scholarly journals Data-Driven Robust Credit Portfolio Optimization for Investment Decisions in P2P Lending

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Guotai Chi ◽  
Shijie Ding ◽  
Xiankun Peng
Keyword(s):  
Portfolio Optimization ◽  
Relative Entropy ◽  
Investment Decision ◽  
Entropy Method ◽  
Data Driven ◽  
Expected Return ◽  
Robust Model ◽  
P2p Lending ◽  
Kernel Approach ◽  
Nonparametric Kernel

Peer-to-Peer (P2P) lending has attracted increasing attention recently. As an emerging micro-finance platform, P2P lending plays roles in removing intermediaries, reducing transaction costs, and increasing the benefits of both borrowers and lenders. However, for the P2P lending investment, there are two major challenges, the deficiency of loans’ historical observations about the certain borrower and the ambiguity problem of estimated loans’ distribution. In order to solve the difficulties, this paper proposes a data-driven robust model of portfolio optimization with relative entropy constraints based on an “instance-based” credit risk assessment framework. The model exploits a nonparametric kernel approach to estimate P2P loans’ expected return and risk under the condition that the historical data of the same borrower is unavailable. Furthermore, we construct a robust mean–variance optimization problem based on relative entropy method for P2P loan investment decision. Using the real-world dataset from a notable P2P lending platform, Prosper, we validate the proposed model. Empirical results reveal that our model provides better investment performances than the existing model.

scholarly journals Relative Entropy Method Applied for the Fatigue Life Distribution of Carbon Fiber/Epoxy Composites

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 224
Author(s):  
Changsheng Yuan ◽  
Yingjie Liang
Keyword(s):  
Experimental Data ◽  
Carbon Fiber ◽  
Relative Entropy ◽  
Least Squares Method ◽  
Entropy Method ◽  
Epoxy Composites ◽  
Order Moment ◽  
Kolmogorov Smirnov ◽  
Relative Entropy Method ◽  
Carbon Fiber Epoxy

This paper verifies the feasibility of the relative entropy method in selecting the most suitable statistical distribution for the experimental data, which do not follow an exponential distribution. The efficiency of the relative entropy method is tested through the fractional order moment and the logarithmic moment in terms of the experimental data of carbon fiber/epoxy composites with different stress amplitudes. For better usage of the relative entropy method, the efficient range of its application is also studied. The application results show that the relative entropy method is not very fit for choosing the proper distribution for non-exponential random data when the heavy tail trait of the experimental data is emphasized. It is not consistent with the Kolmogorov–Smirnov test but is consistent with the residual sum of squares in the least squares method whenever it is calculated by the fractional moment or the logarithmic moment. Under different stress amplitudes, the relative entropy method has different performances.

Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Nurali Virani ◽  
Devesh K. Jha ◽  
Zhenyuan Yuan ◽  
Ishana Shekhawat ◽  
Asok Ray
Keyword(s):  
Particle Filtering ◽  
Dynamic Models ◽  
Training Data ◽  
Data Driven ◽  
Conditional Density ◽  
Bayesian Filtering ◽  
State Dependent ◽  
State Evolution ◽  
And Control ◽  
Nonparametric Kernel

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

A COMBINATION WEIGHTING ALGORITHM USING RELATIVE ENTROPY FOR DOCUMENT CLUSTERING

2014 ◽  
Vol 28 (03) ◽  
pp. 1453002 ◽  
Author(s):  
BO JI ◽  
YANGDONG YE ◽  
YU XIAO
Keyword(s):  
Relative Entropy ◽  
Document Clustering ◽  
Multiple Attribute Decision Making ◽  
Entropy Method ◽  
Data Object ◽  
Information Bottleneck ◽  
Subjective Weight ◽  
Multiple Attribute ◽  
Sequential Information ◽  
Relative Entropy Method

