Stock Portfolio Research Articles

Analisis Pembentukan Portofolio Optimal di Bursa Efek Indonesia dengan Menggunakan Model Markowitz

The purpose of this study is to utilize the Markowitz Model to build an optimal portfolio of stocks on the Indonesia Stock Exchange in 2019 to 2023, focusing on stocks in the IDX30 index. Based on information from KSEI, the public premium in capital market speculation continues to increase. On that basis, this study is important to be conducted in order to provide a reference for the public to invest. The IDX30 index was chosen as the subject of the study because it includes stocks with large market capitalization, high liquidity, and stable financial conditions. This study determines the optimal fund allocation for each stock and examines stocks in the IDX30 index from 2019 to 2023 that are worthy of consideration for inclusion in the optimal portfolio. In this test, it produces four stocks that become the optimal portfolio with the most extreme profits, namely ADRO 16.12%, BBCA 65.97%, BBRI 4.13%, and ICBP 13.76% with a return of 1.01% and a risk of 0.22%. In contrast, the minimum risk consists of ADRO of 5.29%, BBCA of 26.14%, CPIN of 13.91%, ICBP of 16.17%, INDF of 8%, KLBF of 0.48%, TLKM of 13.47%, and UNVR of 16.55%, with a risk of 0.1% and an expected portfolio return of 0.11%.

ANALYSIS OF OPTIMAL PORTFOLIO FORMATION ON IDX30 INDEXED STOCK WITH THE MEAN ABSOLUTE DEVIATION METHOD

In investing in stocks, an investor must be able to form a stock portfolio to obtain optimal results. Factor analysis is one way to select stocks to form a portfolio. Factor analysis with Principal Component Analysis (PCA) extraction is used to summarize many variables into new smaller factors by producing the same information. The new factor formed is called a portfolio. This study aims to form an optimal portfolio using the Mean Absolute Deviation (MAD) method, which is an alternative to Markowitz optimization, and assess the stock portfolio's performance using the Sharpe index. This research uses IDX30-indexed stocks because the stocks in this index have high market capitalization and liquidity. The data used in this study are daily close stock price data on the IDX30 index from September 20, 2022, to September 20, 2023. The data used is secondary data obtained from the official website https://finance.yahoo.com/. From the analysis, three stock portfolios were obtained. With MAD optimization, the investment weight of each stock is obtained namely, in the first portfolio, the investment weight of AMRT shares is 21.95%, BBCA shares are 30%, BBNI shares are 18.05%, and BBRI shares are 30%. In the second portfolio, the investment weight of AKRA shares is 34.03%, BRPT shares are 40%, and MEDC shares are 25.97%. In the third portfolio, the investment weight of BMRI shares is 50%, and INDF shares are 50%. By measuring the performance of the Sharpe index, the optimal portfolio is obtained in the second portfolio with an expected return portfolio of 0.155% and a portfolio risk of 1.927%.

Analysis of optimal portfolio formation using the single index model approach on Bisnis-27 index shares on the Indonesia Stock Exchange for the 2018-2022 period

Based on data from the Indonesian Central Securities Depository (KSEI), the number of investors in the Indonesian stock market is increasing, indicating the popularity of stocks as an investment instrument. This study aims to examine the returns and risks of stocks in the optimal portfolio, compared to those not under the optimal portfolio, using the Single Index Model. Secondary data from the Bisnis-27 index were used for the analysis. The results show that returns differ significantly between stocks in the optimal portfolio and those that are not. However, these risks were found to be insignificant. The determination of the optimal portfolio in this study can be used as a reference by investors when making investment decisions.

