We generalize the classic Shiller cyclically adjusted price-earnings ratio used for prediction of future total returns of the American stock market. We split total returns into three components: earnings growth, dividend yield, and valuation change. The first two components are fundamental, the third is speculative. We develop two time series models: one for valuation change, and the other for dividend yield plus valuation change. These models are simple auto-regressions of order 1. The second model shows that long-run real return is equal to earnings growth plus 4-5%. We verify the classic 4% withdrawal rate: A retiree should invest in stocks and withdraw 4% of initial wealth after adjusting for inflation.

We consider the Bachelier model with information delay where investment decisions can be based only on observations from $H>0$ time units before. Utility indifference prices are studied for vanilla options and we compute their non-trivial scaling limit for vanishing delay when risk aversion is scaled liked $A/H$ for some constant $A$. Using techniques from [7], we develop discrete-time duality for this setting and show how the relaxed form of martingale property introduced by [9] results in the scaling limit taking the form of a volatility control problem with quadratic penalty.

Scoring rules aggregate individual rankings by assigning some points to each position in each ranking such that the total sum of points provides the overall ranking of the alternatives. They are widely used in sports competitions consisting of multiple contests. We study the tradeoff between two risks in this setting: (1) the threat of early clinch when the title has been clinched before the last contest(s) of the competition take place; (2) the danger of winning the competition without finishing first in any contest. In particular, four historical points scoring systems of the Formula One World Championship are compared with the family of geometric scoring rules that have favourable axiomatic properties. The formers are found to be competitive or even better. The current scheme seems to be a reasonable compromise in optimising the above goals. Our results shed more light on the evolution of the Formula One points scoring systems and contribute to the issue of choosing the set of point values.

The phenomenon of brain drain, that is the emigration of highly skilled people, has many undesirable effects, particularly for developing countries. In this study, an agent-based model is developed to understand the dynamics of such emigration. We hypothesise that skilled people's emigration decisions are based on several factors including the overall economic and social difference between the home and host countries, people's ability and capacity to obtain good jobs and start a life abroad, and the barriers of moving abroad. Furthermore, the social network of individuals also plays a significant role. The model is validated using qualitative and quantitative pattern matching with real-world observations. Sensitivity and uncertainty analyses are performed in addition to several scenario analyses. Linear and random forest response surface models are created to provide quick predictions on the number of emigrants as well as to understand the effect sizes of individual parameters. Overall, the study provides an abstract model where brain drain dynamics can be explored. Findings from the simulation outputs show that future socioeconomic state of the country is more important than the current state, lack of barriers results in a large number of emigrants, and network effects ensue compounding effects on emigration. Upon further development and customisation, future versions can assist in the decision-making of social policymakers regarding brain drain.

To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of AIS receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: CNM of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June, when the most severe restrictions were in force. We quantify a variation of mobility between -5.62% and -13.77% for container ships, between +2.28% and -3.32% for dry bulk, between -0.22% and -9.27% for wet bulk, and between -19.57% and -42.77% for passenger traffic. This study is unprecedented for the uniqueness and completeness of the employed dataset, which comprises a trillion AIS messages broadcast worldwide by 50000 ships, a figure that closely parallels the documented size of the world merchant fleet.

The instability of financial system issues might trigger a bank failure, evoke spillovers, and generate contagion effects which negatively impacted the financial system, ultimately on the economy. This phenomenon is the result of the highly interconnected banking transaction. The banking transactions network is considered as a financial architecture backbone. The strong interconnectedness between banks escalates contagion disruption spreading over the banking network and trigger the entire system collapse. This far, the financial instability is generally detected using macro approach mainly the uncontrolled transaction deficits amount and unpaid foreign debt. This research proposes financial instability detection in another point of view, through the macro view where the banking network structure are explored globally and micro view where focuses on the detailed network patterns called motif. Network triadic motif patterns used as a denomination to detect financial instability. The most related network triadic motif changes related to the instability period are determined as a detector. We explore the banking network behavior under financial instability phenomenon along with the major religious event in Indonesia, Eid al-Fitr. We discover one motif pattern as the financial instability underlying detector. This research helps to support the financial system stability supervision.

The construction of approximate replication strategies for derivative contracts in incomplete markets is a key problem of financial engineering. Recently Reinforcement Learning algorithms for pricing and hedging under realistic market conditions have attracted significant interest. While financial research mostly focused on variations of $Q$-learning, in Artificial Intelligence Monte Carlo Tree Search is the recognized state-of-the-art method for various planning problems, such as the games of Hex, Chess, Go,... This article introduces Monte Carlo Tree Search as a method to solve the stochastic optimal control problem underlying the pricing and hedging of financial derivatives. As compared to $Q$-learning it combines reinforcement learning with tree search techniques. As a consequence Monte Carlo Tree Search has higher sample efficiency, is less prone to over-fitting to specific market models and generally learns stronger policies faster. In our experiments we find that Monte Carlo Tree Search, being the world-champion in games like Chess and Go, is easily capable of directly maximizing the utility of investor's terminal wealth without an intermediate mathematical theory.

Forecasting economic growth of India has been traditionally an uncertain exercise. The indicators and factors affecting economic structures and the variables required to model that captures the situation correctly is point of concern. Although the forecast should be specific to the country we are looking at however countries do have interlinkages among them. As the time series can be more volatile and sometimes certain variables are unavailable it is harder to predict for the developing economies as compared to stable and developed nations. However it is very important to have accurate forecasts for economic growth for successful policy formations. One of the hypothesized indicators is the nighttime lights. Here we aim to look for a relationship between GDP and Nighttime lights. Specifically we look at the DMSP and VIIRS dataset. We are finding relationship between various measures of economy.

