Short Horizons Research Articles

8.P. Scientific session: Lessons-learned from recent foresight studies to support policy making?

Abstract Though foresight is still relative new regarding applications to public health, their number is rapidly growing. Recently, several foresight studies have been published with different health angles. These foresight studies have in general the purpose to support and improve the policy making process. Policy making that is foresight-informed, e.g. policies that account for possible futures and the uncertainty they encompass, will hopefully be better in achieving policy goals than without foresight. However, policy makers in general have a relatively short time horizon, may not welcome uncertainty and have many other aspects to account for when developing their policies. In this workshop, the link of foresight studies with policy making, also referred to as knowledge translation will be the focal point. The workshop will start with and interactive Mentimeter session to get a better view on how the audience is familiar with foresight and knowledge translation. Then, a brief presentation is given as introduction to foresight and a systematic way of doing knowledge translation, applied to foresight, explaining the basic concepts to level understanding of the audience. This is followed by three presentations of foresight studies on specific topics (Screening, AMR, Substance use) focussing on their link withing policy making. Next, two broader foresight studies will be presented (i.e. Dutch PHFS 2024 and the future of public health in Europe). These provide good examples on how broad future public health are analyzed and can be linked to public health policy making. The workshop will be concluded with a discussion with presenters on the lessons learned regarding foresight and the link to policy making. Key messages • Foresight studies in the field of health are maturing by a stronger link to policy making. • We can learn from recent foresight studies how to do better knowledge translation regarding foresight.

Technological shocks and stock market volatility over a century

This paper provides a novel perspective on the innovation-stock market nexus by examining the predictive relationship between technological shocks and stock market volatility using data over a period of more than 140 years. Utilizing annual patent data for the U.S. and a large set of economies to create proxies for local and global technological shocks and a mixed-sampling data (MIDAS) framework, we present robust evidence that technological shocks capture significant predictive information regarding future realizations of stock market volatility, both in- and out-of-sample and at both the short and long forecast horizons. Further economic analysis shows that investment portfolios created by the volatility forecasts obtained from the forecasting models that incorporate technological shocks as predictors in volatility models experience significantly lower return volatility in the out-of-sample horizons, which in turn helps to improve the risk-return profile of those portfolios. Our findings present a novel take on the nexus between technological innovations and stock market dynamics and pave the way for several interesting avenues for future research regarding the role of technological innovations on asset pricing tests and portfolio models.

Dynamical complexity and multifractal analysis of geomagnetic activities at high temporal scales over three solar cycles

Activities in geospace occur at different time scales. Understanding geomagnetic activity at high temporal scales will give insight into fast dynamics in geospace. This study aims to investigate dynamical complexities in geomagnetic activities at a high temporal scale across three solar cycles. Geomagnetic activities, as represented by 5-min SYM-H data, were considered in this study over three solar cycles (22–24) from 1986 to 2019. Chaos analysis using sample entropy, Lyapunov exponent, and correlation dimension indicates that the geomagnetic activities are driven by intrinsic complex and chaotic processes. Positive Lyapunov exponent values between 0.13 and 0.18, 0.15–0.18, and 0.16–0.19 were obtained for solar cycles 22, 23, and 24 respectively. Furthermore, geomagnetic activities were also found to have multifractal structures driven by high fractal exponents with fine structures. A positive relationship was obtained between the annual mean values of SYM-H and the degree of complexity. It is concluded that geomagnetic activities have a short prediction horizon.

Public spending on Energy innovations and CO2 impacts: Evidence from selected OECD countries

