To improve the efficient frontier of the classical mean-variance model in continuous time, we propose a varying terminal time mean-variance model with a constraint on the mean value of the portfolio asset, which moves with the varying terminal time. Using the embedding technique from stochastic optimal control in continuous time and varying the terminal time, we determine an optimal strategy and related deterministic terminal time for the model. Our results suggest that doing so for an investment plan requires minimizing the variance with a varying terminal time.

We propose an algorithm which predicts each subsequent time step relative to the previous time step of intractable short rate model (when adjusted for drift and overall distribution of previous percentile result) and show that the method achieves superior outcomes to the unbiased estimate both on the trained dataset and different validation data.

We propose and investigate the concept of commuting service platforms (CSP) that leverage emerging mobility services to provide commuting services and connect directly commuters (employees) and their worksites (employers). By applying the two-sided market analysis framework, we show under what conditions a CSP may present the two-sidedness. Both the monopoly and duopoly CSPs are then analyzed. We showhowthe price allocation, i.e., the prices charged to commuters and worksites, can impact the participation and profit of the CSPs. We also add demand constraints to the duopoly model so that the participation rates ofworksites and employees are (almost) the same. With demand constraints, the competition between the two CSPs becomes less intense in general. Discussions are presented on how the results and findings in this paper may help build CSP in practice and how to develop new, CSP-based travel demand management strategies.

This paper acts as a collection of various trading strategies and useful pieces of market information that might help to implement such strategies. This list is meant to be comprehensive (though by no means exhaustive) and hence we only provide pointers and give further sources to explore each strategy further. To set the stage for this exploration, we consider the factors that determine good and bad trades, the notions of market efficiency, the real prospect amidst the seemingly high expectations of homogeneous expectations from human beings and the catch-22 connotations that arise while comprehending the true meaning of rational investing. We can broadly classify trading ideas and client market color material into Delta-One and Derivative strategies since this acts as a natural categorization that depends on the expertise of the various trading desks that will implement these strategies. For each strategy, we will have a core idea and we will present different flavors of this central theme to demonstrate that we can easily cater to the varying risk appetites, regional preferences, asset management styles, investment philosophies, liability constraints, investment horizons, notional trading size, trading frequency and other preferences of different market participants.

We introduce here very briefly, through some selective choices of problems and through the sample computer simulation programs (following the request of the editor for this invited review in the Journal of Physics Through Computation), the newly developed field of econophysics. Though related attempts could be traced much earlier (see the Appendix), the formal researches in econophysics started in 1995. We hope, the readers (students \& researchers) can start themselves to enjoy the excitement, through the sample computer programs given, and eventually can undertake researches in the frontier problems, through the indicated survey literature provided.

Some consumers, such as householders, are unwilling to face volatile electricity prices, and perceive as unfair price differentiations based on location. For these reasons, nodal prices in distribution networks are rarely employed. However, the increasing availability of renewable resources in distribution grids, and emerging price-elastic behaviour, pave the way for the effective introduction of marginal nodal pricing schemes in distribution networks. The aim of the proposed framework is to show how traditional non-flexible consumers can coexist with flexible users in a local distribution area, where the latter pay nodal prices whereas the former are charged a fixed price, which is derived by the underlying nodal prices. In addition, it determines how the distribution system operator should manage the local grid by optimally determining the lines to be expanded, and the collected network tariff levied on network users, while accounting for both congestion rent and investment costs. The proposed framework is formulated as a non-linear integer bilevel model, which is then recast as an equivalent single optimization problem, by using integer algebra and complementarity relations. The power flows in the distribution area are modelled by resorting to a second-order cone relaxation, whose solution is exact for radial networks under mild assumptions. The final model results in a mixed-integer quadratically constrained program, which can be solved with off-the-shelf solvers. Numerical test cases based on a 5-bus and a 33-bus networks are reported to show the effectiveness of the proposed method.

In this paper, we revisit the equity premium puzzle reported in 1985 by Mehra and Prescott. We show that the large equity premium that they report can be explained by choosing a more appropriate distribution for the return data. We demonstrate that the high-risk aversion value observed by Mehra and Prescott may be attributable to the problem of fitting a proper distribution to the historical returns and partly caused by poorly fitting the tail of the return distribution. We describe a new distribution that better fits the return distribution and when used to describe historical returns can explain the large equity risk premium and thereby explains the puzzle.

In this note we discuss the mathematical tools to define trend indicators which are used to describe market trends. We explain the relation between averages and moving averages on the one hand and the so called exponential moving average (EMA) on the other hand. We present a lot of examples and give the definition of the most frequently used trend indicator, the MACD, and discuss its properties.

