Portfolio Value Research Articles

Scenario selection with LASSO regression for the valuation of variable annuity portfolios

Variable annuities (VAs) are increasingly becoming popular insurance products in many developed countries which provide guaranteed forms of income depending on the performance of the equity market. Insurance companies often hold large VA portfolios and the associated valuation of such portfolios for hedging purposes is a very time-consuming task. There have been several studies focusing on inventing techniques aimed at reducing the computational time including the selection of representative VA contracts and the use of a metamodel to estimate the values of all contracts in the portfolio. In addition to the selection of representative contracts, this paper proposes using LASSO regression to select a set of representative scenarios, which in turn allows for the set of representative contracts to expand without significant increase in computational load. The proposed approach leads to a remarkable improvement in the computational efficiency and accuracy of the metamodel.

Univariate and Multivariate Volatility Models for Portfolio Value at Risk

Univariate and Multivariate Volatility Models for Portfolio Value at Risk

Improving Cointegration-Based Pairs Trading Strategy with Asymptotic Analyses and Convergence Rate Filters

A pairs trading strategy (PTS) constructs a mean-reverting portfolio whose logarithmic value moves back and forth around a mean price level. It makes profits by longing (or shorting) the portfolio when it is underpriced (overpriced) and closing the portfolio when its value converges to the mean price level. The cointegration-based PTS literature uses the historical sample mean and variance to establish their open/close thresholds, which results in bias thresholds and less converged trades. We derive the asymptotic mean around which the portfolio value oscillates. Revised open/close thresholds determined by our asymptotic mean and standard derivations significantly improve PTS performance. The derivations of asymptotic means can be extended to construct a convergence rate filter mechanism to remove stock pairs that are unlikely to be profitable from trading to further reduce trading risks. Moreover, the PTS literature oversimplifies the joint problem of examining a stock pair’s cointegration property and selecting the fittest vector error correction model (VECM). We propose a two-step model selection procedure to determine the cointegration rank and the fittest VECM via the trace and likelihood ratio tests. We also derive an approximate simple integral trading volume ratio to meet no-odd-lot trading constraints. Experiments from Yuanta/P-shares Taiwan Top 50 Exchange Traded Fund and Yuanta/P-shares Taiwan Mid-Cap 100 Exchange Traded Fund constituent stock tick-by-tick backtesting during 2015–2018 show remarkable improvements by adopting our approaches.

K-Medoids Clustering dan Mean-Value at Risk untuk Optimasi Portofolio Saham Jakarta Islamic Index

<p>The problem of the portfolio is how to choose stocks and determine their weights in order to generate maximum returns with minimal risk. Portfolios are formed by selecting stocks that have different characteristics. K-Medoids Clustering can be used to group data sets that contain outliers. Validate cluster results using the Davies Bouldin Index to determine the best number of clusters. Portfolio weighting is determined using the Mean-VaR method by taking into account the expected return value and minimizing the VaR risk value. Stocks are grouped based on Return on Assets, Return on Equity, Debt to Asset Ratio, and Debt to Equity Ratio. The results of cluster formation on the Jakarta Islamic Index stocks obtained six portfolio constituent stocks based on the highest expected return value from each cluster, consisting of PTBA, ADRO, AKRA, EXCL, PTPP, and UNVR. The results of calculating the weight of the optimal portfolio with Mean-VaR obtained a weight for PTBA of 0.46536; AKRA of 0.24018; EXCL of 0.25421; and UNVR of 0.25392. ADRO and PTPP stocks have a negative weight value of -0,07775 and -0,13593 this indicates the occurrence of short selling in the weighting. At the 95% confidence level, the VaR portfolio value is 5.06%.</p><p><strong>Keywords: </strong>Clustering; K-Medoids; Daveis Bouldin Index; Portfolio; Mean-VaR</p>

ESTIMASI TAIL VALUE AT RISK SAHAM BLUE CHIPS MENGGUNAKAN COPULA ALI-MIKHAIL-HAQ

When making investments, investors definitely want high returns with low risk. However, high returns are usually accompanied by high risks and vice versa. Value at Risk (VaR) and other measurement tools help manage risk. VaR measures possible losses. However, VaR has weaknesses, thus Tail Value at Risk (TVaR) can be used to evaluate the likelihood of larger losses than VaR. Copula can be used in risk management because it does not require normal distribution assumptions so it is well applied to financial data. The purpose of this research is to use the Ali-Mikhail-Haq copula to estimate TVaR value of blue chip stock portfolios, including those of BRI, BCA, and Bank Mandiri. Data used is the closing price of daily stocks for the period Jan 1 2021 to Jun 30 2023. The results of calculating TVaR value at the 90%, 95%, and 99% confidence level of the combination of BMRI and BBRI stocks are 0,0574; 0,0668; dan 0,0807. At a confidence level of 90%, 95% and 99% TVaR value of the combination of BMRI and BBCA stocks is 0,0569; 0,0669; dan 0,0886. The combination of BBRI and BBCA stocks resulted in TVaR at 90%, 95% and 99% confidence levels are 0,0238; 0,0283; 0,0376.

