Quantitative Finance

To achieve the ambitious aims of the Paris climate agreement, the majority of fossil-fuel reserves needs to remain underground. As current national government commitments to mitigate greenhouse gas emissions are insufficient by far, actors such as institutional and private investors and the social movement on divestment from fossil fuels could play an important role in putting pressure on national governments on the road to decarbonization. Using a stochastic agent-based model of co-evolving financial market and investors' beliefs about future climate policy on an adaptive social network, here we find that the dynamics of divestment from fossil fuels shows potential for social tipping away from a fossil-fuel based economy. Our results further suggest that socially responsible investors have leverage: a small share of 10--20\,\% of such moral investors is sufficient to initiate the burst of the carbon bubble, consistent with the Pareto Principle. These findings demonstrate that divestment has potential for contributing to decarbonization alongside other social movements and policy instruments, particularly given the credible imminence of strong international climate policy. Our analysis also indicates the possible existence of a carbon bubble with potentially destabilizing effects to the economy.

This paper asks, "Do classics exist in megaproject management?" We identify three types of classic texts: conventional, Kuhnian, and citation classics. We find that the answer to our question depends on the definition of "classic" employed. First, "citation classics" do exist in megaproject management, and they perform remarkably well when compared to the rest of the management literature. A preliminary Top Ten of citation classics is presented. Second, there is no indication that "conventional classics" exist in megaproject management, i.e., texts recognized as definitive by a majority of experts. Third, there is also no consensus as to whether "Kuhnian classics" exist, i.e., texts with paradigmatic clout. The importance of classics seems to be accepted, however, just as work to develop, discuss, and consolidate classics is seen as essential by megaproject scholars. A set of guidelines is presented for developing classics in megaproject management research.

The prevalent view in the economics literature is that a high level of infrastructure investment is a precursor to economic growth. China is especially held up as a model to emulate. Based on the largest dataset of its kind, this paper punctures the twin myths that, first, infrastructure creates economic value, and, second, China has a distinct advantage in its delivery. Far from being an engine of economic growth, the typical infrastructure investment fails to deliver a positive risk adjusted return. Moreover, China's track record in delivering infrastructure is no better than that of rich democracies. Where investments are debt-financed, overinvesting in unproductive projects results in the buildup of debt, monetary expansion, instability in financial markets, and economic fragility, exactly as we see in China today. We conclude that poorly managed infrastructure investments are a main explanation of surfacing economic and financial problems in China. We predict that, unless China shifts to a lower level of higher-quality infrastructure investments, the country is headed for an infrastructure-led national financial and economic crisis, which is likely also to be a crisis for the international economy. China's infrastructure investment model is not one to follow for other countries but one to avoid.

Feed in tariff (FiT) is one of the most efficient ways that many governments throughout the world use to stimulate investment in renewable energies (REs) technology. For governments, financial management of the policy is very challenging as that it needs a considerable amount of budget to support RE producers during the long remuneration period. In this paper, we illuminate that the early growth of REs capacity could be a temporary boost and the system elements would backlash the policy if financial circumstances are not handled well. To show this, we chose Iran as the case, which is in the infancy period of FiT implementation. Iran started the implementation of FiT policy in 2015 aiming to achieve 5 GW of renewable capacity until 2021. Analyses show that the probable financial crisis will not only lead to inefficient REs development after the target time (2021), but may also cause the existing plants to fail. Social tolerance for paying REs tax and potential investors trust emanated from budget related mechanisms are taken into consideration in the system dynamics model developed in this research to reflect those financial effects, which have rarely been considered in the previous researches. To prevent the financial crisis of the FiT funding and to maintain the stable growth in long term, three policy scenarios are analyzed: continuation of the current program with higher FiT rates, adjusting the FiT rates based on the budget status, and adjusting the tax on electricity consumption for the development of REs based on the budget status. The results demonstrate that adjusting the tax on electricity consumption for the development of REs based on budget status leads to the best policy result for a desired installed capacity development without any negative social effects and financial crises.

This paper examines the pricing of short-term and long-term dynamic network risk in the cross-section of stock returns. Stocks with high sensitivities to dynamic network risk earn lower returns. We rationalize our finding with economic theory that allows the stochastic discount factor to load on network risk through the precautionary savings channel. A one-standard deviation increase in long-term (short-term) network risk loadings associate with a 7.66% (6.71%) drop in annualized expected returns.

