On the Identifiability of Artificial Financial Time Series
學年 112
學期 2
出版（發表）日期 2024-05-01
作品名稱 On the Identifiability of Artificial Financial Time Series
作品名稱（其他語言）
著者 Bo-Hsin Lin, Chuang-Chieh Lin, Chih-Chieh Hung, Chien-Chang Chen and Yu-Hsin Chen
單位
出版者
著錄名稱、卷期、頁數 Journal of Information Science and Engineering, 40(3), p.567-579
摘要 Financial time series are often considered to be difficult to model and unlikely to predict. In this study, we assume that financial time series are based on a stochastic series generated by a Markov decision process. Based on this assumption, we investigate two problems related to the identification of the price time series of financial instruments. We try to distinguish the real price-volume time series from the artificial one. First, we investigate whether there is any machine learning model that can distinguish between real price-volume time series and those with time horizon reversed. Then, we investigate whether there is any machine learning model that can distinguish the price-volume time series from the real one when they are subjected to random manipulations of different proportions. The data we use are the daily prices and trading volumes of six U.S. stocks and one crypto-currency BTC/USD. We apply Long-Short Term Memory (LSTM) as the main machine learning model for the binary classification due to its success in fitting time series data. Based on the experimental results, we give positive answers to the above two questions. Our results also partially support the conjecture that the dynamics of a financial time series are driven by an underlying Markov decision processes.
關鍵字 machine learning;long-short term memory;predictability;Markov decision process;price-volume data
語言 en_US
ISSN 1016-2364
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Lin, Chuang-chieh; Chen, Chien-chang
審稿制度 是
國別 TWN
公開徵稿
出版型式 ,電子版,紙本
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/125089 )

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