The study efforts to explore and extend the crisis predictability by synthetically reviewing and comparing a full mixture of early warning models into two constitutions: crisis identifications and predictive models. Given empirical results on Chinese currency and stock markets, three-strata findings are concluded as (i) the SWARCH model conditional on an elastic thresholding methodology can most accurately classify crisis observations and greatly contribute to boosting the predicting precision, (ii) stylized machine learning models are preferred given higher precision in predicting and greater benefit in practicing, (iii) leading factors sign the crisis in a diversified way for different types of markets and varied prediction periods.

This thesis serves three primary purposes, first of which is to forecast two stocks, i.e. Goldman Sachs (GS) and General Electric (GE). In order to forecast stock prices, we used a long short-term memory (LSTM) model in which we inputted the prices of two other stocks that lie in rather close correlation with GS. Other models such as ARIMA were used as benchmark. Empirical results manifest the practical challenges when using LSTM for forecasting stocks. One of the main upheavals was a recurring lag which we called "forecasting lag".

The second purpose is to develop a more general and objective perspective on the task of time series forecasting so that it could be applied to assist in an arbitrary that of forecasting by ANNs. Thus, attempts are made for distinguishing previous works by certain criteria (introduced by a review paper written by Ahmed Tealab) so as to summarise those including effective information. The summarised information is then unified and expressed through a common terminology that can be applied to different steps of a time series forecasting task.

The last but not least purpose of this thesis is to elaborate on a mathematical framework on which ANNs are based. We are going to use the framework introduced in the book "Neural Networks in Mathematical Framework" by Anthony L. Caterini in which the structure of a generic neural network is introduced and the gradient descent algorithm (which incorporates backpropagation) is introduced in terms of their described framework. In the end, we use this framework for a specific architecture, which is recurrent neural networks on which we concentrated and our implementations are based. The book proves its theorems mostly for classification case. Instead, we proved theorems for regression case, which is the case of our problem.

The Covid-19 pandemic exposed firms, organisations and their respective supply chains which are directly involved in the manufacturing of products that are critical to alleviating the effects of the health crisis, collectively referred to as the Crisis-Critical Sector,to unprecedented challenges. Firms from other sectors, such as automotive, luxury and home appliances, have rushed into the Crisis-Critical Sector in order to support the effort to upscale incumbent manufacturing capacities, thereby introducing Intellectual Property (IP)related dynamics and challenges. We apply an innovation ecosystem perspective on the Crisis-Critical Sector and adopt a novel visual mapping approach to identify IP associated challenges and IP specific dynamic developments during and potentially beyond the crisis.In this paper, we add methodologically by devising and testing a visual approach to capturing IP related dynamics in evolving innovation ecosystems and contribute to literature on IP management in the open innovation context by proposing paraground IP as a novel IP type.Finally, we also deduce managerial implications for IP management practitioners at both incumbent firms and new entrants for navigating innovation ecosystems subject to crisis-induced dynamic shifts.

In this investigation we analyze impact of diversification of agriculture on farmer's income, a study from primitive tribal groups from eastern ghats of India. We have taken crop diversification index to measure the extent and regression formalism to analyze the impact, of crop diversification. Descriptive statistics is employed to know the average income of the farmers, paired results of crop diversification index. We observed a positive impact on crop diversification in scheduled areas and investigated reasons where it did not work.

Pairwise comparisons are used in a wide variety of decision situations where the importance of alternatives should be measured on a numerical scale. One popular method to derive the priorities is based on the right eigenvector of a multiplicative pairwise comparison matrix. We consider two monotonicity axioms in this setting. First, increasing an arbitrary entry of a pairwise comparison matrix is not allowed to result in a counter-intuitive rank reversal, that is, the favoured alternative in the corresponding row cannot be ranked lower than any other alternative if this was not the case before the change (rank monotonicity). Second, the same modification should not decrease the normalised weight of the favoured alternative (weight monotonicity). Both properties are satisfied by the geometric mean method but violated by the eigenvector method. The axioms do not uniquely determine the geometric mean. The relationship between the two monotonicity properties and the Saaty inconsistency index are investigated for the eigenvector method via simulations. Even though their violation turns out not to be a usual problem even for heavily inconsistent matrices, all decision-makers should be informed about the possible occurrence of such unexpected consequences of increasing a matrix entry.

