A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing

The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with Smart beta strategies emerging as a trend among institutional investors. While they perform well in the long run, these strategies often suffer from severe short-term drawdown (peak-to-trough decline) with fluctuating performance across cycles. To address cyclicality and underperformance, we build a dynamic asset allocation system using Hidden Markov Models (HMMs). We test our system across multiple combinations of smart beta strategies and the resulting portfolios show an improvement in risk-adjusted returns, especially on more return oriented portfolios (up to 50$\%$ in excess of market annually). In addition, we propose a novel smart beta allocation system based on the Feature Saliency HMM (FSHMM) algorithm that performs feature selection simultaneously with the training of the HMM, to improve regime identification. We evaluate our systematic trading system with real life assets using MSCI indices; further, the results (up to 60$\%$ in excess of market annually) show model performance improvement with respect to portfolios built using full feature HMMs

Analysis of Financial Health Level Using the Z-Score (Altman) Method in Transportation Companies Listed on the Indonesia Stock Exchange for the 2019-2021 Period

This study aims to determine the company's financial condition based on the Altman (Z-Score) method and the effect of the ratio of Working Capital to Total Assets, Retained Earning to Total Assets, Earning Before interest and tax to Total Assets and Market Value of Equity to Book Value of Debt on the company's financial soundness (Altman Z-score). The population in this study is the transportation sector listed on the Indonesia Stock Exchange for the period 2019-2021. Sampling in this study was to use the purposive sampling method so as to obtain a sample of 36 from 12 companies. The data used in this study is the company's annual financial statements obtained from the Indonesia Stock Exchange. The data is then grouped and entered into the Altman Z-score formula and analyzed using the multiple linear regression method using the SPSS version 23 program. The results from the Altman Z-score show that in 2019 there were 5 companies in good health, 4 companies in an emergency (grey area) and 3 companies in a potentially bankrupt condition. In 2020 there are 6 companies in good health, 4 companies in an emergency (grey area) and 2 companies in a potentially bankrupt condition. In 2021 there are 6 companies in good health, 3 companies in an emergency (grey area) and 3 companies in a potentially bankrupt condition. Meanwhile, the results of the analysis test show that the variables Working Capital to Total Assets, Retained Earning to Total Assets, Earning Before Interest and Tax to Total Assets and Market Value Of Equity to Book Value Of Debt, individually and jointly significant effect the level of financial health of the company (Altman Z-score)

Determinants of Financial Risk: An Empirical Application on Low-Cost Carriers

The airline industry has entered a rapid development and transformation process, especially after the Second World War. In this process, it is seen that the market structure changed and many private airlines were established. Due to increased competition, airlines have begun to follow various strategies and business models in order to gain a competitive advantage over each other. One of the business models successfully applied recently is the low-cost business model. Therefore, this study focuses on airline companies that applied the low-cost business model. The study aims to reveal the factors that determine the financial risk in airlines, which implements the low-cost business model. For this purpose, firstly, airline companies that implement the low-cost business model have been identified according to the classification in the literature. The study included an analysis of 13 airlines with the low-cost business model that was fully accessible to financial data for the 2004-2017 period. Panel data analysis was used in the study and Altman (1968) Z-Score and Springate (1978) S-Score were used in measuring financial risk. Empirical findings of the study reveal that firm leverage, asset structure, firm size, firm profitability, and liquidity ratio have an effect on financial risk.JEL Codes - G32, L93, C1

Models and methodologies for credit scoring in personal banking: A literature review

Este trabajo pretende aportar literariamente una revisión de los modelos para la calificación del riesgo crediticio (modelos de Credit Score) utilizados en el otorgamiento de crédito personal; teniendo en cuenta los métodos de Abdou & Pointon (2011); Glennon, Kiefer, Larson, & Choi (2008); Saavedra-García & Saavedra-García (2010), se pretende crear un esquema de orden para explicar los múltiples modelos matemáticos y econométricos utilizados en el credit score, con el fin de generar un listado actualizado que esté sustentado por académicos y expertos en el tema.This paper provides a literature review on risk scoring models for credit granting in personal banking. The methods by Abdou & Pointon (2011), Glennon, Kiefer, Larson, & Choi (2008), and Saavedra-García (2010) are considered. The aim is to create a sorting scheme to explain the multiple mathematical and econometrical models used for credit scoring and to produce an up-to-date list supported by scholars and experts in the field

