Deep Learning Models for Predicting Monthly TAIEX to Support Making Decisions in Index Futures Trading

Futures markets offer investors many attractive advantages, including high leverage, high liquidity, fair, and fast returns. Highly leveraged positions and big contract sizes, on the other hand, expose investors to the risk of massive losses from even minor market changes. Among the numerous stock market forecasting tools, deep learning has recently emerged as a favorite tool in the research community. This study presents an approach for applying deep learning models to predict the monthly average of the Taiwan Capitalization Weighted Stock Index (TAIEX) to support decision-making in trading Mini-TAIEX futures (MTX). We inspected many global financial and economic factors to find the most valuable predictor variables for the TAIEX, and we examined three different deep learning architectures for building prediction models. A simulation on trading MTX was then performed with a simple trading strategy and two different stop-loss strategies to show the effectiveness of the models. We found that the Temporal Convolutional Network (TCN) performed better than other models, including the two baselines, i.e., linear regression and extreme gradient boosting. Moreover, stop-loss strategies are necessary, and a simple one could be sufficient to reduce a severe loss effectively.

De novo variants in H3-3A and H3-3B are associated with neurodevelopmental delay, dysmorphic features, and structural brain abnormalities

The histone H3 variant H3.3, encoded by two genes H3-3A and H3-3B, can replace canonical isoforms H3.1 and H3.2. H3.3 is important in chromatin compaction, early embryonic development, and lineage commitment. The role of H3.3 in somatic cancers has been studied extensively, but its association with a congenital disorder has emerged just recently. Here we report eleven de novo missense variants and one de novo stop-loss variant in H3-3A (n = 6) and H3-3B (n = 6) from Baylor Genetics exome cohort (n = 11) and Matchmaker Exchange (n = 1), of which detailed phenotyping was conducted for 10 individuals (H3-3A = 4 and H3-3B = 6) that showed major phenotypes including global developmental delay, short stature, failure to thrive, dysmorphic facial features, structural brain abnormalities, hypotonia, and visual impairment. Three variant constructs (p.R129H, p.M121I, and p.I52N) showed significant decrease in protein expression, while one variant (p.R41C) accumulated at greater levels than wild-type control. One H3.3 variant construct (p.R129H) was found to have stronger interaction with the chaperone death domain-associated protein 6.

Whole-exome analysis of adolescents with low VWF and heavy menstrual bleeding identifies novel genetic associations

Adolescents with low von Willebrand factor (VWF) levels and heavy menstrual bleeding (HMB) experience significant morbidity. There is a need to better genetically characterize these patients and improve our understanding of the pathophysiology of bleeding. We performed whole-exome sequencing on 86 post-menarchal patients diagnosed with low-VWF levels (30-50 IU/dL) and HMB and compared them to 660 in-house controls. We compared the number of rare stop-gain/stop-loss and rare ClinVar pathogenic variants between cases and controls, as well as performed gene-burden and gene-set burden analyses. We found an enrichment in cases of rare stop-gain/stop-loss variants in genes involved in bleeding disorders, and an enrichment of rare ClinVar pathogenic variants in genes involved in anemias. The two most significant genes in the gene-burden analysis, CFB and DNASE2, are associated with atypical hemolytic uremia (aHUS) and severe anemia, respectively. VWF also surpassed exome-wide significance in the gene-burden analysis (p=7.31x10-6). Gene-set burden analysis revealed an enrichment of rare nonsynonymous variants in cases in several hematologically relevant pathways. Further, common variants in FERMT2, a gene involved in regulation of hemostasis and angiogenesis surpassed genome-wide significance. We demonstrate that adolescents with HMB and low-VWF have an excess of rare nonsynonymous and pathogenic variants in genes involved in disorders of bleeding and anemia. Variants of variable penetrance in these genes may contribute to the spectrum of phenotypes observed in HMB patients, and could partially explain the bleeding phenotype. By identifying the HMB patients who possess these variants, we may be able to improve risk stratification and patient outcomes.

