Stock Portfolio Research Articles

An Analysis of the Relationship between Business Confidence and Macroeconomic Factors: Türkiye Case

Investor sentiment plays an important role in the all level of economic activities. Confidence as an indicator of investor sentiment has become an important and interesting concept in recent years. Confidence indicators are a way in assessing economic developments in the short term. The aim of this study is to analyze the long and short term relationship between the Real Sector Confidence Index and macroeconomic/financial variables for the period of January 2007 – December 2021. The Johansen Cointegration Test results prove a long-term cointegration relationship. Vector Error Correction Models for both macroeconomic and financial variables are statistically significant at the 5% level. In the short run, Manufacturing Capacity Utilization Ratio, Official Reserves, Domestic Debt Stocks, Real Exchance Rate, US Dollar/TRL Buying Rate, Non-Residents’ Stock Portfolio and Volatility Index have a statistically significant effect on the changes in Real Sector Confidence Index. Thus, this study informs investors to predict the effects of changes in the confidence index on financial instruments bilaterally and contribute to their investment decision.

Developing a hybrid system for stock selection and portfolio optimization with many-objective optimization based on deep learning and improved NSGA-III

Portfolio management is a critical aspect of investment strategies, with the goal to balance the low-risk and high-return investments. Despite this, existing portfolios frequently overlook the integration of stock selection outcomes and underutilize data from listed companies, leading to suboptimal portfolio performance. Addressing these shortcomings, this paper introduces a hybrid system involving stock selection and portfolio optimization. In stock selection, the system employs a combination of convolutional neural network and bi-directional recurrent neural network to predict stock trends. This approach enables the identification of stocks likely to appreciate in value, setting the stage for their inclusion in the subsequent optimization process. For portfolio optimization, the study formulates a five-objective optimization problem that incorporates mean, variance, skewness, kurtosis, and distance-to-default as key considerations. To solve the many-objective constrained optimization problem, an advanced strategy employing a static penalty function and an improved Non-dominated Sorting Genetic Algorithm III (NSGA-III) based on tent chaotic mapping is utilized. The efficacy of the proposed hybrid system is rigorously tested through three sets of ablation experiments alongside two discussions focused on its robustness and computational efficiency. The findings from these investigations reveal that the hybrid system outperforms traditional approaches, reducing risks and improving returns for investors.

LQ45 Stock Portfolio Optimization by Considering Return Predictions Using the Support Vector Regression (SVR) Method

Investors require stock portfolio optimization to achieve desired outcomes. This process involves prediction to obtain optimal weights in the expected portfolio. Historically, portfolio optimization has primarily considered risk and expected value. However, a new approach has integrated return prediction into traditional time series models, enhancing the performance of portfolio optimization models. Machine learning has proven effective in predicting stock market trends, with many researchers applying these models in portfolio formation with satisfactory results. This study employs Support Vector Regression (SVR) method to integrate return prediction in portfolio formation, utilizing stock data from the LQ45 index. Test results indicate that return prediction using SVR method yields a Root Mean Square Error (RMSE) of 0.34973. Portfolios considering return prediction demonstrate superior performance compared to the LQ45 index, as evidenced by portfolio return averages, standard deviation, and Sharpe ratio.

Portfolio Performance Analysis Using the Treynor Model in the Early Period of the Covid-19 Pandemic

This study aims to determine portfolio performance using the Treynor model for companies listed on IDX30 in the early period of the COVID-19 pandemic (February-July 2020). This research method is a quantitative descriptive method which in this study tries to analyze, describe and explain the performance of a stock portfolio using the Treynor method in companies listed on IDX30-BEI. The sampling method was purposive sampling method in order to obtain 5 issuers, namely PT. Aneka Tambang Tbk, PT Bank Negara Indonesia Tbk, PT. Erajaya Swasembada Tbk, PT. Indofood Sukses Makmur Tbk and PT. Kalbe Farma Tbk. The results showed that the beta portfolio or the highest investment risk was found in the portfolio composition 22. The lowest risk was in the portfolio composition 12. The highest rate of return in portfolio composition is 28. The lowest rate of return is portfolio 16. Optimal portfolio performance using the Treynor method is found in the 18th portfolio.

