We propose a new valuation measure for 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 treat earnings growth as exogenous. Combining the other two components gives us a new valuation measure, which fits autoregression of order 1 with Gaussian innovations, centered at 4.6%. Therefore, long-term total returns equals long-term earnings growth plus 4.6%. We confirm the classic 4% withdrawal rule. A retiree should invest in stocks and withdraw 4% of initial wealth after adjusting for inflation.

The social and psychological concept of herding behavior provides a suitable solution to give an understanding of the behavioral biases that often occur in the capital market. The aim of this paper is to provide an overview of the broader bibliometric literature on the term and concept of herding behavior. Articles are collected through the help of software consisting of Publish or Perish (PoP), Google Scholar, Mendeley, and VOSViewer through a systematic approach, explicit and reproductive methods. In addition, the articles were scanned by Scimagojr.com (Q1, Q2, Q3, and Q4), analyzing 83 articles of 261 related articles from reputable and non-reputable journals from 1996 to 2021. Mendeley software is used to manage and resume references. To review this database, classification was performed using the VOSviewer software. Four clusters were reviewed; The words that appear most often in each group are the type of stock market, the type of crisis, and the factors that cause herding. Thus these four clusters became the main research themes on the topic of herding in times of crisis. Meanwhile, methodology and strategy are the themes for future research in the future.

This article aims to combine factor investing and reinforcement learning (RL). The agent learns through sequential random allocations which rely on firms' characteristics. Using Dirichlet distributions as the driving policy, we derive closed forms for the policy gradients and analytical properties of the performance measure. This enables the implementation of REINFORCE methods, which we perform on a large dataset of US equities. Across a large range of parametric choices, our result indicates that RL-based portfolios are very close to the equally-weighted (1/N) allocation. This implies that the agent learns to be *agnostic* with regard to factors, which can partly be explained by cross-sectional regressions showing a strong time variation in the relationship between returns and firm characteristics.

Actors in research and scientific publishing are gradually joining the Open-Access (OA) movement, which is gaining momentum to become nowadays at the heart of scientific policies in high-income countries. The rise of OA generates profound changes in the chain of production and dissemination of knowledge. Free access to peer-reviewed research methods and results has contributed to the dynamics of science observed in recent years. The modes of publication and access have also evolved; the classic model, based on journal subscriptions is gradually giving way to new economic models that have appeared with the arrival of OA. The objective of this article is twofold. First, propose a model for the publishing market based on the literature as well as on changes in open science policies. Second, analyze publishing strategies of publishers and institutions. To do so, we relied on game theory in economics. Results show that in the short term, the publisher's equilibrium strategy is to adopt a hybridpublishing model, while the institutions' equilibrium strategy is to publish in OA. This equilibrium is not stable and that in the medium/long term, the two players will converge on an OA publishing strategy. The analysis of the equilibrium in mixed-strategies confirms this result.

Pension reform is a crucial societal problem in many countries, and traditional pension schemes, such as Pay-As-You-Go and Defined-Benefit schemes, are being replaced by more sustainable ones. One challenge for a public pension system is the management of a systematic risk that affects all individuals in one generation (e.g., that caused by a worse economic situation). Such a risk cannot be diversified within one generation, but may be reduced by sharing with other (younger and/or older) generations, i.e., by intergenerational risk sharing (IRS). In this work, we investigate IRS in a Collective Defined-Contribution (CDC) pension system. We consider a CDC pension model with overlapping multiple generations, in which a funding-ratio-liked declaration rate is used as a means of IRS. We perform an extensive simulation study to investigate the mechanism of IRS. One of our main findings is that the IRS works particularly effectively for protecting pension participants in the worst scenarios of a tough financial market. Apart from these economic contributions, we make a simulation-methodological contribution for pension studies by employing Bayesian optimization, a modern machine learning approach to black-box optimization, in systematically searching for optimal parameters in our pension model.

