From Big Data to Econophysics and Its Use to Explain Complex Phenomena

Ferreira, Paulo; Pereira, Éder Johnson de Area Leão; Pereira, Hernane Borges de Barros

doi:10.3390/jrfm13070153

Cited by 5 publications

(4 citation statements)

References 105 publications

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“…), and (iii) strong-form tests (do any financial backers have private data that is not completely reflected in market costs?). This work can also be categorized as big data research, as we have considered stock data of the timeframe 2000-2020 [10].…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains

Dey¹,

Hossain²,

Hossain

et al. 2021

Algorithms

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Investors in the stock market have always been in search of novel and unique techniques so that they can successfully predict stock price movement and make a big profit. However, investors continue to look for improved and new techniques to beat the market instead of old and traditional ones. Therefore, researchers are continuously working to build novel techniques to supply the demand of investors. Different types of recurrent neural networks (RNN) are used in time series analyses, especially in stock price prediction. However, since not all stocks’ prices follow the same trend, a single model cannot be used to predict the movement of all types of stock’s price. Therefore, in this research we conducted a comparative analysis of three commonly used RNNs—simple RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU)—and analyzed their efficiency for stocks having different stock trends and various price ranges and for different time frequencies. We considered three companies’ datasets from 30 June 2000 to 21 July 2020. The stocks follow different trends of price movements, with price ranges of $30, $50, and $290 during this period. We also analyzed the performance for one-day, three-day, and five-day time intervals. We compared the performance of RNN, LSTM, and GRU in terms of R2 value, MAE, MAPE, and RMSE metrics. The results show that simple RNN is outperformed by LSTM and GRU because RNN is susceptible to vanishing gradient problems, while the other two models are not. Moreover, GRU produces lesser errors comparing to LSTM. It is also evident from the results that as the time intervals get smaller, the models produce lower errors and higher reliability.

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Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains

Dey¹,

Hossain²,

Hossain

et al. 2021

Algorithms

View full text Add to dashboard Cite

show abstract

“…Since then, many studies have already been developed and financial events explained by physical theories, such as the power law that is widely applied by economists and used as a mathematical basis in the development of new pricing asset models (Gabaix, 2016;Singer, 2020). The popularity of Econophysics increased after successful attempts to price stock exchange assets, as physical models are able to explain high frequency data and randomness patterns (Ferreira et al, 2020;Mainardi, 2020). Volatility is an example of random variable and the statistical metric most analyzed in financial modeling, due to its relevance in portfolio risk management (Cheng, 2020;Stilger et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Can the Heston Model Forecast Energy Generation? A Systematic Literature Review

Reichert

Souza

2022

IJEEP

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The ability to predict the price of stock exchange assets has attracted the attention of economists and physicists around the world, as physical models are useful to predict volatility behaviors. Knowing that volatility is crucial for energy sector planning, the research aim was to investigate whether the Heston pricing model is useful to predict energy generation, trough the steps established by the systematic review protocol. In a corpus of 25 documents, it was possible to identify: lots of financial studies, energy and demography researches; a low level of interaction among universities; the largest number of publications from Australia and China; the most important journal; and the advantages of applying Econophysics models to solve volatility problems. In conclusion, the Heston model can be applied to predict energy generation, since it is a closed-form model and capable of modeling the stochastic volatility, reversing it to the predicted value of average energy generation.

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“…The econophysics literature is currently extremely broad. It covers, not only subjects, such as nonlinear dynamics, chaos and stochastic and diffusive processes, (Mantegna and Stanley 1999), but also more recent topics, such as big data (Ferreira et al 2020). Here, we look at a relatively small sub-field of econophysics, which is that of the applicability of relativity theories to finance, hoping to provide a smooth, yet rigorous, read to both finance professionals and physicists.…”

Section: Introductionmentioning

confidence: 99%

Relativistic Option Pricing

Carvalho

Gaspar

2021

IJFS

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The change of information near light speed, advances in high-speed trading, spatial arbitrage strategies and foreseen space exploration, suggest the need to consider the effects of the theory of relativity in finance models. Time and space, under certain circumstances, are not dissociated and can no longer be interpreted as Euclidean. This paper provides an overview of the research made in this field while formally defining the key notions of spacetime, proper time and an understanding of how time dilation impacts financial models. We illustrate how special relativity modifies option pricing and hedging, under the Black–Scholes model, when market participants are in two different reference frames. In particular, we look into maturity and volatility relativistic effects.

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From Big Data to Econophysics and Its Use to Explain Complex Phenomena

Cited by 5 publications

References 105 publications

Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains

Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains

Can the Heston Model Forecast Energy Generation? A Systematic Literature Review

Relativistic Option Pricing

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