The relationship between public spending on Energy innovations (R&D of renewable and non-renewable energy, and energy efficiency) and CO2 intensity at constant purchasing power are investigated in this study. A Panel-NARDL model was used for the first time in 10 OECD countries (1990–2020) to evaluate the asymmetric relationship between energy innovative variables and CO2 impacts in the long and short run. In other words, this study aims to monitor how changes in public spending on R&D for renewable and non-renewable influence CO2 emissions. The results show that while an increase in the public budget for R&D of renewable energy will decrease the CO2 intensity in the long run, a positive relationship exists between these variables in the short run. Likewise, the result demonstrates that a cut in public spending on Fossil fuel R&D leads to an increase in the short horizon and a decrease in the long-run CO2 emissions per unit of GDP. The finding implies the substantial impact of R&D in both renewable and non-renewable energy on reducing CO2 emissions over time. In addition, CO2 emissions per unit of GDP respond positively to R&D public spending on energy efficiency. An increase in public expenditure on R&D of energy efficiency can induce economic activity and enhance CO2 emissions. All in all, the 30 years’ analysis of data from the selected OECD countries substantiates the right strategy of reducing the CO2 emissions of these countries by applying the public budget to promote R&D in energy sectors. This study results in contrast with those studies that found no relationship between R&D on renewable energy and CO2 impacts. Significance Statement This paper evaluated the asymmetric impacts of public spending on R&D of renewable energy and fossil fuel, and energy efficiency on CO2 intensity at constant purchasing power. Although a growing body of research has analyzed the impact of R&D on CO2 emission, research on the asymmetric effects of R&D public expenditure in energy sectors on CO2 emission is still narrow. Moreover, by employing Panel Nonlinear ARDL, we delve into the spillover impacts of selected OECD public budget on R&D over the short and long horizons. In addition, we estimate the impacts of both Renewable and non-renewable R&D budgets which help us to explore and compare the effectiveness of budget allocation on R&D with the strategy of reducing CO2 emission. Finally, this study found how the R&D and technology budget surging and declining in energy sectors would influence CO2 emission per unit of GDP.

Money/Asset Ratio as a Predictor of Inflation

This paper modifies the quantity theory of money to forecast inflation, relating the latter to scale variables such as monetary aggregate M2 and government bonds that measure the money demand for asset transactions. The out-of-sample forecast results show that at least since the early 1990s, the money/asset model that uses the money supply/government debt ratio as a predictor has been significantly improved upon univariate and multivariate models, such as Phillips curve and term spread models, for forecasting U.S. inflation over one- to three-year horizons. In using real-time vintage data, I find that, since 2000Q1, the forecasts derived from the money/asset model have slightly improved upon those from the Greenbook in forecasting quarter-over-quarter CPI inflation at short horizons, from two- to four-quarter. These results imply that the Federal Reserve can use the money supply/government debt ratio to forecast and control the inflation rates, coordinating monetary policy with fiscal policy. Moreover, the money supply/government debt ratio can partly explain the U.S. inflation dynamics from the early 1960s until COVID-19.

Safe haven between European ESG and energy sector under Russian-Ukraine war: Role of sustainable investments for portfolio diversification

The shortage of oil and gas supply to Europe during the Russia-Ukraine conflict has triggered a price hike in the European energy sector. To mitigate the impact of higher energy prices on consumers, the EU introduced a price ceiling mechanism to curb gas prices from exceeding market norms. This interplay between supply shortages and price controls further heightened volatility in energy market prices. Consequently, our study aims to explore the efficacy of ESG indices as a safe haven against the highly volatile European oil and gas sectors to reduce portfolio losses during conflict. Using advanced analytical techniques such as cross-quantilogram to predict directional predictability, we find that bullish ESG markets are a safe haven against the bearish oil and gas markets in short, medium, and long-term investment horizons. Further, to identify the net receiver/transmitter of spillover between ESG indices and oil and gas sectors, our quantile time-frequency connectedness analysis shows ESG is a net receiver of spillover before the conflict and during wartime. Additionally, we explored the indicator of uncertainty that influences quantile spillover between ESG indices and European oil and gas sectors. We find that uncertainty's role in quantile spillover is significant and asymmetric, and it becomes more volatile during wartime. This feature tends to be amplified during short and long-term spillovers. Finally, the portfolio rebalance strategy suggests that a safe haven portfolio based on a one-month investment horizon yields positive returns during wartime. Hence, this study suggests investors include ESG indices as safe haven assets at times of crisis and for policymakers to understand the role of uncertainties in reducing price volatility and risk contagion among ESG and energy markets.

