The stock market prediction has always been crucial for stakeholders, traders and investors. We developed an ensemble Long Short Term Memory (LSTM) model that includes two-time frequencies (annual and daily parameters) in order to predict the next-day Closing price (one step ahead). Based on a four-step approach, this methodology is a serial combination of two LSTM algorithms. The empirical experiment is applied to 417 NY stock exchange companies. Based on Open High Low Close metrics and other financial ratios, this approach proves that the stock market prediction can be improved.

Improving road safety and setting targets for reducing traffic-related crashes and deaths are highlighted as part of the United Nation's sustainable development goals and vision zero efforts around the globe. The advent of transportation network companies, such as ridesourcing, expands mobility options in cities and may impact road safety outcomes. In this study, we analyze the effects of ridesourcing use on road crashes, injuries, fatalities, and driving while intoxicated (DWI) offenses in Travis County Texas. Our approach leverages real-time ridesourcing volume to explain variation in road safety outcomes. Spatial panel data models with fixed effects are deployed to examine whether the use of ridesourcing is significantly associated with road crashes and other safety metrics. Our results suggest that for a 10% increase in ridesourcing trips, we expect a 0.12% decrease in road crashes (p<0.05), a 0.25% decrease in road injuries (p<0.001), and a 0.36% decrease in DWI offenses (p<0.0001) in Travis County. Ridesourcing use is not associated with road fatalities at a 0.05 significance level. This study augments existing work because it moves beyond binary indicators of ridesourcing presence or absence and analyzes patterns within an urbanized area rather than metropolitan-level variation. Contributions include developing a data-rich approach for assessing the impacts of ridesourcing use on our transportation system's safety, which may serve as a template for future analyses of other US cities. Our findings provide feedback to policymakers by clarifying associations between ridesourcing use and traffic safety, while helping identify sets of actions to achieve safer and more efficient shared mobility systems.

We consider a system consisting of multiple interdependent assets, and a set of defenders, each responsible for securing a subset of the assets against an attacker. The interdependencies between the assets are captured by an attack graph, where an edge from one asset to another indicates that if the former asset is compromised, an attack can be launched on the latter asset. Each edge has an associated probability of successful attack, which can be reduced via security investments by the defenders. In such scenarios, we investigate the security investments that arise under certain features of human decision-making that have been identified in behavioral economics. In particular, humans have been shown to perceive probabilities in a nonlinear manner, typically overweighting low probabilities and underweighting high probabilities. We show that suboptimal investments can arise under such weighting in certain network topologies. We also show that pure strategy Nash equilibria exist in settings with multiple (behavioral) defenders, and study the inefficiency of the equilibrium investments by behavioral defenders compared to a centralized socially optimal solution.

We explore the effect of past market movements on the instantaneous correlations between assets within the futures market. Quantifying this effect is of interest to estimate and manage the risk associated to portfolios of futures in a non-stationary context. We apply and extend a previously reported method called the Principal Regression Analysis (PRA) to a universe of $84$ futures contracts between $2009$ and $2019$. We show that the past up (resp. down) 10 day trends of a novel predictor -- the eigen-factor -- tend to reduce (resp. increase) instantaneous correlations. We then carry out a multifactor PRA on sectorial predictors corresponding to the four futures sectors (indexes, commodities, bonds and currencies), and show that the effect of past market movements on the future variations of the instantaneous correlations can be decomposed into two significant components. The first component is due to the market movements within the index sector, while the second component is due to the market movements within the bonds sector.

The use of the social accounting matrix (SAM) in income distribution analysis is a method recommended by economists. However, until now, there have only been a few SAM developed in Malaysia. The last SAM produced for Malaysia was developed in 1984 based upon data from 1970 and has not been updated since this time despite the significance changes in the structure of the Malaysian economy. The paper proposes a new Malaysian SAM framework to analyse public expenditure impact on income distribution in Malaysia. The SAM developed in the present paper is based on more recent data, providing an up-to date and coherent picture of the complexity of the Malaysian economy. The paper describes the structure of the SAM framework with a detailed aggregation and disaggregation of accounts related to public expenditure and income distribution issues. In the SAM utilized in the present study, the detailed framework of the different components of public expenditure in the production sectors and household groups is essential in the analysis of the different effects of the various public expenditure programmes on the incomes of households among different groups.

This paper used a primary data collected through a surveys among farmers in rural Kedah to examine the effect of non farm income on poverty and income inequality. This paper employed two method, for the first objective which is to examine the impact of non farm income to poverty, we used poverty decomposition techniques - Foster, greer and Thorbecke (FGT) as has been done by Adams (2004). For the second objective, which is to examine the impact of non farm income to income inequality, we used Gini decomposition techniques.

