本文是计算机专业的留学生作业范例，题目是“Exploring Dow Index Variance using Data Mining Technology（利用数据挖掘技术探讨道琼斯指数方差）”，道琼斯工业平均指数(DJIA)的趋势经常被用作未来经济趋势的指标。利用快速傅里叶变换(FFT)等常用统计工具来研究复杂数据集中隐藏的时间段，无法有效地分析DJIA数据的复杂性。雅虎财经下载了1986年至2018年33年的道琼斯工业指数数据。应用原始周期分析方法“峰值分析”，揭示了一个准十年周期。股市和经济市场在经过十年的稳定发展后，可以预测其下行趋势。这一结论与其他著名专家不谋而合。
Abstract 摘要
Trends in the Dow Jones Industrial Average (DJIA) are often used as an indicator of future economic tendency.The DJIA data complexity cannot be effectively analyzed using the common statistical tools such as Fast Fourier Transform (FFT) to study the time period hidden in complex datasets. Thirty-three years’ DJIA data, from 1986 to 2018, were downloaded in Yahoo Finance. Applying “Peak Analysis”, the primitive period analysis method, a quasi-ten-year cycle was revealed. Downward trend of the stock and economic markets can be predicted after ten years of steady development. This conclusion coincides with other well-known experts.
Modern data mining technology offers the possibility to achieve accurate and reliable predictions. The Google open source, TensorFlow, will be experimentally adopted in our future stock prediction project. Data ingesting, pro-processing, training TensorFlow, stock predicting, post-processing and decision making are the schemas of the stock trading system currently under development.
现代数据挖掘技术为实现准确可靠的预测提供了可能性。我们未来的股票预测项目将实验性地采用谷歌开源的TensorFlow。数据摄取、前处理、TensorFlow培训、股票预测、后处理和决策都是目前正在开发的股票交易系统的模式。
1.Introduction引言
The Dow Jones Industrial Average (DJIA) index, also known as the Dow, is a stock market index that reflects on how the thirty largest public companies traded in the market behaves. The DJIA average is not a weighted mean, nor is it representative of the market capitalization of each individual company included within the index. It is simply used to indicate the average stock price, per share, for each company. The DJIA is the sum of the prices of all 30 stocks divided by the Dow Divisor. The divisor is adjusted in case of stock splits, spinoffs or similar structural changes, to ensure that such events do not in themselves alter the numerical value of the DJIA. As of the end of June 2018, the Dow divisor is 0.14748071991788. It means that for every $1 of change in price for any given stock within the index, the average is equal to a 6.781-point movement in the market. The timely Dow index is critical and essential for the traders to make decision when to buy or sell stocks.
道琼斯工业平均指数(DJIA)，也被称为道，是一种股票市场指数，反映了30家最大的上市公司在市场上的交易行为。道琼斯工业平均指数不是加权平均值，也不能代表指数中每一家公司的市值。它只是用来表示每个公司每股的平均股价。道琼斯指数是所有30只股票的价格之和除以道琼斯指数除数。在股票分割、剥离或类似的结构变化的情况下，该除数被调整，以确保这些事件本身不会改变道琼斯工业平均指数的数值。截至2018年6月底，道琼斯指数的除数为0.14748071991788。这意味着，指数中任何一只股票的价格每变化1美元，市场的平均波动就等于6.781点。及时的道琼斯指数对于交易者决定何时买进或卖出股票是至关重要的。
The stock variance seems erratic, volatile. On Christmas Eve of 2018, the DJIA finished down 653 points, or 2.9%, representing its worst decline on the session prior to Christmas in the 122-year-old blue-chip gauge’s history. Despite this occurrence, the next day the market rallied strongly in a clear display of large volatility .It rallied in volatile trade on Wednesday, the first full trading session after the worst Christmas Eve trading session in history. The index ended 1,086 points higher in the last hour of trade, an increase of 5 percent over Monday’s abbreviated session. It is the biggest ever point jump in U.S. stock history. The irrational stock market brings huge risk and challenge in its investment to the trader while they pursue hard to profit. On the flip side, a non-efficient market presents great opportunity as well. Through more than one hundred years’ Dow index data recorded, the data mining technologies including commonly used statistic methodologies can be attempted to predict the future stock price movements. This trend analysis is based on the idea that what has happened in the past gives the traders the ideas what will happen in the future. The traders can perform decision making from trend analysis results to have maximum profits.