This paper proposes a combination weighting algorithm using relative entropy for document clustering. Combination weighting is widely used in multiple attribute decision making (MADM) problem. However, there exist two difficulties to hinder the applications of combination weighting on document clustering. First, combination weighting is based on the integration of subjective weighting and objective weighting. However, there are so many attributes in documents that the subjective weights which rely on manual annotation by experts are impracticable. Secondly, a document data object might contain hundreds or even thousands of features. It is an extremely time-consuming task to calculate the combination weights. To address the issues, we suggest to simplify the combination weighting by not distinguishing subjective weight and objective weight. Meanwhile, we choose relative entropy method to reduce running time. In our algorithm, we obtain a combination weight set with 14 combination forms. The experiments on real document data show that both on the AC/PR/RE measures and the mutual information (MI) measure, the proposed CWRE-sIB algorithm is superior to the original sequential information bottleneck (sIB) algorithm and a series of weighting-sIB algorithms, which are built by applying a single weighting scheme to the original sIB algorithm.

scholarly journals Investment decision by using Capital Asset Method Pricing Model (CAPM) (Case studies on five automotive companies listed in stock exchange)

2021 ◽  
Vol 1 (2) ◽  
pp. 165-175
Author(s):  
Ahmad Musodik ◽  
Arrum Sari ◽  
Ida Nur Fitriani
Keyword(s):  
Asset Pricing ◽  
Automotive Industry ◽  
Stock Exchange ◽  
Investment Decision ◽  
Capital Asset Pricing Model ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Expected Return ◽  
Asset Pricing Model

Investment is a tool for investors to get more profit than what has been invested. Investors must be able to predict the possibilities that occur when investing. Capital Asset Pricing Model is a tool to predict the development of investment in a particular company used to calculate and determine the Expected Return in minimizing risk investments. The authors conducted research using a sample of 5 companies in the automotive industry, namely PT Astra International Tbk, PT Indokordsa Tbk, PT Indomobil Sukses Internasional Tbk, PT Astra Otoparts Tbk, and PT Gajah Tunggal Tbk. This study uses a descriptive quantitative approach with Microsoft Excel 2016 analysis tools. This study aims to determine Portfolio Analysis with the Capital Asset Pricing Model (CAPM) approach which is used as the basis for making stock investment decisions in automotive industry sector companies listed on the Indonesia Stock Exchange. Use from the results of the analysis of the results by comparing the value of E(Ri) has a directly proportional relationship, meaning that the higher the value of, then the stock return (E(Ri)) will be high as well. Of the 5 companies, there are 2 companies that are in the Undervalued category and 3 companies that are in the overvalued category. This means that investors who will invest in companies engaged in the automotive industry can decide to buy shares of the companies PT Indomobil Sukses Internasional Tbk and PT Gajah Tunggal Tbk, because they are classified as undervalued. Meanwhile, investors who want to invest in shares are not advised to buy company shares that are in the overvalued category, but are advised to sell them to investors who already have shares in the company.

Understanding the Determinants of Analyst Target Price Implied Returns

2020 ◽  
Vol 95 (6) ◽  
pp. 125-149
Author(s):  
Patricia M. Dechow ◽  
Haifeng You
Keyword(s):  
Stock Returns ◽  
Investment Decision ◽  
Optimistic Bias ◽  
Target Price ◽  
Expected Return ◽  
Cross Sectional ◽  
Empirical Risk ◽  
Investment Decision Making ◽  
The Cross ◽  
Jel Classifications

ABSTRACT We investigate the determinants of analysts' target price implied returns and the implication of our findings for investment decision-making. We identify four broad sets of factors that help explain the cross-sectional variation in target price implied returns: future realized stock returns, errors in forecasting fundamentals, errors in forecasting the expected return to risk, and biases relating to analysts' incentives. Our results suggest that all four sets help explain target price implied returns, with errors in forecasting the expected return to empirical risk proxies having the greatest impact. Collectively, these variables explain nearly a quarter of the cross-sectional variation in target price implied returns. We use our model to predict the optimistic bias in target price implied returns and evaluate whether investors correctly ignore the predictable bias. The results suggest that investors make similar valuation errors to analysts and/or do not perfectly back out the predicted bias in target prices. JEL Classifications: M40; M41; G14.

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