Multi indicator approach via mathematical inference for price dynamics in information fusion context

Iovane, Gerardo; Amorosia, A.; Leone, Marco; Nappi, Michele; Tortora, Genoveffa

doi:10.1016/j.ins.2016.08.063

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…Based on the prospect theory [95] and price fluctuations being considered as a dynamic process, Ref. [96] proposed to develop a multiparametric analysis framework for decision making in financial investments (MIAMI model) on short time-frames; the approaches including knowledge discovery, technical analysis, information fusion, and soft computing were applied along with fuzzification such that the contributions were fused in energy and entropy decision variables to derive useful trend analysis. While fusion–fission approach was applied to study and predict a market crash [94] , a potential integration of such statistical approaches can be desired for higher-level of fusion.…”

Section: Discussionmentioning

confidence: 99%

Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions

2021

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Investment in a financial market is aimed at getting higher benefits; this complex market is influenced by a large number of events wherein the prediction of future market dynamics is challenging. The investors’ etiquettes towards stock market may demand the need of studying various associated factors and extract the useful information for reliable forecasting. Fusion can be considered as an approach to integrate data or characteristics, in general, and enhance the prediction based on the combinational approach that can aid each other. We conduct a systematic approach to present a survey for the years 2011–2020 by considering articles that have used fusion techniques for various stock market applications and broadly categorize them into information fusion, feature fusion, and model fusion. The major applications of stock market include stock price and trend prediction, risk analysis and return forecasting, index prediction, as well as portfolio management. We also provide an infographic overview of fusion in stock market prediction and extend our survey for other finely addressed financial prediction problems. Based on our surveyed articles, we provide potential future directions and concluding remarks on the significance of applying fusion in stock market.

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Section: Discussionmentioning

confidence: 99%

Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions

2021

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“…The idea of the granular models introduced by Pedrycz [30] illustrates that generalizations of numerical models are formed as a result of an optimal allocation of information granularity. Specifically, information fusion for stock price prediction is a multidisciplinary research field involving integration of information from multiple sources for data mining (subsuming statistics and machine learning), signal processing, text mining, knowledge discovery, and expert systems modeling [17] , [32] . However, using multiple data sources instead of a single source is a considerable challenge because solving this problem requires not only improving the efficiency of information fusion, but also dealing with high levels of uncertainty, complexity [24] , nonlinearity [33] , and the dynamism of the market itself.…”

Section: Introductionmentioning

confidence: 99%

Attention enhanced long short-term memory network with multi-source heterogeneous information fusion: An application to BGI Genomics

Zhang

Yang

Zhou

2021

Information Sciences

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The recent availability of enormous amounts of both data and computing power has created new opportunities for predictive modeling. This paper compiles an analytical framework based on multiple sources of data including daily trading data, online news, derivative technical indicators, and time-frequency features decomposed from closing prices. We also provide a real-life demonstration of how to combine and capitalize on all available information to predict the stock price of BGI Genomics. Moreover, we apply a long short-term memory (LSTM) network equipped with an attention mechanism to identify long-term temporal dependencies and adaptively highlight key features. We further examine the learning capabilities of the network for specific tasks, including forecasting the next day’s price direction and closing price and developing trading strategies, comparing its statistical accuracy and trading performance with those of methods based on logistic regression, support vector machine,gradient boosting decision trees, and the original LSTM model. The experimental results for BGI Genomics demonstrate that the attention enhanced LSTM model remarkably improves prediction performance through multi-source heterogeneous information fusion, highlighting the significance of online news and time-frequency features, as well as exemplifying and validating our proposed framework.

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“…Description of complex systems dynamics, from financial markets [19,20,21] to the neural networks of living beings [22,23], require appropriate mathematical tools. Among them there are stochastic processes, Information Theory and statistical methods and recently, fuzzy numbers [24,25].…”

Section: Introductionmentioning

confidence: 99%

“…This is because in these processes when describing spikes arrival times, current time and the time from the last spike is primarily taken into account [18]. Description of complex systems dynamics, from financial markets [19,20,21] to the neural networks of living beings [22,23], require appropriate mathematical tools. Among them there are stochastic processes, Information Theory and statistical methods and recently, fuzzy numbers [24,25].…”

Section: Introductionmentioning

confidence: 99%

Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels

Pręgowska¹

2020

Preprint

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(1) Background: In nervous system information is conveyed by a sequence of action potentials, called spikes-trains. As MacKay and McCulloch proposed, spike-trains can be represented as bits sequences coming from Information Sources. Previously, we studied relations between Information Transmission Rates (ITR) carried out by the spikes, their correlations, and frequencies. Here, we concentrate on the problem of how spikes fluctuations affect ITR. (2) Methods: The Information Theory Method developed by Shannon is applied. Information Sources are modeled as stationary stochastic processes. We assume such sources as two states Markov processes. As a spike-trains' fluctuation measure, we consider the Standard Deviation sigma, which, in fact, measures average fluctuation of spikes around the average spike frequency. (3) Results: We found that character of ITR and signal fluctuations relation strongly depends on the parameter s which is a sum of transitions probabilities from no spike state to spike state and vice versa. It turned out that for smaller s (s<1) the quotient ITR\sigma has a maximum and can tend to zero depending on transition probabilities. While for s large enough (1<s) the ITR\sigma is separated from 0 for each s. Similar behavior was observed also when we replaced Shannon entropy terms in Markov entropy formula by their approximation with polynomials. We also show that the ITR quotient by Variance behaves in a completely different way. (4) Conclusions: Our results show that for large transition parameter s the Information Transmission Rate by sigma will never decrease to zero. Specifically, for 1<s<1.7 the ITR will be always, independently on transition probabilities which form this s, above the level of fluctuations, i.e. in this case we have sigma<ITR. Thus, we conclude that in a more noisy environment, to get appropriate reliability and efficiency of transmission, Information Sources with higher tendency of transition from the state no spike to spike state and vice versa should be applied.

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Multi indicator approach via mathematical inference for price dynamics in information fusion context

Cited by 8 publications

References 26 publications

Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions

Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions

Attention enhanced long short-term memory network with multi-source heterogeneous information fusion: An application to BGI Genomics

Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels

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