This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17th INFORMS Revenue Management and Pricing Section Conference on June 29-30, 2017 in Amsterdam, The Netherlands. For this challenge, participants submitted algorithms for pricing and demand learning of which the numerical performance was analyzed in simulated market environments. This allows consideration of market dynamics that are not analytically tractable or can not be empirically analyzed due to practical complications. Our findings implicate that the relative performance of algorithms varies substantially across different market dynamics, which confirms the intrinsic complexity of pricing and learning in the presence of competition.

Modelling all possible life cycles of a company in a highly competitive economic environment gives a significant advantage to the owner in his business investment activities. This article proposes and analyses a dynamic model of a company's life cycle with known action costs and transition probabilities, that can be affected by an outside influence. For this task, the Markov model was utilized. The proposed model is illustrated on a task of determining an advertising policy for a car dealership, that would increase the stock equity of a company. The result demonstrates the usefulness of a model for use in determining future actions of a company. We also review multiple models of the influence of outside factors on a company's total capitalization.

We propose a reduced form set of two coupled continuous time equations linking the price of a representative asset and the price of a bond, the later quantifying the cost of borrowing. The feedbacks between asset prices and bonds are mediated by the dependence of their "fundamental values" on past asset prices and bond themselves. The obtained nonlinear self-referencing price dynamics can induce, in a completely objective deterministic way, the appearance of periodically exploding bubbles ending in crashes. Technically, the periodically explosive bubbles arise due to the proximity of two types of bifurcations as a function of the two key control parameters b and g , which represent, respectively, the sensitivity of the fundamental asset price on past asset and bond prices and of the fundamental bond price on past asset prices. One is a Hopf bifurcation, when a stable focus transforms into an unstable focus and a limit cycle appears. The other is a rather unusual bifurcation, when a stable node and a saddle merge together and disappear, while an unstable focus survives and a limit cycle develops. The lines, where the periodic bubbles arise, are analogous to the critical lines of phase transitions in statistical physics. The amplitude of bubbles and waiting times between them respectively diverge with the critical exponents γ=1 and ν=1/2 , as the critical lines are approached.

We study in this paper the time evolution of stock markets using a statistical physics approach. Each agent is represented by a spin having a number of discrete states q or continuous states, describing the tendency of the agent for buying or selling. The market ambiance is represented by a parameter T which plays the role of the temperature in physics. We show that there is a critical value of T , say T c , where strong fluctuations between individual states lead to a disordered situation in which there is no majority: the numbers of sellers and buyers are equal, namely the market clearing. We have considered three models: q=3 ( sell, buy, wait), q=5 (5 states between absolutely buy and absolutely sell), and q=∞ . The specific measure, by the government or by economic organisms, is parameterized by H applied on the market at the time t 1 and removed at the time t 2 . We have used Monte Carlo simulations to study the time evolution of the price as functions of those parameters. Many striking results are obtained. In particular we show that the price strongly fluctuates near T c and there exists a critical value H c above which the boosting effect remains after H is removed. This happens only if H is applied in the critical region. Otherwise, the effect of H lasts only during the time of the application of H . The second party of the paper deals with the price variation using a time-dependent mean-field theory. By supposing that the sellers and the buyers belong to two distinct communities with their characteristics different in both intra-group and inter-group interactions, we find the price oscillation with time.

Stock prices are influenced over time by underlying macroeconomic factors. Jumping out of the box of conventional assumptions about the unpredictability of the market noise, we modeled the changes of stock prices over time through the Markov Decision Process, a discrete stochastic control process that aids decision making in a situation that is partly random. We then did a "Region of Interest" (RoI) Pooling of the stock time-series graphs in order to predict future prices with existing ones. Generative Adversarial Network (GAN) is then used based on a competing pair of supervised learning algorithms, to regenerate future stock price projections on a real-time basis. The supervised learning algorithm used in this research, moreover, is original to this study and will have wider uses. With the ensemble of these algorithms, we are able to identify, to what extent, each specific macroeconomic factor influences the change of the Brownian/random market movement. In addition, our model will have a wider influence on the predictions of other Brownian movements.