Using Rough logic for predicting price movements on financial markets

Financial markets and especially their movements are very difficult to predict. For this reason, it cannot be clearly concluded what market will do. We cannot use basic logical operators such as if A happens, then comes B. Since we cannot use simple decision rules and we work in high uncertainty we cannot easily build classical mathematical model because of uncertainty of each and every result. However to analyze this type of data we can used Rough logic which is design to work with uncertainty. The aim of this thesis is use of Rough logic to create a mathematical model, which will be able to some extent to understand and eventually predict individual market movements. Market uncertainty Purpose of the article: Using Rough logic for predicting price movements. Scientific aim: Rough Set. Conclusions: Methodology for using Rough set in financial markets

Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network

In view of the applications of artificial neural networks in economic and financial forecasting, a stochastic time strength function is introduced in the backpropagation neural network model to predict the fluctuations of stock price changes. In this model, stochastic time strength function gives a weight for each historical datum and makes the model have the effect of random movement, and then we investigate and forecast the behavior of volatility degrees of returns for the Chinese stock market indexes and some global market indexes. The empirical research is performed in testing the prediction effect of SSE, SZSE, HSI, DJIA, IXIC, and S&P 500 with different selected volatility degrees in the established model

Relationship between Chinese and International Crude Oil Prices: A VEC-TARCH Approach

Many studies focus on the impact of international crude oil price volatility on various economic variables in China with a hypothesis that international crude oil price affected Chinese crude oil price first and then other economic variables. However, there has been little research to explore whether or not international and Chinese oil market are integrated. This study aims to investigate the relationship between Chinese and international crude oil prices by VAR and VEC-TARCH models. It was found that the two crude oil markets have been integrated gradually. But the impact of external shocks on the Chinese crude oil market was stronger and the Chinese crude oil price was sensitive to changes in international crude oil price, implying that the centrally controlled oil market in China is less capable of coping with external risk. In addition, the volatility of both Chinese and international crude oil prices was mainly transmitted by prior fluctuation forecast and the impact of external shocks was limited, demonstrating that in both cases volatility would disappear rather slowly. Furthermore, Chinese and international crude oil markets have established a stable relationship. When the direction of external shocks on the two variables’ respective stochastic term was consistent, the impact on the two variables’ joint volatility was aggravated and vice versa

The Importance of Quantum Information in the Stock Market and Financial Decision Making in Conditions of Radical Uncertainty

The Universe is a coin that’s already been flipped, heads or tails predetermined: all we’re doing is uncovering it the ‘paradox’ is only a conflict between reality and your feeling of what reality ‘ought to be’.Richard FeynmanThe aim of the research takes place through two parallel directions. The first is gaining an understanding of the applicability of quantum mechanics/quantum physics to human decision-making processes in the stock market with quantum information as a decision-making lever, and the second direction is neuroscience and artificial intelligence using postulates analogous to the postulates of quantum mechanics and radical uncertainty in conditions of radical uncertainty.The world of radical uncertainty (radical uncertainty is based on the knowledge of quantum mechanics from the claim that there is no causal certainty). it is everywhere in our world. "Radical uncertainty is characterized by vagueness, ignorance, indeterminacy, ambiguity and lack of information. He prefers to create 'mysteries' rather than 'puzzles' with defined solutions. Mysteries are ill-defined problems in which action is required, but the future is uncertain, the consequences unpredictable, and disagreement inevitable. "How should we make decisions in these circumstances?" (J. Kay and M. King, 2020), while "uncertainty and ambiguity are at the very core of the stock market. "Narratives are the currency of uncertainty" (N. Mangee, 2022)

The Importance of Quantum Information in the Stock Market and Financial Decision Making in Conditions of Radical Uncertainty

The Universe is a coin that’s already been flipped, heads or tails predetermined: all we’re doing is uncovering it the ‘paradox’ is only a conflict between reality and your feeling of what reality ‘ought to be’.Richard FeynmanThe aim of the research takes place through two parallel directions. The first is gaining an understanding of the applicability of quantum mechanics/quantum physics to human decision-making processes in the stock market with quantum information as a decision-making lever, and the second direction is neuroscience and artificial intelligence using postulates analogous to the postulates of quantum mechanics and radical uncertainty in conditions of radical uncertainty.The world of radical uncertainty (radical uncertainty is based on the knowledge of quantum mechanics from the claim that there is no causal certainty). it is everywhere in our world. "Radical uncertainty is characterized by vagueness, ignorance, indeterminacy, ambiguity and lack of information. He prefers to create 'mysteries' rather than 'puzzles' with defined solutions. Mysteries are ill-defined problems in which action is required, but the future is uncertain, the consequences unpredictable, and disagreement inevitable. "How should we make decisions in these circumstances?" (J. Kay and M. King, 2020), while "uncertainty and ambiguity are at the very core of the stock market. "Narratives are the currency of uncertainty" (N. Mangee, 2022)

Artificial Neural Network and its Applications in the Energy Sector – An Overview

