Financial time series forecasting using rough sets with time-weighted rule voting

Podsiadło, Mariusz; Rybiński, Henryk

doi:10.1016/j.eswa.2016.08.066

Cited by 37 publications

(17 citation statements)

References 54 publications

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“…Both, the classification accuracy and financial performance of the examined models were tested using large real life data sets of several well known market-neutral indices. This work extends also on research described in [12] by considering Fuzzy Rough Sets.…”

Section: Introductionsupporting

confidence: 57%

“…The latter required a calibration sample, thus increasing the time distance between the training and test samples. One can conclude that consideration of time distance as a factor in the classification process would most likely improve it [12]. It is also worth noticing that both, DAX and S&P500 had a strong upwards trend in more than a half of the testing period, whereas HSI growth was relatively moderate with a sidewards trend in the same time period (see Fig.…”

Section: Experiments Resultsmentioning

confidence: 92%

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Application of Fuzzy Rough Sets to Financial Time Series Forecasting

Podsiadło¹,

Rybiński

2015

Lecture Notes in Computer Science

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This paper investigates experimentally the feasibility of Fuzzy Rough Sets in building trend prediction models for financial time series, as related research is scarce. Aside of the standard classification accuracy measures, financial profit and loss backtesting using a sample market timing strategy was performed, and profit related quality of the tested methods compared against that of buy&hold strategy applied to the used market indices. The experiments show that Fuzzy Rough Sets models present a viable basis for forecasting market movement direction and thus can support profitable market timing strategies.

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Section: Introductionsupporting

confidence: 57%

Section: Experiments Resultsmentioning

confidence: 92%

Application of Fuzzy Rough Sets to Financial Time Series Forecasting

Podsiadło¹,

Rybiński

2015

Lecture Notes in Computer Science

Self Cite

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“…Bollen et al [250] analyzed moods of Tweets and based on their investigations they could predict daily up and down changes in Dow Jones Industrial Average values with an accuracy of 87.6%. Some qualitative method is developed for the prediction of stock market trend including using the concept of dynamical Bayesian factor graph [251], the adaptive time-weighted rule voting model [252], the ensemble version of empirical mode decomposition and adding look-ahead bias [253]. With this regard, many machine learning approaches are also used to improve the prediction results.…”

Section: Trend Prediction In the Stock Marketmentioning

confidence: 99%

Structure-oriented prediction in complex networks

2018

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Complex systems are extremely hard to predict due to its highly nonlinear interactions and rich emergent properties. Thanks to the rapid development of network science, our understanding of the structure of real complex systems and the dynamics on them has been remarkably deepened, which meanwhile largely stimulates the growth of effective prediction approaches on these systems. In this article, we aim to review different networkrelated prediction problems, summarize and classify relevant prediction methods, analyze their advantages and disadvantages, and point out the forefront as well as critical challenges of the field. Contents

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“…The proposed methodology is developed to cater to diverse market conditions. Podsiadlo and Rybinski in 2016 set out to experimentally determine the feasibility of rough sets to build productive prediction models [33]. In 2016 Chiang et al proposed a dynamic stock prediction system using Predicted Square Error (PSE) and neural network [34].…”

Section: Artificial Intelligence Systems With Trading Rulesmentioning

confidence: 99%

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

et al. 2018

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Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges—New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies—Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

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Financial time series forecasting using rough sets with time-weighted rule voting

Cited by 37 publications

References 54 publications

Application of Fuzzy Rough Sets to Financial Time Series Forecasting

Application of Fuzzy Rough Sets to Financial Time Series Forecasting

Structure-oriented prediction in complex networks

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

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