LQ45 Stock Portfolio Optimization by considering Return Predictions using the Holt Winter Method

A portfolio represents a selection of assets owned by individuals or groups with the goal of generating profit. Stocks are among the various forms of investments available. Investors typically consider two key factors: expected returns and associated risks. Portfolio optimization endeavors to maximize returns while minimizing risks. With the progression of time, portfolio optimization strategies increasingly incorporate return predictions using machine learning techniques. In this study, the Holt-Winter method is employed to forecast stock prices and returns, known for its accuracy in estimating seasonal time series data. Previous research primarily utilized the Mean- Variance method for portfolio optimization, but the outcomes were deemed unsatisfactory. Therefore, this study adopts a novel approach by integrating the Mean-Variance model with forecasting. Performance testing of the optimal portfolio emphasizes sensitivity, aiming for substantial average returns, minimal standard deviation, and a high Sharpe ratio. Comparative analysis with the LQ45 index portfolio reveals that the sensitivity-driven portfolio exhibits superior performance, yielding higher values in terms of average returns, standard deviation, and Sharpe ratio.

The Fama-French Three Factor Model Test on Excess Stock Return: Evidence From Hong Kong, Indonesia and Singapore Capital Market

The presence of the capital market has a very important role in the world economy because the capital market carries out economic and financial functions. One of the instruments in the capital market is stock. In stock investment, investors expect a return with a size that is in accordance with the level of risk they can afford. This study wants to test whether the Fama-French Three Factor Model variables, namely market excess returns, book-to-market equity and firm size, can be used to explain excess returns on the stock markets of Hong Kong and Indonesia with the observation period of 2018–2022. Based on the test results multiple linear regression, Gibbons, Ross, and Shanken test (GRS test) and spanning test, this study found that the variables in the Fama-French Three Factor Model can simultaneously be used to explain the excess return on all stock portfolios on the stock market in Hong Kong and Indonesia, and can only be used to explain the excess return on Big and S-H portfolios in Singapore. Based on the results of the GRS-test, in the Hong Kong market the most efficient portfolio is S-M, in the Indonesian market the most efficient portfolio is the Big portfolio, while in the Singapore market all portfolios are efficient and based on the spanning test, in the Hong Kong market the potential redundant factor is SMB, in in the Indonesian market, the potential redundant factor is HML, while in the Singapore market, there is no redundant potential factor.

Stock portfolio optimization for risk-averse investors: A novel hybrid possibilistic and flexible robust approach

This study proposes stock portfolio optimization models for risk-averse investors under uncertain conditions. To accomplish this, the study measures risk based on the conditional drawdown-at-risk (CDaR) measure, which can prevent major declines in investment as a conservative investment strategy. Given the uncertain character of the input parameters of the problem, the study first proposes a hybrid possibilistic and flexible model by considering the CDaR measure (CDaR-HPFM) to handle uncertainty. Furthermore, to offer more robust outcomes, the study constructs a hybrid possibilistic and flexible robust model by considering the CDaR measure (CDaR-HPFRM), in the forms of a non-linear and a linear mathematicalprogramming problem. The CDaR-HPFRM can process the robustness of output decisions while dealing with uncertain parameters. The real stock exchange data of 100 companies registered on the Tehran Stock Exchange are investigated to validate the functionality of the proposed models. The results reveal that, when various penalty costs are factored in, the CDaR-HPFRM yields more efficient and more robust results than the CDaR-HPFM. Comparative results also indicate that the proposed models almost always outperform existing methods, in terms of both CDaR and rate of return measures. Proposed models can be used for all types of stock market investments (including micro-investing and investment funds) and can handle portfolio optimization and selection processes in project-based organizations.

Deep Reinforcement Learning Model for Stock Portfolio Management Based on Data Fusion

Deep reinforcement learning (DRL) can be used to extract deep features that can be incorporated into reinforcement learning systems to enable improved decision-making; DRL can therefore also be used for managing stock portfolios. Traditional methods cannot fully exploit the advantages of DRL because they are generally based on real-time stock quotes, which do not have sufficient features for making comprehensive decisions. In this study, in addition to stock quotes, we introduced stock financial indices as additional stock features. Moreover, we used Markowitz mean-variance theory for determining stock correlation. A three-agent deep reinforcement learning model called Collaborative Multi-agent reinforcement learning-based stock Portfolio management System (CMPS) was designed and trained based on fused data. In CMPS, each agent was implemented with a deep Q-network to obtain the features of time-series stock data, and a self-attention network was used to combine the output of each agent. We added a risk-free asset strategy to CMPS to prevent risks and referred to this model as CMPS-Risk Free (CMPS-RF). We conducted experiments under different market conditions using the stock data of China Shanghai Stock Exchange 50 and compared our model with the state-of-the-art models. The results showed that CMPS could obtain better profits than the compared benchmark models, and CMPS-RF was able to accurately recognize the market risk and achieved the best Sharpe and Calmar ratios. The study findings are expected to aid in the development of an efficient investment-trading strategy.

