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Tvorba jezika japonskih svečnikov in uporaba NLP algoritma Word2Vec za napovedovanje trendov gibanja vrednosti delnic
Savić, Boris (Author), Lavbič, Dejan (Mentor) More about this mentor... This link opens in a new window

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Abstract
V magistrskem delu rešujemo problem napovedovanja prihodnjih trendov vrednostnih papirjev s pomočjo strojnega učenja. Predstavimo nov inovativen model napovedovanja, ki temelji na uporabi japonskih svečnikov ter na NLP algoritmu Word2Vec. V delu pokažemo, da je možno iz zaporedja japonskih svečnikov tvoriti preprost jezik, katerega slovar je v primerjavi z naravnim jezikom sicer precej omejen. Z obdelavo Word2Vec sistem naučimo kontekst posameznih besed ter ta kontekst uporabimo pri gradnji napovedi. Napovedni model testiramo s simulatorjem trgovanja, ki v svojem delovanju upošteva tudi stroške trgovalne provizije. Rezultate predlaganega napovednega modela primerjamo s tremi osnovnimi modeli: Kupi in zadrži, Tekoča povprečja ter MACD. V analizi pokažemo, da predlagani napovedni model v okviru zastavljene trgovalne strategije znotraj testnega obdobja ustvari dobiček ter deluje mnogo bolje od prej omenjenih napovednih modelov. Delovanje modela preverimo tudi v validacijskem obdobju, kjer ravno tako dosežemo zadovoljive rezultate.

Language:Slovenian
Keywords:borza, NLP, Word2Vec, japonski svečniki, strojno učenje
Work type:Master's thesis/paper (mb22)
Organization:FRI - Faculty of computer and information science
Year:2016
Views:755
Downloads:478
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Secondary language

Language:English
Title:The formation of Japanese candlesticks language and using NLP algorithm Word2Vec for shares trend forecasting
Abstract:
Our primary objective is predicting the future trends for stock market data with machine learning. We present a new innovative prediction model, based on centuries old Japanese candlesticks and modern NLP algorithm Word2Vec. Suggested model constructs a simple language of Japanese candlesticks with a very limited vocabulary. In the following steps the prediction model uses Word2Vec to discover semantic context of each word within the language vocabulary. To test the model we develop a simulation tool that takes into account the most important aspect of stock market trading -- trade fees. To compare the success of the suggested prediction model we also develop simple TA models such as: Buy and Hold, Simple Moving Averages and MACD. Analysis of the results show the superiority of suggested prediction model over previously mentioned models in the test data-set. Additional testing is done with validation data-set in order to verify the results.

Keywords:stock market, NLP, Word2Vec, candlesticks, machine learning

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