Stock Selection Research Articles

Selective and Market Timing Skills: An Empirical Evaluation of Equity Mutual Funds in an Emerging Market

This study evaluates the selective and market timing skills of mutual funds managers using monthly net asset values of all 30 actively managed equity-based funds that operated from January 2012 to December 2021 obtained from the Securities and Exchange Commission. Selectivity is the ability to search for and choose undervalued securities to exploit the sub-optimal market weights of securities, while market timing is the ability to forecast the direction of the market and adjust portfolio betas to generate alpha. Two regression models, namely the Treynor-Mazuy (1966) and Henriksson-Merton (1981) were deployed for the study. The findings indicate that mutual funds did not exhibit significant selective skills or market timing abilities. This outcome is consistent with the Efficient Market Hypothesis (EMH). Consequently, it becomes challenging for investors or fund managers to consistently outperform the market through stock selection or market timing. Based on the study's results, the recommendation for investors is to consider low-cost passive investment strategies, such as index funds or exchange-traded funds (ETFs), over actively managed funds.

Hedging Market Risk with the Index: Experiment Based on Chinese Stock Market

The index can be seen as a reflection of the market. Index futures, also known as stock index futures, are financial derivatives whose value is based on the performance of the whole market. As a consequence, investors can hedge the market risk by longing or shorting some specific value of index futures. Based on the idea, the paper tried to hedge the market risk and have a deeply look into whether it works and what characteristics it has. The paper chooses 10 stocks from those who are the components of the three main Index in China. The results show that with all the hedging strategies, volatility is effectively reduced and the ROI significantly increased during the period with acute value changes. The paper refined the whole experiment by randomly choosing the stocks with python and comparing the stock selection within the industry. The experiment verify the feasibility and the effectiveness of the hedging strategies based on the Index.

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.

Hedge fund activism, R&D efficiency and firm value

We investigate the effectiveness of hedge fund activism in addressing inefficient R&D investment and creating long-term value at target firms. Using an endogenous growth model to estimate efficient R&D investment levels, we find target firms exhibit a tendency to overinvest in R&D. The likelihood of being targeted and activism announcement returns increase with R&D overinvestment. Despite observable cuts in R&D expenditure following activism, our analysis shows no significant improvement in R&D efficiency over the subsequent three years. Additional tests, controlling for the activist’s objectives, reputation, and stock selection ability, reveal a negative impact of activism on value creation. Our study offers important insights into how hedge fund activism shapes corporate R&D strategies and highlights challenges in achieving sustainable innovation and value enhancement through activist interventions.

A Mean-Variance Approach to Portfolio Optimisation for Effective Stock Selection in the Malaysian Stock Market

Optimization of portfolio selection is very important for overall financial engineering performance. Recent developments in portfolio selection have been developed by many researchers. However, many studies show that prediction with manual models is not enough to achieve highly accurate predictions and prosperous returns. In this paper, a novel portfolio construction approach is developed using the mean-variance (MV) model and maximizing the Sharpe ratio for selecting the best portfolio. The approach model is by choosing the best ratio from the comparison between optimal return and annual standard deviation. Experimental data was taken from the Malaysian Stock Exchange from 2014 to 2020, from several securities the ten most active securities were selected. The purpose of this study is to assist investors in verifying the Malaysian stock market to reduce risk and maximize returns. The results of the study show that the ratio of the optimal return and the annual standard deviation is 0.711. These results are obtained from a comparison between the optimal return of 33.96% and the annual standard deviation of 47.73%. The proposed model by maximizing the comparison between the optimal return and the annual standard deviation can be used to support portfolio selection decisions. The results showed that the efficiency limits of the selected stocks and optimal portfolio weights could be achieved.

Political investing of mutual funds

We study whether and how political investing influences the performance of mutual funds. We provide robust evidence of the positive relation between mutual funds' political connection intensity exposure and future performance. The gross return gap between the top and bottom quintiles of mutual funds ranked by exposure to political connection intensity reaches 4.38% on an annualized basis. The performance gap is larger and more significant for mutual funds with high characteristic-selectivity skills. Our findings show that political connection intensity exposure is more closely related to stock selection skill than to political investing risk premium, political investing timing ability or corruption.

