One-year Horizon Research Articles

Forecasting A Major Banking Corporation Stock Prices Using LSTM Neural Networks

The increasing complexity of stock market predictions necessitates advanced computational techniques to address the unique challenges posed by financial data's non-linear and volatile nature. This study aims to leverage Long Short-Term Memory (LSTM) neural networks to accurately forecast stock prices, using historical data collected from a major banking corporation as a primary source. The LSTM model excels at processing sequential time-series data, allowing it to predict monthly stock closing prices over a one-year horizon with a high degree of precision. Our findings indicate a Root Mean Squared Error (RMSE) of 3.2, underscoring the model's efficiency and reliability in financial forecasting tasks. The novelty of this research lies in the systematic incorporation of preprocessing techniques and fine-tuned hyperparameters to optimize model performance. Furthermore, this study explores the practical implications of implementing LSTM models in real-world trading scenarios, analyzing their adaptability to dynamic market conditions and their potential integration into automated trading systems. These findings contribute to the growing body of knowledge in financial analytics and demonstrate the viability of machine learning-based solutions for accurate and robust market predictions.

Predicting Mortgage Loan Defaults Using Machine Learning Techniques

Mortgage default prediction is always on the table for financial institutions. Banks are interested in provision planning, while regulators monitor systemic risk, which this sector may possess. This research is focused on predicting defaults on a one-year horizon using data from the Ukrainian credit registry applying machine-learning methods. This research is useful for not only academia but also policymakers since it helps to assess the need for implementation of macroprudential instruments. We tested two data balancing techniques: weighting the original sample and synthetic minority oversampling technique and compared the results. It was found that random forest and extreme gradient-boosting decision trees are better classifiers regarding both accuracy and precision. These models provided an essential balance between actual default precision and minimizing false defaults. We also tested neural networks, linear discriminant analysis, support vector machines with linear kernels, and decision trees, but they showed similar results to logistic regression. The result suggested that real gross domestic product (GDP) growth and debt-service-to-income ratio (DSTI) were good predictors of default. This means that a realistic GDP forecast as well as a proper assessment of the borrower’s DSTI through the loan history can predict default on a one-year horizon. Adding other variables such as the borrower’s age and loan interest rate can also be beneficial. However, the residual maturity of mortgage loans does not contribute to default probability, which means that banks should treat both new borrowers equally and those who nearly repaid the loan.

Study of Phase Changes in Operational Risk for Trucks

This study concerns the management of operational risk in truck transport using the reliability theory of risks. In this regard, the risk analysis of changes in the vehicle unavailability represents an important topic. In this study, the authors present their own method for analysing the phase changes in risk corresponding to successive sections (phases) of vehicle mileage. The presented risk analysis method is based on an integrated assessment of losses associated with the costs of incidental repairs and losses caused by lost income during vehicle downtime. This includes the following: assessment of differences in average risk and differences in the coefficient of variation in the time series of vehicle mileage phases, indicating the outliers and extremes of phase risk, identifying their physical causes and testing the statistical significance of phase risk differences. The proposed method is described mathematically and verified experimentally based on the operational data concerning trucks from two selected brands (20 trucks from each brand). We show that the method can be used to predict the continuity of transport services in the short term (one-year horizon). The method could also be useful to compare vehicles of different brands in the context of their sensitivity to operational risks.

Asimetrías en la traslación de los incrementos y de los descensos de los precios de la energía a la inflación subyacente del área del euro y de España

Rationale Energy prices rose sharply in early 2022 and then began to decline towards the end of the year. Given this situation, it is worth analysing whether an energy price increase raises underlying inflation more than an energy price decrease reduces it. Takeaways •Over a one-year horizon, a large hike in energy prices increases underlying inflation by around twice as much as an equivalent decrease reduces it. •The asymmetry in the response of underlying inflation to changes in energy prices is greater in components that use more energy in their production process. •Although asymmetry can be identified by analysing historical relationships, there is considerable uncertainty about the validity of such patterns in the current setting, which can be characterised by unprecedentedly large shocks that have driven both energy prices and underlying inflation.

Cost-effectiveness analysis of intravascular ultrasound-guided peripheral vascular interventions in patients with femoropopliteal peripheral artery disease.

