High-performance stock index trading via neural networks and trees

Chalvatzis, Chariton; Hristu-Varsakelis, Dimitrios

doi:10.1016/j.asoc.2020.106567

Cited by 23 publications

(5 citation statements)

References 23 publications

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“…The ability to predict the direction of significant movements of the index future values on a daily basis and thereafter the effectiveness of the proposed trading systems to generate profitable signals (BUY/SELL/WAIT) have been verified over five index futures viz: Dow Jones Industrial Average (DOW JONES), NSE India top 50 companies index (NIFTY50), Banking sectoral index of NSE India (NIFTY BANK), Nasdaq Composite (NASDAQ), and S&P 500. The five index futures employed in the experiments are widely used by researchers 3,5,51,53 . These indices are from different stock exchanges across the globe and represent movements of the financial market due to several global and local factors (such as political factors, economic factors, climatic factors, seasonal factors and govt.…”

Section: Methodsmentioning

confidence: 99%

A hybrid multicategory framework for generating automated trading systems

Kumar

Sahoo

Thakur

2023

Concurrency and Computation

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This work proposes a framework for producing automatic trading systems that suggest making trading decisions in financial markets. The trading systems thus generated utilize multicategory classifiers. A collection of important technical indicators are considered as input features for the underlying multicategory classifiers. The proposed trading systems also have an option to use random forest (RF) algorithms for feature selection. The trading range breakout strategy is used to train the proposed trading systems thus generating “BUY/SELL/WAIT” trading signals on daily open prices. The performances of the proposed trading systems are evaluated over five future indices. Empirical findings suggest that the day trading systems based on the proposed multicategory SVM classifiers along with the RF technique outperform the day trading systems built using the multicategory classifiers taken from the literature. The trading range breakout strategy‐based systems are found to be superior to the traditional BUY‐HOLD strategy and that of RF‐PSVM (BUY/SELL) based strategy.

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Section: Methodsmentioning

confidence: 99%

A hybrid multicategory framework for generating automated trading systems

Kumar

Sahoo

Thakur

2023

Concurrency and Computation

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“…Fed into CNN for pair trading strategy, this helps to improve accuracy and profitability. It is also common to observe LSTM-based strategies, either for converting futures into options (Wu et al 2020), in combination with Autoencoders for training market data (Koshiyama et al 2020), or in more general trade strategy applications (Sun et al 2019;Silva et al 2020;Wang et al 2020;Chalvatzis and Hristu-Varsakelis 2020).…”

Section: Findings: Trade Strategymentioning

confidence: 99%

Deep learning in the stock market—a systematic survey of practice, backtesting, and applications

Olorunnimbe

Viktor

2022

Artif Intell Rev

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The widespread usage of machine learning in different mainstream contexts has made deep learning the technique of choice in various domains, including finance. This systematic survey explores various scenarios employing deep learning in financial markets, especially the stock market. A key requirement for our methodology is its focus on research papers involving backtesting. That is, we consider whether the experimentation mode is sufficient for market practitioners to consider the work in a real-world use case. Works meeting this requirement are distributed across seven distinct specializations. Most studies focus on trade strategy, price prediction, and portfolio management, with a limited number considering market simulation, stock selection, hedging strategy, and risk management. We also recognize that domain-specific metrics such as “returns” and “volatility” appear most important for accurately representing model performance across specializations. Our study demonstrates that, although there have been some improvements in reproducibility, substantial work remains to be done regarding model explainability. Accordingly, we suggest several future directions, such as improving trust by creating reproducible, explainable, and accountable models and emphasizing prediction of longer-term horizons—potentially via the utilization of supplementary data—which continues to represent a significant unresolved challenge.

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“…A backtest was performed on past data [3], comparing the results of traditional strategies (without LSTM integration) with hybridized traditional strategies (with LSTM integration) to see if hybridized strategies are better than simple traditional ones. For an additional comparison of the effectiveness of traditional strategies (without and with LSTM), the "buy and hold" strategy was applied [10].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Traditional Stock Market Strategies Using the LSTM Hybrid Approach

Botunac,

Bosna,

Matetić

2024

Information

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Investment decision-makers increasingly rely on modern digital technologies to enhance their strategies in today’s rapidly changing and complex market environment. This paper examines the impact of incorporating Long Short-term Memory (LSTM) models into traditional trading strategies. The core investigation revolves around whether strategies enhanced with LSTM technology perform better than traditional methods alone. Traditional trading strategies typically depend on analyzing current closing prices and various technical indicators to take trading action. However, by applying LSTM models, this study aims to forecast closing prices with greater accuracy, thereby improving trading performance. Our findings indicate that trading strategies that utilize LSTM models outperform traditional strategies. This improvement suggests a significant advantage in using LSTM models for market prediction and trading decision making. Acknowledging that no one-size-fits-all strategy works for every market condition or stock is crucial. As such, traders are encouraged to select and tailor their strategies based on thorough testing and analysis to best suit their needs and market conditions. This study contributes to a better understanding of how integrating LSTM models can enhance traditional trading strategies, offering a path toward more effective decision making in the unpredictable stock market.

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High-performance stock index trading via neural networks and trees

Cited by 23 publications

References 23 publications

A hybrid multicategory framework for generating automated trading systems

A hybrid multicategory framework for generating automated trading systems

Deep learning in the stock market—a systematic survey of practice, backtesting, and applications

Optimization of Traditional Stock Market Strategies Using the LSTM Hybrid Approach

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