Extension and calibration of a Hawkes-based optimal execution model

We provide some theoretical extensions and a calibration protocol for our former dynamic optimal execution model. The Hawkes parameters and the propagator are estimated independently on financial data from stocks of the CAC40. Interestingly, the propagator exhibits a smoothly decaying form with one or two dominant time scales, but only so after a few seconds that the market needs to adjust after a large trade. Motivated by our estimation results, we derive the optimal execution strategy for a multi-exponential Hawkes kernel and backtest it on the data for round trips. We find that the strategy is profitable on average when trading at the midprice, which is in accordance with violated martingale conditions. However, in most cases, these profits vanish when we take bid-ask costs into account

Copula methods for evaluating relative tail forecasting performance

We apply our method to analyze which portfolios are capable of providing superior performance to those based on the Sharpe ratio. In this paper we illustrate the use of conditional copulas for identifying differences in alternative portfolio performance strategies. We analyze which portfolios are capable of providing superior performance to those based on the Sharpe ratio. Our results show that under the Gaussian copula, both expected tail ratio and skewness-kurtosis ratio portfolios exhibit remarkably low correlations respecting the Sharpe ratio (SR) portfolio. This means that these two portfolios are different respecting the SR one. We also find that copulas which focus on either the upper tail (Gumbel) or the lower tail (Clayton) render significant differences. In short, our copula analysis is useful to understand what kind of equity-screening strategy based on its corresponding performance measure performs better in relation to the SR portfolio. Our copula methods to evaluate models' performance differences is significant because when models’ performance is rather similar, conclusions on statistical differences, can be defective as they may hinge on the subsample type or size used, leading to inefficient investment decisions. Our method contributes to address this issue

Should new or rapidly growing banks have more equity?

There is substantial evidence that new banks and rapidly growing banks are risk prone. We study this problem by designing a relationship-lending model in which a bank operates as a financial intermediary and centralised monitor. In the absence of deposit insurance, the bank’s limited liability option creates an incentive problem between the bank and its depositors, the likely outcome of which is a reduction in the amounts of resources allocated to monitoring its borrowers. Hence, the bank must signal its safety to depositors by maintaining the equity ratio held. The optimal equity ratio is dynamic, ie new banks need relatively more equity than established banks, which enjoy profitable old lending relationships – charter value – that reduce the incentive problem. However, if an established bank grows rapidly, its share of old relationships also decreases and the bank will have to raise its equity ratio. With deposit insurance, regulators should set higher equity requirements for new banks and rapidly growing banks than for those in a more established position. The results of the model can be extended to more general inter-firm control of credit institutions.financial intermediation; relationship banking; financial fragility; bank regulation; deposit insurance; moral hazard; product quality

Value of Research

Transportation research in Kentucky has been ongoing since 1914. The Road Materials Testing Laboratory was established that year at the University of Kentucky. Operation of the laboratory commenced in 1915 under the guidance of Professor D.V. Terrell who later became Dean of the College of Engineering. Dean Terrell often noted that the purpose of research was not to save money but to make it go farther. Public funding was never sufficient to accomplish all that various agency personnel considered essential nor that which the public deemed necessary and research was conducted most often for the purpose of gaining more for the money available. The purposes of this report are: 1) to provide an abbreviated background of the evolution of transportation research in Kentucky; 2) a discussion of research and the administration of research within the Kentucky Transportation Cabinet and its predecessor agencies - the Department of Highways and the Department of Transportation; and 3) a compilation of recent and current research accomplishments which have contributed to the design, construction, maintenance, management, and operations of the highway network within the Commonwealth. Significant benefits of research endeavors are derived through implementation of research findings and technology transfer to the users. Steps have been initiated by Transportation Cabinet and Federal Highway Administration officials to greatly enhance implementation and technology transfer. Dividends from investments in research multiply in proportion to implementation of research discoveries

Deep reinforcement learning for investing: A quantamental approach for portfolio management

The world of investments affects us all. The way surplus capital is allocated by ourselves or investment funds can determine how we eat, innovate and even educate kids. Portfolio management is an integral albeit challenging process in this task (Leković, 2021). It entails managing a basket of financial assets to maximize the returns per unit of risk, considering all the micro and macro economical, societal, political and environmental complex causal relations. This study aims to evaluate how a machine learning technique called deep reinforcement learning (DRL) can improve the activity of portfolio management. It also has a second goal of understanding if financial fundamental features (i.e., revenue, debt, assets, cash flow) improve the model performance. After conducting a literature review to establish the current state-of-the-art, the CRISP-DM method was followed: 1) Business understanding; 2) Data understanding; 3) Data preparation – two datasets were prepared, one with market only features (i.e., close price, daily volume traded) and another with market plus fundamental features; 4) Modeling – Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG) and Twin-delayed DDPG (TD3) DRL models were optimized on both datasets; 5) Evaluation. On average, models had the same sharpe ratio performance in both datasets – average sharpe ratio of 0.35 vs 0.30 for the baseline, in the test set. DRL models outperformed traditional portfolio optimization techniques and financial fundamental features improved model robustness and consistency. Hence, supporting the use of both DRL models and quantamental investment strategies in portfolio management.Todos somos afetados pelo mundo dos investimentos. A forma como o excedente de capital é alocado tanto por nós como por fundos de investimentos determina a forma como comemos, inovamos e até mesmo como fornecemos educação às crianças. Gestão de portfólio é uma tarefa essencial e desafiadora neste processo (Leković, 2021). Envolve gerir um conjunto de ativos financeiros com o objetivo de maximizar os retornos por unidade de risco, tendo em consideração todas as relações complexas entre fatores macro e microeconómicos, sociais, políticos e ambientais. Este estudo pretende avaliar de que forma a técnica de machine learning intitulada de Aprendizagem por Reforço Profunda (ARP) consegue melhorar a tarefa de gestão de portfólios. Também tem um segundo objetivo de entender se variáveis relacionadas com a performance financeira de uma empresa (i.e., vendas, passivos, ativos, fluxos de caixa) melhoram a performance do modelo. Após o estado-de-arte ter sido definido com a revisão de literatura, utilizou-se o método CRISP-DM da seguinte forma: 1) Entendimento do negócio; 2) Entendimento dos dados; 3) Preparação dos dados – dois conjuntos de dados foram preparados, um apenas com variáveis de mercado (i.e., preço de fecho, volume transacionado) e o outro com variáveis de mercado mais variáveis de performance financeira; 4) Modelagem – usou-se os modelos Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG) e Twin-delayed DDPG (TD3) em ambos os conjuntos de dados; 5) Avaliação. Em média, os modelos apresentaram o mesmo índice sharpe nos dois conjuntos de dados – média de 0.35 vs 0.30 para o modelo base, no conjunto de teste. Os modelos ARP apresentaram uma melhor performance do que os modelos tradicionais de otimização de portfólios e a utilização de variáveis de performance financeira melhoraram a robustez e consistência dos modelos. Tais conclusões suportam o uso de modelos ARP e de estratégias de investimentos quantamentais na gestão de portfólios