Budget impact of resmetirom for the treatment of adults with non-cirrhotic non-alcoholic steatohepatitis (NASH) with moderate to advanced liver fibrosis (consistent with stages F2 to F3 fibrosis)

Aims This study assessed the budget impact of resmetirom as a treatment for adults with non-cirrhotic non-alcoholic steatohepatitis (NASH) with moderate-to-advanced liver fibrosis and estimated total costs for a hypothetical private payer in the United States. Materials and methods A three-year budget impact analysis based on an open cohort state transition model was developed for a hypothetical one-million-member private health plan. The comparator was Standard of Care (SOC), defined as routine care for non-cirrhotic NASH patients with moderate-to-advanced liver fibrosis. Each year, the number of resmetirom treatment-eligible patients was estimated through prevalent, incident, and diagnostic rate estimates. Costs included resources incurred by the medical and pharmacy benefits of private payers, including resmetirom drug acquisition costs, diagnosis and monitoring, other medical and other prescription costs stratified by disease progression status (i.e. non-cirrhotic vs. cirrhotic/advanced liver diseases). Resmetirom adverse event management costs were included in sensitivity analysis. Drug costs were estimated based on the average wholesale acquisition cost as of March 2024. Other costs were based on published sources and inflated to 2023 US dollars. Budget impact outcomes were presented in aggregate, net, and on a per-member per-month (PMPM) basis. Results Compared with a scenario without resmetirom, the introduction of resmetirom yielded results ranging from 50 to 238 treated patients, net budget impact of $2.2 to $9.5 million, and PMPM from $0.19 to $0.80 over years one and three. Net costs excluding resmetirom declined over time. In sensitivity analyses, results were most sensitive to diagnostic and epidemiologic inputs. Limitations Market shares are based on internal forecasts, a short time horizon, average treatment effects, and other limitations common to BIMs. Conclusion The adoption of resmetirom on the formulary for the treatment of non-cirrhotic NASH with moderate-to-advanced liver fibrosis resulted in a moderate increase in budget impact with declining costs related to NASH progression.

The role of the end time in experimental asset markets

There are hundreds of scientific articles on experimental asset markets. Almost all of them use a short and definite horizon. This may be one of the starkest differences between experimental settings and real-world financial markets, which usually have indefinite and comparatively long horizons. We analyze the implications of different end time assumptions in an asset market experiment in which we vary the length of the horizon and whether the end time is definite or indefinite. We find very similar price dynamics with recurring bubbles in all treatments.

Forecasting the solar modulation potential: Tests of time series models

This study analyzes the predictability of the solar modulation potential using time series models. Recently, new data sets for the modulation potential have become available, at daily, monthly, and annual resolutions. At lower frequencies, the data show the well-known 11-22-year cycle. Both the periodicity and amplitude vary over time. At higher resolutions, the probability distribution has heavy tails, while the data show the intermittent outliers characteristic of multifractal processes. Forecasting experiments are run using regressions in levels and differences, frequency domain methods, models with sinusoidal terms and neural networks. For the daily data, all the models achieve high degrees of accuracy at proximate horizons. As the horizon extends, accuracy falls away rapidly. At 27 days, corresponding to one solar rotation, a transfer function in differences achieves a more accurate forecast than either regressions or neural nets, since it is able to replicate the range of the data. At the annual resolution, both the regression and neural net predict well at horizons of 1 year. Again, forecast accuracy deteriorates sharply as the forecast horizon extends. At the monthly resolution, forecasting is problematic. The resolution is not low enough to bring out the low frequency cycles, but there is so much short-term dependence that the data are completely dominated by serial correlation. Any model incorporating proximate lags will generate inertial forecasts. Any model using lower frequency cyclical terms will be unable to pick up on near-term patterns. The forecasting skill of time series models appears limited to short horizons. The recommendation for forecasting over longer intervals is some combination of physics and statistical models.

Cost-effectiveness of neuropsychological rehabilitation for acquired brain injuries: Update of Stolwyk etal.'s (2019) review.

