n. mohammadkarimi

This research work is concerned with the predictability of ensemble and singular tree-based machine learning algorithms during the recession and prosperity of the two companies listed in the Tehran Stock Exchange in the context of big data. In this regard, the main issue is that economic managers and the academic community require predicting models with more accuracy and reduced execution time; moreover, the prediction of the companies recession in the stock market is highly significant. Machine learning algorithms must be able to appropriately predict the stock return sign during the market downturn and boom days. Addressing the stated challenge will upgrade the quality of stock purchases and, subsequently, will increase profitability. In this article, the proposed solution relies on the utilization of tree-based machine learning algorithms in the context of big data. The proposed solution exploits the decision tree algorithm, which is a traditional and singular tree-based learning algorithm. Furthermore, two modern and ensemble tree-based learning algorithms, random forest and gradient boosted tree, has been utilized for predicting the stock return sign during recession and prosperity. The mentioned cases were implemented by applying the machine learning tools in python programming language and PYSPARK library that is used explicitly for the big data context. The utilized research data of the current study are the shares information of two companies of the Tehran Stock Exchange. The obtained results reveal that the applied ensemble learning algorithms have performed better than the singular learning algorithms. Additionally, adding 23 technical features to the initial data and subsequent applying of the PCA feature reduction method have demonstrated the best performance among other modes. In the meantime, it has been concluded that the initial data do not possess the proper resolution or generalizability, either during prosperity or recession.

This paper proposes fuzzy modeling using obtained data. Fuzzy system is known as knowledge-based or rule-bases system. The most important part of fuzzy system is rule-base. One of problems of generation of fuzzy rule with training data is inconsistence data. Existence of inconsistence and uncertain states in training data causes high error in modeling. Here, Probability fuzzy system presents to improvement the above challenge. A zero order Sugeno fuzzy model used as fuzzy system structure. At first by using clustering obtains the number of rules and input membership functions. A set of candidate amounts for consequence parts of fuzzy rules is considered. Considering each pair of training data, according which rules fires and what is the output in the pair, the amount of probability of consequences candidates are change. In the next step, eligibility probability of each consequence candidate for all rules is determined. Finally, using these obtained probability, two probable outputs is generate for each input. The experimental results show superiority of the proposed approach rather than some available well-known approaches that makes reduce the number of rule and reduce system complexity.