Multi-factor Stock Selection Model Research Articles

CNN-BiLSTM-Attention Based Stock Price Prediction and Quantitative Investment Strategy

With the rapid development of technology and the increasing maturity of financial markets, stock price prediction has become a hot topic and an important trend in the financial field. However, the stock market has complexity and volatility, in order to reduce the investment risk and ensure the maximization of benefits, selecting the optimal stock to predict the stock price and formulating the quantitative trading strategy are extremely important issues for financial academics and investors. For the research of stock price prediction and quantitative trading, firstly, the quantitative stock selection model combining XGBoost and multi-factor stock selection model is constructed, and four stocks are screened out, and then the CNN-BiLSTM-Attention model is proposed to predict the stock price trend of the selected stocks, and it is found from the prediction results of the four stocks that the prediction accuracies all reach more than 95%, which is higher than that of the single model prediction. accuracy and passed the validity test of the fitted model. Secondly, based on the quantitative investment portfolio given in the above analysis and the quantitative stock trading strategy of MACD, the prediction results are verified, and the total return of most stocks based on the strategy is higher than 40% at the highest, and the loss is controlled within 10%, which indicates that the trading strategy in this paper is effective and feasible. The study concludes that greater returns can be obtained by calculating the total returns of stock portfolios based on different portfolio approaches.

Portfolio Analysis of Multi-Factor Stock Selection Model Based on Investor Sentiment

Since 1990, China's securities market has been gradually improved. Domestic and foreign scholars have proposed many theoretically perfect investment models, but not many of them have been applied in practice because the market cannot satisfy the strict assumption conditions of the models. Foreign quantitative investment models are not necessarily effective in the Chinese market. Based on this, this paper takes the portfolio theory as a guide, uses the scoring method to construct a multi-factor stock selection model, and selects the relevant factors in combination with the characteristics of the Chinese market. Through empirical analyses, the validity of the multi-factor stock selection model with equal weight allocation in China's market is verified. In addition, this paper innovatively constructs an investor sentiment factor, combines the sentiment timing with the multi-factor stock selection model, optimises the timing on the basis of the multi-factor stock selection model, and proposes an investment strategy to obtain excess returns stably.

Application of machine learning in quantitative timing model based on factor stock selection

<abstract><p>In this paper, we integrated machine learning into the field of quantitative investment and established a set of automatic stock selection and investment timing models. Based on the validity test of factors, a multi-factor stock selection model was established to select stocks with the highest investment value to create a stock pool. By comparing the cumulative returns and the overall market returns of different timing signals over the same time period, both the decision tree and the long short-term memory (LSTM) models had great results. Finally, empirical research was reported to show that it is a good combination to introduce machine learning algorithms into quantitative timing.</p></abstract>

Analysis of the effectiveness of multi-factor stock selection model under COVID-19

Contemporarily, quantitative finance and multi-factor stock selection have begun to develop in China. Meanwhile, the new coronavirus from 2020 has brought a huge impact on the market, so it is very important to explore the effectiveness of multi-factor stock selection model during the period. In this study, stocks in the medical industry are selected as the research object, and price-earnings ratio, total market value and market sales ratio are selected as the research indicators. In this study, RiceQuant quantitative trading platform was used to conduct backtesting during the epidemic period, and the effectiveness of stock selection indicators during the epidemic period was analysed through data results. The results showed that under the epidemic situation, changing the P/E ratio, total market value and price-to-sales ratio had an impact on the earnings of stock selection portfolio, which proved that the P/E ratio, total market value and price-to-sales ratio were all effective indicators. These results prove the effectiveness of multi-factor stock selection model under the COVID-19, and list the effective stock selection factors under the epidemic situation. It can provide an in-depth understanding of the impact of the epidemic on the market and enrich the study of multi-factor stock selection model.

Fundamental Multi-Factor Stock Selection Strategy Research - Taking the Constituents of the Shanghai and Shenzhen 300 Index as an Example

The most important aspect of stock investment is the ability to select good stocks, which can bring investors good excess returns before the market becomes strong and efficient. With the rapid development of computer technology and big data application, the multi-factor stock selection theory in quantitative investment is becoming increasingly sophisticated, and has become a popular and stable stock selection method. Based on the "RiceQuant" quantitative investment analysis platform, this study adjusts the parameters of the profitability factors (return on net assets and gross profit margin) in the fundamental multi-factor stock selection model using data from the constituents of the Shanghai and Shenzhen 300 Index from 2017 to 2020, selects the optimal parameters based on backtesting returns and risk screening, and further optimizes the model by including both profitability factors. These results reflect the screening ability of different profitability factors for stocks and can provide reference for the strategy design of multi-factor models.

