Jeon https://dotbig.com/ et al.

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 https://dotbig.com/ 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.

• TMX facilitates fully electronic trading on Canada’s premier equities Exchanges, Toronto Stock Exchange , TSX Venture Exchange and TSX Alpha Exchange .
• Commodity and historical index data provided by Pinnacle Data Corporation.
• One or more NASDAQ market makers will always provide a bid and ask the price at which they will always purchase or sell ‘their’ stock.
• After we get the best combination of i and j, we process the data into finalized the feature set and feed them into the LSTM model to get the price trend prediction result.
• According to the previous works, some researchers who applied both financial domain knowledge and technical methods on stock data were using rules to filter the high-quality stocks.

The dataset of this work consists of five well-known stock market indices, i.e., SSE Composite Index , CSI 300 Index , All Ordinaries Index , Nikkei 225 Index , and Dow Jones Index . Evaluation of the model was based on different stock market indices, and the result was convincing https://dotbig.com/markets/stocks/QCOM/ with generality. By using Rough Set for optimizing the feature dimension before processing reduces the computational complexity. However, the author only stressed the parameter adjustment in the discussion part but did not specify the weakness of the model itself.

Stocks (Mac)

We note the feature selection dataset and model testing dataset as DS_test_f and DS_test_m, respectively. Financial https://dotbig.com/ ratios of a listed company are used to present the growth ability, earning ability, solvency ability, etc.

We set the parameter to retain i numbers of features, and at each iteration of feature selection retains Si top-ranked features, then refit the model and assess the performance again to begin another iteration. The ranking algorithm will eventually determine the top https://dotbig.com/markets/stocks/QCOM/ Si features. Normalize method preserves the relative frequencies of the terms, and transform the technical indices into the range of . Max-min (or min-max) scaling is a transformation method often used as an alternative to zero mean and unit variance scaling.

The Long Short-term Memory is different from other NNs, and it is a variant of standard RNN, which also has time steps with memory and gate architecture. In the previous work , the author performed an analysis Stock Price Online of the RNN architecture complexity. They introduced a method to regard RNN as a directed acyclic graph and proposed a concept of recurrent depth, which helps perform the analysis on the intricacy of RNN.

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As explained, the process of recursive feature elimination can be split into the ranking algorithm, resampling, and external validation. It is known that the training DotBig cost of deep learning models is expansive in both time and hardware aspects; another advantage of our system design is the optimization procedure—PCA.

The authors have introduced the description of the background knowledge in detail. While the weakness of their work DotBig is that it is a lack of data set description. This is a combination of the model proposed by other previous works.

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It can retain the principal components of the features while reducing the scale of the feature matrix, thus help the system to save the training cost of processing the large time-series feature matrix. The very first step before leveraging PCA is feature pre-processing. qcom Because some of the features after RFE are percentage data, while others are very large numbers, i.e., the output from RFE are in different units. Thus, before feeding the data into the PCA algorithm , a feature pre-processing is necessary.

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Different from the prediction model, the authors more focused on stock rankings, selection, and performance evaluation. But in the model validation part, they did not compare the model with existed algorithms but the statistics of the benchmark, which made it challenging to identify if GA would outperform other algorithms. From the result of the previous subsection, we can see that when predicting the price trend for every other day or biweekly, the best result is achieved by selecting a large number of features. Within the selected features, some features DotBig processed from extension methods have better ranks than original features, which proves that the feature extension method is useful for optimizing the model. We involved an evaluation of how feature extension affects RFE and use the test result to measure the improvement of involving feature extension. Based on the literature review, we select the most commonly used technical indices and then feed them into the feature extension procedure to get the expanded feature set. We will select the most effective i features from the expanded feature set.

], many studies have shown a marked tendency for the stock market to trend over time periods of weeks or longer. Various explanations for such large and apparently non-random price movements have been promulgated. For instance, some research has shown that changes qcom stock in estimated risk, and the use of certain strategies, such as stop-loss limits and value at risk limits, theoretically could cause financial markets to overreact. But the best explanation seems to be that the distribution of stock market prices is non-Gaussian .

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