Acquired brain injuries (ABI), resulting from stroke or traumatic brain injury, cause a range of neuropsychological impairments and many patients continue to experience neuropsychological deficits years after onset. The increasing average age of the population highlights the importance of effective management strategies for the consequences of ABI. Despite the well-documented impact of rehabilitation interventions, the cost-effectiveness of neuropsychological rehabilitation remains largely unknown. This study conducted a scoping review to update the findings of Stolwyk etal. (Neuropsychological Rehabilitation, 2021, 31, 316), focusing on the economic evaluations of neuropsychological rehabilitation for individuals with ABI. Following the PIO framework, PRISMA ScR guidelines, and systematic review reporting checklist, the review screened 1027 articles and included eight studies published between 2019 and 2024. The studies encompassed either language rehabilitation or general neuropsychological programs, including neuropsychological interventions. The economic analyses, including two cost-effectiveness, five cost-utility, and one cost-benefit study, mostly adhered to CHEERS guidelines, enhancing the transparency and methodological rigour of their reporting. These studies demonstrated varying degrees of cost-effectiveness for interventions targeting post-stroke language disorders and neuropsychological rehabilitation for ABI, with significant cost savings and health benefits observed, particularly for home-based rehabilitation interventions. The included studies suffered from a short time horizon, limiting the ability to capture the long-term economic impacts and effectiveness of the interventions. Future research should focus on longer-term follow-up data and include broader search strategies to enhance understanding and optimise health care interventions. A comprehensive implementation of these economic analyses is crucial for informing policymakers, enabling them to introduce rehabilitative interventions based on solid evidence.

Valuing the Future: Changing Time Horizons and Policy Preferences

AbstractThe short time horizons of citizens are a prominent explanation for why governments fail to tackle significant long-term public policy problems. Evidence for the influence of time horizons is mixed, complicated by the difficulty of determining how attitudes would differ if individuals were more concerned about the future. This paper approaches the challenge by leveraging a personal experience that leads people to place more value on the future: parenthood. The analysis compares new parents with otherwise similar individuals using a matched difference-in-differences design with a three-wave panel. The results show that parenthood increases support for stopping climate change. Falsification tests and two survey experiments suggest that longer time horizons explain part of this shift in support. Not only are scholars right to emphasize the role of individual time horizons, but changing valuations of the future offer a new way to understand how policy preferences evolve.

Bridging ‘infrastructural solutions’ and ‘infrastructures as solution’: Regional promises and urban pragmatism

The potential of infrastructure ‘as a solution’ is currently at the forefront of American political consciousness. Historic levels of investment in infrastructure proffer seismic material, economic, and symbolic transformations at a near-continental scale. However, the present policy context for infrastructure planning in the US is confounded by a mosaic of decision-making authorities that hamper the development of cohesive approaches to sustainable and equitable development. This situation underscores the need to identify how infrastructural futures are assembled and scaled as simultaneously continuous and emergent, old and new, and marked by the diverse capacities of various stakeholders. This paper makes a case for ‘seeing like a region’ when examining transformative approaches to infrastructural change, as infrastructure systems regularly transcend the boundaries of urban space and hence become enmeshed in the goals of broader constituencies and interests. Through a case study of the Southwestern Pennsylvania Commission, we question how infrastructural futures are understood and materialised by the region’s central planning stakeholders. Our analysis pays particular attention to the challenges faced by regional planning organisations when navigating the spatial–temporal frames of incremental and radical change. As the Southwestern Pennsylvania Commission operates with limited staff capacity, high regulatory burdens, and short time horizons for budgeting processes, incremental changes to infrastructure often are the best hope for solving regional challenges of structural inequality and uneven access to resources. This demonstrates how the solutions proffered by infrastructural development are confounded by the dynamics that come into focus when evaluated from the regional scale. Yet we also identify possibilities for regional approaches that foster equitable urban futures within the spatial envelopes created by infrastructural systems and imaginaries that transition from reactive ‘infrastructural solutions’ to a proactive materialisation of ‘infrastructures as solutions’.

What charge-off rates are predictable by macroeconomic latent factors?

Charge-offs provide critical insights into the risk level of loan portfolios within the banking system, signaling potential systemic risks that could lead to deep recessions. Utilizing consolidated financial statements, we have compiled the net charge-off rate (COR) data from the 10 largest U.S. bank holding companies (BHCs) for disaggregated loans, including business loans, real estate loans, and consumer loans, as well as the average COR for each loan category among these top 10 banks. We propose factor-augmented forecasting models for CORs that incorporate latent common factor estimates, including targeted factors, via an array of data dimensionality reduction methods for a large panel of macroeconomic predictors. Our models have demonstrated superior performance compared with benchmark forecasting models, particularly well for business loan and real estate loan CORs, while predicting consumer loan CORs remains challenging especially at short horizons. Notably, real activity factors improve the out-of-sample predictability over the benchmarks for business loan CORs even when financial sector factors are excluded.

