financeФинансы: теория и практика/Finance: Theory and PracticeFinance: Theory and Practice2587-56712587-7089Financial University under The Government of Russian Federation10.26794/2587-5671-2022-26-4-267-276finance-1740Research ArticleИНВЕСТИЦИОННАЯ ПОЛИТИКАINVESTMENT POlICYНовая взвешенная гибридная система рекомендаций с использованием коэффициента Шарпа для прибыльного диверсифицированного инвестиционного портфеляA Novel Weighted Hybrid Recommendation System using Sharpe Ratio for a Profitable Diversified Investment Portfoliohttps://orcid.org/0000-0001-5205-5263СайниДж. Р.SainiJ. R.

Джатиндеркумар Р. Сайни - доктор философии, профессор, директор

Пуна

Jatinderkumar R. Saini - PhD, Prof., Director, Symbiosis Institute of Computer Studies and Research

Pune

saini_expert@yahoo.comhttps://orcid.org/0000-0002-0417-4070ВазК.VazC.

Корин Ваз — MBA

Пуна

Махараштра

Corinne Vaz - MBA

Pune

Maharashtra

corinnevaz98@gmail.comИнститут компьютерных исследований и разработок Symbiosis, Symbiosis International (Deemed University)ИндияSymbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed UniversityIndiaАналитик по принятию решений, EarlySalaryИндияDecision Analyst, EarlySalaryIndia202212092022264267276Copyright © Сайни Д.Р., Ваз К., 20222022Сайни Д.Р., Ваз К.Saini J.R., Vaz C.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://financetp.fa.ru/jour/article/view/1740

Выбор того, куда и сколько инвестировать, может быть очень сложной задачей для обычных людей, которые имеют ограниченные знания в этой области. Портфолио-менеджеры — это финансовые профессионалы, которые тратят много времени и усилий, чтобы помочь инвесторам в размещении средств и реализации финансовых стратегий, но не все могут позволить себе обратиться к ним за консультацией. Цель исследования — разработать взвешенную систему гибридных рекомендаций оптимизированного инвестиционного портфеля на основе предпочтений инвестора относительно риска и доходности. Как правило, инвесторы спрашивают совета по инвестициям у друзей или родственников со схожими предпочтениями в отношении риска, или, если их интересует конкретный товар, у того, кто уже инвестировал в тот же товар. Поэтому методология учитывает прошлое поведение инвестора и его ближайших соседей-инвесторов со схожими предпочтениями риска. С помощью коллаборативной фильтрации на основе интересов пользователя авторы рекомендуют выбрать определенное количество акций, используя метод корреляции Пирсона на основе дохода инвестора. Затем с помощью другой коллаборативной фильтрации на основе интересов пользователя рекомендуется определенное количество акций на основе возраста инвестора. Рекомендованному количеству акций, сгенерированному на основе дохода и возраста инвестора, присваиваются веса, и в итоге считается их средневзвешенное значение. В заключение проведена оценка реализуемости предложенной системы с помощью различных экспериментов. На основании полученных результатов авторы делают вывод, что предложенный взвешенный гибридный подход достаточно надежен для реализации в реальных условиях. Новизна работы заключается в том, что ни один из существующих подходов не использует более одного типа алгоритма взвешенных рекомендаций. Кроме того, конечные результаты, полученные таким образом, также никогда не отличались максимальным коэффициентом Шарпа и минимальным риском для инвестора. Такая комбинация гибридной фильтрации и коэффициента Шарпа никогда ранее не исследовалась.

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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The authors declare that there are no conflicts of interest present.