The Dow index is driven by a handful of public companies. Currently, the biggest components of the Dow are Boeing (NYSE:BA); McDonald’s (NYSE: MCD); Goldman Sachs (NYSE:GS); United Health (NYSE:UNH); Exxon Mobile (NYSE: XOM); Intel (NASDAQ: INTC); and Apple (NASDAQ: AAPL) etc. All the attention given to the Dow hitting new record highs, or falling to new lows, can certainly be regarded as reflecting the performance of these 30 companies. Even if those 30 public companies are only a fraction of the overall U.S. economy, the Dow index is still the reflectance generated from the whole economic, political and human social activities. Following lead of the natural science, the social science has been categorized in a state of chaos [1]. With the new discoveries, the uncertainty, nonlinearity, and unpredictability in the natural realm had piqued the interest of social scientists. Measuring chaos and discovering the nonlinear dynamics become the new research subjects. With the development of advanced computing technologies including large hard disk storage, fast Central Process Unit (CPU), and superior Graphics Processing Unit (GPU), the deep learning has been rapidly applied in the recent tens of years. The deep learning and data mining technologies had been used for stock market returns [2].
In this short paper, we downloaded 33 years DJIA data from 1986 to 2018 in Yahoo Finance and analyzed the primary period cycles from the view of statistics. The future plan to utilize the Google’s open source TensorFlow to develop a stock market predicting and decision system is added.
在这篇短文中，我们从雅虎财经中下载了1986 - 2018年33年的DJIA数据，从统计的角度分析了主要的周期周期。未来还计划利用谷歌的开源TensorFlow开发一个股票市场预测和决策系统。
2.Data数据
The 33 years’ Dow index, DJIA, were downloaded in Yahoo Finance. The trade dataset covers “open”, “high”, “low”, “close”, “Adj close” and “volume”. “open” is the data value at the starting each day. “high” and “low” represent the highest and lowest record traded on the same day. “close” is the close price adjusted for splits. “Adj close” stands for “adjusted close price adjusted for both dividends and splits”. “volume” is the total amount of trading happened in the whole day.
33年的道琼斯指数(DJIA)是在雅虎财经(Yahoo Finance)上下载的。交易数据集包括“open”、“high”、“low”、“close”、“Adj close”和“volume”。“open”是每天开始时的数据值。“高”和“低”代表同一天交易的最高和最低纪录。“close”是经过分割调整的收盘价。“Adj close”代表“经股息和分拆调整后的收盘价”。“volume”是指一天内发生的交易总量。
3.Dow index period exploring with statistics technology用统计技术探索道琼斯指数周期
Many factors could impact the stock market and brings it up and down. Among all these factors, economy is the main element. The business cycle is the pattern of expansion, contraction and recovery. It is mainly measured by the Gross Domestic Product (GDP) and the unemployment. GDP rising and unemployment shrinking means in the expansion phases, while reversing in periods of recession. The economy thus can be observed to go through four periods – expansion, peak, contraction and trough. The intrinsic relationship between stock market and the economic period leads the existing of the period of stock. The period could be investigated, analyzed through the trade data peaks and troughs. Usually, a period is measured through the two continuous peaks. Discovering the stock period turns to count the peaks of the long series data.
许多因素都可能影响股票市场并使其上下波动。在所有这些因素中，经济是主要的因素。商业周期是扩张、收缩和复苏的模式。它主要是由国内生产总值(GDP)和失业率来衡量的。GDP上升和失业率下降意味着在扩张阶段，而在衰退时期则会逆转。因此，可以观察到经济经历四个阶段——扩张、高峰、收缩和低谷。股票市场与经济周期的内在关系决定了股票周期的存在。这一时期可以通过贸易数据的波峰和波谷进行调查和分析。通常，周期是通过两个连续的峰值来测量的。发现股票周期就是计算长序列数据的峰值。
4. Dow index and stock prediction with deep learning technology道琼斯指数和股票预测深度学习技术
The stock pattern with certain period is meaningful from a long time perspective. Pattern lets us know some future stock variance, but it is not very helpful to the stock traders who need the timely product. Predicting stock and being aware of the actual stock value in advance has practical significance for the traders to make decision to buy or sell stocks for profiting most. However, the movement in the stock exchange depends mainly on capital gains and losses, most people consider the erratic stock market unpredictable.
