Machine Learning for Stock Selection

A Correlation-Embedded Attention Module to Mitigate Multicollinearity: An Algorithmic Trading Application

Algorithmic trading is a common topic researched in the neural network due to the abundance of data available. It is a phenomenon where an approximately linear relationship exists between two or more independent variables. It is especially prevalent in financial data due to the interrelated nature of the data. The existing feature selection methods are not efficient enough in solving such a problem due to the potential loss of essential and relevant information. These methods are also not able to consider the interaction between features. Therefore, we proposed two improvements to apply to the Long Short-Term Memory neural network (LSTM) in this study. It is the Multicollinearity Reduction Module (MRM) based on correlation-embedded attention to mitigate multicollinearity without removing features. The motivation of the improvements is to allow the model to predict using the relevance and redundancy within the data. The first contribution of the paper is allowing a neural network to mitigate the effects of multicollinearity without removing any variables. The second contribution is improving trading returns when our proposed mechanisms are applied to an LSTM. This study compared the classification performance between LSTM models with and without the correlation-embedded attention module. The experimental result reveals that a neural network that can learn the relevance and redundancy of the financial data to improve the desired classification performance. Furthermore, the trading returns of our proposed module are 46.82% higher without sacrificing training time. Moreover, the MRM is designed to be a standalone module and is interoperable with existing models.

A Multicriteria Decision Trading System Based on Prospect Theory: A Risk Return Analysis of the TODIM Method

This paper proposes a trading system (TS) based on a multicriteria decision aid (MCDA) process known as TODIM, (Multicriteria Interactive Decision Making) a Portuguese acronym for interactive and multicriteria decision-making. MCDA has been employed to solve financial questions because of its ability to deal with a complex environment populated with different sorts of criteria and alternatives, such as financial markets. The aim is to propose a general and adaptive tool for supporting the trading strategies of investors and market practitioners in such an environment. The reason for selecting TODIM among the different MCDA methods is that it is based on prospect theory, which assumes that the risk profile of the investor varies according to different situations, considering the risk of loss or gain. A list of simulations using some of the most prominent Brazilian stocks is performed, and the results are compared with the Buy-and-Hold benchmark and a TS based on an ensemble method for selecting trading rules. The results show that, compared to Buy-and-Hold, a TODIM-based TS provides the same level of return with a lower level of risk exposure. The consequence is superior risk adjustment parameters. As a result, we have a model with similar results in profit, but with superior results in relation to risk-based performance, which makes the method advantageous in relation to similar ones.

Intelligent Optimization Based Multi-Factor Deep Learning Stock Selection Model and Quantitative Trading Strategy

With the rapid development of financial research theory and artificial intelligence technology, quantitative investment has gradually entered people’s attention. Compared with traditional investment, the advantage of quantitative investment lies in quantification and refinement. In quantitative investment technology, quantitative stock selection is the foundation. Without good stock selection ability, the effect of quantitative investment will be greatly reduced. Therefore, this paper builds an effective multi-factor stock selection model based on intelligent optimization algorithms and deep learning and proposes corresponding trading strategies based on this. First of all, this paper selects 26 effective factors of financial indicators, technical indicators and public opinion to construct the factor database. Secondly, a Gated Recurrent Unit (GRU) neural network based on the Cuckoo Search (CS) optimization algorithm is used to build a stock selection model. Finally, a quantitative investment strategy is designed, and the proposed multi-factor deep learning stock selection model based on intelligent optimization is applied to practice to test its effectiveness. The results show that the quantitative trading strategy based on this model achieved a Sharpe ratio of 127.08%, an annualized rate of return of 40.66%, an excess return of 13.13% and a maximum drawdown rate of −17.38% during the back test period. Compared with other benchmark models, the proposed stock selection model achieved better back test performance.

Innovative deep matching algorithm for stock portfolio selection using deep stock profiles

Construction of a reliable stock portfolio remains an open issue in quantitative investment. Multiple machine learning models have been trained for stock portfolio selection, but their practical applicability remains limited due to the challenges posed by the characteristic of a low signal-to-noise ratio (SNR), the nature of time-series data, and non-independent identical distribution in financial data. Here, we transformed the stock selection task into a matching problem between a group of stocks and a stock selection target. We proposed a novel representation algorithm of stock selection target and a novel deep matching algorithm (TS-Deep-LtM). Then we proposed a deep stock profiling method to extract the optimal feature combination and trained a deep matching model based on TS-Deep-LtM algorithm for stock portfolio selection. Especially, TS-Deep-LtM algorithm was obtained by setting statistical indicators to filter and integrate three deep text matching algorithms. This parallel framework design made it good at capturing signals from time-series data and adapting to non-independent identically distributed data. Finally, we applied the proposed model to stock selection and tested long-only portfolio strategies from 2010 to 2017. We demonstrated that the risk-adjusted returns obtained by our portfolio strategies outperformed those obtained by the CSI300 index and learning-to-rank approaches during the same period.

Is Artificial Intelligence Ready to Assess an Enterprise’s Financial Security?

This study contributes to the literature on financial security by highlighting the relevance of the perceptions and resulting professional judgment of stakeholders. Assessing a company’s financial security using only economic indicators—as suggested in the existing literature—would be inaccurate when undertaking a comprehensive study of financial security. Specifically, indices and indicators based on financial or managerial reporting calculated at any particular point in time, provide only a superficial understanding—and may even distort the overall picture. It has also been suggested that expert assessment is the most objective method, although it has disadvantages related to individual cognitive limitations. These limitations are not particular to artificial intelligence, which could assess an enterprise’s financial security in a less biased way. However, by only imitating human behavior, it is not able to perceive and evaluate with intuition the dynamics of the company’s development and holistically assess the financial condition—despite the possibility of learning and forecasting—because artificial intelligence is not able to think and predict, which, in an enterprise, is the most important skill of a manager. Therefore, the risk of developing artificial intelligence to assess a firm’s financial security lies in a biased assessment of the enterprise’s activities in general—and its financial security in particular.