Background: Intravascular ultrasound (IVUS)-guided percutaneous transluminal angioplasty (PTA) might offer clinical benefits compared to angiography-guided PTA in patients with peripheral artery disease (PAD). A cost-effectiveness model was developed to examine the benefits and costs of IVUS-guided PTA versus angiography-guided PTA in PAD patients with femoropopliteal (FP) occlusive disease. Methods: A two-step model (a one-year decision tree followed by a lifetime semi-Markov model) was developed from a German healthcare payer perspective to estimate the costs and outcomes over a one-year and lifetime horizon. Clinical events included target lesion revascularization (TLR), amputation, and death. Transition probabilities and utility values were derived from published literature. Healthcare costs were based on German Diagnosis Related Groups (DRG) codes. Costs and outcomes were discounted at a rate of 3% per year. The incremental cost-effectiveness ratio (ICER) was calculated, and sensitivity analyses were performed to assess the robustness of the results. Results: In the one-year horizon, IVUS-guided PTA resulted in incremental quality-adjusted life-years (QALY) and costs of 0.02 and €919 per patient respectively, with a corresponding ICER of €45,195/QALY gained versus angiography-guided PTA. In the lifetime horizon, IVUS-guided PTA outperforms angiography-guided PTA; it was associated with a cost saving of €46 per patient and incremental QALY of 0.22. Utility value for post-TLR, as well as probabilities of death and TLR had the greatest impact on the one-year ICER, while cost of TLR and probabilities of TLR and amputation influenced the lifetime ICER most. The probability of IVUS-guided PTA being cost-effective at a willingness-to-pay (WTP) threshold of €50,000/QALY was 50.4% in the one-year horizon and increased to 85.9% in the lifetime horizon. Conclusions: In this analysis IVUS-guided PTA among patients with symptomatic FP atherosclerosis was cost-saving in a lifetime horizon from the German healthcare payer perspective.

Extreme illiquidity and stock returns: Evidence from Thailand market

Extreme illiquidity (EIL) is priced in the cross-section of stock returns in the Thailand market. The EIL premium permeates stock categories with various risk factor loadings and characteristics. EIL is persistent and has predictive power for expected stock returns to at least a one-year horizon. The Market for Alternative Investment (MAI) with a higher proportion of individual investors, who are more active in trading than institutional investors, carries a higher EIL premium than the Stock Exchange of Thailand (SET). While the tradable EIL factor captures an important source of risk missed by the conventional risk factors, EIL as a characteristic has higher explanatory power in the cross-section than EIL as a covariance risk.

What does anticipated monetary policy do?

Applying sign restrictions to measures of expectations in a VAR, we quantify the economic effects of imperfectly and perfectly anticipated monetary policy innovations—the type of shocks induced by partially and fully credible forward guidance (FG). We find that fully credible FG one-year ahead has large near-term effects on prices and real activity, but, consistent with the forward guidance puzzle, these effects do not grow much larger as the FG horizon extends into the future. We also estimate that anticipated policy innovations are very noisy. At the one-year horizon, over 70 percent of the monetary-policy signal consists of noise, and the prevalence of noise increases at longer horizons. FG subject to this level of noise would be only partially credible and have significantly smaller macroeconomic effects.

Pension fund investments in infrastructure

AbstractInfrastructure investment is increasingly important for institutional investors. This paper focuses on pension fund allocations to infrastructure, using the CEM Benchmarking database. We document that larger pension funds, public pension funds, and pension funds with a higher allocation to other alternative assets are more likely to invest in infrastructure. Pension funds across the globe face infrastructure investment costs that are comparable, but we observe significant scale advantages in infrastructure investments, with larger pension funds exhibiting lower investment costs, and higher net returns. Importantly, infrastructure has been among the best-performing asset classes, as measured by net returns, and we document persistence in pension fund infrastructure investment performance over a one-year horizon.

Verbal Qualitative Information from Social Networks and Stock Performance of Tunisian Financial Companies

Social networks have invaded our world and changed the way we communicate. Even the financial world has turned to social media, with Twitter leading the way. As a result, social media is becoming an undeniable tool that affects the stock market. In this study, we consulted the articles published on the facebook page ilboursa of Tunis. The published articles contain an announcement of the news of the movements of the shares of the listed companies by using negative and positive words. Our database is composed by a manual counting of these qualitative verbal information. We used the list of words of the psychological dictionary "Harvard-IV-4". Our research focuses on 26 Tunisian financial companies listed on the Tunis Stock Exchange, over a one-year horizon, from 01 January 2015 until 31 December 2015. We used the GMM (Generalized Method of Moment) on dynamic panel. The generalized method of moments analyzes two models in which five days of delay of the dependent variable appear as explanatory variable. The results of the study are twofold. First, the Tunis stock exchange seems to react positively to positive qualitative information. Second, it reacts negatively to negative qualitative information. Among other things, the impact of stock returns on information is quite important. It is always necessary to master the tool of social networks to disseminate good and relevant qualitative financial information on the financial market.

