The Cross-Sectional Intrinsic Entropy—A Comprehensive Stock Market Volatility Estimator

Vințe, Claudiu; Ausloos, Marcel

doi:10.3390/e24050623

Cited by 4 publications

(2 citation statements)

References 45 publications

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“…To measure the market uncertainty related to price volatility, various variants of entropy have been proposed based on information entropy. Claudiu Vinte introduced the approach of cross-sectional intrinsic entropy to estimate the uncertainty in stock markets [ 10 ]. This approach takes into account the trading volume and price movements of various assets, allowing for a more holistic understanding of market dynamics.…”

Section: Introductionmentioning

confidence: 99%

Transaction Entropy: An Alternative Metric of Market Performance

Zhong,

Liang,

Wang

2023

Entropy

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Market uncertainty has a significant impact on market performance. Previous studies have dedicated much effort towards investigations into market uncertainty related to information asymmetry and risk. However, they have neglected the uncertainty inherent in market transactions, which is also an important aspect of market performance, besides the quantity of transactions and market efficiency. In this paper, we put forward a concept of transaction entropy to measure market uncertainty and see how it changes with price. Transaction entropy is defined as the ratio of the total information entropy of all traders to the quantity of transactions, reflecting the level of uncertainty in making successful transactions. Based on the computational and simulated results, our main finding is that transaction entropy is the lowest at equilibrium, it will decrease in a shortage market, and increase in a surplus market. Additionally, we make a comparison of the total entropy of the centralized market with that of the decentralized market, revealing that the price-filtering mechanism could effectively reduce market uncertainty. Overall, the introduction of transaction entropy enriches our understanding of market uncertainty and facilitates a more comprehensive assessment of market performance.

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Section: Introductionmentioning

confidence: 99%

Transaction Entropy: An Alternative Metric of Market Performance

Zhong,

Liang,

Wang

2023

Entropy

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“…Since market indices started to be traded on the exchanges as regular securities, they became one the most, if not the most, sought-after assets in portfolios by individual and institutional investors alike, investment funds, pension funds, etc. The attractiveness of stock market indices is rightly justified due to their relatively broad base of constituents and, corroborated with this, a historically proven lower exposure to risk compared to the market as a whole (Vințe and Ausloos 2022). It is as if portfolio selection is already solved by owning a single asset that offers exposure to many stocks.…”

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confidence: 98%

Portfolio Volatility Estimation Relative to Stock Market Cross-Sectional Intrinsic Entropy

Vințe

Ausloos

2023

JRFM

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Selecting stock portfolios and assessing their relative volatility risk compared to the market as a whole, market indices, or other portfolios is of great importance to professional fund managers and individual investors alike. Our research uses the cross-sectional intrinsic entropy (CSIE) model to estimate the cross-sectional volatility of the stock groups that can be considered together as portfolio constituents. The CSIE market volatility estimate is based on daily traded prices—open, high, low, and close (OHLC)—along with the daily traded volume for symbols listed on the considered market. In our study, we benchmark portfolio volatility risks against the volatility of the entire market provided by the CSIE and the volatility of market indices computed using longitudinal data. This article introduces CSIE-based betas to characterise the relative volatility risk of the portfolio against market indices and the market as a whole. We empirically prove that, through CSIE-based betas, multiple sets of symbols that outperform the market indices in terms of rate of return while maintaining the same level of risk or even lower than the one exhibited by the market index can be discovered, for any given time interval. These sets of symbols can be used as constituent stock portfolios and, in connection with the perspective provided by the CSIE volatility estimates, to hierarchically assess their relative volatility risk within the broader context of the overall volatility of the stock market.

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CEGH: A Hybrid Model Using CEEMD, Entropy, GRU, and History Attention for Intraday Stock Market Forecasting

Liu

Zhang

et al. 2022

Entropy

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Intraday stock time series are noisier and more complex than other financial time series with longer time horizons, which makes it challenging to predict. We propose a hybrid CEGH model for intraday stock market forecasting. The CEGH model contains four stages. First, we use complete ensemble empirical mode decomposition (CEEMD) to decompose the original intraday stock market data into different intrinsic mode functions (IMFs). Then, we calculate the approximate entropy (ApEn) values and sample entropy (SampEn) values of each IMF to eliminate noise. After that, we group the retained IMFs into four groups and predict the comprehensive signals of those groups using a feedforward neural network (FNN) or gate recurrent unit with history attention (GRU-HA). Finally, we obtain the final prediction results by integrating the prediction results of each group. The experiments were conducted on the U.S. and China stock markets to evaluate the proposed model. The results demonstrate that the CEGH model improved forecasting performance considerably. The creation of a collaboration between CEEMD, entropy-based denoising, and GRU-HA is our major contribution. This hybrid model could improve the signal-to-noise ratio of stock data and extract global dependence more comprehensively in intraday stock market forecasting.

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The Cross-Sectional Intrinsic Entropy—A Comprehensive Stock Market Volatility Estimator

Cited by 4 publications

References 45 publications

Transaction Entropy: An Alternative Metric of Market Performance

Transaction Entropy: An Alternative Metric of Market Performance

Portfolio Volatility Estimation Relative to Stock Market Cross-Sectional Intrinsic Entropy

CEGH: A Hybrid Model Using CEEMD, Entropy, GRU, and History Attention for Intraday Stock Market Forecasting

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