Hippocampal network activity forecasts epileptic seizures.

Seizures in people with epilepsy were long thought to occur at random, but recent methods for seizure forecasting enable estimation of the likelihood of seizure occurrence over short horizons. These methods rely on days-long cyclical patterns of brain electrical activity and other physiological variables that determine seizure likelihood and that require measurement through long-term, multimodal recordings. In this retrospective cohort study of 15 adults with bitemporal epilepsy who had a device that provides chronic intracranial recordings, functional connectivity of hippocampal networks fluctuated in multiday cycles with patterns that mirrored cycles of seizure likelihood. A functional connectivity biomarker of seizure likelihood derived from 90-s recordings of background hippocampal activity generalized across individuals and forecasted 24-h seizure likelihood as accurately as cycle-based models requiring months-long baseline recordings. Larger, prospective studies are needed to validate this approach, but our results have the potential to make reliable seizure forecasts accessible to more people with epilepsy.

Exploring the impact of entrepreneurial experience on future entrepreneurship aspirations

This paper evaluates how different types of direct and vicarious experience in entrepreneurship determine the perceived entrepreneurial intention in short, medium, and long-term horizons. The paper concludes with a discussion of the importance of considering both personal experiences and social influences in designing curriculum. A survey was conducted on four types of entrepreneurial experience and four intention horizons. Two of the experiences were direct experiences: current and previous, whilst two were vicarious: family and knowing someone. The four intention horizons were now, short-term, long-term and no intention. The study looks at 679 agricultural students from universities in both China and the United Kingdom. The paper uses the integrated model of entrepreneurial intentions together with a temporal horizon model of intention as the theoretical underpinning for this approach. The study’s results reinforce the positive relationship between family experience and long-term intentions, thus supporting the idea that familial exposure to entrepreneurship can shape individuals’ long-term entrepreneurial aspirations. The lack of a significant association between current direct experience and long-term intentions deviates from previous research and may be attributed to other factors influencing individuals’ long-term intentions, such as changing circumstances or evolving entrepreneurial opportunities. The cohorts available, whilst from China and UK, as participants were limited which presents several opportunities to expand upon this work and challenge its central premise. An understanding of the role of family in shaping long-term entrepreneurial intention has widespread social implications. The discovery that individuals with entrepreneurial experience, be it direct or through vicarious means, generally show heightened entrepreneurial intentions, coupled with the insight that knowing an entrepreneur correlates with shorter-term intention horizons, whereas family-influenced experiences align with longer-term venture creation ambitions, offers valuable new perspectives for those involved in entrepreneurial education and support.

Effectiveness of Using Candlestick Charts to Forecast Ethereum Price Direction: A Machine Learning Approach

Cryptocurrency is a form of decentralized digital currency. Ethereum is the second-largest cryptocurrency by market capitalization and the largest altcoin. Cryptocurrencies including Ethereum are highly volatile. Hence, shortterm directional forecasts in the cryptocurrency market have become a widely discussing topic. Candlestick charts are useful visualizations of the open, high, low and close prices which can identify patterns and gauge the near-term direction of prices. This research explores the effectiveness of forecasting hourly Ethereum closing price direction based on candlestick charts within a short time horizon. The proposed forecasting algorithm incorporates clustering methods such as fuzzy K-means, K-means and partition around medoids clustering to cluster candlestick chart properties namely upper shadow length, body length and lower shadow length. Classification methods such as random forest, support vector machine and K-nearest neighbour were used to forecast closing price direction using 16 different predictor variable sets including open, high, low and close prices, candlestick chart price direction, USL, BL and LSL. The accuracy for all considered cases was around 50%. Clustering improved the accuracy slightly and including the CPD with the predictor variable sets under consideration can increase the accuracy slightly. However, this approach is performing better in predicting the Down cases to the total number of actual Down cases because there is a higher sensitivity of 81.20% based on the SVM with Open, High, Low and Close at t in the clustering ignored method.