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.

Natural Spawning Breeding Techniques Analysis of Carp (Cyprinus carpio) in the Fish Seed Center (BBI) Bolangan, Tabanan, Bali

Carp (Cyprinus carpio) is a freshwater fish species with a high tolerance to various environmental conditions. The present study aimed to analyze the seeding process of carp using a natural spawning method at the Fish Seed Center (BBI) Bolangan, Tabanan, Bali. The descriptive analysis had been used to analyze of the data collected through active participation, observation, and interviews. The carp natural spawning techniques at BBI Bolangan were conducted in an earthen pond. The breeding techniques include pond preparation, brood stock selections, fish spawning, larvae or seed maintenance, water quality control, disease control, and fish harvesting. Male and female parents in a 2:1 ratio were required for effective breeding. This natural hatching system produced up to 100806 eggs with a hatching rate of 74.4%, yielding 75000 larvae per female parent. After a week of care, the larvae have a 30% survival rate and are ready for cultivation in the following section (nursery II). We conclude that the Carp (Cyprinus carpio) breeding techniques at the Fish Seed Center (BBI) Bolangan, Tabanan, Bali are technically sound and feasible to apply.

Exploring the Timing Stock Selection Ability of U.S. Active Index Fund ETF Based on T-M Model

Based on past research experiences, the passage briefly reviews the important tools used as an important tool for the analysis of stock selectivity ability and market timing ability, The T-M model. Results found that from 2022 to 2023, the funding groups selected do not have the effective ability of timing and stock selection, also the government policies and other external factors during the period affect the whole market trending. The Jensen model and the subsequent T-M model have been widely used to measure whether the return-on-investment index funding portfolio has achieved the expected effect, which has a strong correlation and provides a good calculation standard for investors and funding investment managers holding funding portfolios. The model provides an effective reference for following stakeholders to know the market trend and control their investment risks better.

Technological scheme and biotechnical indices of industrial cultivation of Australian redclaw crayfish juveniles in aquaculture

The aim of the work: forming technological scheme and biotechnical indices of industrial cultivation of Australian redclaw crayfish Cherax quadricarinatus juveniles under controlled conditions based on a generalization of available research investigations.Used methods: the study was carried out on the basis of the aquarium room of Invertebrate Aquaculture Department of VNIRO on Australian redclaw crayfish juveniles and adults in the period from 2010 till 2023. Standard methods were used to determine hydrochemical parameters and fish-cultural and biological indicators of individuals. Studies were carried out on the characteristics of the life cycle, early stages of development, physiology and nutrition. The data was processed using statistical methods.Novelty: A technological scheme and biotechnical indicators of industrial rearing under RAS conditions have been formulated for Australian redclaw crayfish juveniles.Results: A technological scheme and biotechnical indicators for cultivation of Australian redclaw crayfish juveniles have been developed for a modular basin complex. The technological scheme includes six stages: “Selection and housing of brood stock” (1); “Mating and obtaining gravid females” (2); “Maintenance of females with eggs and early juveniles” (3); “Ongrowing of juveniles to 0.2–0.8 g” (4); “Ongrowing of juveniles from 0.2–0.8 g to 2–5 g” (5); “Ongrowing of juveniles for broodstock remount” (6). The success of stages 1 and 2 depends on the spawners’ physiological state, which is determined by the housing conditions during the preparation of crayfish for mating. Stages 3–6 require the use of single-type basins, specialization of which is achieved through the installation of sedimentation tanks for holding females with eggs, shelters and volume-structuring substrates of different types.Practical significance: application of the proposed biotechnical methods and approaches to the production and cultivation of juvenile Australian redclaw crayfish will optimize the cultivation of the species, which will be beneficial for the development of its cultivation in fish farms.

