Stock Selection Strategy Research Articles

ESG Scores’ Impact on Portfolio Performance, An Evidence from Indonesia

This study is to determine the effect of using the Refinitiv ESG score in stock picking and its impact on portfolio performance using the ESG Score. Methodology for this research using a two-sample t-test of Net Asset Value (NAV) price-return portfolios and the Sharpe ratio as a proxy for risk adjustment return for portfolio evaluation. The results of this study indicate that the use of a Refinitiv rating may serve as a suitable metric for stock selection strategy because companies with higher ESG scores tend to exhibit superior performance. Since ESG ratings in Indonesia are still difficult to obtain, those with access to ESG ratings will have more advantages. Greater return performance is followed by an increased level of risk. Given the assumption of a well-diversified portfolio, it is hypothesized that the increased risk can be attributed to a larger variance coefficient. So that the Sharpe ratio must be used for portfolio evaluation to measure risk-adjusted returns.

Research on Stock Selection Strategies Based on the Basic Value of Stocks Based on Buffett's Theory

The paper presented is highly significant as it explores the applicability and effectiveness of Warren Buffett's stock value investing theory in China's A-share market. By employing five key indicatorsdividend yield, debt-to-asset ratio, ROE, net profit growth rate, and the 200-day moving average (MA200), the paper attempts to adjust the values of these indicators to construct a model. This model is tested on the Chinese A-share market to verify its universality and effectiveness. Ultimately, the author concludes that the stock value investing theory is practical in the Chinese market, although the numerical values differ significantly from those recommended in traditional Western literature. Specifically, the return on equity (ROE) within a specific parameter range yields better returns, which are a quarter lower than the values mentioned by Buffett; the forecasted net profit growth rate for the coming year shows a slight negative correlation with excess returns within a certain range; and the latest balance sheet debt-to-asset ratio and dividend yield are positively correlated with excess returns within a specific range.

Quantitative Stock Selection Model Using Graph Learning and a Spatial–Temporal Encoder

In the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models. Yet, prevailing models frequently neglect the fluid dynamics of asset relationships and market shifts, a gap that undermines their predictive and risk management efficacy. This oversight renders them vulnerable to market volatility, adversely affecting investment decision quality and return consistency. Addressing this critical gap, our study proposes the Graph Learning Spatial–Temporal Encoder Network (GL-STN), a pioneering model that seamlessly integrates graph theory and spatial–temporal encoding to navigate the intricacies and variabilities of financial markets. By harnessing the inherent structural knowledge of stock markets, the GL-STN model adeptly captures the nonlinear interactions and temporal shifts among assets. Our innovative approach amalgamates graph convolutional layers, attention mechanisms, and long short-term memory (LSTM) networks, offering a comprehensive analysis of spatial–temporal data features. This integration not only deciphers complex stock market interdependencies but also accentuates crucial market insights, enabling the model to forecast market trends with heightened precision. Rigorous evaluations across diverse market boards—Main Board, SME Board, STAR Market, and ChiNext—underscore the GL-STN model’s exceptional ability to withstand market turbulence and enhance profitability, affirming its substantial utility in quantitative stock selection.

Utilizing Genetic Algorithms in Conjunction with ANN-Based Stock Valuation Models to Enhance the Optimization of Stock Investment Decisions

Navigating the stock market’s unpredictability and reducing vulnerability to its volatility requires well-informed decisions on stock selection, capital allocation, and transaction timing. While stock selection can be accomplished through fundamental analysis, the extensive data involved often pose challenges in discerning pertinent information. Timing, typically managed through technical analysis, may experience delays, leading to missed opportunities for stock transactions. Capital allocation, a quintessential resource optimization dilemma, necessitates meticulous planning for resolution. Consequently, this thesis leverages the optimization attributes of genetic algorithms, in conjunction with fundamental analysis and the concept of combination with repetition optimization, to identify appropriate stock selection and capital allocation strategies. Regarding timing, it employs deep learning coupled with the Ohlson model for stock valuation to ascertain the intrinsic worth of stocks. This lays the groundwork for transactions to yield favorable returns. In terms of experimentation, this study juxtaposes the integrated analytical approach of this thesis with the equal capital allocation strategy, TAIEX, and the Taiwan 50 index. The findings affirm that irrespective of the Taiwan stock market’s bullish or bearish tendencies, the method proposed in this study indeed facilitates investors in making astute investment decisions and attaining substantial profits.