Cost-benefit analysis of Intensive Care Unit with Activity-Based Costing approach in the era COVID-19 pandemic: A case study from Iran.

Providing intensive care to acute patients is a vital part of health systems. However, the high cost of Intensive Care Units (ICU) has limited their development, especially in low-income countries. Due to the increasing need for intensive care and limited resources, ICU cost management is important. This study aimed to analyze the cost-benefit of ICU during COVID-19 in Tehran, Iran. This cross-sectional study is an economic evaluation of health interventions. The study was conducted in the COVID-19 dedicated ICU, from the provider's point of view and within one-year horizon. Costs were calculated using a top-down approach and the Activity-Based Costing technique. Benefits were extracted from the hospital's HIS system. Benefit Cost ratio (BCR) and Net Present Value (NPV) indexes were used for cost-benefit analysis (CBA). A sensitivity analysis was performed to evaluate the dependence of the CBA results on the uncertainties in the cost data. Analysis was performed with Excel and STATA software. The studied ICU had 43 personnel, 14 active beds, a 77% bed occupancy rate, and 3959 occupied bed days. The total costs were $2,372,125.46 USD, of which 70.3% were direct costs. The highest direct cost was related to human resources. The total net income was $1,213,314.13 USD. NPV and BCR were obtained as $-1,158,811.32 USD and 0.511 respectively. Despite operating with a relatively high capacity, ICU has had high losses during the COVID-19. Proper management and re-planning in the structure of human resources is recommended due to its importance in the hospital economy, provision of resources based on needs assessment, improvement of drugs management, reduction of insurance deductions in order to reduce costs and improve ICU productivity.

Motivating non-physician health workers to reduce the behavioral risk factors of non-communicable diseases in the community: a field trial study

BackgroundNon-communicable diseases behavioral risk factors can be improved if effective interventions are designed considering the health system’s capabilities and local resources. This study evaluated the effectiveness of interventions that aimed at increasing non-physician community health workers’ motivation in reducing non-communicable diseases behavioral risk factors in the community.MethodsA randomized field trial study was conducted in 32 community health centers in 4 Iranian districts after a baseline population survey on the status of NCDs of 30–70-year-old individuals (n = 1225). The interventions were performed to improve insufficient physical activity, insufficient fruit consumption, insufficient vegetable consumption, high salt intake, and tobacco use. Four intervention packages were implemented in 24 community health centers; the other 8 centers were used as control groups. The non-physician community health workers performed the interventions. The packages additively included goal-setting, evidence-based education, operational planning, and incentive payments. A second survey was conducted 1 year after the start of the interventions to identify the effects on an independent random sample of 30–70-year-old individuals (n = 1221). Difference-in-difference method was used to quantify the interventions’ effects.ResultsThe average age of participants in both surveys was about 49 years. Also, about half of the participants were female, and about 43% were illiterate or had a primary school education. The interventions had statistically significant effects only on decreasing the prevalence of insufficient physical activity. The package with all the intervention components decreased the odds of insufficient physical activity to 0.24 (95% CI, 0.08, 0.72). The package with operational planning but no performance-based financing did not change the odds of insufficient physical activity.ConclusionsThis study highlighted the importance of components, design, and implementation details of interventions intended to reduce NCDs behavioral risk factors. Some risk factors, such as insufficient physical activity, seem more easily modifiable with limited low-cost interventions in a one-year horizon. However, risk factors related to healthy food consumption and tobacco use need more extensive interventions.Trial registrationThis trial was registered on the Iranian Registry of Clinical Trials (IRCT20081205001488N2) on 3 June 2018 (https://en.irct.ir/trial/774).

Analysts versus the random walk in financial forecasting: evidence from the Czech National Bank’s Financial Market Inflation Expectations survey

ABSTRACT We analyse how financial market analysts’ expectations in the Czech National Bank’s Financial Market Inflation Expectations survey perform relative to the random-walk forecast when it comes to predicting five financial variables. Using data from 2001 to 2022, our results indicate that the analysts are able to significantly outperform the random-walk forecast in terms of forecast precision for the repo rate and Prague Interbank Offered Rate at the one-month forecasting horizon. For the five- and ten-year interest rate swap rates, the random walk significantly outperforms the analysts at both the one-month and one-year forecasting horizons. For the CZK/EUR exchange rate, the random-walk forecast has a lower root mean squared forecast error than that of the analysts’ forecast at the one-month horizon whereas at the one-year horizon the opposite is found; however, none of these differences are statistically significant.

