布莱克-斯科尔斯期权定价模型(BSOPM)长期以来一直用于股票期权的估值,以找到股票的价格。在这项工作中,我们使用BSOPM提出了一种比较分析方法和数值技术,以找到看涨期权和看跌期权的价格,并将这两个价格视为前沿市场中股票的买入价和卖出价,以便我们可以预测股票价格(收盘价)。该模型已进行了更改,以找到诸如“行使价”和“到期时间”之类的参数来计算前沿市场的股票价格。为了验证使用改进的BSOPM获得的结果,我们使用了使用Rapidminer软件的机器学习方法,在该方法中,我们采用了决策树,集成学习方法和神经网络等不同算法。据观察,使用机器学习的收盘价预测与使用BSOPM获得的收盘价非常相似。机器学习方法是优于BSOPM的更好的预测器,因为Black-Scholes-Merton方程包含不断变化的风险和股利参数。我们还通过数值计算了波动率。由于定价过高导致股票价格上涨时,波动率以极大的速度增加,并且当波动率变得很高时;市场趋于下降,这可以通过我们改进的BSOPM来观察和确定。还基于量子物理学的薛定inger方程(和热方程)的类比,对提出的改进的BSOPM进行了解释。因为Black-Scholes-Merton方程包含风险和股利参数,该参数不断变化。我们还通过数值计算了波动率。由于定价过高导致股票价格上涨时,波动率以极大的速度增加,并且当波动率变得很高时;市场趋于下降,这可以通过我们改进的BSOPM来观察和确定。还基于量子物理学的薛定inger方程(和热方程)的类比,对提出的改进BSOPM进行了解释。因为Black-Scholes-Merton方程包含风险和股利参数,该参数不断变化。我们还通过数值计算了波动率。由于定价过高导致股票价格上涨时,波动性会以极大的速度增加,并且当波动性变得很高时;市场趋于下降,这可以通过我们改进的BSOPM来观察和确定。还基于量子物理学的薛定inger方程(和热方程)的类比,对提出的改进的BSOPM进行了解释。可以使用我们改进的BSOPM进行观察和确定。还基于量子物理学的薛定inger方程(和热方程)的类比,对提出的改进BSOPM进行了解释。可以使用我们改进的BSOPM进行观察和确定。还基于量子物理学的薛定inger方程(和热方程)的类比,对提出的改进的BSOPM进行了解释。
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Predicting the stock price of frontier markets using machine learning and modified Black–Scholes Option pricing model
The Black–Scholes Option pricing model (BSOPM) has long been in use for valuation of equity options to find the price of stocks. In this work, using BSOPM, we have come up with a comparative analytical approach and numerical technique to find the price of call option and put option and considered these two prices as buying price and selling price of stocks in the frontier markets so that we can predict the stock price (close price). Changes have been made in the model to find the parameters such as ‘strike price’ and the ‘time of expiration’ for calculating stock price of frontier markets. To verify the result obtained using modified BSOPM, we have used machine learning approach using the software Rapidminer, where we have adopted different algorithms like the decision tree, ensemble learning method and neural network. It has been observed that, the prediction of close price using machine learning is very similar to the one obtained using BSOPM. Machine learning approach stands out to be a better predictor over BSOPM, because Black-Scholes-Merton equation includes risk and dividend parameter, which changes continuously. We have also numerically calculated volatility. As the price of the stocks goes up due to overpricing, volatility increases at a tremendous rate and when volatility becomes very high; market tends to fall, which can be observed and determined using our modified BSOPM. The proposed modified BSOPM has also been explained based on the analogy of Schrodinger equation (and heat equation) of quantum physics.