Comparative Analysis of LSTM and ARIMA Models in Stock Price Prediction: A Technology Company Example

Stock price forecasting has been an important area of interest for economists and computer scientists. In addition to traditional statistical methods, advanced artificial intelligence techniques such as machine learning can stand out with their ability to process complex data sets and adapt to historical data. In recent years, hybrid models combining deep learning and time series methods have demonstrated superior performance in stock selection and portfolio optimisation. This study comparatively analyses the performance of LSTM and ARIMA models in time series forecasting. In the study, the stock prices of Oracle company are predicted using two different models, LSTM and ARIMA. Model performance is evaluated using metrics like MSE, MAE, RMSE, and MAPE. Both models have been found to be successful in different metrics. The LSTM model has lower error values; meanwhile, the ARIMA model produced proportionally more accurate forecasts. The study concludes that given the potential offered by deep learning, models such as LSTM are essential for time series forecasting. The flexibility of deep learning allows the development of customized models for different data types and time series problems.

A Heuristic for Fat-Tailed Stock Market Returns

Large negative stock and equity portfolio rates of return occur more frequently than they should under the Gaussian normal distribution. They tend to be kurtotic, roughly as if they were drawn from Student T-distributions with 2 to 5 degrees of freedom. A very easy adjustment to help assess the probability of losses is to work with a transformed Z score, Z ′ = − 1.25 − log ( − Z ) . For example, one should expect a stock return that is 17 standard deviations below the mean under the empirical distribution as often as one would expect to see a draw that is Z ′ = | – 1.25 − log ( 17 ) ≈ − 4 | standard deviations below the mean under the idealized normal distribution.

Index tracking via reparameterizable subset sampling in neural networks

Index tracking aims at replicating the benchmark index performance by constructing a tracking portfolio consisting of partial component stocks of the index. This is an NP-hard question because of the cardinality constraint which limits the number of component stocks in the tracking portfolios. This paper proposes a new method to solve this problem in an end-to-end way by using a continuous subset sampling relaxation which can provide differentiable gradients calculation in neural networks. The proposed approach is tested with the FTSE-100 index data and the S&P100 index data, and compared with widely used index tracking methods, such as the forward and backward selection and optimization solvers. According to the empirical results, our proposed approach achieves the best tracking performance for different evaluation criteria.

Do clean energy stocks diversify the risk of FinTech stocks? Connectedness and portfolio implications

The FinTech sector is growing rapidly, prompting a need to explore effective investment diversification strategies for stocks in this sector. The existing literature has identified the benefits of using clean energy stocks to diversify stock portfolios and the purpose of this research is to estimate how useful clean energy stocks are for diversifying an investment in FinTech stocks. This study uses a QVAR model to estimate the dynamic return connectedness between FinTech stocks and clean energy stocks for the period September 2016 to April 2024. Total connectedness is time varying and is higher in the tails than at the median. The onset of the COVID-19 pandemic had a large but short-term impact on connectedness. Under normal market conditions, systemic risk increases by 3.5% per year. FinTech is a net transmitter of shocks to nuclear energy but is mostly unaffected by shocks from wind, solar, and nuclear energy stocks illustrating the diversification benefits of these sub-sectors. Portfolio analysis shows that adding solar, wind, and nuclear energy to a portfolio with FinTech can produce higher risk adjusted returns and lower drawdown than an investment solely in FinTech stocks. These results are robust across various portfolio rebalancing frequencies (daily, weekly, monthly). For example, a minimum connectedness portfolio rebalanced daily has an average annual return of 11% and a Sharpe ratio of 0.37. These values are higher than their respective values for an investment solely in FinTech stocks (5.4%, 0.11). Thus, clean energy stocks do provide diversification benefits for investments in FinTech stocks.

Asset Selection and Portfolio Management on LQ 45 Using Active and Passive Management Strategy

The aim of this research is to build a stock portfolio contained in the LQ 45 index using passive and active strategy methods based on financial report data published during the 2016-2019 period, choosing that year to avoid abnormal performance during Covid year between 2020 - 2022. To form the portfolio, it is grouped based on high, medium and low Tobin's Q values and high, medium and low of ROE. To measure the performance of the portfolio using we used Sharpe, Treynor and Jensen methods. The research results show that using an active strategy shows the highest average total return of 19.97% with a risk of 18.23%, followed by an annual active management strategy and then semi-annual active management. Meanwhile, using a passive management strategy shows that the medium ROE group produces an optimum portfolio, while the annual active and semi-annual active groups show that the optimum portfolio is in the medium Tobis' Q group. The varied results contradict the research conducted by Pasaribu (2009), Hidayat and Hendrawan (2017), Hendrawan and Salim (2017), and Hendrawan et al. (2020). In contrast to the research conducted by Pham (2018), the composition of ROE is more capable of providing higher returns than the Tobin's Q composition. This research also shows varied results of the ROE portfolio, in contrast to Barka and Hamza (2019).