Climate change and credit risk: The effect of carbon tax on Italian banks' business loan default rates

Climate change poses severe systemic risks to the financial sector through multiple transmission channels. In this paper, we estimate the potential impact of different carbon taxes (€50, €100, €200 and €800 per ton of CO2) on the Italian banks’ default rates at the sector level in the short term using a counterfactual analysis. We build on the micro-founded climate stress test approach proposed by Faiella et al. (2022), which estimates the energy demand of Italian firms using granular data and simulates the effects of the alternative taxes on the share of financially vulnerable agents (and their debt). Credit risks stemming from the introduction of a carbon tax - during periods of low default rates - are modest for banks: on average, over a one-year horizon, the default rates of firms increase but remain below their historical averages. The effect is heterogeneous across different sectors and rises with the tax value; however, even assuming a tax of €800 per ton of CO2, the default rates are below their historical peaks.

Mathematical Modeling of Concentration Risk under the Default Risk Charge Using Probability and Statistics Theory

In the Fundamental Review of the Trading Book (FRTB), the latest regulation for minimum capital market risk requirements, one of the major changes, is replacing the Incremental Risk Charge (IRC) with the Default Risk Charge (DRC). The DRC measures only the default and does not consider the migration rating risk. The second new change in this approach was that the DRC now includes equity assets, contrary to the IRC. This paper studies DRC modeling under the Internal Model Approach (IMA) and the regulator conditions that every DRC component must respect. The FRTB presents the DRC measurement as Value at Risk (VaR) over a one-year horizon, with the quantile equal to 99.9%. We use multifactor adjustment to measure the DRC and compare it with the Monte Carlo Model to understand how the approach fits. We then define concentration in the DRC and propose two methods to quantify the concentration risk: the Ad Hoc and Add-On methods. Finally, we study the behavior of the DRC with respect to the concentration risk.

Optimal sizing of residential battery energy storage systems for long-term operational planning

Appropriate battery storage capacity plays an important role in the performance and cost of residential energy systems. However, the load demand and renewable energy generation vary seasonally. To address the long-term operational planning problem of battery energy storage, two battery sizing methods are developed based on the consensus alternating direction method of multipliers (C-ADMM). The residential system layout and convex battery model considering cycling aging are first established. Then, for the one-year horizon planning, C-ADMM decomposes the original large-scale problem into 365 sub-problems. The global optimal battery size and operation strategy are obtained by coordinating these sub-problems in parallel until convergence. In addition, to obtain the optimal battery size based on receding horizon control (RHC) strategies in a year, a new bi-level optimization method is developed, which uses the particle swarm optimization (PSO) algorithm in the outer loop and C-ADMM in the inner loop. Thus the inner loop can be accelerated through parallel computing. The results show that the long-term storage planning problem of residential systems can be solved quickly through C-ADMM due to the parallel computing capability. Furthermore, the optimal battery size calculated under the RHC strategy in a year can contribute to more economic benefits than the global battery size when applied online.

Dominant Currencies: How Firms Choose Currency Invoicing and Why it Matters

Abstract We analyze how firms choose the currency of invoicing and the implications of this choice for exchange rate pass-through into export prices and quantities. Using a new data set for Belgian firms, we find currency invoicing to be an active firm-level decision, shaped by the firm’s size, exposure to imported inputs, and the currency choices of its competitors. Our results show that a firm’s currency choice, in turn, has a direct causal effect on the exchange rate pass-through into prices and quantities. Moreover, the differential price response of similar firms that invoice in different currencies is large, persists beyond a one-year horizon, and gradually wanes in the long run. This results in allocative expenditure-switching effects on export quantities, which build up over time, suggesting a role for quantity adjustment frictions in addition to price stickiness. Our findings shed light on the mechanisms that make or break a dominant currency and the consequences it has for the international transmission of shocks.

Economic evaluation of a national vitamin D supplementation program among Iranian adolescents for the prevention of adulthood type 2 diabetes mellitus