从长期的角度看，具有一定时期的股票格局是有意义的。模式让我们知道一些未来的股票变化，但它对需要及时的产品的股票交易者并不是很有帮助。对股票进行预测，提前了解股票的实际价值，对于交易者做出最大收益的买卖股票决策具有重要的现实意义。然而，股票交易的动态主要取决于资本的损益，大多数人认为不稳定的股票市场是不可预测的。
The rapidly growing artificial intelligence (AI) technique is a computer based system to interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation. AI has been successfully used in understanding human speech, competing at the highest level in strategic game systems, autonomously operating cars, cancer research, and intelligent routing in content delivery networks and military simulations. AI has been applied to a diverse range of topics: cancer research, self-driving cars, and image recognition.
AI has been successfully applied in a diverse multitude of fields. The stock market can also be viewed as a particular artificial intelligence problem. It can be considered as an intelligent treatment of past and present financial data in order to predict the stock market future behavior [3,4,5]. Data mining techniques are used to evaluate past stock prices and acquire useful knowledge through the calculation of some financial indicators. Data mining techniques use past data points, and gather useful information to help predict future behavior. Attempts to forecast the stock price movement that is generally subject to many forces, GDP growth, employment rate, interest rate, monetary policies such as weakening or strong currency, high or low tax rate, corporate earnings, business and consumer spending, new technology appearance and development, political and social upheavals etc. [6]. All these factors could be used as the inputs to feed a deep learning system.
人工智能已成功应用于众多领域。股票市场也可以被看作是一个特殊的人工智能问题。它可以被认为是对过去和现在的财务数据的一种智能处理，以预测股票市场的未来行为[3,4,5]。利用数据挖掘技术对过去的股票价格进行评估，并通过一些财务指标的计算获得有用的知识。数据挖掘技术使用过去的数据点，并收集有用的信息来帮助预测未来的行为。试图预测股票价格的运动，这通常是受许多力量，GDP增长，就业率，利率，货币政策，如贬值或强势货币，高或低的税率，公司盈利，企业和消费者的支出，新技术的出现和发展，政治和社会动荡等[6]。所有这些因素都可以作为深度学习系统的输入。
Google TensorFlow Application Programming Interface (API) is used as the machining learning model to predict the future stock price and provide decision making suggestion [7]. TensorFlow is an open source software library for data flowing programming. It is a flexible interface for expressing, implementing and executing machine learning algorithm. It was borne from real world experience in conducting research and Google products and services, and this system has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer sciences and other fields. Google TensorFlow programs and source codes are available at www.tensorflow.org. We ran it on a Linux platform.
From the past tons of data to find the reliable variables and parameters during the deep learning to mostly achieve the best possible goals, training is a critical and indispensable step. Training plays more important roles in selecting the algorithm and determining more significant variables in prediction through looking at the past performance. Pre-processing the historical data is important before the data is ingested into the machine learning system. Data-gathering is often loosely controlled, resulting in out-of-range values, impossible data combinations, missing values, etc. Analyzing data that has not been carefully screened can produce misleading results. Thus, making quality control (QC) data is first and foremost before running training the learning system. Any good learning system is a concept hypotheses model trained from the post backgrounds. It can only better represents the past events. The generated data as the future stock price should be evaluated, interpreted, incorporated into other possible evaluation system to check for potential conflicts with previously induced stock variance. Post-processing cannot be left out to avoid any possible risk. Decision making is the last step of a stock trading system. Trading is not similar to find or predict any pattern from the long time datasets. Facing the instant stock change, purely relying on any computer based AI system is to be prudent. Qian [5] investigated the predictability of DJIA index with some inductive machine-learning classifiers, pointed out the prediction accuracy up to 65 percent. The role from the human being cannot be disregarded. The traders should make the final choice to buy or sell based their experiences or another independent decision making system.
