Construction of Investment Strategies for WIG20, DAX and Stoxx600 with Random Forest Algorithm

Tratkowski, Grzegorz

doi:10.1007/978-3-030-43078-8_15

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…The results from the predictive process of this algorithm mostly derive from trees in ranking issues, as well as an average prediction in regression problems, according to Tratkowski (2020). The author also highlights that the random forest algorithm can be used in complex economic environments, considered useful in the formulation of investment strategies.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Investments in Times of Uncertainty: Formation of Portfolios Using Random Forest

Silva¹,

Montebello

Silva

2023

regea

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Objective: based on a systematic approach using machine learning, this research aims to propose a model of selection and allocation of assets that allows for building profitable and safe portfolios, even in times of insecurity and low predictability.Methodology: we used the machine learning algorithm called random forest to associate the independent variables with a dependent one and learn the probability of positive returns in the month following the data collection. According to the probabilities, the stocks were allocated into long, short, or non-allocated portfolios. Finally, we allocated a share of gold, which is a protection asset much used in times of crisis and uncertainty.Results and contributions: the study reached its goal and demonstrated being possible to build profitable and safe investment portfolios, even in times of greater uncertainty and volatility, as in 2020 due to the Covid-19 pandemic. We found that the model is effective in moments of crisis and also of greater predictability, as in the period from 2016 to 2019 when the stock exchange has an uptrend.Relevance: the relevance of this study points to an unprecedented historical context in Brazil, where uncertainties regarding both the local and world economy have demanded advanced studies of prediction to minimize risks and contribute to results for investors. In addition, we highlight that following a short period of low Selic (2019 to 2021), the Central Bank increased the rate again, raising the interest more in profitable and safer assets than the investment in stocks.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Investments in Times of Uncertainty: Formation of Portfolios Using Random Forest

Silva¹,

Montebello

Silva

2023

regea

View full text Add to dashboard Cite

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“…Further, ensemble techniques were adopted for stock price prediction like Random Forest and Gradient Boosting. Random Forest was adopted by Tratkowski (2020) for predicting the stock market in terms of formulating long-term investment strategies. It outperforms other baseline classifiers.…”

Section: Existing Studies In Machine Learning and Financementioning

confidence: 99%

“…First, the existing studies like Wang and Shang (2014) and Tratkowski (2020) have investigated the impact of technical indicators on stock price independently but not in combination with historical stock prices. Technical indicators help provide an estimation of the price trend but, by itself, are not sufficient to forecast the future price trend accurately.…”

Section: Limitations Of Prior Studiesmentioning

confidence: 99%

Deep Neural Network and Time Series Approach for Finance Systems

Srivastava

Zhang

Eachempati

2021

Journal of Organizational and End User Computing

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The stock market is an aggregation of investor sentiment that affects daily changes in stock prices. Investor sentiment remained a mystery and challenge over time, inviting researchers to comprehend the market trends. The entry of behavioral scientists in and around the 1980s brought in the market trading's human dimensions. Shortly after that, due to the digitization of exchanges, the mix of traders changed as institutional traders started using algorithmic trading (AT) on computers. Nevertheless, the effects of investor sentiment did not disappear and continued to intrigue market researchers. Though market sentiment plays a significant role in timing investment decisions, classical finance models largely ignored the role of investor sentiment in asset pricing. For knowing if the market price is value-driven, the investor would isolate components of irrationality from the price, as reflected in the sentiment. Investor sentiment is an expression of irrational expectations of a stock's risk-return profile that is not justified by available information. In this context, the paper aims to predict the next-day trend in the index prices for the centralized Indian National Stock Exchange (NSE) deploying machine learning algorithms like support vector machine, random forest, gradient boosting, and deep neural networks. The training set is historical NSE closing price data from June 1st, 2013-June 30th, 2020. Additionally, the authors factor technical indicators like moving average (MA), moving average convergence-divergence (MACD), K (%) oscillator and corresponding three days moving average D (%), relative strength indicator (RSI) value, and the LW (R%) indicator for the same period. The predictive power of deep neural networks over other machine learning techniques is established in the paper, demonstrating the future scope of deep learning in multi-parameter time series prediction.

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Construction of Investment Strategies for WIG20, DAX and Stoxx600 with Random Forest Algorithm

Cited by 2 publications

References 12 publications

Investments in Times of Uncertainty: Formation of Portfolios Using Random Forest

Investments in Times of Uncertainty: Formation of Portfolios Using Random Forest

Deep Neural Network and Time Series Approach for Finance Systems

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