Linear complementarity problems on extended second order cones

In this paper, we study the linear complementarity problems on extended second order cones. We convert a linear complementarity problem on an extended second order cone into a mixed complementarity problem on the non-negative orthant. We state necessary and sufficient conditions for a point to be a solution of the converted problem. We also present solution strategies for this problem, such as the Newton method and Levenberg-Marquardt algorithm. Finally, we present some numerical examples

Machine Learning for Stock Prediction Based on Fundamental Analysis

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks’ historical data. Most of these existing approaches have focused on short term prediction using stocks’ historical price and technical indicators. In this thesis, we prepared 22 years’ worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for stock prediction based on fundamental analysis. In addition, we applied RF based feature selection and bootstrap aggregation in order to improve model performance and aggregate predictions from different models. Our results show that RF model achieves the best prediction results, and feature selection is able to improve test performance of FNN and ANFIS. Moreover, the aggregated model outperforms all baseline models as well as the benchmark DJIA index by an acceptable margin for the test period. Our findings demonstrate that machine learning models could be used to aid fundamental analysts with decision making regarding to stock investment

Machine Learning for Stock Prediction Based on Fundamental Analysis

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks’ historical data. Most of these existing approaches have focused on short term prediction using stocks’ historical price and technical indicators. In this paper, we prepared 22 years’ worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for stock prediction based on fundamental analysis. In addition, we applied RF based feature selection and bootstrap aggregation in order to improve model performance and aggregate predictions from different models. Our results show that RF model achieves the best prediction results, and feature selection is able to improve test performance of FNN and ANFIS. Moreover, the aggregated model outperforms all baseline models as well as the benchmark DJIA index by an acceptable margin for the test period. Our findings demonstrate that machine learning models could be used to aid fundamental analysts with decision-making regarding stock investment

Portfolio selection problems in practice: a comparison between linear and quadratic optimization models

Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio. We present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional Value-at-Risk (LACVaR) models, where the assets are limited with the introduction of quantity and cardinality constraints. We propose a completely new approach for solving the LAM model, based on reformulation as a Standard Quadratic Program and on some recent theoretical results. With this approach we obtain optimal solutions both for some well-known financial data sets used by several other authors, and for some unsolved large size portfolio problems. We also test our method on five new data sets involving real-world capital market indices from major stock markets. Our computational experience shows that, rather unexpectedly, it is easier to solve the quadratic LAM model with our algorithm, than to solve the linear LACVaR and LAMAD models with CPLEX, one of the best commercial codes for mixed integer linear programming (MILP) problems. Finally, on the new data sets we have also compared, using out-of-sample analysis, the performance of the portfolios obtained by the Limited Asset models with the performance provided by the unconstrained models and with that of the official capital market indices

Essays on Risk Pricing in Insurance

Pricing risks in the insurance business is an essential task for actuaries. Implementing the appropriate pricing techniques to improve risk management and optimize its financial gain requires a thorough understanding of underlying risks and their interactions. In this dissertation, I address risk pricing in the context of insurance company by reviewing methods applied in practice, proposing new models, and also exploring different aspects of insurance risks. This dissertation consists of three chapters. The first chapter provides a survey of existing capital allocation methods, including common approaches based on the gradients of risk measures and “economic” allocation arising from counterparty risk aversion. All methods are implemented in two example settings: binomial losses and using loss realizations from a catastrophe reinsurer. The stability of allocations is assessed based on sensitivity analysis with regards to losses. The results show that capital allocations appear to be intrinsically (geometrically) related, although the stability varies considerably. Stark differences exist between common and “economic” capital allocations. The second chapter develops a dynamic profit maximization model for a financial institution with liabilities of varying maturity, and uses it for determining the term structure of capital costs. iii As a key contribution, the theoretical, numerical, and empirical results show that liabilities with different terms are assessed differently, depending on the company’s financial situation. In particular, for a financially constrained firm, value-adjustments due to financial frictions for liabilities in the far future are less pronounced than for short-term obligations, resulting in a strongly downward sloping term structure. The findings provide guidance for performance measurement in financial institutions. The third chapter estimates a flexible affine stochastic mortality model based on a set of US term life insurance prices using a generalized method of moments approach to infer forward-looking, market-based mortality trends. The results show that neither mortality shocks nor stochasticity in the aggregate trend seem to affect the prices. In contrast, allowing for heterogeneity in the mortality rates across carriers is crucial. The major conclusion is that for life insurance, rather than aggregate mortality risk, the key risks emanate from the composition of the portfolio of policyholders. These findings have consequences for mortality risk management and emphasize important directions of mortality-related actuarial research

A Systematic Review on Robo-Advisors in Fintech

Technology has been the main driver for the financial sector. Fintech tools emerged to support the provision of financial services, especially Robo-Advisors (RAs), which allow the automation of the investment management process. The main functions are the creation of an investment portfolio and allocating assets, and daily management of investment portfolios based on a machine learning algorithm. This paper presents a literature review to summarise the importance of RAs in the financial sectors as well as the perception of investors. Also, this literature review presents the main algorithm’s characteristics behind the intelligence of RAs and the primary concerns. The Scopus and Web of Science databases revealed 114 research papers. It was found that investor acceptance of these technologies is affected by aspects of high volatility, which includes financial markets. The algorithm\u27s mathematical models and system architecture might be improved so that this instrument can better suit the needs of investors

Sector participation in labour supply models: Preferences or rationing ?

Developing Countries;Wage Differentials;Labour Market Segmentation