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

A Study on Investors Behavior on Stock Selection Decision: Evidence from Odisha

A Study on Investors Behavior on Stock Selection Decision: Evidence from Odisha

A STUDY ON THE ROLE OF TECHNICAL ANALYSIS IN PREDICTING SHORT-TERM PRICE MOVEMENTS IN THE STOCK MARKET

This research explores the efficacy of technical analysis in predicting short-term stock market movements. The study investigates various technical analysis tools, including support and resistance levels, Fibonacci retracement, and swing highs and lows, applied to a selection of stocks from Bank Nifty. The primary objective is to evaluate the effectiveness of these tools in forecasting price trends and providing actionable trading insights. Data was analyzed over a five-year period, utilizing a blend of qualitative and quantitative research methods. Findings indicate that while technical analysis provides significant insights into market behavior, its effectiveness can vary widely depending on market conditions and the specific tools used. The research contributes to academic discourse on market efficiency and offers practical implications for traders and financial analysts by enhancing the understanding of technical analysis as a robust component of trading strategy development in the volatile environment of stock trading. Keywords : Short term Price Movements, Support and Resistance Level, Fibonacci Retracement, Swing highs and lows, Technical Analysis, Stock Market, Bank Nifty.

Multi-factor Strategy Using Linear Weighting Model and Time Series Analysis of Returns

Quantitative methods of stock picking and timing have become a popular approach today, and choosing good factors and quality models can often lead to high yields. In the current Chinese stock market, the small-cap factor has an amazing performance. In recent years, simply through the small-cap factor stock selection can get more than 40% annualized return. In this paper, the author intends to perform factor clustering through the clustering method, select quality factors from each category to form a factor portfolio, and solve the pre-coefficients of each factor through the linear regression model as the calculation of the weights, and then combine the small-cap factor with the resulting composite score to pick stocks. After the completion of the strategy, the robustness and feasibility of the strategy is analyzed through time series analysis of daily profit and loss, and the ARIMA model is used to predict the strategy's future returns in the future.

A Comparative Study of Stock Selection Models Based on Decision Tree Algorithms

In this paper, the decision tree model in data mining is applied to select stock characteristics that can be effectively used for stock selection by using the C4.5 algorithm and the CART algorithm, respectively, in combination with the strategies of fundamental analysis and technical analysis. The paper concludes that the decision tree models constructed by the C4.5 and CART algorithms both have better classification ability for stock selection and portfolio construction, but the decision tree model constructed by the C4.5 algorithm is simpler. The stock portfolios determined by the decision tree model are able to achieve an excess return of 13.4% relative to the CSI 300 index, thus proving that the decision tree model is effective in stock selection and stock portfolio construction.

Relational Fusion-based Stock Selection with Neural Recursive Ordinary Differential Equation Networks

Stock selection attempts to rank a list of stocks for optimizing investment decision making, aiming at minimizing investment risks while maximizing profit returns. Recently, researchers have developed various (recurrent) neural network-based methods to tackle this problem. Without exceptions, they primarily leverage historical market volatility to enhance the selection performance. However, these approaches greatly rely on discrete sampled market observations, which either fail to consider the uncertainty of stock fluctuations or predict continuous stock dynamics in the future. Besides, some studies have considered the explicit stock interdependence derived from multiple domains (e.g., industry and shareholder). Nevertheless, the implicit cross-dependencies among different domains are under-explored. To address such limitations, we present a novel stock selection solution—StockODE, a latent variable model with Gaussian prior. Specifically, we devise a Movement Trend Correlation module to expose the time-varying relationships regarding stock movements. We design Neural Recursive Ordinary Differential Equation Networks (NRODEs) to capture the temporal evolution of stock volatility in a continuous dynamic manner. Moreover, we build a hierarchical hypergraph to incorporate the domain-aware dependencies among the stocks. Experiments conducted on two real-world stock market datasets demonstrate that StockODE significantly outperforms several baselines, such as up to 18.57% average improvement regarding SR.

Exploring Investment Optimization and “Greenwashing” from ESG Disclosure: A Dual Examination of Investor Perception

This study investigates the intricate correlation between environmental, social, and governance (ESG) information and the financial performance of companies, with a focus on the fundamental nature of ESG and its influence on the choices made by investors. This study examines available literature and data analysis to uncover how disclosing ESG information impacts investment optimization. Additionally, it clarifies the relationship between greenwashing and the advancement of green financial products. The study employs the XGBoost ensemble learning method, using non-financial features of ESG combined with financial features to construct a prediction model, achieving a prediction accuracy rate of 71.26%. Furthermore, applying this model aims to further utilize it in stock selection and constructing a stock pool. By analyzing the financial performance of companies predicted by the model, we will select potential high-performance stocks to build an investment portfolio. Then, we use the Markowitz portfolio theory to optimize the weight combination of stocks in the pool to maximize expected returns and minimize risk. After backtesting the investment portfolio using the closing prices in 2021, its annualized return was a positive 5.76%, significantly higher than the benchmark portfolio. Additionally, this study provides theoretical references and practical guidance for insight and addressing the potential large-scale greenwashing behavior under the trend of increasing ESG information disclosure in the future.

