The efficiency of the stock market has a significant impact on the potential return on investment. An efficient market eliminates the possibility of arbitrage and unexploited profit opportunities. This study analyzes the weak form efficiency of the Indian Stock market based on the two major Indian stock exchanges, viz., BSE and NSE. The daily closing values of Sensex and Nifty indices for the period from April 2010 to March 2019 are used to perform the Runs test, the Autocorrelation test, and the Autoregression test. The study confirms that the Indian Stock market is weak form inefficient and can thus be outperformed.

We present a constructive approach to Bernstein copulas with an admissible discrete skeleton in arbitrary dimensions when the underlying marginal grid sizes are smaller than the number of observations. This prevents an overfitting of the estimated dependence model and reduces the simulation effort for Bernstein copulas a lot. In a case study, we compare different approaches of Bernstein and Gaussian copulas w.r.t. the estimation of risk measures in risk management.

Retailers and major consumers of electricity generally purchase an important percentage of their estimated electricity needs years ahead in the forward market. This long-term electricity procurement task consists of determining when to buy electricity so that the resulting energy cost is minimised, and the forecast consumption is covered. In this scientific article, the focus is set on a yearly base load product from the Belgian forward market, named calendar (CAL), which is tradable up to three years ahead of the delivery period. This research paper introduces a novel algorithm providing recommendations to either buy electricity now or wait for a future opportunity based on the history of CAL prices. This algorithm relies on deep learning forecasting techniques and on an indicator quantifying the deviation from a perfectly uniform reference procurement policy. On average, the proposed approach surpasses the benchmark procurement policies considered and achieves a reduction in costs of 1.65% with respect to the perfectly uniform reference procurement policy achieving the mean electricity price. Moreover, in addition to automating the complex electricity procurement task, this algorithm demonstrates more consistent results throughout the years. Eventually, the generality of the solution presented makes it well suited for solving other commodity procurement problems.

We present results demonstrating that an appropriately configured deep learning neural network (DLNN) can automatically learn to be a high-performing algorithmic trading system, operating purely from training-data inputs generated by passive observation of an existing successful trader T. That is, we can point our black-box DLNN system at trader T and successfully have it learn from T's trading activity, such that it trades at least as well as T. Our system, called DeepTrader, takes inputs derived from Level-2 market data, i.e. the market's Limit Order Book (LOB) or Ladder for a tradeable asset. Unusually, DeepTrader makes no explicit prediction of future prices. Instead, we train it purely on input-output pairs where in each pair the input is a snapshot S of Level-2 LOB data taken at the time when T issued a quote Q (i.e. a bid or an ask order) to the market; and DeepTrader's desired output is to produce Q when it is shown S. That is, we train our DLNN by showing it the LOB data S that T saw at the time when T issued quote Q, and in doing so our system comes to behave like T, acting as an algorithmic trader issuing specific quotes in response to specific LOB conditions. We train DeepTrader on large numbers of these S/Q snapshot/quote pairs, and then test it in a variety of market scenarios, evaluating it against other algorithmic trading systems in the public-domain literature, including two that have repeatedly been shown to outperform human traders. Our results demonstrate that DeepTrader learns to match or outperform such existing algorithmic trading systems. We analyse the successful DeepTrader network to identify what features it is relying on, and which features can be ignored. We propose that our methods can in principle create an explainable copy of an arbitrary trader T via "black-box" deep learning methods.

Ad exchanges, i.e., platforms where real-time auctions for ad impressions take place, have developed sophisticated technology and data ecosystems to allow advertisers to target users, yet advertisers may not know which sites their ads appear on, i.e., the ad context. In practice, ad exchanges can require publishers to provide accurate ad placement information to ad buyers prior to submitting their bids, allowing them to adjust their bids for ads at specific domains, subdomains or URLs. However, ad exchanges have historically been reluctant to disclose placement information due to fears that buyers will start buying ads only on the most desirable sites leaving inventory on other sites unsold and lowering average revenue. This paper explores the empirical effect of ad placement disclosure using a unique data set describing a change in context information provided by a major private European ad exchange. Analyzing this as a quasi-experiment using diff-in-diff, we find that average revenue per impression rose when more context information was provided. This shows that ad context information is important to ad buyers and that providing more context information will not lead to deconflation. The exception to this are sites which had a low number of buyers prior to the policy change; consistent with theory, these sites with thin markets do not show a rise in prices. Our analysis adds evidence that ad exchanges with reputable publishers, particularly smaller volume, high quality sites, should provide ad buyers with site placement information, which can be done at almost no cost.