In order to realize the goal of optimal use of energy sources and cleaner environment at a minimal cost, researchers; field professionals; and industrialists have identified the expediency of harnessing the computational benefits provided by artificial intelligence (AI) techniques. This article provides an overview of AI, chronological blueprints of the emergence of artificial neural networks (ANNs) and some of its applications in the energy sector. This short survey reveals that despite the initial hiccups at the developmental stages of ANNs, ANN has tremendously evolved, is still evolving and have been found to be effective in handling highly complex problems even in the areas of modeling, control, and optimization, to mention a few

Forecasting Crude Oil Price and Stock Price by Jump Stochastic Time Effective Neural Network Model

The interacting impact between the crude oil prices and the stock market indices in China is investigated in the present paper, and the corresponding statistical behaviors are also analyzed. The database is based on the crude oil prices of Daqing and Shengli in the 7-year period from January 2003 to December 2009 and also on the indices of SHCI, SZCI, SZPI, and SINOPEC with the same time period. A jump stochastic time effective neural network model is introduced and applied to forecast the fluctuations of the time series for the crude oil prices and the stock indices, and we study the corresponding statistical properties by comparison. The experiment analysis shows that when the price fluctuation is small, the predictive values are close to the actual values, and when the price fluctuation is large, the predictive values deviate from the actual values to some degree. Moreover, the correlation properties are studied by the detrended fluctuation analysis, and the results illustrate that there are positive correlations both in the absolute returns of actual data and predictive data

A New Theory to Forecast the Price of Non Renewable Energy Resources with Mass and Energy-Capital Conservation Equations.

The mass and energy-capital conservation equations are employed to study the time evolution of mass and price of non-renewable energy resources, extracted and sold to the market, in case of no-accumulation and no-depletion; i.e. when the resources are extracted and sold to the market at the same mass flow rate. The Hotelling rule for non-renewable resources, i.e. an exponential increase of the price at the rate of the current interest multiplied the time, is shown to be a special case of the general energy-capital conservation equation when the mass flow rate of extracted resources is unity. The mass and energy-capital conservation equations are solved jointly to investigated the time evolution of the extracted resources. The parameter PIFE, “Price Increase Factor of Extracted resources”, is the difference between the interest rate of capital, typically the inflation rate, and the mass flow rate of extraction of non-renewable resources. The price of the extracted resources increases if PIFE is greater than zero, i.e. the mass flow rate of extraction is smaller than the inflation rate. The price is constant if PIFE is zero, i.e. the mass flow rate of extraction is equal to the inflation rate. The price is decreasing with time if PIFE is smaller than zero, i.e. the mass flow rate of extraction is greater than the inflation rate. The price of selling resources varies with time according to the relation between the parameters PIFE and PIFS, “Price Increase Factor of Selling resources”, which is the difference between the extraction rate and the interest rates of selling resources, prime or discount rate. The price of selling resources increases with time if the initial price is greater than CIPS, “Critical Initial Price of Sold resources”, which depends on the initial price of extracted resources, the interest rate of non-extracted resources, and the difference between PIFS and PIFE or is greater than CIPES, “Critical Initial Price Extreme of Selling resources”, which depends on the initial price of extracted resources, the interest rate of non-extracted resources, and PIFS. The price of selling resources increases temporarily with time if the interest rates of non-extracted and extracted resources are equal, i.e. PIFE is equal to PIFS, and the initial price is greater than CIPES, “Critical Initial Price Extreme of Selling resources”. The price evolutions of the difference between selling and extracted resources are investigated according to the relation between extraction rate and interest rate of extracted and selling resources. The price difference increases with time if PIFS is greater than PIFE of the extracted resources and the initial price is greater than the critical price of selling resources, which depends on the initial price of extracted resources and the interest rate of non-extracted and extracted resources. The price difference decreases with time if PIFS is greater than PIFE and the initial price is smaller than the critical price of selling resources. The other cases are discussed extensively in the paper. The price evolution of non-renewable resources versus the consumption rate is investigated with the aim of constructing the energy supply curve. The case studied is without accumulation nor depletion of the resources and the mass and energy-capital conservation equations are solved under the condition of the same mass flow rate of extraction and sale. The energy supply curve of extracted resource is dependent on the new parameter, RINE, “Rate of Interest of Non-extracted resources on the Extraction rate”. The energy supply curve of selling resource is dependent on the new parameter, RISE, “Rate of Interest of Sold resources on the Extraction rate”, in case the rate of interest of non-extracted resources, rN, is nil. The energy supply curve of selling resources is dependent also on two dimensionless parameters, “Dimensionless Critical Initial Price of Sold resources”, i.e. DCIPS, and “Dimensionless Critical Initial Price Extreme of Sold resources”, i.e. DCIPES. The energy supply curve of selling resources is investigated under different relations between three parameters, i.e. extraction rate and interest rates of extracted and selling resources. New trends are observed in the economic market of non-renewable energy resources. The energy supply curve of the difference between selling and extracted resource is dependent on two dimensionless parameters, “Critical Initial Price Difference”, i.e. CIPD, and “Critical Extreme of the Initial Price Difference”, i.e. CEIPD. The price difference between selling and extracted resources is investigated versus the dimensionless mass flow rate of extraction. The evolution is dependent on four parameters: RINE, RISE, DCIPS, and DCIPES