Construction of a Scoring and Ranking System for Multi-Factor Quantitative Stock Selection Strategy with Daily Rebalancing: An Analysis of SSE A-shares Component Stocks

Construction of a Scoring and Ranking System for Multi-Factor Quantitative Stock Selection Strategy with Daily Rebalancing: An Analysis of SSE A-shares Component Stocks

Research on the Current Situation of Quantitative Investment in China

Quantitative investment, as one of the many tools in the investment toolkit, plays a significant role in investment practice. Quantitative investment strategies encompass quantitative industry allocation, stock selection, and market timing strategies, among others. In the highly developed capital markets of the United States, quantitative investment has a history spanning several decades. It has garnered praise from numerous corporations and individual investors for its stable investment returns and rational investment style, making it an essential decision-making factor for other investors as well. In China, quantitative investment is still in its nascent stages. This article analyzes several issues existing in the Chinese quantitative investment landscape, such as limited hedging mechanisms for investment strategies, some strategies having a significant impact on the market environment, and the incompatibility of the unique Chinese financial market environment with traditional quantitative investment strategies. The article proposes corresponding solutions to these problems, which hold positive implications for further improving China's financial markets.

Research on Stock Selection Strategy with the Fusion of Data of Multi-Factor, Volume and Price Based on Support Vector Machine

Research on Stock Selection Strategy with the Fusion of Data of Multi-Factor, Volume and Price Based on Support Vector Machine

Multi Factor Quantitative Stock Selection Strategy Based on Support Vector Machine Model

Multi Factor Quantitative Stock Selection Strategy Based on Support Vector Machine Model

Quantitative Stock Selection Strategy Analysis Based on Multi-factor Model Under the Epidemic Situation

Contemporarily, COVID-19 undoubtedly had a profound impact on the current Chinese economy, as well as presented various challenges and opportunities for different industries in China. As a matter of fact, these changes may profoundly affect the future development trends and competitive landscape of industries. In recent years, the number of stock investors had continued to increase, surpassing 200 million in 2022. In a highly competitive market, the ability to select high-quality stocks often determines the final returns. This study selected the constituent stocks of the “CSI 500” as the “stock pool” for stock selection between 2020 and 2023. By analysing the degree of factor exposure, the correlation between factors, and IC values of factors, suitable and effective factor combinations were selected, and an evaluation model was established using multiple regression analysis to obtain a trading strategy. To ensure its feasibility and rationality, the strategy was simulated using backtesting before actual trading, ultimately aiming to achieve excess alpha returns.

Analysis of stock selection strategy of multi-factorial model based on rotation factor and investor sentiment

With the wide application of advanced technologies including AI which with high precision and automation level, financial technology has shown a progressive trend, and quantitative trading strategies have bright prospect. Based on China’s A-share market, this study constructs a quantitative strategy for stock selection by combining the industry rotation effect with a multi-factor model considering investor sentiment. Firstly, the strategy screens the target industries according to the rotation factor calculated by PER, and then determine the stocks by the multi-factor model formed by the factors after effective test. In this study, the effectiveness of the strategy is verified through six constructed back-test portfolios from both sides. According to the analysis, the return of the strategy considering the rotation effect and investor sentiment is higher than the market. The comparison of the impact of the rotation factor and the emotional factor shows that the change of macro and middle environment has a greater impact on stock prices than the micro level. Up to now, there are few stock selection strategies considering both rotation effect and investor sentiment. These results attempt to supplement this aspect and enrich the field of asset pricing.

Analysis of the Effectiveness of Alpha Strategy for Optimized Multi-Factor Models

The emergence of ChatGPT signifies the advent of a new artificial intelligence revolution. Generative AI technologies, represented by ChatGPT, will profoundly afferct the research paradigm in finance and the systemic ecosystem of the financial industry. This study primarily focuses on the stocks within the ChatGPT concept sector of China's A-share market and investigates the effectiveness of a multi-factor Alpha stock selection strategy in this sector. Firstly, the research optimizes the Fama-French three-factor model and establishes a four-factor model. Using this optimized four-factor model, stock selection is performed within the ChatGPT concept sector, resulting in the formation of an investment portfolio. Based on backtesting, the strategy is found to be highly effective, though it requires a certain level of risk tolerance from investors. These results represent a proactive attempt to apply the Alpha strategy to the ChatGPT concept sector of China's A-share market, providing a preliminary examination of its performance characteristics and offering valuable insights for further research by subsequent scholars.