The emergence of autonomous vehicles (AVs) is anticipated to influence the public transportation (PT) system. Many possible relationships between AV and PT are proposed depending on the policy and institution, where competition and cooperation are two main categories. This paper focuses on the former in a hypothetical scenario-"if both AV and PT operators were only profit-oriented." We aim to quantitatively evaluate the system performance (e.g. level of service, operators' financial viability, transport efficiency) when AV and PT are profit-oriented competitors with dynamic adjustable supply strategies under certain policy constraints. We assume AV can adjust the fleetsize and PT can adjust the headway. Service fare and bus routes are fixed. The competition process is analyzed through an agent-based simulation platform, which incorporates a proposed heuristic dynamic supply updating algorithm (HDSUA). The first-mile scenario in Singapore Tampines area is selected as the case study, where only bus is considered for PT system. We found that when AV and bus operators are given the flexibility to adjust supply, both of them will re-distribute their supply spatially and temporally, leading to higher profits. In temporal dimension, both AV and bus will concentrate their supplies in morning and evening peak hours, and reduce the supplies in off-peak hours. The competition between AV and PT decreases passengers' travel time but increase their travel cost. The generalized travel cost is still reduced when counting the value of time. The bus supply adjustment can increase the bus average load and reduce total passenger car equivalent (PCE), which is good for transport efficiency and sustainability. But the AV supply adjustment shows the opposite effect. Overall, the competition does not necessarily bring out loss-gain results. A win-win outcome is also possible under certain policy interventions.

The main objective of this paper is to fill a critical gap in the literature by analyzing the effects of decentralization on the macroeconomic stability. A survey of the voluminous literature on decentralization suggests that the question of the links between decentralization and macroeconomic stability has been relatively scantily analyzed. Even though there is still a lot of room for analysis as far as the effects of decentralization on other aspects of the economy are concerned, we believe that it is in this area that a more thorough analyses are mostly called for. Through this paper, we will try to shed more light on the issue notably by looking at other dimension of macroeconomic stability than the ones usually employed in previous studies as well as by examining other factors that might accentuate or diminish the effects of decentralization on macroeconomic stability. Our results found that decentralization appears to lead to a decrease in inflation rate. However, we do not find any correlation between decentralization with the level of fiscal deficit. Our results also show that the impact of decentralization on inflation is conditional on the level of perceived corruption and political institutions.

Foreign power interference in domestic elections is an existential threat to societies. Manifested through myriad methods from war to words, such interference is a timely example of strategic interaction between economic and political agents. We model this interaction between rational game players as a continuous-time differential game, constructing an analytical model of this competition with a variety of payoff structures. All-or-nothing attitudes by only one player regarding the outcome of the game lead to an arms race in which both countries spend increasing amounts on interference and counter-interference operations. We then confront our model with data pertaining to the Russian interference in the 2016 United States presidential election contest. We introduce and estimate a Bayesian structural time series model of election polls and social media posts by Russian Twitter troll accounts. Our analytical model, while purposefully abstract and simple, adequately captures many temporal characteristics of the election and social media activity. We close with a discussion of our model's shortcomings and suggestions for future research.

Convolutional neural networks can do time series forecasting. They can learn local patterns in time, and they can be modified to learn only from the history (ignoring the future) and to use a long look-back window when doing so. A further simple modification enables them to forecast not the mean, but arbitrary quantiles of the distribution. And one last thing to make this all work: the CNN forecaster's complexity and flexibility requires much data, that is, preferably multiple time series. When this is met, the proposed QCNN framework can be competitive. It is demonstrated on a financial problem of huge practical importance: Value at Risk forecasting. By contributing to the stability of financial systems, deep learning may find one further way to improve our lives.

A standing assumption in the literature on proportional transaction costs is efficient friction. Together with robust no free lunch with vanishing risk, it rules out strategies of infinite variation, as they usually appear in frictionless markets. In this paper, we show how the models with and without transaction costs can be unified.

The bid and the ask price of a risky asset are given by c\'adl\'ag processes which are locally bounded from below and may coincide at some points. In a first step, we show that if the bid-ask model satisfies "no unbounded profit with bounded risk" for simple strategies, then there exists a semimartingale lying between the bid and the ask price process.

In a second step, under the additional assumption that the zeros of the bid-ask spread are either starting points of an excursion away from zero or inner points from the right, we show that for every bounded predictable strategy specifying the amount of risky assets, the semimartingale can be used to construct the corresponding self-financing risk-free position in a consistent way.

Economists are showing increasing interest in the use of text as an input to economic research. Here, we analyse online text to construct a real time metric of welfare. For purposes of description, we call it the Feel Good Factor (FGF). The particular example used to illustrate the concept is confined to data from the London area, but the methodology is readily generalisable to other geographical areas. The FGF illustrates the use of online data to create a measure of welfare which is not based, as GDP is, on value added in a market-oriented economy. There is already a large literature which measures wellbeing/happiness. But this relies on conventional survey approaches, and hence on the stated preferences of respondents. In unstructured online media text, users reveal their emotions in ways analogous to the principle of revealed preference in consumer demand theory. The analysis of online media offers further advantages over conventional survey-based measures of sentiment or well-being. It can be carried out in real time rather than with the lags which are involved in survey approaches. In addition, it is very much cheaper.

What resources and technologies are strategic? This question is often the focus of policy and theoretical debates, where the label "strategic" designates those assets that warrant the attention of the highest levels of the state. But these conversations are plagued by analytical confusion, flawed heuristics, and the rhetorical use of "strategic" to advance particular agendas. We aim to improve these conversations through conceptual clarification, introducing a theory based on important rivalrous externalities for which socially optimal behavior will not be produced alone by markets or individual national security entities. We distill and theorize the most important three forms of these externalities, which involve cumulative-, infrastructure-, and dependency-strategic logics. We then employ these logics to clarify three important cases: the Avon 2 engine in the 1950s, the U.S.-Japan technology rivalry in the late 1980s, and contemporary conversations about artificial intelligence.