Do individual investors gamble to recoup losses?

ABSTRACT This study presents novel empirical insights into the speculative behaviours of individual investors within the stock market. Using detailed data on individual investors’ stock portfolios, we find that investors are more likely to invest in lottery stocks and to invest a larger proportion of their stock portfolio in lottery stocks after experiencing a large loss. These results are consistent with Thaler and Johnson’s (1990) experimental findings, showing that investors, when faced with losses, tend to increase their risk appetite, displaying a preference for investment options that present a perceived opportunity to recoup losses.

Research on Stock Portfolio Construction Based on Bi-LSTM Neural Networks

Research on Stock Portfolio Construction Based on Bi-LSTM Neural Networks

Implementing the Variance-Covariance Method for Assessing Market Transaction Risks in Raw Material Sector Stocks

The capital market plays a crucial role in supporting a country's economic growth. Besides being a funding source, the capital market also serves as an investment avenue for investors, particularly through stocks. Every investor must be willing to bear risks in line with their targeted returns. Risk is defined as the uncertainty of future outcomes due to market condition changes, and VaR (Value at Risk) is used to determine the tolerated loss at a certain confidence level. This study discusses the application of the Value at Risk (VaR) method using the Variance-Covariance approach to mitigate market risks in the portfolio of raw material sector stocks. The study focuses on two raw material sector stocks in Indonesia, assuming a normal distribution of asset price changes. The measurement results indicate that with an investment of Rp. 100,000,000.00, a 95% confidence level, and a 1-day period, the VaR of the portfolio of these five stocks is Rp. 2,769,750.00. This research provides critical insights to assist investors in understanding and managing portfolio risks, making VaR a key indicator to measure potential future risks and laying the foundation for decision-making in risk management.

Value and quality investing strategy in Indian stock market

PurposeThe objective of the study is to shed light on the notion of quality investing in the Indian stock market. The study also attempts to combine the value and quality metrics to test their ability to generate a higher risk-adjusted return.Design/methodology/approachThe paper employs asset pricing models to examine the excess risk-adjusted returns and panel regression model (random estimates) to determine the price of quality in the cross-section of Bombay Stock Exchange (BSE) listed stocks from 2003 to 2020.FindingsThe results indicate that the quality-only strategy failed to produce substantial risk-adjusted returns in the Indian stock market. The returns to long/short hedging strategy quality-minus-junk (QMJ) are significantly positive with the majority of the returns attributable to the short leg of the stock portfolio. The findings further discovered that the explanatory effect of quality on prices is limited. In particular, a strategy that combines value and quality investing generated positive and significant alphas as well as a higher Sharpe ratio.Practical implicationsThe study provides investors and portfolio managers with valuable insights for navigating undervalued high-quality equities in the Indian stock market.Originality/valueThis is the first research of its kind to examine the performance of quality (Q score indicator) combined with value investing in the Indian stock market. As majority of research have concentrated on developed economies, this study offers out-of-sample evidence to validate the strategy’s success in an emerging market.

