Fluctuation-driven price dynamics and investment strategies

PLOS ONE, Nov 2019

Investigation of the driven mechanism of the price dynamics in complex financial systems is important and challenging. In this paper, we propose an investment strategy to study how dynamic fluctuations drive the price movements. The strategy is successfully applied to different stock markets in the world, and the result indicates that the driving effect of the dynamic fluctuations is rather robust. We investigate how the strategy performance is influenced by the market states and optimize the strategy performance by introducing two parameters. The strategy is also compared with several typical technical trading rules. Our findings not only provide an investment strategy which extends investors’ profits, but also offer a useful method to look into the dynamic properties of complex financial systems.

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Fluctuation-driven price dynamics and investment strategies

December Fluctuation-driven price dynamics and investment strategies Yan Li 0 Bo Zheng 0 Ting-Ting Chen 0 Xiong-Fei Jiang 0 Alejandro Raul Hernandez Montoya, Universidad Veracruzana, MEXICO 0 Department of Physics, Zhejiang University , Hangzhou 310027 , P.R. China , 2 Collaborative Innovation Center of Advanced Microstructures , Nanjing 210093 , P.R. China , 3 School of Information Engineering, Ningbo Dahongying University , Ningbo 315175 , P.R. China Investigation of the driven mechanism of the price dynamics in complex financial systems is important and challenging. In this paper, we propose an investment strategy to study how dynamic fluctuations drive the price movements. The strategy is successfully applied to different stock markets in the world, and the result indicates that the driving effect of the dynamic fluctuations is rather robust. We investigate how the strategy performance is influenced by the market states and optimize the strategy performance by introducing two parameters. The strategy is also compared with several typical technical trading rules. Our findings not only provide an investment strategy which extends investors' profits, but also offer a useful method to look into the dynamic properties of complex financial systems. - Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: Bo Zheng was supported by National Natural Science Foundation of China under Grant Nos. 11375149, 11775186, and Xiong-Fei Jiang was supported by National Natural Science Foundation of China under Grant No. 11505099. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Introduction Financial markets, as a typical complex dynamic system with many-body interactions, have drawn much attention of scientists from different fields during the past decades and much progress has been achieved [1±10]. Quantification of the price dynamics in financial markets would provide a great basis for deepening our understanding of the financial market behaviours [8, 11±20]. There have been various approaches in researches on the comprehension of financial markets. Recently, it is reported that massive data sources, such as Twitter and Google Trends, can be linked to the transaction frequency and price movements in the stock markets [21±24]. Since changes in these ªbig dataº can be interpreted as early signals of market moves, several hypothetical strategies have been constructed for validation of this argument [25±28]. The empirical analysis of financial time series' properties provides new insights into the non trivial nature of the stochastic process of stock prices [29±34]. Besides, some agent-based modeling methods have been proposed to investigate the role of heterogeneity of agents with respect to the price dynamics [35±41]. The temporal correlation functions can be used to characterize the dynamic properties of the financial markets [ 42, 43 ]. Since the autocorrelating time of returns is extremely short, which is on the minute time scale, our understanding on the movement of the price return itself is limited. Understanding the driven mechanism of the price dynamics in financial markets is important and challenging. Recently, a dynamic observable nonlocal in time is constructed to explore the correlation between past volatilities and future returns [ 42 ]. This nonlocal correlation is designated as the ªfluctuation-driven effectº, which may be concerning the nonstationary dynamic property of the complex systems [ 44 ]. In this paper, we construct an investment strategy to study how dynamic fluctuations drive the price movements in stock markets. We should emphasize that the fluctuation-driven effect based strategy is different from other information-driven strategies. It is constructed from the perspective of the internal price dynamics in the financial markets instead of the external information such as search volumes or investors' sentiments. With the strategy, we not only advance our understanding to the financial markets but also provide a concrete application for financial practitioners. According to the efficient market hypothesis [ 45 ], the strategies based on the analysis of historical price movements should not be useful because all agents were rational and able to respond promptly to all market information so that there will be no arbitrage opportunity. However, accumulating evidences are presented against this hypothesis [32, 46±49]. Some technical trading rules have been proved to be effective [50±56]. Different algorithms are utilized to forecast the price movements and quantify the price dynamics [57±60]. Various researches have suggested that trading strategies can be regarded not only as a technique to generate excessive trading profits but also as a powerful instrument to (...truncated)


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Yan Li, Bo Zheng, Ting-Ting Chen, Xiong-Fei Jiang. Fluctuation-driven price dynamics and investment strategies, PLOS ONE, 2017, Volume 12, Issue 12, DOI: 10.1371/journal.pone.0189274