When does society eventually learn the truth, or underlying state, via sequential observational learning? This paper develops the interplay of preferences satisfying single-crossing differences (SCD) and a new informational condition, directionally unbounded beliefs (DUB). SCD preferences and DUB information are a jointly minimal pair of sufficient conditions for learning. When there are more than two states, DUB is weaker than unbounded beliefs, which characterizes learning for all preferences (Smith and Sorensen, 2000). Unlike unbounded beliefs, DUB is compatible with the monotone likelihood ratio property, and satisfied, for example, by normal information.

The main objective of this paper is to present an algorithm of pricing perpetual American put options with asset-dependent discounting. The value function of such an instrument can be described as \begin{equation*} V^{\omega}_{\text{A}^{\text{Put}}}(s) = \sup_{\tau\in\mathcal{T}} \mathbb{E}_{s}[e^{-\int_0^\tau \omega(S_w) dw} (K-S_\tau)^{+}], \end{equation*} where $\mathcal{T}$ is a family of stopping times, $\omega$ is a discount function and $\mathbb{E}$ is an expectation taken with respect to a martingale measure. Moreover, we assume that the asset price process $S_t$ is a geometric L\'evy process with negative exponential jumps, i.e. $S_t = s e^{\zeta t + \sigma B_t - \sum_{i=1}^{N_t} Y_i}$. The asset-dependent discounting is reflected in the $\omega$ function, so this approach is a generalisation of the classic case when $\omega$ is constant. It turns out that under certain conditions on the $\omega$ function, the value function $V^{\omega}_{\text{A}^{\text{Put}}}(s)$ is convex and can be represented in a closed form; see Al-Hadad and Palmowski (2021). We provide an option pricing algorithm in this scenario and we present exact calculations for the particular choices of $\omega$ such that $V^{\omega}_{\text{A}^{\text{Put}}}(s)$ takes a simplified form.

In this note we consider a system of financial institutions and study systemic risk measures in the presence of a financial market and in a robust setting, namely, where no reference probability is assigned. We obtain a dual representation for convex robust systemic risk measures adjusted to the financial market and show its relation to some appropriate no-arbitrage conditions.

The growth of internet users in Indonesia gives an impact on many aspects of daily life, including commerce. Indonesian small-medium enterprises took this advantage of new media to derive their activity by the meaning of online commerce. Until now, there is no known practical implementation of how to predict their sales and revenue using their historical transaction. In this paper, we build a sales prediction model on the Indonesian footwear industry using real-life data crawled on Tokopedia, one of the biggest e-commerce providers in Indonesia. Data mining is a discipline that can be used to gather information by processing the data. By using the method of classification in data mining, this research will describe patterns of the market and predict the potential of the region in the national market commodities. Our approach is based on the classification decision tree. We managed to determine predicted the number of items sold by the viewers, price, and type of shoes.

This paper examines the impact of coronavirus (COVID-19) on stock market volatility (SMV) in Pakistan by controlling the effect of exchange rate, interest rate and government/central bank interventions to combat the pandemic. We used the vector autoregressive (VAR) model over a sample period ranging from February 25, 2020 to December 7, 2020. We find that a shock to total daily coronavirus cases in Pakistan lead to a significant increase in SMV. This result is aligned with a vast literature on pandemics and investors uncertainty and remains robust to several robustness checks applied in our analysis.

When it comes to conversations about funding, the questions of whether the United States should be spending its resources on space-based research often rears its head. Opponents of the idea tend to share the opinion that the resources would be better spent helping citizens on the ground. With an estimated homeless population around 562,000 throughout the country, roughly 39.4 million Americans (12.3% of the population) living below the poverty level, and 63.1 million tons of food waste per year, it's hard to argue that the United States does not have its share of problems that need to be addressed. However, a history of space-based research has proven time and time again to bring forth advances in technology and scientific understanding that benefit humans across the globe and provide crucial protection for life on Earth.

We investigate the possibility of a harvesting effect, i.e. a temporary forward shift in mortality, associated with the COVID-19 pandemic by looking at the excess mortality trends of an area that registered one of the highest death tolls in the world during the first wave, Northern Italy. We do not find any evidence of a sizable COVID-19 harvesting effect, neither in the summer months after the slowdown of the first wave nor at the beginning of the second wave. According to our estimates, only a minor share of the total excess deaths detected in Northern Italian municipalities over the entire period under scrutiny (February - November 2020) can be attributed to an anticipatory role of COVID-19. A slightly higher share is detected for the most severely affected areas (the provinces of Bergamo and Brescia, in particular), but even in these territories, the harvesting effect can only account for less than 20% of excess deaths. Furthermore, the lower mortality rates observed in these areas at the beginning of the second wave may be due to several factors other than a harvesting effect, including behavioral change and some degree of temporary herd immunity. The very limited presence of short-run mortality displacement restates the case for containment policies aimed at minimizing the health impacts of the pandemic.

India has had an established universal immunization program since 1985 and immunization services are available for free in healthcare facilities. Despite this, India has one of the lowest vaccination rates globally and contributes to the largest pool of under-vaccinated children in the world. Why is the vaccination rate low in India? This paper explores the importance of historical events in shaping India's current vaccination practices. We examine India's aggressive family planning program implemented during the period of emergency rule in the 1970s, under which millions of individuals were sterilized. We find that greater exposure to the forced sterilization policy has had negative effects on the current vaccination rate. We explore the mechanism and find that institutional delivery and antenatal care are low in states where policy exposure is high. Finally, we examine the consequence of lower vaccination suggesting that child mortality is currently high in states with greater sterilization exposure. Together, the evidence suggests that the forced sterilization policy has had a persistent effect on health-seeking behavior in India.