Two-stage stock portfolio optimization based on AI-powered price prediction and mean-CVaR models

With the advancement of prediction methods in the field of artificial intelligence, accurate price predictions can effectively support financial portfolio selection. This paper proposes an intelligent stock portfolio selection method based on a prediction neural network, incorporating signal processing and hyperparameter optimization techniques. The method is divided into two key stages: stock price prediction and portfolio selection. In the first stage, we apply Savitzky–Golay filtering to denoise price data and reveal its patterns, and optimize the hyperparameters of the long short-term memory network using the sparrow search algorithm to achieve high-precision stock price predictions. In the second stage, we use the mean-Conditional Value-at-Risk (mean-CVaR) model to select the optimal stock allocation, considering factors such as potential returns, prediction accuracy, and growth rate. Numerical comparisons based on multiple public financial datasets demonstrate that the proposed two-stage method significantly outperforms seven benchmark methods. Specifically, on the Shanghai and Shenzhen 300 (CSI 300) Index dataset, the proposed method achieves a determination coefficient of 0.9980 and an accuracy rate of 97.05%. Additionally, its cumulative returns reach 9.38%, 8.63%, and 7.54% at different confidence levels.

Optimal Portfolio Analysis on Stocks Listed in Lq45

The purpose of this study is to determine the optimal portfolio of stocks that are listed in the LQ-45 period (January 2023 – January 2024) and compare the return and risk in stocks that are included in the LQ-45 but not included in the optimal portfolio. The method used is the Single Index Method, which uses the ERB (Excess Return to Beta) assessment reference. The results showed that out of 45 stocks there were 10 stocks that had large ERB, which were included in the optimal portfolio were GGRM, BBTN, KLBF, EXCL, ICBP, MAPI, UNVR, CPIN, INDF, and TBIG, which provided a return of 6.61% per year and a risk of 0.08% per year. Meanwhile, the remaining thirty-five stocks were made up as well as 10 other portfolios. Each of these portfolios consists of 10 randomly selected stocks. These ten portfolios yield higher returns than optimal portfolios. However, they also have a higher risk. The results of the comparison of the coefficient of variation between the optimal portfolio and the other 10 portfolios show that the optimal portfolio is the best portfolio

The volatility of stock investor returns

The volatility of stock investor returns depends not only on the volatility of the stocks they hold but also on their time-varying capital exposure to these holdings. Using individual stocks, portfolios of stocks, and indexes across U.S. and international stock markets, we provide comprehensive evidence that the volatility of investor returns is consistently higher than the corresponding volatility of stock returns across nearly all specifications. The relative magnitude of the volatility differential ranges from 10% to 75%, increasing with investment horizon. This discrepancy is driven primarily by investors’ propensity to "flee volatility," withdrawing equity capital following periods of high volatility.

PERFORMANCE OF DIA AND FORWARD-LOOKING OPTIMAL PORTFOLIOS OF DOW STOCKS

Purpose- This paper compares the performance of DIA, trailing optimal portfolio and forward-looking optimal portfolio constructed from a pool of DOW stocks, applying a modified contrarian portfolio construction to the forward-looking optimization. The modified contrarian optimization of this study is based on the premise that loser stocks, in the short run, would have reversal performance and become winner stocks in the short-run future. The investigative question is: Do forward-looking optimal portfolios of DOW stocks perform better than trailing optimal portfolios of DOW stocks in the short run after DJIA hit the year's lowest point in 2022? Methodology- To answer the investigative question, this study compares the short-run performance of forward-looking optimal portfolios with the performance of trailing optimal portfolios. Elton, Gruber, and Padberg (1987) originally introduced the optimal portfolio technique. Findings- The primary focus was on the case related to September 30, 2022, when DJIA hit the lowest level in 2022. To get the trend analysis of the cases of DJIA hitting the lowest level of the year, this study examined two comparable findings, having examined the performance properties of trailing vs. forward-looking optimal portfolios using the same method. One examined the case related to March 23, 2020, and another examined the case related to December 24, 2018. It finds a robust performance of DIA compared to the performance of two forms of optimal portfolios. It also finds that forward-looking optimal portfolios performed better than trailing optimal portfolios regarding the average performance of three cases. Conclusion- It concludes the potential usefulness of DIA as evidence of the market efficiency of DOW stocks. At the same time, forward-looking optimal portfolios for short-run investment in DOW stocks are a viable alternative to investing in the DIA.