This paper presents a novel approach to analyze human decision-making that involves comparing the behavior of professional chess players relative to a computational benchmark of cognitively bounded rationality. This benchmark is constructed using algorithms of modern chess engines and allows investigating behavior at the level of individual move-by-move observations, thus representing a natural benchmark for computationally bounded optimization. The analysis delivers novel insights by isolating deviations from this benchmark of bounded rationality as well as their causes and consequences for performance. The findings document the existence of several distinct dimensions of behavioral deviations, which are related to asymmetric positional evaluation in terms of losses and gains, time pressure, fatigue, and complexity. The results also document that deviations from the benchmark do not necessarily entail worse performance. Faster decisions are associated with more frequent deviations from the benchmark, yet they are also associated with better performance. The findings are consistent with an important influence of intuition and experience, thereby shedding new light on the recent debate about computational rationality in cognitive processes.

I analyze how a careerist delegate carries out reform decisions and implementation under alternative information environments. Regardless of his true policy preference, the delegate seeks retention and tries to signal to a principal that he shares an aligned policy predisposition. Given this pandering incentive, the principal best motivates the delegate's implementation if she can commit to a retention rule that is pivotal on reform outcomes. I characterize an informativeness condition under which this retention rule is endogenous, provided that the principal uses an opaque information policy -- she observes the delegate's policy choice and outcomes, but not the effort. With other information policies, the principal has to reward congruent policy choices rather than good policy outcomes; her policy interest is damaged by failing to sufficiently motivate reform implementation.

We obtain error estimates for strong approximations of a diffusion with a diffusion matrix $\sigma$ and a drift b by the discrete time process defined recursively X_N((n+1)/N) = X_N(n/N)+N^{1/2}\sigma(X_N(n/N))\xi(n+1)+N^{-1}b(XN(n/N)); where \xi(n); n\geq 1 are i.i.d. random vectors, and apply this in order to approximate the fair price of a game option with a diffusion asset price evolution by values of Dynkin's games with payoffs based on the above discrete time processes. This provides an effective tool for computations of fair prices of game options with path dependent payoffs in a multi asset market with diffusion evolution.

Individuals' behavior in economic decisions depends on such factors as ethnicity, gender, social environment, personal traits. However, the distinctive features of decision making have not been studied properly so far between indigenous populations from different ethnicities in a modern and multinational state like the Russian Federation. Addressing this issue, we conducted a series of experiments between the Russians in Moscow (the capital of Russia) and the Yakuts in Yakutsk (the capital of Russian region with the mostly non-Russian residents). We investigated the effect of socialization on participants' strategies in the Prisoner's Dilemma game, Ultimatum game, and Trust game. At the baseline stage, before socialization, the rates of cooperation, egalitarianism, and trust for the Yakuts are higher than for the Russians in groups composed of unfamiliar people. After socialization, for the Russians all these indicators increase considerably; whereas, for the Yakuts only the rate of cooperation demonstrates a rising trend. The Yakuts are characterized by relatively unchanged indicators regardless of the socialization stage. Furthermore, the Yakutsk females have higher rates of cooperation and trust than the Yakuts males before socialization. After socialization, we observed the alignment in indicators for males and females both for the Russians and for the Yakuts. Hence, we concluded that cultural differences can exist inside one country despite the equal economic, politic, and social conditions.