Actuarial Technique of Modelling the Out of Pocket with Deductible and Stop Loss in Health Care Insurance

The paper is intended to numerically estimate health insurance out of pocket spending from the perspective of the enrollees in order to inform whether health insurance scheme pricing policy delivers value to the enrollees. We study the implications of financing health care insurance using a classification model for health care insurance under the framework of deductibles and stop loss. The paper is designed for health underwriting professionals, particularly those who consult for health insurance schemes. This is essential given the form and emergence of treating enrollees fairly, based on regulations that impact on actuarial consultants advising on health insurance scheme product lines. The objectives of this paper are to demonstrate actuarial methods for estimating (i) total health expenses (ii) out of pocket costs (iii) reimbursements by third party insurance. (iv) examine the actuarial implications of the chosen model. In computing the out of pocket and reimbursement, the Pitacco’s model was adopted because of its relatively computational superiority and the fact that both deductible and stop loss form its core parameters. Our results show that there is a positive relationship between total health expense and the share funded by out of pocket spending. As the health expenditure increases, the share funded by out of pocket progressively increases.

Institutional Disruptions and the Philanthropy of Multinational Firms

This paper studies philanthropy by multinational enterprises (MNEs) during institutional disruptions—the sudden and unexpected, temporary, and systemic breakdowns in market-oriented institutions. The central argument is that, under institutional disruptions, MNEs aim to restore factors that are essential for the market to function, such as infrastructure and labor markets, and the strength of the market restoration motive is positively associated with the economic importance of the affected country to the MNE. Analyses of donations from 2,000 MNEs headquartered in 63 countries in the aftermath of 265 major epidemics, natural disasters, and terrorist attacks affecting 129 countries suggest that the economic importance of the country to the firm strongly explains donations. Country market concentration, public aid, and the country’s regulatory quality moderate this effect. These associations are robust to a matching method; a vector of firm-, country-, and event-specific time-varying and -constant variables; and alternative motives, such as reputation, altruism, media salience, market standing, and poverty-gap avoidance. They offer evidence that company philanthropy in the aftermath of institutional disruptions may deviate from predicted behavior under stable conditions. Particularly, the findings contest the expectation that philanthropy rises in market competition. Monopolistic firms are comparatively large donors and may act as an economic stop-loss mechanism during large disruptions.

Optimal Insurance Indemnity and Reinsurance Strategy for Health Insurance

“Health insurance + health management” package has recently become one of the most important nonlife insurance products, and its pricing technique has drawn attention from both academia and industry. This paper investigates the optimal indemnity design and per-loss reinsurance strategy for the health insurance package, where the reinsurance contract is assumed to combine the quota-share type and the excess-of-loss type. By applying the Lagrange multiplier method and optimal control technique, we develop the solutions to the corresponding optimization problems and obtain the optimal deductible. Then, we proceed to solve the optimal quota-share proportion and the optimal stop-loss retention based on the optimal insurance indemnity. In addition to theoretical results, numerical examples are also given to illustrate the effects of various key parameters on the optimal indemnity design and combinational reinsurance strategy.

A Quantitative Model for Option Sell-Side Trading with Stop-Loss Mechanism by Using Random Forest

Due to the characteristics of high leverage and low margin, option is very suitable for quantitative trading by applying portfolio management to control the profit and risk. The money management is an important issue to build a portfolio especially for option sell-side trader, since the profit is only the premium, while the loss is unlimited. In this research, we propose a model for option sell-side strategy to estimate the win-rate of option by the premium, time to maturity, and volatility based on statistical approach and random forest algorithm. The prediction of the model is visualized through heatmap which can reveal the profitable trading range intuitively, we use the precision score to evaluate the performance in these two models and proof the effectiveness and robustness of predictive model proposed by random forest algorithm. In the future, we plan to apply other machine learning algorithm to propose the predictive model for spread trading.