5.Conclusions and discussions结论和讨论
The DJIA is an important index to describe the economy variance and its future trend. The conventional and widely used statistical analysis tools such as FFT, Exponential smoothing etc. cannot be effectively used to explore the trend from the complicate DJIA dataset. “Peak analysis”, the very primitive method through measuring two continuous peaks, is applied on analyzing 33 years DJIA data, from 1986 to 2018, downloaded from Yahoo Finance. A quasi-ten-year period was revealed existing in stock market. This quasi-ten-year period is an approximate value instead of a precise number. Over about a decade steady growth, the stock market faces downward.
道琼斯工业指数是描述经济变化及其未来趋势的重要指标。传统的、广泛使用的统计分析工具如FFT、指数平滑等无法有效地从复杂的DJIA数据集中探索趋势。“峰值分析”是一种非常原始的方法，通过测量两个连续的峰值来分析1986 - 2018年33年的DJIA数据，下载自雅虎财经。股票市场显示出一个准10年的周期。这个准十年的周期是一个近似值，而不是一个精确的数字。在大约10年的稳定增长中，股市面临下行。
Several famous economists cited by Klebnikov [8] made coincident statements similar to ours. Ray Dalio, hedge fund manager, said “the probability of a recession prior to the next presidential election would be relatively high, 70%”. JPMorgan’s real time recession forecast model suggests the chance of a market downturn at 70% by 2020. The Duke University/CFO Global Business Outlook survey released 80% of U.S. chief financial officers a recession will hit the economy by 2020.
As of May, 2019, the stock had just climbed near the top within 33 years, and the DOW has been over 26000. Not everyone trusted the economy will fall in any recession depending on the strong economy performance, such as the lowest unemployment since 1950. Larry Kudlow [8], National Economic Council director, kept optimistic, “I know there’s a lot of permission out there. I do not share that permission.” However, the economic development follows some certain regular pertain as we pointed a quasi-ten-year period exists in the DOW variance. Volatility was been driven by signs of a global economic activities including concerns about monetary policy, political dysfunction, inflation fears, trade wars etc.. The stock is in the stage of downward tendency. Facing this tough situation, investment should be wary.
Stock analysis deals with the study of these patterns. It uses different techniques and strategies, mostly automatic that trigger buying and selling orders depending on different decision making algorithms. Therefore it can be viewed as an artificial intelligence problem in the data mining field. The analysis have techniques from artificial intelligence applied to it , due to the automated nature of modern stock trading. Deeping learning has been revolutionizing virtually every aspect of financial and investment decision making. It has been employed world widely to tackle difficult tasks involving intuitive judgement or requiring the detection of data patterns which elude conventional analytic techniques. Neural networks are already being used to trade the securities markets, to forecast the economy and to analyze credit risk.
Currently, machine learning is being applied to finance in areas including, but not limited to economic forecasts and credit risks. The Google open source, TensorFlow, has been downloaded and installed on our Linux platform, it will be trained and applied in our future deep learning project to predict the stock movement including the Dow index as the first experimental development. The input data has to be QCed in the pro-pressing stage to remove any erratic value. Post-processing the forecasted stock value cannot be overlooked even if many observers believe neural networks will eventually outperform even the best traders and investors. Date ingesting, pro-processing, training the deep learning system and making prediction, post-processing and decision making are the schema or our future stock trading system. After system processing, the decision can be made to buy or sell the stock.
目前，机器学习正被应用于金融领域，包括但不限于经济预测和信用风险。谷歌开源软件TensorFlow已经下载安装在我们的Linux平台上，它将作为第一个实验性开发，在我们未来的深度学习项目中进行培训和应用，预测包括道琼斯指数在内的股票走势。输入数据必须在预按阶段进行QCed以删除任何不稳定的值。即使许多观察人士认为神经网络最终将超越最优秀的交易员和投资者，对预测的股票价值进行后处理也不能被忽视。数据的摄取、前处理、训练深度学习系统，以及预测、后处理和决策，都是我们未来的股票交易系统的模式。在系统处理之后，就可以做出买卖股票的决定。
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