Successful quantitative investment usually relies on precise predictions of the future movement of the stock price. Recently, machine learning based solutions have shown their capacity to give more accurate stock prediction and become indispensable components in modern quantitative investment systems. However, the i.i.d. assumption behind existing methods is inconsistent with the existence of diverse trading patterns in the stock market, which inevitably limits their ability to achieve better stock prediction performance. In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns. Essentially, TRA is a lightweight module that consists of a set of independent predictors for learning multiple patterns as well as a router to dispatch samples to different predictors. Nevertheless, the lack of explicit pattern identifiers makes it quite challenging to train an effective TRA-based model. To tackle this challenge, we further design a learning algorithm based on Optimal Transport (OT) to obtain the optimal sample to predictor assignment and effectively optimize the router with such assignment through an auxiliary loss term. Experiments on the real-world stock ranking task show that compared to the state-of-the-art baselines, e.g., Attention LSTM and Transformer, the proposed method can improve information coefficient (IC) from 0.053 to 0.059 and 0.051 to 0.056 respectively. Our dataset and code used in this work are publicly available: https://github.com/microsoft/qlib/tree/main/examples/benchmarks/TRA.

We study the link between lobbying and industrial concentration. Using data for the past 20 years in the US, we show how lobbying increases when an industry becomes more concentrated, using mergers as shocks to concentration. This holds true both for expenditures on federal lobbying as well as expenditures on campaign contributions. Results are in line with the predictions of a model where lobbying is akin to a public good for incumbents, and thus typically underprovided, while a merger solves the coordination problem.

Pricing financial derivatives, in particular European-style options at different time-maturities and strikes, is a relevant financial problem. The dynamics describing the price of vanilla options when constant volatilities and interest rates are assumed, is governed by the Black-Scholes model, a linear parabolic partial differential equation with terminal value given by the pay-off of the option contract and no additional boundary conditions. Here, we present a digital quantum algorithm to solve Black-Scholes equation on a quantum computer for a wide range of relevant financial parameters by mapping it to the Schr\"odinger equation. The non-Hermitian nature of the resulting Hamiltonian is solved by embedding the dynamics into an enlarged Hilbert space, which makes use of only one additional ancillary qubit. Moreover, we employ a second ancillary qubit to transform initial condition into periodic boundary conditions, which substantially improves the stability and performance of the protocol. This algorithm shows a feasible approach for pricing financial derivatives on a digital quantum computer based on Hamiltonian simulation, technique which differs from those based on Monte Carlo simulations to solve the stochastic counterpart of the Black Scholes equation. Our algorithm remarkably provides an exponential speedup since the terms in the Hamiltonian can be truncated by a polynomial number of interactions while keeping the error bounded. We report expected accuracy levels comparable to classical numerical algorithms by using 10 qubits and 94 entangling gates on a fault-tolerant quantum computer, and an expected success probability of the post-selection procedure due to the embedding protocol above 60\%.

We consider an incomplete multi-asset binomial market model. We prove that for a wide class of contingent claims the extremal multi-step martingale measure is a power of the corresponding single-step extremal martingale measure. This allows for closed form formulas for the bounds of a no-arbitrage contingent claim price interval. We construct a feasible algorithm for computing those boundaries as well as for the corresponding hedging strategies. Our results apply, for example, to European basket call and put options and Asian arithmetic average options.

We prove a rate of convergence for the $N$-particle approximation of a second-order partial differential equation in the space of probability measures, like the Master equation or Bellman equation of mean-field control problem under common noise. The rate is of order $1/N$ for the pathwise error on the solution $v$ and of order $1/\sqrt{N}$ for the $L^2$-error on its $L$-derivative $\partial_\mu v$. The proof relies on backward stochastic differential equations techniques.

This paper provides a general overview of different perspectives and studies on trust, offers a definition of trust, and provides factors that play a substantial role in developing social trust, and shows from which perspectives it can be fostered. The results showed that trust is playing an important role in success for organizations involved in cross-national strategic partnerships. Trust can reduce transaction costs, promotes inter-organizational relationships, and improve subordinate relationships between managers.

Sovereign wealth funds are created in those countries whose budget is highly dependent on market factors, usually world commodity prices. At the same time, these funds are large institutional investors. An analysis of the nature of investments by the State Pension Fund Global of Norway showed that investments of the Fund are based on a seven-level model of diversifying its investments. This model can also be applied to the investments of the National Wealth Fund of Russia to increase its profitability.

We estimate the relationship between GDP per capita growth and the growth rate of the national savings rate using a panel of 130 countries over the period 1960-2017. We find that GDP per capita growth increases (decreases) the growth rate of the national savings rate in poor countries (rich countries), and a higher credit-to-GDP ratio decreases the national savings rate as well as the income elasticity of the national savings rate. We develop a model with a credit constraint to explain the growth-saving relationship by the saving behavior of entrepreneurs at both the intensive and extensive margins. We further present supporting evidence for our theoretical findings by utilizing cross-country time series data of the number of new businesses registered and the corporate savings rate.