Unveiling Outperformance: A Portfolio Analysis of Top AI-Related Stocks against IT Indices and Robotics ETFs

In this study, we delve into the financial market to compare the performance of prominent AI and robotics-related stocks against traditional IT indices, such as the Nasdaq, and specialized AI and robotics ETFs. We evaluate the role of these stocks in diversifying portfolios, analyzing their return potential and risk profiles. Our analysis includes various investment scenarios, focusing on common AI-related stocks in the United States. We explore the influence of risk management strategies, ranging from “buy and hold” to daily rebalancing, on AI stock portfolios. This involves investigating long-term strategies like buy and hold, as well as short-term approaches, such as daily rebalancing. Our findings, covering the period from 30 April 2021, to 15 September 2023, show that AI-related stocks have not only outperformed in recent years but also highlight the growing “AI bubble” and the increasing significance of AI in investment decisions. The study reveals that these stocks have delivered superior performance, as indicated by metrics like Sharpe and Treynor ratios, providing insights into market trends and financial returns in the technology and robotics sectors. The results are particularly relevant for investors and traders in the AI sector, offering a balanced view of potential returns against the risks in this rapidly evolving market. This paper adds to the financial market literature by demonstrating that investing in emerging trends, such as AI, can be more advantageous in the short term compared to traditional markets like the Nasdaq.

Portfolio Optimization Analysis Using Markowitz Model on Idx30 Stock Index in 2022 and 2023

Economic growth today has an impact on the lives of residents of a country. Investing in the stock market involves a relatively high degree of risk, as stock prices can fluctuate very quickly. However, a proper analysis in forming a portfolio is very important before making any investment decision to get maximal return. This research will use Markowitz Model to get optimal. This model explains the importance of diversification and how it can reduce overall risk while increasing returns. Data to be use are from IDX30 Index Companies in 2022 and 2023 because this index includes stocks from various industrial sectors that reflects significant developments in the Indonesian stock market. Data processing in this research will use Python. This model will determine best combination to become optimal portfolio from list companies in IDX30 Index. The result from this research shows that there 7 stocks become the combination for the optimal portfolio with the expected returns rate of is 65.776% and the portfolio risk is 20.0008%. The weights for each stock in optimal portfolio obtained using PyPortfolioOpt package in Python, are BBNI.JK = 12.024%, BMRI.JK = 16.142%, INCO.JK= 5.033%, INDF.JK = 12.36%, ITMG.JK = 38.606%, KLBF.JK = 11.148%, UNVR.JK.=4.688%. Markowitz model can investor investor an consideration to select the stocks recommendation with the optimal weight for their portfolio to get maximum return with low risk.

The effect of the 2008–09 short selling sales ban on UK security equities in relation to market metrics of volatility, liquidity, and price discovery

In this study we look at the Financial Services Authority imposed short sale ban of UK financial equities in the 2008–2009 financial crisis to explore the effects of the short selling ban on liquidity, volatility, and price discovery on alternative portfolios of UK equity stocks. To this end we employ a control portfolio as well a portfolio of banned stocks. By using a GARCH model to explore the effects of short selling on portfolios’ returns volatility, we find a decline of volatility for selected UK financial stocks during the short sale restriction period. Further, we employ a bid-ask spread model to explore the effects of short selling on liquidity and find that liquidity deteriorates during the short sale ban period. We also employ a Wald-Wolfowitz Runs Test to investigate the effects of the short sale ban on price discovery and find that price discovery deteriorated during the short sale ban period. Finally, we examine the causal relationships among the market metrics in order to provide additional insight into the structure of the UK equity market during the period under study.