Analysis of Multifactor Fundamentals Stock Selection Based on Backtesting

In recent years, quantitative finance has become a major trend for investing which brings stable returns with controllable risks. Among various different quantitative strategies, multifactorial stock selection strategy based on fundamental data (e.g., financial statements, macro- and micro-economy data) is one of the widely investigated strategies. On this basis, this study chooses Chinese listed company to verify the feasibility and effectiveness of the stock selection strategy. To be specific, the Ricequant platform is utilized to realize the backtesting as well as data retrieving in order to estimate and evaluate the performances of the strategies. According to the analysis, several indicators show great ability to gain extra returns compared with systematic risks and market performances. In other words, the feasibility of explicability of the quantitative strategy based on multifactorial model is verified in Chinese market. Overall, these results shed light on guiding further exploration of fundamental analysis of different underlying assets based on multifactorial analysis.

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.

An analysis of the applicability of Markowitz Model in China

This research project aims to analyze whether Markowitz Model fits with the Chinese market and whether it can be applied to the Chinese market. The logical chain of this paper is to verify the rationality and stability of the stock selection strategy in the Chinese market by comparing it with the market benchmark and related indexes, and finally verify whether the Markowitz Model applies to the Chinese market with the conclusion of whether the stock selection strategy is reasonable and stable. I selected 20 representative stocks among the CSI 300 stocks and used Markowitz Model to conduct linear regression. Then I used the stock selection strategy to update the stock selection to maximize benefits regularly and compared it with the CSI 300 index to verify the similarity and correlation between it and CSI 300. Finally, the conclusion is that Markowitz's Model fits the Chinese market.

A Study of Multifactor Quantitative Stock-Selection Strategies Incorporating Knockoff and Elastic Net-Logistic Regression

In the data-driven era, the mining of financial asset information and the selection of appropriate assets are crucial for stable returns and risk control. Multifactor quantitative models are a common method for stock selection in financial assets, so it is important to select the optimal set of factors. Elastic Net, which combines the benefits of the L1 and L2 penalty terms, performs better at filtering features due to the complexity of the features in high-dimensional datasets than Lasso and Ridge regression. At the same time, the false discovery rate (FDR), which is important for making reliable investment decisions, is not taken into account by the current factor-selection methodologies. Therefore, this paper constructs the Knockoff Logistic regression Elastic Net (KF-LR-Elastic Net): combining Logistic regression with Elastic Net and using Knockoff to control the FDR of variable selection to achieve factor selection. Based on the selected factors, stock returns are predicted under Logistic regression. The overall model is denoted as Knockoff Logistic regression Elastic Net-Logistic regression (KL-LREN-LR). The empirical study is conducted with data on the CSI 300 index constituents in the Chinese market from 2016–2022. KF-LREN-LR is used for factor selection and stock-return forecasting to select the top 10 stocks and establish an investment strategy for daily position changing. According to empirical evidence, KF-LR-Elastic Net can select useful factors and control the FDR, which is helpful for increasing the accuracy of factor selection. The KF-LREN-LR forecast portfolio has the advantages of high return and controlled risk, so it is informative for optimizing asset allocation.

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.

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.

Magic Formula: Sebuah Tinjauan dalam Prediksi Perusahaan-Perusahaan Unggulan di Indonesia

Purpose: This study aimed at investigating the performance of a value investing strategy called the “Magic Formula”, introduced by Joel Greenblatt (2006), applied to the Indonesian Stock Exchange. Magic Formula is a simple stock selection strategy by ranking stocks based on calculating Return on Capital and Earnings Yield. Methodology: First, this study examined stocks listed on the Indonesia Stock Exchange represented by the Kompas 100 index measuring the price performance of 100 stocks that have good liquidity and large market capitalization. This step was followed by eliminating finance stocks because they have different debt postures which will affect the calculation in the next stage. The next step was to choose the top thirty stocks from the combined rank to become a portfolio. Portfolio rebalancing is conducted annually. Sharpe, Treynor, and Jensen’s Alpha analysis were used to measure returns adjusted for risk. Results: This study showed that using return on capital and earnings yields can produce higher average returns than the market index from April 2015 to April 2019. The Magic Formula portfolio yields an average return of 12.76% and the market produces average returns of 6.89% during the period under test. Limitations: The obtained data were derived from 5 years-data of the Indonesia Stock Market. in order to neglect the Efficient Market Hypothesis, the collection of wider data was required. besides, the result of this study is unusable to test the Efficient Market Hypothesis at weak or strong level. Contribution: This study contributes empirical evidence that the Magic Formula is a solid investment model especially considering its simplicity. Magic Formula can be used as an early stage in the process of selecting a company to invest in. Keywords: 1. Value Investing 2. Magic Formula 3. Earning Yield 4. Return on Capital 5. Investment Strategy