In- sample errors: VRS model for return forecasting, but in its favor, it does not perform worse in any noticeable way than the linear model While these results not show overwhelming support for ... model), forecast onevar svjdmodel new1 .m (for the stochastic volatility jump diﬀusion model), forecast onevar markovmodel new1 .m (for the Markov regime switching model), and forecast onevar dlm new1 .m...

New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_7 doc

... dependence, since it is well known that when the forecasting interval h exceeds the sampling 176 In ation and Deﬂation: Hong Kong and Japan interval (in this case we are forecasting for one year ... estimation errors The information from Table 7.8 gives strong support for abandoning a linear approach for understanding in ation/deﬂation dynamics in Japan 7.2.4 Out-of-Sample Performance Figure 7.17 ... been forthcoming Chen, Racine, and Swanson (2001) have used a ridgelet neural network for forecasting in ation in the United States McNelis and McAdam (2004) used a thick model approach (combining...

New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_8 pptx

... next observation, and applied these coeﬃcients and functions for estimating the new principal component We used this new forecast principal component 9.1 Hong Kong 215 15 Linear Principal Component ... nonlinear component outperforms the linear component for all maturities except for four years.1 9.3 Conclusion In this chapter we examined the practical uses of linear and nonlinear components for ... variables remain signiﬁcant in the modiﬁed models 9 Dimensionality Reduction and Implied Volatility Forecasting In this chapter we apply the methodologies of linear and nonlinear principal component...

Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_1 docx

... parametric and semi-parametric models, and models that have and not have closed-form solutions The typology appears in Table 2.1 Both linear and polynomial models have closed-form solutions for estimation ... [−1, 1] For any variable x, the transformation to a variable x∗ is given by the following formula: x∗ = 2x min(x) + max(x) − max(x) − min(x) max(x) − min(x) (2.17) The exact formulae for these ... simple and straightforward Current and lagged values of interest rates, exchange rates, changes in GDP, and other types of economic and ﬁnancial news aﬀect further developments in the economy by...

Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_3 pot

... particular point forecasts Granger and Jeon (2002) have suggested “thick modeling” as a strategy for neural networks, particularly for forecasting The idea is simple and straightforward We should ... drawing two real numbers r1 and r2 from the [0, 1] interval and one random number s from a standard normal distribution The mutated coeﬃcient Ω i,p is given by the following formula: Ωi,p    Ωi,p ... overall network forecast They call this forecast a thick model forecast We can also use this method for obtaining intervals for our forecasts of the network Granger and Jeon have pointed out an intriguing...

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