Research on Quantitative Investment Strategy Based on Multifactor and Double Average Model

In recent years, with the gradual maturity of China's capital market, the number of rational investors is also increasing, and the demand and application for quantitative stock selection is becoming increasingly widespread. Among many quantitative stock selection theories, the multi factor stock selection model and the double moving average stock selection model are favored by institutional investors due to their inherent characteristics such as discipline, systematicness, and accuracy. This article conducts quantitative stock selection analysis for listed companies based on a quantitative investment research model based on multiple factor and double average line models, and studies whether the model can help investment and obtain additional benefits, providing some ideas for future quantitative analysis.

Application of DeepForest-CQP multi-factor model in quantitative stock selection

The stock market is a chaotic system, and stock forecasting has been the research focus. This paper proposes a multi-factor model based on DeepForest-CQP to make it more applicable to the stock domain. A t-test is used for selecting factors, and orthogonalization and heteroskedasticity tests are performed for the combined factors, which are particularly important in stock forecasting. DeepForest-CQP was combined with the multi-factor model to construct a stock selection model that can achieve higher returns. The obtained multi-factor quantitative stock selection model is used to study stock selection strategies, and simulated trading is used to evaluate the multi-factor model and stock selection strategies and compare them with various machine learning multi-factor models. The experimental results show that the DeepForest-CQP-based multi-factor stock selection model achieves significant performance advantages in all backtesting metrics.

Multi-Factor Stock Selection Model Based on Categorical Prediction Model

Multi-Factor Stock Selection Model Based on Categorical Prediction Model

Synergy frontier of multi-factor stock selection model

Synergy frontier of multi-factor stock selection model

Fuzzy Decision-Making Analysis of Quantitative Stock Selection in VR Industry Based on Random Forest Model

Since Professor L.A. Zadeh published “Fuzzy Set Theory” in the 1960s, the theory of fuzzy mathematics has been formally established and developed and has been gradually introduced into work in all walks of life. At the same time, fuzzy mathematics theory has also been widely used in VR industry selection. In the stock strategy, the advantages of improving unit classification accuracy, screening high-quality stocks, and constructing near-perfect investment portfolios continue to emerge. On the other hand, with the increasing maturity and continuous development of China’s computer and Internet technologies, the VR industry has gained a new round of development space, and its own investment value and the investable space between related industries have been gradually tapped. Different from the analysis of quantitative stock selection by constructing a logistics multifactor stock selection model in the existing research, the research mainly adopts the random forest algorithm based on fuzzy mathematics to construct the initial investment strategy portfolio. Secondly, different from the single effective frontier algorithm, the research is based on the random forest algorithm, calculates the average AUC of the index, and continuously checks and tests the results to obtain the optimal investment portfolio. Finally, select appropriate risk indicators and performance indicators to evaluate the performance of the strategy portfolio. The study concludes that the portfolios selected by the random forest model are highly investable and have good stability.

Empirical Analysis of Multi-factor Stock Selection Model Based on Weight Assignment of Momentum and Discrete Degree

Through the stock selection of a large number of literature, we found that factors contribute differently at different market styles or stages, and the factor weight is different. Therefore, in this paper, the factors were preliminarily screened by IC mean and IR equal weight scoring method and T test, and then the screened factors were tested by correlation test and sorting method to obtain the final factors. After five methods, such as factor equal weight, momentum and dispersion comprehensive average weight, were tested and compared, we found that the method of momentum and dispersion comprehensive average weight on the effect of the stock selection is more excellent than other methods, so we chose the momentum and discrete degree of comprehensive weighted average approach to dynamic weighting of each stock, the last stock through empowerment scoring method selection. In this way, the speed of market change and the impact of industry on factors are better considered, and the te-test effect is better.

The Multifactor Quantitative Investment Model Based on Association Rule Mining and Machine Learning

The security information database has accumulated a large amount of historical data due to the continuing development of the securities market. People are concerned about how to fully utilize these data to investigate the securities market’s law. In the financial field, financial asset pricing is a major issue. To some extent, the size of the return is determined by the difference between asset prices and their intrinsic value. The total global investment scale of quantitative funds will surpass 20 billion yuan by the end of 2021. Global asset management firms have turned to quantitative funds as their most important investment tool. Quantitative investment applies a specific investment idea to a specific model by creating specific indicators and parameters and then executes the investment strategy, greatly increasing the breadth and depth of investment. The goal of investors is to understand risk and maximize returns on investment. Researchers and investors alike value quantitative investment because of its scientific and efficient operation. In quantitative stock selection, a multifactor stock selection model is a critical tool for building a portfolio. This paper builds a multifactor investment strategy based on the relevant factors of corporate finance and valuation, selects the portfolio, and calculates the excess return using a machine learning classification algorithm.