Optimisasi Portofolio Saham Syariah dengan Pendekatan Fuzzy Goal Programming

This study discusses about solving the problem of stock portfolio optimization. The portfolio optimization problem can be modeled as a multiobjective model with two objective functions, they are maximizing return and minimizing risk. This study uses the mean-variance Markowitz model to model the portfolio. The fuzzy goal programming approach is used to solve stock portfolio problems by converting a multi-objective model into a fuzzy mathematical programming model which is solved using a nonlinear programming method. The results of implementing the model on stocks listed on the Jakarta Islamic Index show that the best investment proportion forms an optimal portfolio according to the level of aspirations. Several different optimal portfolios can be formed using the fuzzy goal programming approach. The difference in the results of these portfolios depends on the level of aspiration of investors.

Monte carlo simulation with bilstm for day-ahead stock portfolio management

<div>Predicting stock price movement and optimizing day-ahead stock portfolios are challenging tasks due to the inherent complexity and volatility of financial markets. This study proposes a novel approach that combines bidirectional long short-term memory (BiLSTM) neural networks with monte carlo simulation (MCS) to enhance day-ahead stock portfolio management. In the proposed methodology, historical data of the top-performing 10 stocks from different sectors of the National Stock Exchange of India (NSEI) is obtained from 1 January 2004 to 30 June 2023 and utilized to train a BiLSTM model. This model effectively extracts intricate patterns and trends from the time series, leading to more accurate and robust stock price predictions. MCS generates different scenarios, considering various market conditions and uncertainties. These scenarios provide a comprehensive view of the portfolio’s performance under different conditions, thus mitigating the risk of relying solely on a single prediction. The study evaluates the proposed framework and compares its performance against traditional portfolio management strategies. Results demonstrate that the MCS with the BiLSTM approach outperforms traditional methods in terms of risk-adjusted returns and portfolio stability.</div>

Determination of Optimal Stock Portfolio Return by Single Index Model (Case Study on Banking Sector Stocks in Indonesia)

The optimal portfolio is a portfolio chosen by investors from the many options available in the collection of efficient portfolios. To get the optimal proportion, which is the maximum return and minimum risk, it is necessary to analyze the stocks to be selected in the investment model. The research objective is to determine the optimal return, risk, and proportion for each banking stock portfolio in Indonesia in the period February - July 2023. The method used is the Single Index Model. The process of determining the optimal proportion of stocks with the Single Index Model requires stock and market return data as the main basis for applying this method. This study involves the formation of an optimal portfolio of daily closing prices of 46 banking stocks. As a result of this research, there are 5 optimal stocks that meet the criteria for optimal portfolio formation with each fund proportion of 21.43% (BNII), 13.52% (BDMN), 35.02% (BBRI), 23.69% (BTPN), and 6.34% (BBCA). Expected return from optimal stocks is 0.152% and the risk that will be borne by investors is 0.0011% per day.

A hybrid model for stock price prediction based on multi-view heterogeneous data

Literature shows that both market data and financial media impact stock prices; however, using only one kind of data may lead to information bias. Therefore, this study uses market data and news to investigate their joint impact on stock price trends. However, combining these two types of information is difficult because of their completely different characteristics. This study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine (SVM). It works by simply inputting heterogeneous multi-view data simultaneously, which may reduce information loss. Compared with the ARIMA and classic SVM models based on single- and multi-view data, our hybrid model shows statistically significant advantages. In the robustness test, our model outperforms the others by at least 10% accuracy when the sliding windows of news and market data are set to 1–5 days, which confirms our model’s effectiveness. Finally, trading strategies based on single stock and investment portfolios are constructed separately, and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.

Investment Portfolio Optimization Using Black-Litterman Model in Smart Carbon Economy Transition

An optimal investment portfolio needs to be formed before an investor invests because it can help investors determine which financial instruments are suitable to choose in order to get the maximum return or profit and the minimum level of risk. In the current situation, where there is an economic transition to a smart carbon economy or low carbon economy, it is necessary to form the optimal portfolio of stocks to facilitate investors in making investments. The purpose of this study is to form the optimal investment portfolio using the Black-Litterman model in a smart carbon economy. The data used is stock data from 24 companies listed on the LQ45 Low Carbon Leaders index for the period 2022-2023. Based on the research results, the Black-Litterman model generates the optimal portfolio with a 0.1% expected return. Thus, the optimal portfolio results with the Black-Litterman model are estimated to generate a profit of 0.1% for smart carbon stock data listed on the LQ45 Low Carbon Leaders index for the 2022-2023 period.

