A survey on recommendation systems for financial services

Decision-making in the stock market is convoluted as it requires significant trading experience and knowledge. Faced with a huge range of stocks, investors in the stock market may be overwhelmed and daunted by the number of choices available on their plates. Financial institutions also face the problem of recommending stocks that cater to the investors’ trading strategies. In this paper, we develop and explore three different approaches that can be used to build a stock recommender system that takes into account the investors’ needs and interests. Furthermore, to ensure the profitability of the recommended portfolios and to support the investors in making risk-informed decisions, we evaluate their Expected Returns and Value-at-Risks. Hierarchical clustering was performed to better understand groups of traders that are similar in their needs and preferences and thus computational efficiency was improved. Three stock recommender systems, the K-Nearest Neighbour (kNN), Singular Value Decomposition (SVD) and Association Rule Mining (ARM) was explored and evaluated. Our short term, medium term and long term portfolios average rate of return was 4.15%, 10.24% and 23.17% respectively. Our study demonstrates promising recommendation results that not only catered to the user's profile but also contributed to the portfolio profitabilities with minimal financial loss. Stock recommendations were beneficial to users when their preferences were considered. 

How can we use artificial intelligence for stock recommendation and risk management? A proposed decision support system

Does portfolio optimization favor sector or broad market investments

Applied human large-scale data are collected from heterogeneous science or industry databases for the purposes of achieving data utilization in complex application environments, such as in financial applications. This has posed great opportunities and challenges to all kinds of scientific data researchers. Thus, finding an intelligent hybrid model that solves financial application problems of the stock market is an important issue for financial analysts. In practice, classification applications that focus on the earnings per share (EPS) with financial ratios from an industry database often demonstrate that the data meet the abovementioned standards and have particularly high application value. This study proposes several advanced multicomponential discretization models, named Models A–E, where each model identifies and presents a positive/negative diagnosis based on the experiences of the latest financial statements from six different industries. The varied components of the model test performance measurements comparatively by using data-preprocessing, data-discretization, feature-selection, two data split methods, machine learning, rule-based decision tree knowledge, time-lag effects, different times of running experiments, and two different class types. The experimental dataset had 24 condition features and a decision feature EPS that was used to classify the data into two and three classes for comparison. Empirically, the analytical results of this study showed that three main determinants were identified: total asset growth rate, operating income per share, and times interest earned. The core components of the following techniques are as follows: data-discretization and feature-selection, with some noted classifiers that had significantly better accuracy. Total solution results demonstrated the following key points: (1) The highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning with a percentage-split method for two classes in one run; (2) the highest accuracy mean, 91.44%, occurred in Models D and E from the use of naive Bayes learning for cross-validation and percentage-split methods for each class for 10 runs; (3) the highest average accuracy mean, 87.53%, occurred in Models D and E with a cross-validation method for each class; (4) the highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning-C4.5 with the percentage-split method and no time-lag for each class. This study concludes that its contribution is regarded as managerial implication and technical direction for practical finance in which a multicomponential discretization model has limited use and is rarely seen as applied by scientific industry data due to various restrictions.

https://www.mdpi.com/2073-8994/12/10/1620/pdf

Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model

Airbnb is a companionate platform that permits moderators (hosts) to establish their properties (listings) online, so a particular recipient can stipend to halt in them. Airbnb has developed into progressively prominent amidst tourists for accommodating athwart the universe. Correspondingly, there endure enormous datasets actuality possessed from Airbnb listings with affluent lineaments. Pricing an estate on Airbnb is a confront exhaust for the proprietor as it regulating the tally of clients for that community or place. The night-time price of Airbnb listings, build upon various aspects. This model is predetermined to be conducive to the intramural pricing apparatus that Airbnb contributes to its moderators. In this project, we desire to analyze Airbnb listing demand in New York City with several machine learning paths and compare price listing with different algorithms to choose one of the best models with efficient accuracy. In this case, Arima [1] is used for evaluating the price of listing on Airbnb. With the coincidental architecture and examination of hypothesis bounds and data wrangling analyzations, this endeavor perpetrates to price estimate analysis for the forthcoming years.

Price Listing Predictions and Forthcoming Analysis of Airbnb

Recommender systems that can suggest the user when to buy and sell stocks can be of immense help to those who wish to trade in stocks but are constrained by their limited knowledge of stock market dynamics. Traditionally, the trading recommendations have been generated on the basis of technical analysis. However, recent research in the field indicates that soft computing/data mining based recommender systems are also capable of generating profitable trading recommendations. An attempt has been made in this study to generate novel classifier based stock trading recommender systems that employ historical stock price data and technical indicators as input features. Moreover, there have been very few studies on the effectiveness recommender systems in the context of India, the world’s sixth largest economy and home to one of the world’s largest stock exchanges: the Bombay Stock Exchange (BSE). This study presents an empirical evaluation the effectiveness of five single classifier and six ensemble classifier based recommender systems on a total of 293 stocks drawn from the BSE. Recommender system performance for each stock is evaluated based on classification accuracy and eight economic performance measures. Results indicate that the proposed approach can indeed be used successfully for generating profitable trading recommendations.

Classifier Based Stock Trading Recommender Systems for Indian stocks: An Empirical Evaluation

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Classifier Based Stock Trading Recommender Systems for Indian stocks: An Empirical Evaluation