Standard, PCA-based factor analysis suffers from a number of well known problems due to the random nature of pairwise correlations of asset returns. We analyse an alternative based on ICA, where factors are identified based on their non-Gaussianity, instead of their variance. Generalizations of portfolio construction to the ICA framework leads to two semi-optimal portfolio construction methods: a fat-tailed portfolio, which maximises return per unit of non-Gaussianity, and the hybrid portfolio, which asymptotically reduces variance and non-Gaussianity in parallel. For fat-tailed portfolios, the portfolio weights scale like performance to the power of $1/3$, as opposed to linear scaling of Kelly portfolios; such portfolio construction significantly reduces portfolio concentration, and the winner-takes-all problem inherent in Kelly portfolios. For hybrid portfolios, the variance is diversified at the same rate as Kelly PCA-based portfolios, but excess kurtosis is diversified much faster than in Kelly, at the rate of $n^{-2}$ compared to Kelly portfolios' $n^{-1}$ for increasing number of components $n$.

We study a portfolio management problem featuring many-player and mean-field competition, investment and consumption, and relative performance concerns under the forward performance processes (FPP) framework. We focus on agents using power (CRRA) type FPPs for their investment-consumption optimization problem an under common noise Merton market model and we solve both the many-player and mean-field game providing closed-form expressions for the solutions where the limit of the former yields the latter.

In our case, the FPP framework yields a continuum of solutions for the consumption component as indexed to a market parameter we coin "market consumption intensity". The parameter permits the agent to set a preference for their consumption going forward in time that, in the competition case, reflects a common market behaviour. We show the FPP framework, under both competition and no-competition, allows the agent to disentangle his risk-tolerance and elasticity of intertemporal substitution (EIS) just like Epstein-Zin preferences under recursive utility framework and unlike the classical utility theory one. This, in turn, allows a finer analysis on the agent's consumption "income" and "substitution" regimes, and, of independent interest, motivates a new strand of economics research on EIS under the FPP framework.

We find that competition rescales the agent's perception of consumption in a non-trivial manner in addition to a time-dependent "elasticity of conformity" of the agent to the market's consumption intensity.

The central idea of the paper is to present a general simple patchwork construction principle for multivariate copulas that create unfavourable VaR (i.e. Value at Risk) scenarios while maintaining given marginal distributions. This is of particular interest for the construction of Internal Models in the insurance industry under Solvency II in the European Union.

We examine the impact of labour law deregulations in the Indian state of Rajasthan on plant employment and productivity. In 2014, after a long time, Rajasthan was the first Indian state that introduced labour reforms in the Industrial Disputes Act (1947), the Factories Act (1948), the Contract Labour (Regulation and Abolition) Act (1970), and the Apprentices Act (1961). Exploiting this unique quasi-natural experiment, we apply a difference-in-difference framework using the Annual Survey of Industries panel data of manufacturing plants. Our results show that reforms had an unintended consequence of the decline in labour use. Also, worryingly, the flexibility resulted in the disproportionate decline in the directly employed worker. Evidence suggests that the reforms positively impacted the plants' value-added and productivity. The strength of these effects varies depending on the underlying industry and reform structure. These findings prove robust to a set of specifications.

With Australia's significant capacity of household PV, decreasing battery costs may lead to widespread use of household PV-battery systems. As the adoption of these systems are heavily influenced by retail tariffs, this paper analyses the effects of flat retail tariffs with households free to invest in PV battery systems. Using Perth, Australia for context, an open-source model is used to simulate household PV battery investments over a 20-year period. We find that flat usage and feed-in tariffs lead to distinct residual demand patterns as households' transition from PV-only to PV-battery systems. Analysing these patterns qualitatively from the bottom-up, we identify tipping point transitions that may challenge future electricity system management, market participation and energy policy. The continued use of flat tariffs incentivises PV-battery households to maximise self-consumption, which reduces annual grid-imports, increases annual grid-exports, and shifts residual demand towards winter. Diurnal and seasonal demand patterns continue to change as PV-battery households eventually become net-generators. Unmanaged, these bottom-up changes may complicate energy decarbonisation efforts within centralised electricity markets and suggests that policymakers should prepare for PV-battery households to play a more active role in the energy system.