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.

Research on multi factor stock selection model based on LightGBM and Bayesian Optimization

With the development of the stock market, the number of individual investors has become more and more. Because of emotional and irrational factors, the risk and instability of stock market in China have been greatly increased. Therefore, it is necessary to introduce the idea of quantitative investment into the financial field. In this paper, firstly we use random forest, XGBoost and LightGBM to conduct rolling tests on multiple factors. After parameter adjustment based on Bayesian Optimization, we find that LightGBM-Bayes has the best effect. Finally, this paper uses the multi factor stock selection model based on LightGBM-Bayes to conduct a rolling back test on CSI300 Constituent Stocks, and the back test results are better than the benchmark CSI300 index.

Discovering optimal weights in weighted-scoring stock-picking models: a mixture design approach

Certain literature that constructs a multifactor stock selection model adopted a weighted-scoring approach despite its three shortcomings. First, it cannot effectively identify the connection between the weights of stock-picking concepts and portfolio performances. Second, it cannot provide stock-picking concepts’ optimal combination of weights. Third, it cannot meet various investor preferences. Thus, this study employs a mixture experimental design to determine the weights of stock-picking concepts, collect portfolio performance data, and construct performance prediction models based on the weights of stock-picking concepts. Furthermore, these performance prediction models and optimization techniques are employed to discover stock-picking concepts’ optimal combination of weights that meet investor preferences. The samples consist of stocks listed on the Taiwan stock market. The modeling and testing periods were 1997–2008 and 2009–2015, respectively. Empirical evidence showed (1) that our methodology is robust in predicting performance accurately, (2) that it can identify significant interactions between stock-picking concepts’ weights, and (3) that which their optimal combination should be. This combination of weights can form stock portfolios with the best performances that can meet investor preferences. Thus, our methodology can fill the three drawbacks of the classical weighted-scoring approach.

Stock selection strategy of A-share market based on rotation effect and random forest

Due to the random nature of stock market, it is extremely difficult to capture market trends with traditional subjective analysis. Besides, the modeling and forecasting of quantitative investment strategies are not easy. Based on the research of experts and scholars at home and abroad and the rotation effect of large and small styles in China's A-share market, a stock picking strategy combining wheeling effect and random forest is proposed. Firstly, judge the style trend of the A-share market, that is, the relatively strong style in the large and small-sized market. The strategy first judges the trend of the A-share market style, then uses a multi-factor stock selection model through random forest to select stocks among the constituent stocks of the dominant style index, and buys the selected stocks according to the optimal portfolio weights determined by the principle of minimum variance. The empirical results show that the annualized rate of return of the strategy in the eight years from January 1, 2012 to April 1, 2020 is 3.6% higher than that of the single-round strategy, far exceeding the performance of the CSI 300 Index during the same period.

Integrated Long-Term Stock Selection Models Based on Feature Selection and Machine Learning Algorithms for China Stock Market

The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. In this paper, the features are selected by various feature selection algorithms, and the parameters of the machine learning-based stock price trend prediction models are set through time-sliding window cross-validation based on 8-year data of Chinese A-share market. Through the analysis of different integrated models, the model performs best when the random forest algorithm is used for both feature selection and stock price trend prediction. Based on the random forest algorithm, a long-short portfolio is constructed to validate the effectiveness of the best model.

FACTOR CROWDING AND LIQUIDITY EXHAUSTION

AbstractWell‐known anomalies and stable patterns in equity returns are widely employed to guide stock selection. The use of overlapping multifactor models built on these patterns induces correlated trade across investors. A stock with a strong signal from a parsimonious multifactor stock selection model exhibits changes in trade activity, net order imbalances, lower volatility, lower liquidity level, and changes in liquidity comovement consistent with correlated trade. These results illustrate that correlated trading among investors can affect the liquidity and risk of the securities they trade, and imply that measures of portfolio liquidity risk that ignore these changes can understate risk.

基于EKPCA算法的多因子选股模型研究

基于EKPCA算法的多因子选股模型研究

Multi-factor Stock Selection Model Based on Adaboost

In this paper, we established multi-factor stock selection model based on Adaboost by using Adaboost to integrate the custom week classifier model, and Shanghai and Shenzhen 300 stocks are taken as the research object. During the stock retest, the first is make a comparative test between Adaboost multi-factor stock selection model and the traditional multi-factor model, among them, the factor large class isn’t considered in the multi-factor stock selection model. And the results of two contrast experiment showed that the multi-factor stock selection model based on Adaboost has stronger profitability and less risk than the traditional multi-factor model.