Inertia and context-dependent choice effects are well-studied classes of behavioural phenomena. While much is known about these effects in isolation, little is known about whether one of them "dominates" the other when both can potentially be present. Knowledge of any such dominance is relevant for effective choice architecture and descriptive modelling. We initiate this empirical investigation with a between-subjects lab experiment that featured a single decision over two or three money lotteries. Our experiment was designed to test for dominance between *status quo bias* and the *decoy effect*. We find strong evidence for status quo bias and no evidence for the decoy effect. We also find that status quo bias is powerful enough so that, at the aggregate level, a fraction of subjects switch from being risk-averse to being risk-seeking. Survey evidence suggests that this is due to subjects focusing on the maximum possible amount when the risky lottery is the default and on the probability of winning more than the smallest amount when there is no default lottery. The observed reversal in risk attitudes is explainable by a large class of reference-dependent preferences.

We show that coherent risk measures are ineffective in curbing the behaviour of investors with limited liability or excessive tail-risk seeking behaviour if the market admits statistical arbitrage opportunities which we term $\rho$-arbitrage for a risk measure $\rho$. We show how to determine analytically whether such $\rho$-arbitrage portfolios exist in complete markets and in the Markowitz model. We also consider realistic numerical examples of incomplete markets and determine whether expected shortfall constraints are ineffective in these markets. We find that the answer depends heavily upon the probability model selected by the risk manager but that it is certainly possible for expected shortfall constraints to be ineffective in realistic markets. Since value at risk constraints are weaker than expected shortfall constraints, our results can be applied to value at risk. By contrast, we show that reasonable expected utility constraints are effective in any arbitrage-free market.

The dynamical evolution of multiscaling in financial time series is investigated using time-dependent Generalized Hurst Exponents (GHE), $H_q$, for various values of the parameter $q$. Using $H_q$, we introduce a new visual methodology to algorithmically detect critical changes in the scaling of the underlying complex time-series. The methodology involves the degree of multiscaling at a particular time instance, the multiscaling trend which is calculated by the Change-Point Analysis method, and a rigorous evaluation of the statistical significance of the results. Using this algorithm, we have identified particular patterns in the temporal co-evolution of the different $H_q$ time-series. These GHE patterns, distinguish in a statistically robust way, not only between time periods of uniscaling and multiscaling, but also among different types of multiscaling: symmetric multiscaling (M) and asymmetric multiscaling (A). We apply the visual methodology to time-series comprising of daily close prices of four stock market indices: two major ones (S\&P~500 and NIKKEI) and two peripheral ones (Athens Stock Exchange general Index and Bombay-SENSEX). Results show that multiscaling varies greatly with time: time periods of strong multiscaling behavior and time periods of uniscaling behavior are interchanged while transitions from uniscaling to multiscaling behavior occur before critical market events, such as stock market bubbles. Moreover, particular asymmetric multiscaling patterns appear during critical stock market eras and provide useful information about market conditions. In particular, they can be used as 'fingerprints' of a turbulent market period as well as provide warning signals for an upcoming stock market 'bubble'. The applied visual methodology also appears to distinguish between exogenous and endogenous stock market crises, based on the observed patterns before the actual events.

We consider the thermodynamic approach to the description of economic systems and processes. The first and second laws of thermodynamics as applied to economic systems are derived and analyzed. It is shown that there is a deep analogy between the parameters of thermodynamic and economic systems (markets); in particular, each thermodynamic parameter can be associated with a certain economic parameter or indicator. The economic meaning of such primordially thermodynamic concepts as pressure, volume, internal energy, heat, etc. has been established. The thermostatistics of the market is considered. It is shown that, as in conventional thermostatistics, many market parameters, such as price of goods, quantity of goods, etc., as well as their fluctuations can be calculated formally using the partition function of an economic system.

While the estimation of risk is an important question in the daily business of banks and insurances, it is surprising that efficient procedures for this task are not well studied. Indeed, many existing plug-in approaches for the estimation of risk suffer from an unnecessary bias which leads to the underestimation of risk and negatively impacts backtesting results, especially in the small sample environment. In this article, we consider efficient estimation of risk in practical situations and provide means to improve the accuracy of risk estimators and their performance in backtesting. In particular, we propose an algorithm for bias correction and show how to apply it for generalized Pareto distributions. Moreover, we propose new estimators for value-at-risk and expected shortfall, respectively, and illustrate the gain in efficiency when heavy tails exist in the data.