Prediction of Investment Patterns Using Data Mining Techniques

Singh, Sakshi; Mittal, Harsh; Purwar, Archana

doi:10.7763/ijcce.2014.v3.309

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…The investment patterns of a person have been found based on their characteristics using fuzzy data mining techniques. The result is in the form of clusters of investment patterns of similar people [8]. Using Big Order Net Inflow of stock, a selection of stocks with a higher value to the net inflow was added to the pre-recommended stock set and presented to the target investor.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Weighted Hybrid Recommendation System using Sharpe Ratio for a Profitable Diversified Investment Portfolio

Saini

Vaz

2022

jour

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Identifying where to invest and how much to invest can be very challenging for common people who have limited knowledge in the domain. Portfolio managers are financial professionals who spend a lot of time and effort to help investors in investing funds and implementing investment strategies, but not all can afford to consult them. The study aims to develop a weighted hybrid recommendation system that recommends an optimized investment portfolio based on the investor’s preferences regarding risk and return. Generally, investors usually ask investment for advice from friends or relatives with similar risk preferences or if they are interested in a particular item, the investors ask for the experience of someone who already has invested in the same item. Therefore, the methodology considers the investor’s past behavior and the past behavior of the nearest neighbor investors with similar risk preferences. Using user-based collaborative filtering the number of stocks is recommended using Pearson correlation based on the investor’s income, then using another user-based collaborative filtering the number of stocks is recommended based on the investor’s age. Weights are assigned to the recommended number of stocks generated based on income and age and their weighted average is finally considered. Finally, the feasibility of the proposed system was assessed through various experiments. Based on the received results, the authors conclude that the proposed weighted hybrid approach is robust enough for implementation in the real world. The novelty of the paper lies in the fact that none of the existing approaches make use of more than one type of weighted recommendation algorithm. Additionally, the final results obtained this way have been never further fortified with the highest Sharpe ratio and minimum risk for the investor. This combination of hybrid and Sharpe ratios has never been explored before.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Weighted Hybrid Recommendation System using Sharpe Ratio for a Profitable Diversified Investment Portfolio

Saini

Vaz

2022

jour

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DBSCANI: Noise-Resistant Method for Missing Value Imputation

Purwar

Singh

2016

Journal of Intelligent Systems

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The quality of data is an important task in the data mining. The validity of mining algorithms is reduced if data is not of good quality. The quality of data can be assessed in terms of missing values (MV) as well as noise present in the data set. Various imputation techniques have been studied in MV study, but little attention has been given on noise in earlier work. Moreover, to the best of knowledge, no one has used density-based spatial clustering of applications with noise (DBSCAN) clustering for MV imputation. This paper proposes a novel technique density-based imputation (DBSCANI) built on density-based clustering to deal with incomplete values in the presence of noise. Density-based clustering algorithm proposed by Kriegal groups the objects according to their density in spatial data bases. The high-density regions are known as clusters, and the low-density regions refer to the noise objects in the data set. A lot of experiments have been performed on the Iris data set from life science domain and Jain’s (2D) data set from shape data sets. The performance of the proposed method is evaluated using root mean square error (RMSE) as well as it is compared with existing K-means imputation (KMI). Results show that our method is more noise resistant than KMI on data sets used under study.

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Prediction of Investment Patterns Using Data Mining Techniques

Cited by 2 publications

References 10 publications

A Novel Weighted Hybrid Recommendation System using Sharpe Ratio for a Profitable Diversified Investment Portfolio

A Novel Weighted Hybrid Recommendation System using Sharpe Ratio for a Profitable Diversified Investment Portfolio

DBSCANI: Noise-Resistant Method for Missing Value Imputation

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