An Efficient Stock Recommendation Model Based on Big Order Net Inflow

Yang, Yujun; Li, Minglu; Yang, Y.B.

doi:10.1155/2016/5725143

Cited by 18 publications

(3 citation statements)

References 24 publications

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“…Fuzzy clustering methods were used to categorize similar investors and stocks were chosen by the stock set that was once operated by a similar investor. This technique proved to show that the recommended stocks have higher gains after the recommendation [9].…”

Section: Literature Reviewmentioning

confidence: 98%

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: 98%

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

Saini

Vaz

2022

jour

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“…For the problem of recommending a single stock to the user, some personalized approaches based on technical analysis have been proposed [1,15] where the trading preferences of the user, in particular their response to technical signals, are learned through feedback on the recommendations. Collaborative Filtering has also been used for personalization, in general combined with other recommendation logics, such as order book analysis [16], contentbased filtering [13] or multiple-criteria decision analysis [7].…”

Section: Related Workmentioning

confidence: 99%

Large-scale recommendation for portfolio optimization

Swezey

Charron

2018

Proceedings of the 12th ACM Conference on Recommender Systems

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Individual investors are now massively using online brokers to trade stocks with convenient interfaces and low fees, albeit losing the advice and personalization traditionally provided by fullservice brokers. We frame the problem faced by online brokers of replicating this level of service in a low-cost and automated manner for a very large number of users. Because of the care required in recommending financial products, we focus on a riskmanagement approach tailored to each user's portfolio and risk profile. We show that our hybrid approach, based on Modern Portfolio Theory and Collaborative Filtering, provides a sound and effective solution. The method is applicable to stocks as well as other financial assets, and can be easily combined with various financial forecasting models. We validate our proposal by comparing it with several baselines in a domain expert-based study. CCS CONCEPTS• Information systems Ñ Recommender systems; • Theory of computation Ñ Convex optimization; • Applied computing Ñ Economics.

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“…Although many algorithms have achieved good results in certain aspects, there are many parameter configurations and data selection problems in the use of machine learning, which is still an important area of research. On the other hand, in the fundamental analysis [16][17][18], people mainly use natural language processing to analyze the company's financial news and financial statements to predict the future stock price trend.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

Yang

Xiao

2020

Complexity

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The stock market is a chaotic, complex, and dynamic financial market. The prediction of future stock prices is a concern and controversial research issue for researchers. More and more analysis and prediction methods are proposed by researchers. We proposed a hybrid method for the prediction of future stock prices using LSTM and ensemble EMD in this paper. We use comprehensive EMD to decompose the complex original stock price time series into several subsequences which are smoother, more regular and stable than the original time series. Then, we use the LSTM method to train and predict each subsequence. Finally, we obtained the prediction values of the original stock price time series by fused the prediction values of several subsequences. In the experiment, we selected five data to fully test the performance of the method. The comparison results with the other four prediction methods show that the predicted values show higher accuracy. The hybrid prediction method we proposed is effective and accurate in future stock price prediction. Hence, the hybrid prediction method has practical application and reference value.

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An Efficient Stock Recommendation Model Based on Big Order Net Inflow

Cited by 18 publications

References 24 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

Large-scale recommendation for portfolio optimization

A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

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