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Building agent-based market simulation models for price forecasting of real-world stocks and other securities Altreva; Utrecht, Netherlands Proprietary; free evaluation version available for research and experimentation (some limitations but no expiration)
Orbiter was developed as a simulator, [14] with accurately modeled planetary motion, gravitation effects (including non-spherical gravity), free space, atmospheric flight and orbital decay. [15] [16] The position of the planets in the solar system is calculated by the VSOP87 solution, while the Earth-Moon system is simulated by the ELP2000 ...
For example, for bond options [3] the underlying is a bond, but the source of uncertainty is the annualized interest rate (i.e. the short rate). Here, for each randomly generated yield curve we observe a different resultant bond price on the option's exercise date; this bond price is then the input for the determination of the option's payoff.
With unexpectedly strong economic data and investors’ AI enthusiasm driving the S&P 500 32% higher over the past 12 months, some experts are worried that the stock market is in a bubble. Bank of ...
Examples of RNN and TDNN are the Elman, Jordan, and Elman-Jordan networks. For stock prediction with ANNs, there are usually two approaches taken for forecasting different time horizons: independent and joint. The independent approach employs a single ANN for each time horizon, for example, 1-day, 2-day, or 5-day.
Orbit modeling is the process of creating mathematical models to simulate motion of a massive body as it moves in orbit around another massive body due to gravity.Other forces such as gravitational attraction from tertiary bodies, air resistance, solar pressure, or thrust from a propulsion system are typically modeled as secondary effects.
Historical simulation in finance's value at risk (VaR) analysis is a procedure for predicting the value at risk by 'simulating' or constructing the cumulative distribution function (CDF) of assets returns over time assuming that future returns will be directly sampled from past returns.
Simulation-based optimization (also known as simply simulation optimization) integrates optimization techniques into simulation modeling and analysis. Because of the complexity of the simulation, the objective function may become difficult and expensive to evaluate. Usually, the underlying simulation model is stochastic, so that the objective ...