The pricing of Bermudan options amounts to solving a dynamic programming principle, in which the main difficulty, especially in high dimension, comes from the conditional expectation involved in the computation of the continuation value. These conditional expectations are classically computed by regression techniques on a finite dimensional vector space. In this work, we study neural networks approximations of conditional expectations. We prove the convergence of the well-known Longstaff and Schwartz algorithm when the standard least-square regression is replaced by a neural network approximation. We illustrate the numerical efficiency of neural networks as an alternative to standard regression methods for approximating conditional expectations on several numerical examples.

Historically, economists have mainly focused on human capital accumulation and considerably less so on the causes and consequences of human capital depreciation in late adulthood. Studying human capital depreciation over the life cycle has powerful economic consequences for decision-making in old age. Using data from the introduction of a retirement program in China, we examine how the introduction of a retirement program influences individual cognition. We find large negative effects of pension benefits on cognitive functioning among the elderly. We detect the most substantial impact of the program to be on delayed recall, which is a significant predictor of the onset of dementia. We show suggestive evidence that the program leads to larger negative impacts among women. We show that retirement plays a significant role in explaining cognitive decline at older ages.

This paper extends the sequential search model of Wolinsky (1986) by allowing firms to choose how much match value information to disclose to visiting consumers. This restores the Diamond paradox (Diamond 1971): there exist no symmetric equilibria in which consumers engage in active search, so consumers obtain zero surplus and firms obtain monopoly profits. Modifying the scenario to one in which prices are advertised, we discover that the no-active-search result persists, although the resulting symmetric equilibria are ones in which firms price at marginal cost.

The first quantum computers are expected to perform well at quadratic optimisation problems. In this paper a quadratic problem in finance is taken, the Portfolio Optimisation problem. Here, a set of assets is chosen for investment, such that the total risk is minimised, a minimum return is realised and a budget constraint is met. This problem is solved for several instances in two main indices, the Nikkei225 and the S\&P500 index, using the state-of-the-art implementation of D-Wave's quantum annealer and its hybrid solvers. The results are benchmarked against conventional, state-of-the-art, commercially available tooling. Results show that for problems of the size of the used instances, the D-Wave solution, in its current, still limited size, comes already close to the performance of commercial solvers.

Real-world problems are becoming highly complex and, therefore, have to be solved with combinatorial optimisation (CO) techniques. Motivated by the strong increase of publications on CO, 8,393 articles from this research field are subjected to a bibliometric analysis. The corpus of literature is examined using mathematical methods and a novel algorithm for keyword analysis. In addition to the most relevant countries, organisations and authors as well as their collaborations, the most relevant CO problems, solution methods and application areas are presented. Publications on CO focus mainly on the development or enhancement of metaheuristics like genetic algorithms. The increasingly problem-oriented studies deal particularly with real-world applications within the energy sector, production sector or data management, which are of increasing relevance due to various global developments. The demonstration of global research trends in CO can support researchers in identifying the relevant issues regarding this expanding and transforming research area.

In this paper we present a sequential hypothesis test for the detection of general jump size distrubution. Infinitesimal generators for the corresponding log-likelihood ratios are presented and analyzed. Bounds for infinitesimal generators in terms of super-solutions and sub-solutions are computed. This is shown to be implementable in relation to various classification problems for a crude oil price data set. Machine and deep learning algorithms are implemented to extract a specific deterministic component from the crude oil data set, and the deterministic component is implemented to improve the Barndorff-Nielsen and Shephard model, a commonly used stochastic model for derivative and commodity market analysis.

We study the problem of the intraday short-term volume forecasting in cryptocurrency exchange markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the outperformance of our model by comparing its outcomes with those obtained with different time series and machine learning methods. Finally, we discuss the predictions conditional to volume and we find that also in this case machine learning methods outperform econometric models.