ANALYSIS OF MAGIC FORMULA FOR INVESTMENT RETURN IN INDIAN STOCK

Magic Formula Investing, popularized by Joel Greenblatt, is a strategy that focuses on selecting stocks based on two main criteria: high earnings yield and high return on capital. Earnings yield is the ratio of a company's earnings per share to its stock price, indicating how much profit a company is generating relative to its price. Return on capital, on the other hand, measures how efficiently a company is using its capital to generate profits. The goal of this strategy is to identify undervalued companies that exhibit strong financial performance. By targeting stocks with high earnings yield and return on capital, investors aim to uncover hidden gems in the stock market that may be overlooked by others. This methodical approach to value investing has garnered attention for its relative simplicity and effectiveness in outperforming the market. One of the key advantages of the Magic Formula is its straightforward nature, which makes it accessible to a wide range of investors. Unlike more complex investment strategies that require in-depth financial analysis, the Magic Formula provides a clear and systematic way to identify potential investment opportunities. Overall, Magic Formula Investing offers investors a disciplined and rules-based approach to stock selection, with the aim of achieving superior returns compared to the broader market. While past performance is not indicative of future results, the Magic Formula's track record

An automated quantitative investment model of stock selection and market timing based on industry information

As a growth asset, many listed companies have distinct characteristics of their respective industries. The listed companies in the same industry have a link effect, which can provide a wealth of information for stock operations. In this paper, a complete automated quantitative investment model is developed based on statistical models that use industry information to select stocks and time the market, thereby bringing out the synergistic effect of the system and ensuring maximum returns on investment. Risk control is taken into consideration in our model, a risk control factor is designed by measuring volatility of stock prices to determine the buying volume and the timing of stop loss, effectively safeguarding capital security. The latest industry classification results published by the China Securities Regulatory Commission are used as the basis for the industry classification. After data preprocessing, there are 70 sub-categories in 18 major categories of industry. We take the stock price of the 70 sub-categories from January 1, 2012 to January 1, 2022 as our research data. The back testing results show that positive returns are obtained in all industries except for six in our model. The average annualized rate of return is 11.10 %, which is higher than the stock indexes of the same period and far higher than the investment model of bank savings. Additionally, in accordance with a real trading system, the experiment simulates the inclusion of all transaction fees in the trading process, demonstrating the practical application value of our expert system.

Machine learning-based stock picking using value investing and quality features

Value Investing stands as one of the most time-honored strategies for long-term equity investment in financial markets, specifically in the domain of stocks. The essence of this approach lies in the estimation of a company's "intrinsic value," which serves as an investor's most refined gage of the company's true worth. Once the investor arrives at an estimation of the intrinsic value for a given company, she proceeds to contemplate purchasing the company's stocks solely if the prevailing market price of the stocks significantly deviates below the estimated intrinsic value, thus presenting an enticing buying opportunity. This deviation, referred to as the "margin of safety," represents the disparity between the intrinsic value and the current market capitalization of the company. Within the scope of this endeavor, our objective is to automate the stock selection process for value investing across a vast spectrum of US companies. To accomplish this, we harness a combination of value-investing principles and quality features derived from historical financial reports and market capitalization data, thereby enabling the identification of favorable value-driven opportunities. Our methodology entails the utilization of an ensemble of classifiers, where the class is determined as a function of the margin of safety. Consequently, the model is trained to discern stocks that exhibit value characteristics warranting investment. Remarkably, our model attains a success rate surpassing 80%, effectively identifying stocks capable of yielding an annualized return of 15% within a three-year timeframe from the recommended stock purchase date provided by the model.