英文标题：
《Predicting the Stock Price of Frontier Markets Using Modified
  Black-Scholes Option Pricing Model and Machine Learning》
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作者：
Reaz Chowdhury, M.R.C. Mahdy, Tanisha Nourin Alam, Golam Dastegir Al
  Quaderi
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最新提交年份：
2018
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英文摘要：
  The Black-Scholes Option pricing model (BSOPM) has long been in use for valuation of equity options to find the prices 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 of frontier markets so that we can predict the stock price (close price). Changes have been made to the model to find the parameters 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 prices of the stocks goes high 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.
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中文摘要：
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，以确定股票的价格。在这项工作中，利用BSOPM，我们提出了一种比较分析方法和数值技术来确定看涨期权和看跌期权的价格，并将这两种价格视为前沿市场股票的买入价和卖出价，以便预测股票价格（收盘价）。对模型进行了修改，以找到计算前沿市场股票价格的参数执行价格和到期时间。为了验证使用改进的BSOPM获得的结果，我们使用了使用软件Rapidminer的机器学习方法，其中我们采用了不同的算法，如决策树、集成学习方法和神经网络。据观察，使用机器学习对收盘价的预测与使用BSOPM获得的预测非常相似。由于Black-Scholes-Merton方程包含了不断变化的风险和红利参数，机器学习方法比BSOPM具有更好的预测能力。我们还对波动率进行了数值计算。随着股票价格因定价过高而走高，波动性以惊人的速度增加，当波动性变得非常高时，市场往往会下跌，这可以使用我们改进的BSOPM进行观察和确定。基于量子物理中薛定谔方程（和热方程）的类比，对所提出的改进BSOPM进行了解释。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Quantitative Finance        数量金融学
二级分类：Mathematical Finance        数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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