Stock portfolio performance

Donated on 4/21/2016

The data set of performances of weighted scoring stock portfolios are obtained with mixture design from the US stock market historical database.

Dataset Characteristics

Multivariate

Subject Area

Business

Associated Tasks

Regression

Feature Type

Real

# Instances

315

# Features

-

Dataset Information

Additional Information

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 aims 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.

Has Missing Values?

No

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
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0 to 10 of 12

Additional Variable Information

The inputs are the weights of the stock-picking concepts as follows X1=the weight of the Large B/P concept X2=the weight of the Large ROE concept X3=the weight of the Large S/P concept X4=the weight of the Large Return Rate in the last quarter concept X5=the weight of the Large Market Value concept X6=the weight of the Small systematic Risk concept The outputs are the investment performance indicators (normalized) as follows Y1=Annual Return Y2=Excess Return Y3=Systematic Risk Y4=Total Risk Y5=Abs. Win Rate Y6=Rel. Win Rate

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Creators

I-Cheng Yeh

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