634 research outputs found
A patent time series processing component for technology intelligence by trend identification functionality
Β© 2014, Springer-Verlag London. Technology intelligence indicates the concept and applications that transform data hidden in patents or scientific literatures into technical insight for technology strategy-making support. The existing frameworks and applications of technology intelligence mainly focus on obtaining text-based knowledge with text mining components. However, what is the corresponding technological trend of the knowledge over time is seldom taken into consideration. In order to capture the hidden trend turning points and improve the framework of existing technology intelligence, this paper proposes a patent time series processing component with trend identification functionality. We use piecewise linear representation method to generate and quantify the trend of patent publication activities, then utilize the outcome to identify trend turning points and provide trend tags to the existing text mining component, thus making it possible to combine the text-based and time-based knowledge together to support technology strategy making more satisfactorily. A case study using Australia patents (year 1983β2012) in Information and Communications Technology industry is presented to demonstrate the feasibility of the component when dealing with real-world tasks. The result shows that the new component identifies the trend reasonably well, at the same time learns valuable trend turning points in historical patent time series
Stock trend prediction framework based on line segment algorithm and deep learning
Stock forecasting is a very complicated task due to its noise and volatile characteristics. How to effectively eliminate the noise has attracted attention from both investors and researchers. This report presents a novel de-noise technique named Line Segment Algorithm (LSA). Compared to those signal processing methods, LSA is based on the characteristic of financial time series. First, the algorithm identified the shape patterns of the historical stock price series and labeled them as turning points and false alarms. Then, a stock trend prediction framework was built and trained with the shape patterns extracted by the algorithm. Eventually, the model could predict whether a shape pattern is turning point or not. To evaluate its performance, experiments on the real stock data were carried out in LSTM and Random Forest, respectively. The results show that LSA demonstrates its effectiveness by better accuracy on prediction. It provides a new perspective for stock trend analysis and can be applied in the
actual stock investment trading as well
A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting
[EN] In this paper we propose and validate a trading rule based on flag pattern recognition, incorporating im- portant innovations with respect to the previous research. Firstly, we propose a dynamic window scheme that allows the stop loss and take profit to be updated on a quarterly basis. In addition, since the flag pat- tern is a trend-following pattern, we have added the EMA indicator to filter trades. This technical analysis indicator is calculated both for 15-min and 1-day timeframes, which enables short and medium terms to be considered simultaneously. We also filter the flags according to the price range on which they are de- veloped and have limited the maximum loss of each trade to 100 points. The proposed methodology was applied to 91,309 intraday observations of the DJIA index, considerably improving the results obtained in the