Stock Portfolio Analysis of Agribusiness Companies on Indonesia Stock Exchange

Stock portfolio analysis is the analysis conducted by diversifying or selective combining shares in investment, with a portfolio of risks to be minimized. The purpose of this study is to find out the formation of optimal portfolios using the Capital Asset Pricing Model (CAPM) model approach and Arbitrage Pricing Model (APT). With both models, an investor will be able to find out the composition of stocks, especially shares of companies engaged in agribusiness, as well as directly to find out the proportion of each stock and its risks and benefits arising from the formation of the portfolio. The results showed that by using the CAPM and APT models in the calculation of the formation of optimal portfolios in stocks excange Agribusiness, produces a portfolio with stocks composision 3 froms, namely AALI, TBLA and UNSP. APT model approach shows a greater value than the CAPM models, the highest return value can be generated is 14.088% while that may happen is losses -0268% while the CAPM models yield the highest return rate of 5.74% and the losses will be experienced by -14.54 %. significant difference in outcome is caused from the different assumptions of the models above. From these results should be expected investors to take investment decisions of agribusiness firms stocks in the Indonesia Stock Exchange with respect to the results obtained from the approach to the CAPM and APT models, in addition to the results obtained from both models are also investors give the attention of fundamental and technical factors companies that investors are not wrong and more confident in make investment decisions.

Optimal Stock Portfolio Analysis using Mean-Value at Risk (Mean-VaR) under Arbitrage Pricing Theory (APT)

Investing in Sharia-compliant stocks is one of the rapidly growing investment options, making it a potential choice for investors' portfolios. Therefore, investors need to understand how to select an optimal composition of stocks in their portfolio. This research aims to calculate the expected return on Sharia-compliant stocks and determine the optimal portfolio. The data used in this study includes stocks within the Indonesian Sharia Stock Index (ISSI) in the energy and mining sectors from November 1, 2022, to October 30, 2023. The analytical models employed are the Arbitrage Pricing Theory (APT) and Mean-Value at Risk (Mean-VaR). Based on the research findings, seven stocks form the composition of the optimal stock portfolio. These stocks are AKRA, ANTM, PGAS, INCO, INDY, PTBA, and MDKA, with weights of 20.54%, 19.58%, 19.02%, 14.24%, 10.97%, 8.00%, and 7.66%, respectively. The expected return for the investor is 0.13% per day, with a corresponding risk of 0.23%.

A novel regret-rejoice cross-efficiency approach for energy stock portfolio optimization

As climate change intensifies, more and more people realize that it is necessary and urgent to optimize energy structure through constructing energy asset portfolios. However, most of existing literature on energy asset portfolio models neglects real market restrictions, and the related portfolio performance evaluation approaches ignore the impact of the psychological factors of decision makers (DMs) on evaluation results, which limit their applications. To overcome these limitations, we first present a mean-WMCVaR (MWMC) model for energy stock portfolio selection with real features. Then, we employ the proposed MWMC model and its closely related works to yield a set of investment schemes and determine their characteristic index values including mean, WMCVaR, carbon intensity and ESG. Subsequently, we present a novel regret-rejoice cross-efficiency (NRRCE) approach to evaluate the performance of these investment schemes, and then make a comparison with the ones of the other seven competitive evaluation approaches by using an empirical example. Empirical results show that the proposed MWMC model can yield an optimal investment scheme with a better trade-off between economic and ecological benefits, and our NRRCE approach can effectively provide reasonable and reliable evaluation results for different investment schemes.

Modern Portfolio Theory and Its Applications

According to the modern portfolio theory, the monthly return data of 4 groups of portfolios (40 stocks) from November 5, 2011, to November 5, 2023, are empirically found: (1) the average monthly yield curve of stock portfolios group#1 and group#4 fluctuates significantly, while the volatility of equity portfolios group#2 and group#3 is relatively moderate. Especially after the new crown epidemic outbreak in 2020, the average monthly returns of all portfolios fluctuated sharply. (2) The expected return of NVDA is the highest among all stocks in the portfolio, and the IMB is the lowest. (3) Compared to the given portfolio returns, the #3 portfolio performs best, while the fund of group #1 performs the worst, and the remaining two portfolios perform well. In addition, if only the portfolio return and standard deviation are used as the portfolio return risk measures, the theoretical portfolio is better than the existing actual portfolio. Therefore, the return of a portfolio of securities will not exceed the yield of the benchmark portfolio at the same level of risk, which, to some extent, supports the hypothesis of the effectiveness of the securities market.