Machine learning no processo de risco de crédito das instituições bancárias

Uma vez que o sistema económico mundial se encontra em constante mudança, o estudo do risco de crédito tem uma grande importância para as instituições bancárias. Por estar associado a possíveis perdas que impactam o mercado financeiro, o processo de análise de crédito deve ser contínuo e progressivo. O atraso nos pagamentos de negócios tornou-se uma tendência, especialmente após as recentes crises financeiras. Desse modo, os bancos devem minimizar dívidas, analisar individualmente os créditos, agir com rapidez e se proteger de não pagamentos. Na mesma conjuntura, machine learning é uma tecnologia emergente para a construção de modelos analíticos, faz com que as máquinas aprendam com os dados. Com isso, efetuem análises preditivas de maneira mais rápida e eficiente. Fazer análises preditivas é muito importante e possui uma ampla gama de atuação para os bancos. Como, por exemplo: • Identificação dos melhores fatores de risco a serem utilizados na antecipação de crédito a clientes; • Obediência dos dispositivos legais; • Qualidade de dados; • Deteção de fraudes. Na criação de uma pontuação de risco de crédito bancário, automatizada, robusta e eficaz, machine learning vai ajudar na previsão da capacidade de crédito do cliente com mais precisão. O objetivo é analisar as diferentes abordagens de gestão de risco de crédito. Para tal, recorre-se a revisão de literatura de tópicos importantes, em destaque a machine learning, e ao uso de questionários. Os principais resultados mostraram que o uso de machine learning no risco de crédito bancário, ainda está em fase inicial. A maioria dos bancos já reconhece os valores que esta tecnologia pode proporcionar. Com base nesses resultados, os bancos que são tão sensíveis a mudanças, têm que sair do âmbito da teoria e investir em pequenos projetos. Só assim esta tecnologia provará a sua capacidade de melhoria, e transmitir a confiança necessária para este sector.As the global economic system is constantly changing, the study of credit risk is of great importance to banking institutions. Because it is associated with possible losses that impact the financial market, the process of credit analysis should be continuous and progressive. Late business payments have become a trend, especially after the recent financial crises. Thus, banks should minimize debt, analyze individual credits, act quickly and protect themselves from non-payment. At the same time, machine learning is an emerging technology for building analytical models, making machines learn from data. As a result, they carry out predictive analyses more quickly and efficiently. Predictive analysis is very important and has a wide range of activities for banks. For example: • Identification of the best risk factors to be used in anticipating credit to customers; • Compliance with legal provisions; • Obedience of legal provisions; • Data quality; • Fraud detection. In creating an automated, robust and effective bank credit risk score, machine learning will help predict the customer's creditworthiness more accurately. The goal is to analyze the different approaches to credit risk management. To this end, a literature review of important topics is used, especially machine learning and the use of questionnaires. The main results showed that the use of machine learning in bank credit risk is still at an early stage. Most banks already recognize the values that this technology can provide. Based on these results, banks that are so sensitive to change have to go beyond the scope of theory and invest in small projects. Only in this way will this technology prove its ability to improve and transmit the necessary confidence to this sector

Promoting the emotional well-being of staff in care homes for older people

Background: Compassion fatigue is associated with negative physical and psychological symptoms. Compassion satisfaction occurs when carers experience reward from helping others. Research has concluded that managerial support can protect against compassion fatigue; however, there is limited evidence exploring how older adult care home managers support their staff. Aims: The study aimed to explore care home managers‟ experiences of mitigating compassion fatigue and promoting compassion satisfaction in their staff teams. Method: Semi-structured interviews were carried out with six care home managers. The interviews were transcribed verbatim and analysed using Interpretive Phenomenological Analysis. Results: Three superordinate themes were developed from the data: 1) Navigating staff-resident relationships; 2) Utilising manager resources; and 3) Promoting satisfaction. Conclusions: The participants described their experiences validating and empathising with staff. They reflected on challenges, the need for relationship-based care, and highlighted staff training needs. Implications and recommendations are discussed