Credit Cycles in a OLG Economy with Money and Bequest

In this paper we develop an extended version of the original Kiyotaki and Moore's model ("Credit Cycles" Journal of Political Economy, vol. 105, no 2, April 1997)(hereafter KM) using an overlapping generation structure instead of the assumption of infinitely lived agents adopted by the authors. In each period the population consists of two classes of heterogeneous interacting agents, in particular: a financially constrained young agent (young farmer), a financially constrained old agent (old farmer), an unconstrained young agent (young gatherer), an unconstrained old agent (old gatherer). By assumption each young agent is endowed with one unit of labour. Heterogeneity is introduced in the model by assuming that each class of agents use different technologies to pro- duce the same non durable good. If we study the effect of a technological shock it is possible to demonstrate that its effects are persistent over time in fact the mechanism that it induces is the reallocation the durable asset ("land")among agents. As in KM we develop a dynamic model in which the durable asset is not only an input for production processes but also collateralizable wealth to secure lenders from the risk of borrowers'default. In a context of intergenerational altruism, old agents leave a bequest to their offspring. Money is a means of payment and a reserve of value because it enables to access consumption in old age. For simplicity we assume that preferences are defined over consumption and bequest of the agent when old. Money plays two different and contrasting roles with respect to landholding. On the one hand, given the bequest, the higher the amount of money the young wants to hold, the lower landholding. On the other hand the higher the money of the old, the higher the resources available to him and the higher bequest and landholding. We study the complex dynamics of the allocation of land to farmers and gatherers - which determines aggregate output - and of the price of the durable asset. If a policy move does not change the ratio of money of the farmer and of the gatherer, i.e. if the central bank changes the rates of growth of the two monetary aggregates by the same amount, monetary policy is superneutral, i.e. the allocation of land to the farmer and to the gatherer does not change, real variables are unaffected and the only e¤ect of the policy move is an increase in the rate of inflation, which is pinned down to the (uniform) rate of change of money, and of the nominal interest rate. If, on the other hand, the move is differentiated, i.e. the central bank changes the rates of growth of the two monetary aggregates by different amounts so that the rates of growth are heterogeneous, money is not superneutral, i.e. the allocation of land changes and real variables are permanently affected, even if the rates of growth of the two aggregates go back to the original value afterwardsCredit Cycles, monetary policy

Generative Adversarial Network to evaluate quantity of information in financial markets

Nowadays, the information obtainable from the markets are potentially limitless. Economic theory has always supported the possible advantage obtainable from having more information than competitors, however quantifying the advantage that these can give has always been a problem. In particular, in this paper we study the amount of information obtainable from the markets taking into account only the time series of the prices, through the use of a specific Generative Adversarial Network. We consider two types of financial instruments traded on the market, stocks and cryptocurrencies: the first are traded in a market subject to opening and closing hours, whereas cryptocurrencies are traded in a 24/7 market. Our goal is to use this GAN to be able to “convert” the amount of information that the different instruments can have in discriminative and predictive power, useful to improve forecast. Finally, we demonstrate that by using the initial dataset with the 5 most important feature useds by traders, the prices of cryptocurrencies present higher discriminatory and predictive power than stocks, while by adding a feature the situation can be completely reversed

How Boltzmann Entropy Improves Prediction with LSTM

In this paper we want to demonstrate how it is possible to improve the forecast by using Boltzmann entropy like the classic financial indicators, throught neural networks. In particular, we show how it is possible to increase the scope of entropy by moving from cryptocurrencies to equities and how this type of architectures highlight the link between the indicators and the information that they are able to contain

Boltzmann Entropy in Cryptocurrencies: A Statistical Ensemble Based Approach

In this paper we try to build a statistical ensemble to describe a cryptocurrency-based system, emphasizing an "affinity" between the system of agents trading in these currencies and statistical mechanics. We focus our study on the concept of entropy in the sense of Boltzmann and we try to extend such a definition to a model in which the particles are replaced by N agents completely described by their ability to buy and to sell a certain quantity of cryptocurrencies. After providing some numerical examples, we show that entropy can be used as an indicator to forecast the price trend of cryptocurrencies

Generative Adversarial Network for Market Hourly Discrimination

In this paper, we consider 2 types of instruments traded on the markets, stocks and cryptocurrencies. In particular, stocks are traded in a market subject to opening hours, while cryptocurrencies are traded in a 24-hour market. What we want to demonstrate through the use of a particular type of generative neural network is that the instruments of the non-timetable market have a different amount of information, and are therefore more suitable for forecasting. In particular, through the use of real data we will demonstrate how there are also stocks subject to the same rules as cryptocurrencies

