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Analyzing the Effects of Bollinger Bands on the Probability of Stock Options Using Support Vector Machines.

機譯：使用支持向量機分析布林帶對股票期權概率的影響。

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The purpose of this research is to efficiently analyze certain data provided and to see if a useful trend can be observed as a result. This trend can be used to analyze certain probabilities. There are three main pieces of data which are being analyzed in this research: The value for delta of the call and put option, the %B value of the stock, and the amount of time until expiration of the stock option. The %B value is the most important. The purpose of analyzing the data is to see the relationship between the variables and, given certain values, what is the probability the trade makes money. This result will be used in finding the probability certain trades make money over a period of time.;Since options are so dependent on probability, this research specifically analyzes stock options rather than stocks themselves. Stock options have value like stocks except options are leveraged. The most common model used to calculate the value of an option is the Black-Scholes Model [1]. There are five main variables the Black-Scholes Model uses to calculate the overall value of an option. These variables are theta, delta, gamma, nu, and rho. The variable, theta is the rate of change in price of the option due to time decay, delta is the rate of change of the option's price due to the stock's changing value, gamma is the rate of change of delta, nu represents the rate of change of the value of the option in relation to the stock's volatility, and rho represents the rate of change in value of the option in relation to the interest rate [2]. In this research, the %B value of the stock is analyzed along with the time until expiration of the option. All options have the same delta. This is due to the fact that all the options analyzed in this experiment are less than two months from expiration and the value of ? reveals how far in or out of the money an option is.;The machine learning technique used to analyze the data and the probability is support vector machines. Support vector machines analyze data that can be classified in one of two or more groups and attempts to find a pattern in the data to develop a model, which reliably classifies similar, future data into the correct group. This is used to analyze the outcome of stock options.
機譯：這項研究的目的是有效分析提供的某些數(shù)據，并查看是否可以觀察到有用的趨勢。此趨勢可用于分析某些概率。本研究正在分析三個主要數(shù)據：看漲和看跌期權的德爾塔價值，股票的％B值以及到股票期權到期為止的時間。 ％B值是最重要的。分析數(shù)據的目的是查看變量之間的關系，以及在給定特定值的情況下，交易賺錢的概率是多少。該結果將用于查找一段時間內某些交易獲利的可能性。由于期權非常依賴概率，因此本研究專門分析股票期權而不是股票本身。股票期權具有與股票一樣的價值，但期權是杠桿的。用于計算期權價值的最常見模型是Black-Scholes模型[1]。 Black-Scholes模型使用五個主要變量來計算期權的整體價值。這些變量是theta，delta，gamma，nu和rho。變量theta是由于時間衰減而導致的期權價格變化率，delta是由于股票的價值變動而導致的期權價格變化率，gamma是delta的變化率，nu表示期權價格的變化率期權價值相對于股票波動率的變化，rho表示期權價值相對于利率的變化率[2]。在這項研究中，將分析股票的％B值以及直至期權到期的時間。所有選項具有相同的增量。這是由于以下事實：本次實驗中分析的所有選項距有效期均不到兩個月，并且？揭示期權的收益或收益。;用于分析數(shù)據的機器學習技術，概率為支持向量機。支持向量機分析可以分為兩個或多個組之一的數(shù)據，并嘗試在數(shù)據中找到模式以開發(fā)模型，該模型將相似的未來數(shù)據可靠地分類為正確的組。這用于分析股票期權的結果。

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  • 作者

    Reeves, Michael.;

  • 作者單位

    Arizona State University.;

  • 授予單位 Arizona State University.;
  • 學科 Computer science.;Finance.
  • 學位 M.S.
  • 年度 2015
  • 頁碼 87 p.
  • 總頁數(shù) 87
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類
  • 關鍵詞

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