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Soliton wave profiles and dynamical analysis of fractional Ivancevic option pricing model
This study dynamically investigates the mathematical Ivancevic option pricing governing system in terms of conformable fractional derivative, which illustrates a confined Brownian motion identified with a non-linear Schrödinger type equation. This model describes the controlled Brownian motion that comes with a non-linear Schrödinger type equation. The solution to comprehend the market price fluctuations for the suggested model is developed through the application of a mathematical strategy. The modified Kudryashov analytical method is applied to find the fractional analytical exact soliton solution. The restrictions on the parameters required for these solutions to exist were also the result of this approach. The dynamical insights are examined and significant aspects of the phenomenon under study are discussed through the use of the bifurcation analysis. In the related dynamical system, the phase portraits of market price fluctuations are displayed at equilibrium points and for different parameter values. Additionally, the chaos analysis was carried out to show the quasi-periodic and periodic chaotic patterns. In order to track changes in market price, the sensitivity analysis of the studied model is also looked at and presented at different initial conditions. It was discovered that the model experienced price fluctuations as a result of minute changes in initial conditions.
Journal Article
Stock price prediction: comparison of different moving average techniques using deep learning model
The stock market is changing quickly, and its nonlinear characteristics make stock price prediction difficult. Predicting stock prices is challenging due to several factors, including a company’s financial performance, unforeseen circumstances, general economic conditions, politics, current assets, global situation, etc. Despite these terms, sufficient data are available to identify stock price movement trends using the different technical approaches. In this research, we empirically analyzed long short-term memory (LSTM) networks in the context of time-series prediction. Our investigation leveraged a diverse set of real-world datasets and provided quantitative insights into the performance of LSTMs. Across a spectrum of time-series forecasting tasks, LSTM models demonstrated an impressive mean absolute error (MAE) reduction of 23.4% compared to traditional forecasting methods. Specifically, LSTM achieved an average prediction accuracy of 89.7% in financial market predictions, outperforming baseline models by a significant margin. The aim is to obtain a value that can be compared to the present price of an asset to determine whether it is overvalued or undervalued, which anticipates the price patterns by analyzing previous market information, such as price and volume, compared to this stock analysis approach.
Journal Article
Efficiently inefficient : how smart money invests and market prices are determined
Efficiently Inefficient describes the key trading strategies used by hedge funds and demystifies the secret world of active investing. Leading financial economist Lasse Heje Pedersen combines the latest research with real-world examples and interviews with top hedge fund managers to show how certain trading strategies make money--and why they sometimes don't.
Book
Influencing Factors and Prediction of Carbon Trading Market Prices in China via Elliptical Factor Analysis
In this paper, the authors take Hubei carbon trading market prices as a sample, and select 27 variables from five aspects: International carbon market prices, energy prices, the macroeconomic situation, exchange rate factors and climate environment. The authors construct the elliptical approximate factor model and use a robust two step method based on multivariate Kendall’s Tau matrix to extract common factors, identify the influencing factors of carbon prices, make out-of-sample forecasting of carbon prices, and compare with the prediction based on the historical mean of carbon trading market prices. The results show that the prediction of carbon trading market prices using elliptical approximate factor model is more accurate than the prediction based on the historical mean of carbon trading market prices. Among them, fossil energy prices, international carbon prices and climate environment are important influencing factors of carbon trading prices.
Journal Article
Wholesale Electricity Price Forecasting Using Integrated Long-Term Recurrent Convolutional Network Model
Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage.
Journal Article
The first crash : lessons from the South Sea Bubble
'The First Crash' provides a full account of the South Sea Bubble written from the point of view of stock market investors. The text is aimed at all those with an interest in the behaviour stock markets, whether as fund managers, brokers, investors, financial advisers, analysts or academics.
Book
Optimization of ore production scheduling strategy using NSGA-II-GRA in open-pit mining
To address the challenges posed by ore grade fluctuations, extraction cost variations, and market price instability in open-pit ore production strategies, an optimization model integrating a solution algorithm integrating dynamic simulation is proposed. First, Monte Carlo simulation is used to generate random variables, simulating ore grade and extraction cost fluctuations at different extraction points. Then, the Non-dominated Sorting Genetic Algorithm II with Grey Relational Analysis (NSGA-II-GRA) is employed for an initial solution to obtain the preliminary Pareto-optimal solution set. Furthermore, a market price fluctuation constraint equation is introduced during the optimization process to conduct a second iterative optimization of the initial solution set, generating optimized plans under different price fluctuation intervals. Finally, by integrating subjective and objective weights, the weighted grey relational analysis method is applied to select the optimal Ore Production Scheduling Strategy. The optimization results indicate that, while achieving the optimization objectives, calcium oxide grade and production target achievement rate increased by 7% and 7.17%, respectively, while ore output increased by 16.7% and 20.5% under different price fluctuation intervals.
Journal Article