Analysis of aggregated load consumption forecasting in short, medium and long term horizons using Dynamic Mode Decomposition

In response to the growing need for precise energy demand forecasting in distribution grids, this study examines the effectiveness of the Dynamic Mode Decomposition (DMD) algorithm for short, medium, and long-term demand forecasting in distribution grids, leveraging diverse time series energy consumption data. The DMD algorithm, a data-driven approach renowned for its capacity to discern frequencies and trends, will be compared against established forecasting techniques, including XGBoosting Regressor, Long Short-Term Memory, and PROPHET. The research is conducted through a comprehensive case study comprising three distinct phases: study on pattern capturing of DMD, comparison in Long term horizon with all models studied and robustness study of DMD. It recalls upon real aggregated hourly energy consumption data sourced from a Spanish distribution grid. The practical applications and benefits of DMD in time series forecasting are examined carefully in order to show its comparative advantages over alternative forecasting models. To ensure a rigorous assessment of the findings, the analysis will employ four key statistical metrics: Root Mean Square Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results obtained with the case studies showed a 75% reduction on computational waste compared to other models and the results are closer to classical models, obtaining an improvement in the best case study of 44% comparing RMSE with Prophet. The insights gained can help future applications and research in the field.

Opacity and frequency dependence of beta

This paper examines the relationship between opacity and frequency dependence of systematic risk (β), estimated over different horizons using Wavelet Transform, for small and large firms. The findings provide evidence for the frequency-specific nature of opacity and suggest that while opacity is positively related to the frequency dependence of beta for large firms at all frequencies, for small firms the relationship is significant at low (long horizon) and insignificant at higher (short horizon) frequencies.

Banking on trust: exploring the relationship between Federal Reserve directors and financial institutions

Purpose This study aims to analyze the risk profile of banks whose managers sit on Federal Reserve district bank boards in 2023. In particular, to analyze the impact tha Federal Reserve bank directors have on their own banks. Design/methodology/approach Use a matched sample approach to perform univariate analysis and multiple regression methodology to study whether banks whose managers sit on Federal Reserve Bank boards differ in risk profile from banks whose managers do not sit on Federal Reserve district boards. Findings There is limited evidence that banks managed by Fed directors have different capital ratios and leverage ratios relative to non-Fed director banks. There does appear to be a slight difference in the growth of Held-to-Maturity (HTM) Securities between the two samples. Specifically, banks managed by a Fed director saw their HTM portfolio grow over the study period, while banks managed by non-Fed directors reduced their HTM securities. Overall, the results suggest that bank directors on Federal Reserve district boards do so with no apparent detriment to the banks that they manage. Research limitations/implications Results of this study suggest that stakeholder director relationships are not associated with higher risk-taking at director banks. This study is unique in that, rather than looking at how director ties might influence the firm that they are on the board of, the focus here is how the firm (the Fed district, in this case) might influence director affiliations. Limitations include a small sample size (70 banks, including the matched sample), and data over a short time horizon. Additional measures of risk can also be analyzed in future research. Practical implications While there has been much speculation in the industry and in the press regarding the conflict of interest involving bank directors on Fed district boards, this research suggests there is little evidence of any risk differential involving these directors and their specialties to the Fed. Originality/value This study involves a unique approach to corporate governance analysis, whereby any conflict of interest that might exist between directors and the firm is studied from an alternate angle – in particular, whether the association with a regulator’s board impacts the director firm’s risk. Furthermore, with the recent events in the banking industry involving the collapse of several banks, including Silicon Valley, the notion that bank management participating on the boards of directors of their own regulator seemed a worthwhile question as to whether this diminished the safety and soundness of the banks that they run.

The short-termism trap: Catering to informed investors with limited horizons

Does the stock market exert short-term pressure on listed firms, do they respond, and is this response value reducing? We show that limited investor horizons indeed have those consequences, as follows. First, informative stock prices increase firm value; in our model, they reduce the agency cost of incentivizing managers. Second, short project maturity improves stock price informativeness by catering to informed investors with short horizons. Third, since informed trading capital is a scarce resource, attracting informed investors cannot increase an individual firm’s price informativeness in equilibrium: it simply destroys shareholder value. This “short-termism trap” can potentially destroy up to 100% of the benefits of stock market listing.