Forecasting financial time series with Boltzmann entropy through neural networks

Neural networks have recently been established as state-of-the-art in forecasting financial time series. However, many studies show how one architecture, the Long-Short Term Memory, is the most widespread in financial sectors due to its high performance over time series. Considering some stocks traded in financial markets and a crypto ticker, this paper tries to study the effectiveness of the Boltzmann entropy as a financial indicator to improve forecasting, comparing it with financial analysts’ most commonly used indicators. The results show how Boltzmann’s entropy, born from an Agent-Based Model, is an efficient indicator that can also be applied to stocks and cryptocurrencies alone and in combination with some classic indicators. This critical fact allows obtaining good results in prediction ability using Network architecture that is not excessively complex

Boltzmann Entropy in Cryptocurrencies: A Statistical Ensemble Based Approach

In this paper we try to build a statistical ensemble to describe a cryptocurrency-based system, emphasizing an "affinity" between the system of agents trading in these currencies and statistical mechanics. We focus our study on the concept of entropy in the sense of Boltzmann and we try to extend such a definition to a model in which the particles are replaced by N agents completely described by their ability to buy and to sell a certain quantity of cryptocurrencies. After providing some numerical examples, we show that entropy can be used as an indicator to forecast the price trend of cryptocurrencies

Effects of dietary level of pantothenic acid and sex on carcass, meat quality traits and fatty acid composition of thigh subcutaneous adipose tissue in Italian heavy pigs.

Two trials were carried out to evaluate the effects of i) supranutritional doses of pantothenic acid (PA) and ii) sex on carcass, meat quality and fatty acid (FA) composition of subcutaneous adipose tissue in Italian heavy pig. In trial 1, 59 Duroc x (LxLW) pigs were fed the same diet containing either 10 [in the control (C) group] or 110 ppm [in the treatment (T) group] PA, from 107 to 168 kg live weight. At slaughtering, forty carcasses were sampled randomly. The T carcasses had lower backfat thickness (P<0.05), lower incidence of adipose cuts (P<0.05), higher lean cuts percentage (63.09 vs 60.64%; P<0.01) and lean meat yield (P<0.07). In trial 2, 42 pigs [Dumeco Cofok x (LxLW)], evenly divided into three groups, were fed the same feed containing respectively 10 (C), 60 (T1) and 110 ppm (T2) PA, from 95 to 165 kg live weight. The treatment lowered total adipose cuts yield (P<0.05) and increased lean/adipose cuts ratio (P<0.07). In the outer layer of thighs subcutaneous adipose tissue, the treatment raised polyunsaturated FA content (P<0.01), unsaturation coefficient (P<0.01) and polyunsaturated/saturated (P/S) FA ratio (P<0.05). In the inner layer, the treatment led to a lower saturated FA (P<0.05) and higher polyunsaturated FA content (P<0.01). In both trials, females generally provided leaner carcasses. In neither trials, vitamin level affected meat quality. Thus, feeding high levels of PA to heavy pigs can yield more valuable carcasses without affecting meat quality. However, effects on FA composition suggest caution in adopting this practice in the Italian heavy pig production

Agent-Based Analysis of Urban Spaces Using Space Syntax and Spatial Cognition Approaches: A Case Study in Bari, Italy

The present study provides a reflection on the agent-based intelligence of urban spatial environments through the comparison of a formal quantitative approach, i.e., space syntax, and a qualitative experimentation based on the spatial cognition approach. Until recently, space syntax was adopted by urban planners and designers to support urban design and planning decisions, based on an analysis of the urban physical environment. Researchers in the cognitive science field have increased their attempts to address space syntax techniques to better understand the relationships of cognitive spatial agents with the spatial features of urban environments. In this context, the experimental approach focuses on the qualities of the environment as interacted, perceived and interpreted by cognitive agents and reflects on the role which it plays in affecting spatial decisions and route choices. The present paper aimed to explore the extent to which possible integration between the different approaches can provide insights on agent-based decisions in actions and behavioural processes in space for useful perspectives in urban analysis and planning. Findings suggest relevant correlations between the experimentation results and space syntax predictions when a correspondence of some aspects can be found. Conversely, interesting qualitative insights from the spatial cognition approach are pointed out to enrich the configurational analysis. The potential and constraints of each approach and the ways of combining these are presented. Evidence supports the suitability of the proposal outlined in the present paper within the framework of urban planning practice