Using intelligent computing and data stream mining for behavioral finance associated with market profile and financial physics

Lin, Chung‐Yen; Chen, Chun Sheng; Chen, An-Pin

doi:10.1016/j.asoc.2017.08.008

Cited by 17 publications

(5 citation statements)

References 14 publications

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“…Bazetska et al (2021) argue that the economic subject is significantly influenced by psychological factors such as the phase of his/her life, temperament type, psychological type, archetype, and metaprograms. Lin et al (2018) state that due to the high level of complexity of forecasting trading trends, applying traditional financial analysis and technical analysis indicators to predict short-term market trends is often ineffective. The main reason is that trading behavior is influenced by psychological factors, such as greed and fear, that influence investors during the execution of trading transactions.…”

Section: Literature Reviewmentioning

confidence: 99%

Drivers of S&P 500’s Profitability: Implications for Investment Strategy and Risk Management

Nagy,

Valaskova,

Kovalova

et al. 2024

Economies

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The financial markets, shaped by dynamic forces, including macroeconomic trends and technological advancements, are influenced by a multitude of factors impacting the S&P 500 stock index, a pivotal indicator in the US equity markets. This paper highlights the significance of understanding the exogenous variables affecting the index’s profitability for academics, portfolio managers, and investment professionals. Amid the global ramifications of the S&P 500, particularly in combating the eroding purchasing power caused by inflation, investing in stock indexes emerges as a means to safeguard wealth. The study employs various statistical techniques, emphasizing a methodical approach to uncover influential variables, and using static regression and autoregressive models for immediate and time-lagged effects. In conclusion, the findings have broad practical implications beyond investment strategy, extending to portfolio construction and risk management. Acknowledging inherent uncertainties in financial market forecasts, future research endeavors should target long-term trends, specific influences, and the impact of exchange rate fluctuations on index evolution. Collaboration across regulatory bodies, academia, and the financial industry is underscored, holding the potential for effective risk monitoring and bolstering overall economic and financial market stability. This research serves as a foundational step towards enhancing market understanding and facilitating more efficient investment decision-making approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Drivers of S&P 500’s Profitability: Implications for Investment Strategy and Risk Management

Nagy,

Valaskova,

Kovalova

et al. 2024

Economies

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show abstract

“…Sezer et al [14] proposed CNN-TA using a 2-D convolutional neural network based on image processing properties. By converting 15 different technical indicators financial time series into 2-D images.…”

Section: Figure 2 Illustration Of the Cnn Architecturementioning

confidence: 99%

“…Lin et al [14] used the market profile theory and neural network to build the model that conducted empirical experiments on intraday trading. They adopted values of POC, VA, price range, and tail as the input and fed into fully connected neural networks for forecasting a short-term market trend.…”

Section: Figure 5 Trend Reversals Example With Buying Tail and Selling Tailmentioning

confidence: 99%

Identifying Financial Market Trend Reversal Behavior With Structures of Price Activities Based on Deep Learning Methods

Chen

2022

IEEE Access

Self Cite

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In recent years, with the popularity of artificial intelligence (AI) applications, financial market forecasting based on deep learning models has gotten more attention in academia and industry. According to statistics, a long short-term memory network (LSTM) is the first choice for deep learning to deal with the financial time series predicting problems due to its internal memory that can process incoming input with the previous state. However, most of the research used the raw financial time series data composed of opening, closing, highest, lowest price, and transactional time as learning features to feed into models. This study proposed a novel approach focusing on appropriate structures that can organize the price activities as a bell shape curve and identify the greed and fear in the market with deep convolutional neural networks. In addition, this study also considered the influence of time causality on patterns. It is difficult to accurately catch the rapid changes in the market, especially the causal link between various patterns and trend reversal behavior while using only static features extracted by AI models. We designed disparate methods while generating images that could keep the time-variant features on structures and be extracted by convolutional neural networks. Each image was labeled as the upward trend to downward trend, downward trend to upward trend, and trend did not change according to reversals of overall price direction. Finally, to evaluate the availability of the trained model, two-stage experiments were carried out. The first stage of the experiment mainly evaluated the accuracy and profitability of trades following the models, and the second stage considered practical trading rules such as the stop-loss mechanism. The results of the first and second stages showed the proposed models had better profitability and extremely well-matched classification capabilities when compared with the state-of-theart deep learning models.

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“…In general terms we can point out that prediction accuracy increases when the available information is more abundant, as expected. This is because the longer we wait, the more information we have and the higher resistance of our prediction to noise [23,31]. Secondly, the return predictabil-ity is particularly important in the shorter forecast horizon.…”

Section: Case Study: Ibex 35mentioning

confidence: 99%

Early classification of time series using multi-objective optimization techniques

Mori

Mendiburu

Miranda

et al. 2019

Information Sciences

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Using intelligent computing and data stream mining for behavioral finance associated with market profile and financial physics

Cited by 17 publications

References 14 publications

Drivers of S&P 500’s Profitability: Implications for Investment Strategy and Risk Management

Drivers of S&P 500’s Profitability: Implications for Investment Strategy and Risk Management

Identifying Financial Market Trend Reversal Behavior With Structures of Price Activities Based on Deep Learning Methods

Early classification of time series using multi-objective optimization techniques

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