The stack bar chart shows that the overall time spends on training the model is decreasing by the number of selected features, while the PCA method is significantly effective in optimizing training dataset preparation. For the time spent on the training stage, PCA is not as effective as the Stock Price Online data preparation stage. While there is the possibility that the optimization effect of PCA is not drastic enough because of the simple structure of the NN model. In the implementation part, we expanded 20 features into 54 features, while we retain 30 features that are the most effective.
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Five Fundamental Reasons for High Oil Volatility
Second, the system also embedded with a forecasting component, which also retains the features of the time series. Last but not least, their input features are a mix of fundamental features and technical indices that aim to fill in the gap between the financial domain and technical domain. Instead of evaluating the proposed system on a large dataset, they chose 25 well-known stocks. There is a high possibility that the well-known stocks https://dotbig.com/markets/stocks/NFLX/ might potentially share some common hidden features. Huang et al. in applied a fuzzy-GA model to complete the stock selection task. They used the key stocks of the 200 largest market capitalization listed as the investment universe in the Taiwan Stock Exchange. Besides, the yearly financial statement data and the stock returns were taken from the Taiwan Economic Journal database at / for the time period from year 1995 to year 2009.
- Other commonly used financial ratios include return on assets , dividend yield, price to book (P/B) ratio, current ratio, and the inventory turnover ratio.
- First, the amount of input features and processing elements in the hidden layer are 12 and not adjustable.
- The smooth functioning of all these activities facilitates economic growth in that lower costs and enterprise risks promote the production of goods and services as well as possibly employment.
- Here shows that the profit target was hit, then later on the ATR Trailing Stop-loss was hit.
- The confusion matrix is the figure on the right in Fig.11, and detailed metrics scores can be found in Table9.
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We tested the RFE algorithm on a range of short-term from 1 day to 2 weeks , to evaluate how the commonly used technical indices correlated to price trends. For evaluating the prediction term length, we fully expanded the features as Table2, and feed them to RFE. During the test, we found that different length Stock Price Online of the term has a different level of sensitive-ness to the same indices set. Lei in exploited Wavelet Neural Network to predict stock price trends. The author also applied Rough Set for attribute reduction as an optimization. Rough Set was utilized to reduce the stock price trend feature dimensions.
Five Fundamental Reasons for High Oil VolatilityWTI crude oil prices fell 25% in the third quarter, but amid economic uncertainty and geopolitical risks, volatility has shown little sign of weakening. Reuters, the news and media division of Thomson Reuters, is the world’s largest multimedia news provider, reaching billions of people worldwide every day. Reuters provides business, financial, national and international news to professionals via desktop terminals, the world’s media organizations, industry events and directly to consumers. The value of your investment will fluctuate over time, and you may gain or lose money. Stock markets are volatile and can fluctuate significantly in response to company, industry, political, regulatory, market, or economic developments. Investing in stock involves risks, including the loss of principal. Choose from common stock, depository receipt, unit trust fund, real estate investment trusts , preferred securities, closed-end funds, and variable interest entity.
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This is a combination of the model proposed by other previous works. Though we did not see the novelty of this work, we can still conclude that the genetic programming algorithm is admitted in stock market research domain. To reinforce the validation strengths, it would be good to consider adding GP models into evaluation if the model is predicting a specific price. Jeon et al. in performed research on millisecond interval-based big dataset by using pattern graph tracking to complete stock price prediction tasks. The dataset they used is a millisecond interval-based big dataset of historical stock data from KOSCOM, from August 2014 to October 2014, 10G–15G capacity.
Other research has shown that psychological factors may result in exaggerated stock price movements (contrary to EMH which assumes such behaviors ‘cancel out’). Psychological research has demonstrated that people are predisposed to ‘seeing’ patterns, and often will perceive a pattern in what is, in fact, just noise, e.g. seeing familiar shapes in clouds or ink blots. In the present context, this means that a succession https://dotbig.com/markets/stocks/NFLX/ of good news items about a company may lead investors to overreact positively, driving the price up. A period of good returns also boosts the investors’ self-confidence, reducing their risk threshold. The racial composition of stock market ownership shows households headed by whites are nearly four and six times as likely to directly own stocks than households headed by blacks and Hispanics respectively.
This research work also evaluates the best combination of i and j, which has relatively better prediction accuracy, meanwhile, cuts the computational consumption. After the PCA step, the system will get a reshaped matrix with j columns. In this research, we focus on the short-term price https://dotbig.com/ trend prediction. We mark the price trend by comparing the current closing price with the closing price of n trading days ago, the range of n is from 1 to 10 since our research is focusing on the short-term. If the price trend goes up, we mark it as 1 or mark as 0 in the opposite case.
Another well-known method used is fluctuation percentage, and we transform the technical indices fluctuation percentage into the range of [− 1, 1]. The function FE is corresponding to the feature extension block. For the feature extension procedure, we apply three different processing methods to translate the findings from the financial domain to a technical DotBig module in our system design. While not all the indices are applicable for expanding, we only choose the proper method for certain features to perform the feature extension , according to Table2. After performing the data pre-processing part, the last step is to feed the training data into LSTM and evaluate the performance using testing data.