Cross-Sectional Variation of Option-Implied Volatility Skew

The stock option-implied volatility skew reflects both the structural risk characteristics of the underlying company and the short-term information flow about the stock price movement. This paper builds a semistructural, cross-sectional option pricing model to separate the structural risk contributions from the information flow. The model identifies two structural risk sources that contribute to the cross-sectional variation of the skew: the company’s business cyclicality and its default risk. The model can explain as much as 44% of the cross-sectional variation in implied volatility skew and is particularly informative during and after recessions. The remaining skew variation reflects mainly short-term information flow and can be used to construct stock portfolios with much better investment performance and without hidden structural risk exposures. This paper was accepted by Agostino Capponi, finance. Funding: L. Wu gratefully acknowledges support by a grant from the City University of New York PSC-CUNY Research Award Program. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4872 .

Optimal investment for asset–liability management with delay and partial information under Ornstein–Uhlenbeck process

In this paper, we investigate the optimal investment strategy of asset liability management (ALM) with bounded memory and partial information. Suppose that investors invest their assets in a financial market consisting of a risk-free bond and a risk-free stock, while also taking on liabilities, in which the value of liabilities and the price of risky assets satisfy the Ornstein–Uhlenbeck (O–U) processes whose drift terms are unobserved. By constructing a dynamic portfolio of risk-free bonds, risky stocks and liabilities, a stochastic delay differential equation is obtained to depict the surplus process of investor. The ALM problem is formulated as finding the best strategy to maximize the terminal utility of the sum of terminal surplus and some historical wealth under partial information, and the corresponding full information case is also studied as a supplement. For both cases of partial information and full information, we apply the dynamic programming method to derive HJB equations, verification theorems, and closed-form solutions of optimal strategies and value functions. Moreover the relationship between optimal strategy and value function under full information and partial information is also given. Finally, numerical examples are carried out to illustrate the influence of some important parameters on the obtained results.

APPLICATION OF QUADRATIC PROGRAMMING ON PORTFOLIO OPTIMIZATION USING WOLFE’S METHOD AND PARTICLE SWARM OPTIMIZATION ALGORITHM

Stock portfolios can be modeled into quadratic programming problems using the Markowitz mean-variance model. Quadratic programming problems can be solved using two methods, namely classical and heuristic methods. In this research, the classical method uses Wolfe’s method, while the heuristic method uses the particle swarm optimization (PSO) algorithm. This research aims to determine optimal results in portfolio problems using two methods, namely Wolfe’s method and the PSO algorithm. The data used in this research is data from 10 stock companies that distribute the highest dividends in the IDX High Dividend 20 category for the 2022 period. The research results discuss the portfolios of PT Astra International Tbk and PT. Indo Tambangraya Megah Tbk. Based on the result, using Wolfe’s method, the ASII and ITMG stock portfolios are obtained, namely the optimal proportion of ASII shares = 0.76401 or 76.401% and ITMG shares = 0.23598 or 23.598%, while the PSO algorithm obtains a portfolio of ASII and ITMG shares, namely ASII shares = 75.02% and ITMG shares = 24.98%. Compared to Wolfe’s method, the PSO algorithm has a smaller Z value 5.7.

Investing in US Timberland Companies

Are common stocks issued by timberland companies a good investment? Portfolios of large US timberland corporations are compared to simultaneous investments in a diversified US common stock index. Over a 20-year sample period it turns out that the US timberland corporations, on average, perform about as well as the highly diversified US stock market index. It is surprising that the timberland companies do not outperform the stock market indexes because, in order to encourage tree planting, the US Congress has almost completely exempted timberland companies from paying federal income taxes. Furthermore, it is scientifically impossible to assess the value of the large amounts of photosynthesis that the timberland companies produce. As a result of these two ambiguities, it is difficult to state decisively that the timberland companies are better investments than a diversified portfolio of common stocks. However, valuing timberland companies is more practical than endeavoring to value the trees directly.