BackgroundThis study aimed to evaluate the cost-effectiveness of vitamin D supplementation in preventing type 2 diabetes mellitus (T2DM) among Iranian adolescents.MethodsThis analytical observational study was conducted, using the decision tree model constructed in TreeAge Pro to assess the cost per quality-adjusted life-year (QALY) of monthly intake vitamin D supplements to prevent T2DM compared to no intervention from the viewpoint of Iran’s Ministry of Health and through an one-year horizon. In the national program of vitamin D supplementation, 1,185,211 Iranian high-school students received 50,000 IU vitamin D supplements monthly for nine months. The costs-related data were modified to 2018. The average cost and effectiveness were compared based on the Incremental Cost-Effectiveness Ratio (ICER).ResultsOur analytical analysis estimated the 4071.25 (USD / QALY) cost per AQALY gained of the monthly intake of 50,000 IU vitamin D for nine months among adolescents over a one-year horizon. Based on the ICER threshold of 1032–2666, vitamin D supplementation was cost-effective for adolescents to prevent adulthood T2DM. It means that vitamin D supplementation costs were substantially less than the costs of T2DM treatments than the no intervention.ConclusionsBased on the findings, the national vitamin D supplementation program for Iranian adolescents could be a cost-effective strategy to reduce the risk of diabetes in adulthood. From an economic perspective, vitamin D supplementation, especially in adolescents with vitamin D deficiency, would be administrated.

Cost-effectiveness of the COVID-19 test, trace and isolate program in Colombia

BackgroundDuring the COVID-19 pandemic, Test-Trace-Isolate (TTI) programs have been recommended as a risk mitigation strategy. However, many governments have hesitated to implement them due to their costs. This study aims to estimate the cost-effectiveness of implementing a national TTI program to reduce the number of severe and fatal cases of COVID-19 in Colombia.MethodsWe developed a Markov simulation model of COVID-19 infection combined with a Susceptible-Infected-Recovered structure. We estimated the incremental cost-effectiveness of a comprehensive TTI strategy compared to no intervention over a one-year horizon, from both the health system and the societal perspective. Hospitalization and mortality rates were retrieved from Colombian surveillance data. We included program costs of TTI intervention, health services utilization, PCR diagnosis test, productivity loss, and government social program costs. We used the number of deaths and quality-adjusted life years (QALYs) as health outcomes. Sensitivity analyses were performed.FindingsCompared with no intervention, the TTI strategy reduces COVID-19 mortality by 67%. In addition, the program saves an average of $1,045 and $850 per case when observed from the social and the health system perspective, respectively. These savings are equivalent to two times the current health expenditures in Colombia per year.InterpretationThe TTI program is a highly cost-effective public health intervention to reduce the burden of COVID-19 in Colombia. TTI programs depend on their successful and speedy implementation.FundingThis study was supported by the Colombian Ministry of Health through award number PUJ-04519-20 received by EPQ AVO and SDS declined to receive any funding support for this study. The contents are the responsibility of all the individual authors.

The cost-effectiveness analysis of a nationwide vitamin D supplementation program among Iranian adolescents for adulthood cardiovascular diseases prevention

ObjectiveThis study aimed to evaluate a national vitamin D supplementation program's cost-effectiveness among Iranian adolescents to prevent cardiovascular diseases (CVDs) in adulthood. Study designA cost-effectiveness analytical study. MethodsA decision tree model was adopted to evaluate the cost per quality-adjusted life-year (QALY) of monthly intake of nine pearls of 50,000 IU vitamin D for nine months to prevent CVD a one-year horizon compared to no intervention. The analysis was conducted in Iranian adolescents in first or second high school grades of 47 climatically different Iran regions. ResultsOur analytical analysis estimated the 1090$ cost per QALY gained of the monthly intake of 50,000 IU vitamin D for nine months among adolescents over a one-year horizon. Based on the incremental cost-effectiveness ratio (ICER) threshold of 1032–2666, vitamin D supplementation was cost-effective for adolescents to prevent adulthood CVD. It means that vitamin D supplementation costs were substantially less than the costs of CVD treatments compared to the no intervention. ConclusionsBased on these findings, the national program of vitamin D supplementation in adolescents would be cost effective to prevent CVD development in adulthood. From an economic perspective, vitamin D supplementation, especially in adolescents with vitamin D deficiency, would be administrated.

Probability of Default Model to Estimate Ex Ante Credit Risk

A genuine measure of ex ante credit risk links borrower’s financial position with the odds of default. Comprehension of a borrower’s financial position is proxied by the derivatives of its filled financial statements, i.e., financial ratios. We identify statistically significant relationships between shortlisted financial ratios and subsequent default events and develop a probability of default (PD) model that assesses the likelihood of a borrower going into delinquency at a one-year horizon. We compare the PD model constructed against alternative measures of ex ante credit risk that are widely used in related literature on bank risk taking, i.e., credit quality groups (prudential reserve ratios) assigned to creditors by banks and the credit spreads in interest rates. We find that the PD model predicts default events more accurately at a horizon of one year compared to prudential reserve rates. We conclude that the measure of ex ante credit risk developed is feasible for estimating risk-taking behaviour by banks and analysing shifts in portfolio composition.