I analyze Osaka factory worker households in the early 1920s, whether idiosyncratic income shocks were shared efficiently, and which consumption categories were robust to shocks. While the null hypothesis of full risk-sharing of total expenditures was rejected, factory workers maintained their households, in that they paid for essential expenditures (rent, utilities, and commutation) during economic hardship. Additionally, children's education expenditures were possibly robust to idiosyncratic income shocks. The results suggest that temporary income is statistically significantly increased if disposable income drops due to idiosyncratic shocks. Historical documents suggest microfinancial lending and saving institutions helped mitigate risk-based vulnerabilities.

We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to capture the spatial structure of the limit order books as well as LSTM modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [1]. In a more realistic setting, we test our model by using one year market quotes from the London Stock Exchange and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments which were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a "black box'' model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features which translate well to other instruments is an important property of our model which has many other applications.

In this paper we investigate a dynamic stochastic portfolio optimization problem involving both the expected terminal utility and intertemporal utility maximization. We solve the problem by means of a solution to a fully nonlinear evolutionary Hamilton-Jacobi-Bellman (HJB) equation. We propose the so-called Riccati method for transformation of the fully nonlinear HJB equation into a quasi-linear parabolic equation with non-local terms involving the intertemporal utility function. As a numerical method we propose a semi-implicit scheme in time based on a finite volume approximation in the spatial variable. By analyzing an explicit traveling wave solution we show that the numerical method is of the second experimental order of convergence. As a practical application we compute optimal strategies for a portfolio investment problem motivated by market financial data of German DAX 30 Index and show the effect of considering intertemporal utility on optimal portfolio selection.

The efficiency of a modern economy depends on what we call the Value-Tracking Hypothesis: that market prices of key assets broadly track some underlying value. This can be expected if a sufficient weight of market participants are valuation-based traders, buying and selling an asset when its price is, respectively, below and above their well-informed private valuations. Such tracking will never be perfect, and we propose a natural unit of tracking error, the 'deciblack'. We then use a simple model to show how large tracking errors can arise if enough market participants are not valuation-based traders, regardless of how much information the valuation-based traders have. We find a threshold above which value-tracking breaks down without any changes in the underlying value of the asset. Because financial markets are increasingly dominated by non-valuation-based traders, assessing how much valuation-based investing is required for reasonable value tracking is of urgent practical interest.

Active management is a term that has many meanings and we have found the defining characteristics needed for success as an "active manager" elusive within the literature. In this paper we offer a set of criteria that defines an active manager and his success. In order to facilitate this, we introduce several definitions, which lead to a logically coherent evaluation framework. We expand on the definitions of these six key concepts: Introduce a specific concept of Ruin, Assets, Risk, Discount rate, Margin of safety, and Optimization.

Through these definitions a strong defense of active management emerges. Furthermore, to the extent one chooses to limit the definitions we offer, our framework reduces to that of Modern Portfolio Theory. Overall, we have aimed to construct a robust expansion of a framework for active management, one that has been found wanting in the current literature.

In this article, inspired by Shi, et al. we investigate the optimal portfolio selection with one risk-free asset and one risky asset in a multiple period setting under cumulative prospect theory (CPT). Compared with their study, our novelty is that we consider a stochastic benchmark, and portfolio constraints. We test the sensitivity of the optimal CPT-investment strategies to different model parameters by performing a numerical analysis.

Trailing stop is a popular stop-loss trading strategy by which the investor will sell the asset once its price experiences a pre-specified percentage drawdown. In this paper, we study the problem of timing buy and then sell an asset subject to a trailing stop. Under a general linear diffusion framework, we study an optimal double stopping problem with a random path-dependent maturity. Specifically, we first derive the optimal liquidation strategy prior to a given trailing stop, and prove the optimality of using a sell limit order in conjunction with the trailing stop. Our analytic results for the liquidation problem is then used to solve for the optimal strategy to acquire the asset and simultaneously initiate the trailing stop. The method of solution also lends itself to an efficient numerical method for computing the the optimal acquisition and liquidation regions. For illustration, we implement an example and conduct a sensitivity analysis under the exponential Ornstein-Uhlenbeck model.

We provide a closed-form analytical solution to a static portfolio optimization problem with two coherent risk measures. The use of two risk measures is motivated by joint decision-making for portfolio selection where the risk perception of the portfolio manager is of primary concern, hence, it appears in the objective function, and the risk perception of an external authority needs to be taken into account as well, which appears in the form of a risk constraint. The problem covers the risk minimization problem with an expected return constraint and the expected return maximization problem with a risk constraint, as special cases. We also consider a variant of the problem with size and magnitude constraints which we formulate using binary variables. In this case, only numerical solutions are possible.

The shift to renewable power is accelerating at a growing pace. Contemporary energy markets undergo profound structural changes. Forecasts as to the wind and solar supply become critically important. This paper studies the impact of these forecasts on electricity market prices. We develop an intraday price model based on day-ahead auction data and errors in wind and solar power prognostications. The model manipulates empirical supply and demand curves recorded in the German EPEX SPOT SE to produce the prices. The obtained results show that forecast errors exert a non-linear impact on intraday prices. Moreover, additional wind and solar power capacities induce non-linear changes in intraday price volatility. Economical and policy implications of these findings are elaborated upon.