This paper investigates the contribution of business model innovations in improvement of food supply chains. Through a systematic literature review, the notable business model innovations in the food industry are identified, surveyed, and evaluated. Findings reveal that the innovations in value proposition, value creation processes, and value delivery processes of business models are the successful strategies proposed in food industry. It is further disclosed that rural female entrepreneurs, social movements, and also urban conditions are the most important driving forces inducing the farmers to reconsider their business models. In addition, the new technologies and environmental factors are the secondary contributors in business model innovation for the food processors. It is concluded that digitalization has disruptively changed the food distributors models. E-commerce models and internet of things are reported as the essential factors imposing the retailers to innovate their business models. Furthermore, the consumption demand and the product quality are two main factors affecting the business models of all the firms operating in the food supply chain regardless of their positions in the chain. The findings of the current study provide an insight into the food industry to design a sustainable business model to bridge the gap between food supply and food demand.

We consider a sequential social-learning environment with rational agents and Gaussian private signals, focusing on how the observation network affects the speed of learning. Agents learn about a binary state and take turns choosing actions based on own signals and network neighbors' behavior. The observation network generally presents an obstruction to the efficient rate of signal aggregation, as agents compromise between incorporating the signals of the observed neighbors and not over-counting the confounding signals of the unobserved early movers. We show that on any network, equilibrium actions are a log-linear function of observations and each agent's accuracy admits a signal-counting interpretation. Adding links to the observation network can harm agents even without introducing new confounds. We then consider a network structure where agents move in generations and observe some members of the previous generation. When this observation structure is sufficiently connected and symmetric, the additional information aggregated by each generation is asymptotically equivalent to fewer than two independent signals, even when generations are arbitrarily large. When agents observe all predecessors from the previous generation, social learning aggregates no more than three signals per generation starting from the third generation, and the long-run learning rate is slower when generations are larger.

This paper considers an optimal dividend distribution problem for an insurance company where the dividends are paid in a foreign currency. In the absence of dividend payments, our risk process follows a spectrally negative L\'evy process. We assume that the exchange rate is described by a an exponentially L\'evy process, possibly containing the same risk sources like the surplus of the insurance company under consideration. The control mechanism chooses the amount of dividend payments. The objective is to maximise the expected dividend payments received until the time of ruin and a penalty payment at the time of ruin, which is an increasing function of the size of the shortfall at ruin. A complete solution is presented to the corresponding stochastic control problem. Via the corresponding Hamilton--Jacobi--Bellman equation we find the necessary and sufficient conditions for optimality of a single dividend barrier strategy. A number of numerical examples illustrate the theoretical analysis.

In this paper we study the pricing of exchange options when underlying assets have stochastic volatility and stochastic correlation. An approximation using a closed-form approximation based on a Taylor expansion of the conditional price is proposed. Numerical results are illustrated for exchanges between WTI and Brent type oil prices.

We develop a network reconstruction model based on the entropy maximization considering the sparsity of network. Here the reconstruction is to estimate network's adjacency matrix from node's local information. We reconstruct the interbank network in Japan from financial data in balance sheets of individual banks using the developed reconstruction model in the period from 2000 to 2016. The sparsity of the interbank network is successfully reproduced in the reconstructed network. We examine the accuracy of the reconstructed interbank network by comparing the actual data and analyze the characteristics of the interbank network. The comparison confirms that the accuracy of the reconstruction model is acceptably good. For the reconstructed interbank network, we obtain the following characteristics which are consistent with the previously known stylized facts: the short path length, the small clustering coefficient, the disassortative property, and the core and peripheral structure. Community analysis shows that the number of communities is 2-3 in the normal period, 1 in the economic crisis (2003, 2008-2013). The major nodes in each community have been the major commercial banks. Since 2013, the major commercial banks have lost the average PageRank and the leading regional banks have obtained both the average degree and the average PageRank. The observed changing role of banks is considered as a result of the quantitative and qualitative easing monetary policy started by Bank of Japan in April of 2013.

Crowding is most likely an important factor in the deterioration of strategy performance, the increase of trading costs and the development of systemic risk. We study the imprints of \emph{crowding} on both anonymous market data and a large database of metaorders from institutional investors in the U.S. equity market. We propose direct metrics of crowding that capture the presence of investors contemporaneously trading the same stock in the same direction by looking at fluctuations of the imbalances of trades executed on the market. We identify significant signs of crowding in well known equity signals, such as Fama-French factors and especially Momentum. We show that the rebalancing of a Momentum portfolio can explain between 1-2\% of order flow, and that this percentage has been significantly increasing in recent years.