Multi-factor Stock Selection 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.

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.

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>

Quantitative Multi-factor Stock Selection Strategy Based on Internet Service Industry

As a matter of fact, with regard to stock investment practice, quantitative strategies have become popular with the rapid development of China's capital market. Plenty of quantitative strategies have been proposed and implemented into the Chinese markets, where multi-factor stock selection is one of them. Based on the multi-factor model commonly used in quantitative investment, this article selects relevant data of the Internet service industry in the A-share market from 2018 to 2022, conducts an industry-level empirical test on 15 factors in 5 categories that are widely used, and constructs investments accordingly combination. According to the analysis, the research found that the stock portfolio constructed based on the above method can outperform the market, far exceeding the performance of the CSI 300 during the same period, indicating that the multi-factor model is still effective in the Internet service industry, and this industry has high investment value. Overall, these results shed light on guiding further exploration of quantitative analysis of stock market.

Multi-factor Stock Selection Strategy Incorporating Liquidity Risk

As an emerging market dominated by retail investors, China's stock market is greatly affected by external shocks such as market noise and policy changes, and the traditional fundamental factors have limited ability to predict the future returns of stocks. Stock liquidity is also affected by noise factors and macro factors, which can be regarded as one of their external performance. Based on the multi-factor stock selection model, this paper uses the 2010-2022 CSI 300 stock data to construct a fundamental fusion factor and another fusion factor considering both fundamental and liquidity risk factors, then compares the stock selection effect of the two fusion factors through a variety of test indicators. The empirical results show that the IC value of the fusion factor considering both fundamental and liquidity risk factors is 0.0267, which is greatly improved compared with the fundamental fusion factor with IC value of only 0.0195, and its excess yield of 64.728% is 13.134% higher than the fundamental fusion factor, while the benchmark win rate is increased by nearly 3% and the maximum retracement is reduced by nearly 12%. The research shows that the effectiveness and stability of the fusion factor after adding the liquidity index to predict the future return of the stock and obtain the excess profits are better than the fundamental fusion factor. Therefore, considering the measurement and inclusion of stock liquidity level can improve the yield of stock selection strategy in the Chinese stock market to a certain extent.

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.

Multi-Factor Stock Selection Strategies Based on Different Securities Markets

In the context of the transformation stage of the epidemic and the 20th National Congress, we selected factors such as Market Capitalization, ROE, EPS, P / E, and Turnover to analyze the trading data of stock markets such as Shanghai and Shenzhen 300 and Shanghai Securities last year. Use quantitative methods to construct multi-factor stock selection strategies. In terms of factor selection, 8 different types of factors were selected with reference to research literature in related fields and research reports of securities companies. Next, the validity of the factors in the factor pool is tested, and then the correlation analysis is carried out on the effective factors that have been screened out. Finally, the method of equal-weight synthesis is used to synthesize a large class of factors for the highly correlated factors for subsequent preparations. strategy. Three effective factors were screened out through IC test and hierarchical method test, and redundant factors were eliminated. Finally, based on the research process and conclusions, this paper puts forward relevant suggestions for quantitative investment.

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.

Application of Random Forest in Multi-Factor Stock Selection

Application of Random Forest in Multi-Factor Stock Selection

Sector rotation multi-factor stock selection strategy based on six boards

This paper proposes an investment strategy based on a regression linear model to predict sector rotation and a multi-factor scoring model to select stocks to achieve excess returns. The strategy uses January 2006 December 2020 as the model construction period and January 2021 June 2021 as the model validation and backtesting period. Using Shenyin & wanguo’s industry classification as a standard, the cyclicality of industries is classified by β value and the existence of sector rotation is verified using the difference in rate of return between cyclical and non-cyclical industry. The factors associated with the annualized rate of return are selected and the fitted formula for the annualized rate of return is obtained by regressing linear equation and neural network multidimensional correlation to select the dominant board. Valid factors for each board were selected for fitting, and the factors were assigned weights for scoring, resulting in 32 stocks. The backtest results significantly outperformed the CSI 300 Index, and the model performance is stable and can achieve better returns.

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

Research on the investment strategy of new energy vehicle industry based on multi-factor stock selection

With the development of the Chinese stock market and the continuous improvement of financial engineering technology, quantitative investment represented by the multi-factor stock selection strategy has been developing and growing in the Chinese stock market. This paper takes the quarterly data of the China Securities New Energy Vehicle Index from January 2017 to March 2021 as the research object, uses the Fama-Macbeth test to select factors, and constructs the regression equation based on the regression method. The regression equation is used to predict the stock return rate of the next quarter through the factor data of each quarter in 2020. The top 7 stocks with returns are selected to construct the investment portfolio in the way of equal-weighted capital allocation, and the investment portfolio is updated quarterly. Through the empirical test, this paper mainly draws the following three conclusions: First, the driving factors of stock return in the new energy vehicle industry mainly include the ratio of long-term debt to working capital (LTDWC), total profit growth rate (TPG), price/earnings to growth ratio (PEG), and equity turnover ratio (ET); secondly, the portfolio return based on the stock selection model in this paper is higher than the average level of the industry, and the investment return obtained is stable, suitable for investment targets; third, the new energy vehicle industry is in a period of rapid development, and the portfolio return rate is higher than the average level of A-share market. Therefore, it has huge potential investment value. This paper provides suggestions and solutions for investors' stock investment in the new energy vehicle industry.

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.

A Stock Selection Model of Image Classification Method Based on Convolutional Neural Network

With the development of artificial intelligence technology, an increasing number of researchers try to apply different machine learning and deep learning methods to quantitative trading fields to obtain more stable and efficient trading models. As a typical quantitative trading strategy, stock selection has also attracted a lot of attention. There are many studies and applications on stock selection. However, the existing research and application cannot meet the continuous expansion of the scale and dimension of stock selection data set and cannot meet the needs in terms of efficiency and accuracy of stock selection. A convolutional neural network has been applied to image classification and achieved better results than the traditional methods. In this study, we first constructed a multifactor stock selection data set based on China's stock market. Then, we apply the convolutional neural network model to analyze stock selection data and select stocks. The main contribution of this study is that we build a stock multifactor data set, construct a “factor picture,” and classify them by convolutional neural network to select stocks. This study also makes comparative experiments on the decision tree, support vector machine, and feedforward neural network in stock selection on the same data set constructed in this study. The results show that the stock selection method based on the convolutional neural network outperforms other methods in terms of the annual return, sharp ratio, and max drawdown.

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.