previous proposals and those obtained by the buy & hold strategy, both for profitability and risk, and also after taking into account the transaction costs. These results seem to challenge market efficiency in line with other similar studies, in the specific analysis carried out on the DJIA index and is also limited to the setup considered.The fourth author of this work was partially supported by MINECO, Project MTM2016-75963-P.ArΓ©valo, R.; GarcΓa, J.; Guijarro, F.; Peris Manguillot, A. (2017). A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Systems with Applications. 81:177-192. https://doi.org/10.1016/j.eswa.2017.03.0281771928
A self-organizing map analysis of survey-based agents' expectations before impending shocks for model selection: The case of the 2008 financial crisis
This paper examines the role of clustering techniques to assist in the selection of the most indicated method to model survey-based expectations. First, relying on a Self-Organizing Map (SOM) analysis and using the financial crisis of 2008 as a benchmark, we distinguish between countries that show a progressive anticipation of the crisis, and countries where sudden changes in expectations occur. We then generate predictions of survey indicators, which are usually used as explanatory variables in econometric models. We compare the forecasting performance of a multi-layer perceptron (MLP) Artificial Neural Network (ANN) model to that of three different time series models. By combining both types of analysis, we find that ANN models outperform time series models in countries in which the evolution of expectations shows brisk changes before impending shocks. Conversely, in countries where expectations follow a smooth transition towards recession, autoregressive integrated moving-average (ARIMA) models outperform neural networks
A self-organizing map analysis of survey-based agents expectations before impending shocks for model selection: the case of the 2008 financial crisis
Β© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper examines the role of clustering techniques to assist in the selection of the most indicated method to model survey-based expectations. First, relying on a Self-Organizing Map (SOM) analysis and using the financial crisis of 2008 as a benchmark, we distinguish between countries that show a progressive anticipation of the crisis, and countries where sudden changes in expectations occur. We then generate predictions of survey indicators, which are usually used as explanatory variables in econometric models. We compare the forecasting performance of a multi-layer perceptron (MLP) Artificial Neural Network (ANN) model to that of three different time series models. By combining both types of analysis, we find that ANN models outperform time series models in countries in which the evolution of expectations shows brisk changes before impending shocks. Conversely, in countries where expectations follow a smooth transition towards recession, autoregressive integrated moving-average (ARIMA) models outperform neural networks.Peer ReviewedPostprint (published version
ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΎΡΠ±ΠΎΡΠ° ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π΄Π»Ρ Π΄ΠΎΠ»Π³ΠΎΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΠΈΠ½Π΄Π΅ΠΊΡΠ° ΠΠΌΠΌΠ°Π½ΡΠΊΠΎΠΉ ΡΠΎΠ½Π΄ΠΎΠ²ΠΎΠΉ Π±ΠΈΡΠΆΠΈ
Π€ΠΎΠ½Π΄ΠΎΠ²ΡΠ΅ Π±ΠΈΡΠΆΠΈ β Π½Π΅ΠΎΡΡΠ΅ΠΌΠ»Π΅ΠΌΠ°Ρ ΡΠ°ΡΡΡ ΠΌΠΈΡΠΎΠ²ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ; Π±Π»Π°Π³ΠΎΠ΄Π°ΡΡ ΠΎΡΡΠ»Π΅ΠΆΠΈΠ²Π°Π½ΠΈΡ Π΅ΠΆΠ΅Π΄Π½Π΅Π²Π½ΡΡ
ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ, ΡΠΎΠ½Π΄ΠΎΠ²ΡΠ΅ ΠΈΠ½Π΄Π΅ΠΊΡΡ ΠΎΡΡΠ°ΠΆΠ°ΡΡ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π½Π° ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΌ ΡΡΠ½ΠΊΠ΅ ΡΠΈΡΠΌ. ΠΠ»Ρ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΠ½Π΄ΠΎΠ²ΠΎΠ³ΠΎ ΠΈΠ½Π΄Π΅ΠΊΡΠ° ΠΠΎΡΠ΄Π°Π½ΠΈΠΈ Π² Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Ρ ΡΠ°ΠΊΡΠΎΡΡ, Π½Π°ΠΏΡΡΠΌΡΡ Π²Π»ΠΈΡΡΡΠΈΠ΅ Π½Π° ΠΈΠ½Π΄Π΅ΠΊΡ ΡΠΎΠ½Π΄ΠΎΠ²ΠΎΠΉ Π±ΠΈΡΠΆΠΈ. Π§ΡΠΎΠ±Ρ Π²ΡΡΠ²ΠΈΡΡ, ΠΊΠ°ΠΊΠΈΠ΅ ΡΠ΅ΠΊΡΠΎΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ ΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ Π½Π°ΠΈΠ±ΠΎΠ»ΡΡΠ΅Π΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π½Π° ΠΌΠΎΠ΄Π΅Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, Π°Π²ΡΠΎΡΡ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠ»ΠΈ ΡΠ΅ΡΡΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΎΡΠ±ΠΎΡΠ° ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π΄Π»Ρ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΡΠ²ΡΠ·ΠΈ ΠΌΠ΅ΠΆΠ΄Ρ 23 ΡΠ΅ΠΊΡΠΎΡΠ°ΠΌΠΈ ΠΈ ΠΈΠ½Π΄Π΅ΠΊΡΠΎΠΌ ΠΠΌΠΌΠ°Π½ΡΠΊΠΎΠΉ ΡΠΎΠ½Π΄ΠΎΠ²ΠΎΠΉ Π±ΠΈΡΠΆΠΈ (ASEI100) Π·Π° ΠΏΠ΅ΡΠΈΠΎΠ΄ 2008β2018 Π³Π³. Π ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π±ΡΠ»ΠΈ Π²ΡΠ΄Π΅Π»Π΅Π½Ρ 10 Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π·Π½Π°ΡΠΈΠΌΡΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ², ΠΊΠΎΡΠΎΡΡΠ΅ Π·Π°ΡΠ΅ΠΌ ΠΎΠ½ΠΈ Π±ΡΠ»ΠΈ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½Π΅Π½Ρ ΠΈ Π²Π½Π΅ΡΠ΅Π½Ρ Π² ΡΠ°Π±Π»ΠΈΡΡ ΡΠ°ΡΡΠΎΡ. ΠΠ»Ρ ΠΏΡΠΎΠ²Π΅ΡΠΊΠΈ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎΡΡΠΈ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ², ΠΊΠΎΡΠΎΡΡΠ΅ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΡΠΎ Π²ΡΡΡΠ΅ΡΠ°Π»ΠΈΡΡ Π² ΡΠ΅ΡΡ-
ΡΠ΅Ρ
ΠΌΠΎΠ΄Π΅Π»ΡΡ
, Π° ΡΠ°ΠΊΠΆΠ΅ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΈΡ
Π²Π»ΠΈΡΠ½ΠΈΡ Π½Π° ASEI ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈΡΡ ΠΌΠ΅ΡΠΎΠ΄Ρ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΉ ΡΠ΅Π³ΡΠ΅ΡΡΠΈΠΈ ΠΈ ΠΎΠ±ΡΡΠ½ΡΡ
Π½Π°ΠΈΠΌΠ΅Π½ΡΡΠΈΡ
ΠΊΠ²Π°Π΄ΡΠ°ΡΠΎΠ². Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΡΡΡΠ΅ΡΡΠ²ΡΠ΅Ρ ΡΠ΅ΡΡΡ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΡΠ΅ΠΊΡΠΎΡΠΎΠ², Π½Π΅ΠΏΠΎΡΡΠ΅Π΄ΡΡΠ²Π΅Π½Π½ΠΎ Π²Π»ΠΈΡΡΡΠΈΡ
Π½Π° ΠΎΠ±ΡΠΈΠΉ ΡΠΎΠ½Π΄ΠΎΠ²ΡΠΉ ΠΈΠ½Π΄Π΅ΠΊΡ Π² ΠΠΎΡΠ΄Π°Π½ΠΈΠΈ: Π·Π΄ΡΠ°Π²ΠΎΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΠ΅, Π³ΠΎΡΠ½ΠΎΠ΄ΠΎΠ±ΡΠ²Π°ΡΡΠ°Ρ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΡΡΡ, ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²ΠΎ ΠΎΠ΄Π΅ΠΆΠ΄Ρ, ΡΠ΅ΠΊΡΡΠΈΠ»Ρ ΠΈ ΠΈΠ·Π΄Π΅Π»ΠΈΠΉ ΠΈΠ· ΠΊΠΎΠΆΠΈ, Π½Π΅Π΄Π²ΠΈΠΆΠΈΠΌΠΎΡΡΡ, ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΠ΅ ΡΡΠ»ΡΠ³ΠΈ, ΡΡΠ°Π½ΡΠΏΠΎΡΡ. ΠΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΡΡΠΈΡ
ΡΠ΅ΠΊΡΠΎΡΠΎΠ² ΠΌΠΎΠΆΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ Π΄Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ ΠΈΠ½Π΄Π΅ΠΊΡΠ° ΠΠΌΠΌΠ°Π½ΡΠΊΠΎΠΉ ΡΠΎΠ½Π΄ΠΎΠ²ΠΎΠΉ Π±ΠΈΡΠΆΠΈ Π² ΠΠΎΡΠ΄Π°Π½ΠΈΠΈ. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, Π»ΠΈΠ½Π΅ΠΉΠ½Π°Ρ ΡΠ΅Π³ΡΠ΅ΡΡΠΈΡ Π²ΡΡΠ²ΠΈΠ»Π° ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΡΡ Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·Ρ ΠΌΠ΅ΠΆΠ΄Ρ ΡΠ΅ΡΡΡΡ ΡΠ΅ΠΊΡΠΎΡΠ°ΠΌΠΈ (Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΡΠ΅ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅) ΠΈ ASEI (Π·Π°Π²ΠΈΡΠΈΠΌΠ°Ρ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ). ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ, ΠΎΠΏΠΈΡΡΠ²Π°ΡΡΠΈΠ΅ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π²Π°ΠΆΠ½ΡΠ΅ ΡΠ΅ΠΊΡΠΎΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ ΠΠΎΡΠ΄Π°Π½ΠΈΠΈ, ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ ΠΈΠ½Π²Π΅ΡΡΠΎΡΠ°ΠΌΠΈ Π΄Π»Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΡΡ
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