Data Mining - Concepts and Techniques.

Matrix Differential Calculus with Applications in Statistics and Econometrics

There are three disadvantages of weighted scoring stock selection models. First, they cannot identify the relations between weights of stock-picking concepts and performances of portfolios. Second, they cannot systematically discover the optimal combination for weights of concepts to optimize the performances. Third, they are unable to meet various investors' preferences. This study aimed to more efficiently construct weighted scoring stock selection models to overcome these disadvantages. Since the weights of stock-picking concepts in a weighted scoring stock selection model can be regarded as components in a mixture, we used the simplex-centroid mixture design to obtain the experimental sets of weights. These sets of weights are simulated with US stock market historical data to obtain their performances. Performance prediction models were built with the simulated performance data set and artificial neural networks. Furthermore, the optimization models to reflect investors' preferences were built up, and the performance prediction models were employed as the kernel of the optimization models, so that the optimal solutions can now be solved with optimization techniques. The empirical values of the performances of the optimal weighting combinations generated by the optimization models showed that they can meet various investors' preferences and outperform those of S&P's 500 not only during the training period but also during the testing period.

Using mixture design and neural networks to build stock selection decision support systems

In Foreign Exchange Markets Compound options (options on options) are traded frequently. Instalment options generalize the concept of Compound options as they allow the holder to prolong a Vanilla Call or Put option by paying instalments of a discrete payment plan. We derive a closed-form solution to the value of such an option in the Black-Scholes model and prove that the limiting case of an Instalment option with a continuous payment plan is equivalent to a portfolio consisting of a European Vanilla option and an American Put on this Vanilla option with a time-dependent strike.

/pdf/instalment-options-a-closed-form-solution-and-the-limiting-29wsvbn382.pdf

Instalment Options: A Closed-Form Solution and the Limiting Case

Valuing continuous-installment options

We present three approaches to value American continuous-installment options written on assets without dividends or with continuous dividend yield. In an American continuous-installment option, the premium is paid continuously instead of up-front. At or before maturity, the holder may terminate payments by either exercising the option or stopping the option contract. Under the usual assumptions, we are able to construct an instantaneous riskless dynamic hedging portfolio and derive an inhomogeneous Black--Scholes partial differential equation for the initial value of this option. This key result allows us to derive valuation formulas for American continuous-installment options using the integral representation method and consequently to obtain closed-form formulas by approximating the optimal stopping and exercise boundaries as multipiece exponential functions. This process is compared to the finite difference method to solve the inhomogeneous Black--Scholes PDE and a Monte Carlo approach.

/pdf/valuation-of-american-continuous-installment-options-3w70041yb4.pdf

Valuation of American Continuous-Installment Options

A major inconvenience of the traditional approach in portfolio choice, based upon historical information, is its inability to anticipate sudden changes of price tendencies. Introducing information about future behavior of the assets fundamentals may help to make more appropriate choices. However the specification and parameterization of a model linking this exogenous information to the asset prices is not straightforward. Classification trees can be used to construct partitions of assets of forecasted similar behavior. We analyze the performance of this approach and apply it to different sectors of the S&P500.

Using Economic and Financial Information for Stock Selection

A major inconvenience of the traditional approach in portfolio choice, based upon historical information, is its inability to anticipate sudden changes of price tendencies. Introducing information about future behavior of the assets fundamentals may help to make more appropriate choices. However, the specification and parameterization of a model linking this exogenous information to the asset prices is not straightforward. Classification trees can be used to construct partitions of assets of forecasted similar behavior. We analyze the performance of this approach and apply it to different sectors of the S&P 500.

/pdf/using-economic-and-financial-information-for-stock-selection-4jbbs4mmke.pdf

Using economic and financial information for stock selection

A major inconvenience of the traditional approach in portfolio choice, based upon historical information, is its inability to anticipate sudden changes of price tendencies. Introducing information about future behavior of the assets fundamentals may help to make more appropiate choices. However the specification and parameterization of a model linking this exogenous information to the asset prices is not straightforward. Classification trees can be used to construct partitions of assets of forecasted similar behaviour. We analyze the performance of this approach and apply it to different sectors of the S&P 500.

/pdf/using-economic-and-financial-information-for-stock-selection-4nok9erzsm.pdf

https://archive-ouverte.unige.ch/unige:41670/ATTACHMENT01

Ilir Roko

Papers

Valuation of American Continuous-Installment Options

Using Economic and Financial Information for Stock Selection

Using economic and financial information for stock selection

Using Economic and Financial Information for Stock Selection

Estimation of SEM with GARCH errors