How to Identify and Forecast Bull and Bear Markets?

The state of the equity market, often referred to as a bull or a bear market, is of key importance for financial decisions and economic analyses. Its latent nature has led to several methods to identify past and current states of the market and forecast future states. These methods encompass semi-parametric rule-based methods and parametric regime-switching models. We compare these methods by new statistical and economic measures that take into account the latent nature of the market state. The statistical measure is based directly on the predictions, while the economic mea- sure is based on the utility that results when a risk-averse agent uses the predictions in an investment decision. Our application of this framework to the S&P500 shows that rule-based methods are preferable for (in-sample) identification of the market state, but regime-switching models for (out-of-sample) forecasting. In-sample only the direction of the market matters, but for forecasting both means and volatilities of returns are important. Both the statistical and the economic measures indicate that these differences are significant

The Big Society and the Conjunction of Crises: Justifying Welfare Reform and Undermining Social Housing

The idea of the “Big Society” can be seen as culmination of a long-standing debate about the regulation of welfare. Situating the concept within governance theory, the article considers how the UK coalition government has justified a radical restructuring of welfare provision, and considers its implications for housing provision. Although drawing on earlier modernization processes, the article contends that the genesis for welfare reform was based on an analysis that the government was forced to respond to a unique conjunction of crises: in morality, the state, ideology and economics. The government has therefore embarked upon a programme, which has served to undermine the legitimacy of the social housing sector (most notably in England), with detrimental consequences for residents and raising significant dilemmas for those working in the housing sector

Assessing the Value of Complex Refractive Index and Particle Density for Calibration of Low-Cost Particle Matter Sensor for Size-Resolved Particle Count and PM2.5 Measurements

Commercially available low-cost particulate matter (PM) sensors provide output as total or size-specific particle counts and mass concentrations. These quantities are not measured directly but are estimated by the original equipment manufacturers' (OEM) proprietary algorithms and have inherent limitations since particle scattering depends on their composition, size, shape, and complex index of refraction (CRI). Hence, there is a need to characterize and calibrate their performance under a controlled environment. We present calibration algorithms for Plantower PMS A003 sensor as a function of particle size and concentration. A standardized experimental protocol was used to control the PM level, environmental conditions and to evaluate sensor-to-sensor reproducibility. The calibration was based on tests when PMS A003 were exposed to different polydisperse standardized testing aerosols. The results suggested particle size distribution from PMS A003 was shifted compared to reference instrument measures. For calibration of number concentration, linear model without adjusting aerosol properties corrects the raw PMS A003 measurement for specific size bins with normalized mean absolute error within 4.0% of the reference instrument. Although the Bayesian Information Criterion suggests that models adjusting for particle optical properties and relative humidity are technically superior, they should be used with caution as the particle properties used in fitting were within a narrow range for challenge aerosols. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM's mass concentrations estimates and demonstrated lower error. These results have significant implications for using PMS A003 in high concentration environments, including indoor air quality and occupational/industrial exposure assessments, wildfire smoke, or near-source monitoring scenarios

The Volatility–Variability Hypotheses Testing and Hedging Effectiveness of Precious Metals for the Indonesian and Malaysian Capital Markets

This study evaluates the use of futures contracts for precious metals to hedge against stock market risks and their hedging effectiveness on the Indonesian Stock Exchange (IDX) and the Kuala Lumpur Stock Exchange (KLSE). This study found that gold was the most effective hedging instrument, since it produced the highest hedging effectiveness both on the IDX and the KLSE among the other precious metals. None of the hedged portfolios had a higher Sharpe’s ratio than the unhedged one on the IDX; however, all the hedged portfolios on the KLSE had a higher Sharpe’s ratio than the unhedged ones. Almost all the hedged portfolios could produce a higher Treynor’s ratio than the unhedged portfolios, both on the IDX and the KLSE. In general, this study concluded that studying some precious metals could reduce the investment risk, which was shown through the variance produced by the smaller portfolios, while gold can improve the risk-adjusted performance