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

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

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

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

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Neural Network Contribute to Reverse Cryptographic Processes in Bitcoin Systems: attention on SHA256

Bitcoin is a digital currency created in January 2009 following the housing market crash that promises lower transaction fees than traditional online payment mechanisms. Though each bitcoin transaction is recorded in a public log, the names of buyers and sellers are never revealed. While that keeps bitcoin users' transactions private, it also lets them buy or sell anything without easily tracing it back to them. Bitcoin is based on cryptographic evidence, which therefore does not suffer from the weakness present in a model based on trust in guarantee authorities. The use of cryptography is of crucial importance in the Bitcoin system. In addition to maintaining data secrecy, in the case of Bitcoin, cryptography is used to make it impossible for anyone to spend money from another user's wallet. In our paper, we develop the idea that it is possible to reverse the cryptography process based on hash functions (one-way) through Machine Translation with neural networks. Assuming this hypothesis is true and considering some quantistic algorithms to decrypt certain types of hash functions, we will highlight their effects on the Bitcoin system