Financial Collaboration Technology Based on Deep Deterministic Strategy Gradient Algorithm

With the rapid development of modern financial technology, the use of machine learning methods to assist enterprises in financial decision-making has become an important trend. This article investigates financial decision-making problems based on the Deep Deterministic Policy Gradient (DDPG). By building a simulation environment and organically integrating the algorithm with the real financial decision-making process, the effectiveness and accuracy of the DDPG algorithm in financial decision-making were verified. The value of the investment portfolio steadily increases with the increase of time step. From the initial $10000 to the final $10985, this indicates that the algorithm is highly effective in optimizing asset portfolios. This development trend demonstrates that the DDPG algorithm can efficiently manage assets in a stable market, thereby achieving the goal of preserving and increasing value. The research results of this article can expand the application of DDPG method in unconventional data processing, and provide new ideas and methods for the research of financial technology and other related issues, which has important theoretical significance and practical value.

Reinforcement learning for deep portfolio optimization

<p>Portfolio optimization is an important financial task that has received widespread attention in the field of artificial intelligence. In this paper, a novel deep portfolio optimization (DPO) framework was proposed, combining deep learning and reinforcement learning with modern portfolio theory. DPO not only has the advantages of machine learning methods in investment decision-making, but also retains the essence of modern portfolio theory in portfolio optimization. Additionaly, it was crucial to simultaneously consider the time series and complex asset correlations of financial market information. Therefore, in order to improve DPO performance, features of assets information were extracted and fused. In addition, a novel risk-cost reward function was proposed, which realized optimal portfolio decision-making considering transaction cost and risk factors through reinforcement learning. Our results showed the superiority and generalization of the DPO framework for portfolio optimization tasks. Experiments conducted on two real-world datasets validated that DPO achieved the highest accumulative portfolio value compared to other strategies, demonstrating strong profitability. Its Sharpe ratio and maximum drawdown also performed excellently, indicating good economic benefits and achieving a trade-off between portfolio returns and risk. Additionally, the extraction and fusion of financial information features can significantly improve the applicability and effectiveness of DPO.</p>

Kinerja Portofolio Investasi Saham Dengan Standar Deviasi Untuk Mengukur Volatilitas Pasar Ekuitas Pada Pasar Modal Indonesia

The Indonesian capital market is a dynamic and challenging area for investment stakeholders, especially stock portfolio holders. This objective is to measure and analyze the performance of stock investment portfolios using standard deviation as a key indicator to measure equity market volatility in the Indonesian capital market. The writing method used is a descriptive method using standard deviation. The result of the standard deviation shows that the higher the investment risk between risk and return to compensate the return corresponding to the greater level of risk taken, but also increases the potential loss. The performance of the stock investment portfolio in 2022 can be said to be good. This can be seen from the growth in portfolio value that continues to increase from month to month. The portfolio's average return on investment (IRR) is 8%.

Investment strategy on indonesia islamic stocks using Greenblatt Magic Formula

Purpose: This study analyzes the portfolio form based on the Magic Formula investment strategy introduced by Greenblatt (2006). Research methodology: The portfolio formed is evaluated using the Sharpe, Treynor, and Jensen indices. Results: The results show that the Magic Formula investment portfolio provides higher returns than the reference index from June 2018 to May 2021, specifically -1.45% compared to -3.26%. The performance evaluation value of the Magic Formula investment portfolio was better than that of the reference index. Limitations: Although the Magic Formula portfolio performs well during the study period, investment portfolios can also be built and evaluated using other portfolio formulas. Contribution: This evidence shows that the Greenblatt Magic Formula investment strategy performs well because it can provide a greater return with less risk.

Value at Risk Calculation of Digital Bank Stocks Portfolio in Indonesia

<p>Nowadays, stock investment has been increasingly growing in society. In investing activities, there are risks that may be experienced by investors. However, sometimes many investors do not realize how much risk that they might suffer in the future. One way that can be done to measure this risk is to calculate the Value at Risk (VaR). This study aims to calculate the VaR value of digital bank stock portfolio in Indonesia. The calculation of VaR will be done using two methods, include the Historical Simulation and Monte Carlo Simulation method. From the calculation, VaR with Historical Simulation and Monte Carlo sequentially generate results of IDR 6,006,718 and IDR 10,797,904 for 99% confidence level, IDR 4,135,857 and IDR 5,376,949 for 95% confidence level, and IDR 3,219,885 and IDR 3,417,553 for 90% confidence level. Based on the results, it is found that VaR result is directly proportional to the confidence level used. Through the calculation results, it also found that VaR value with the Monte Carlo Simulation method are greater than those with the Historical Simulation method.</p>

Clustering-based value investing strategy in the Helsinki Stock Exchange: k-means algorithm

The purpose of this research is to study the possibility of combining quantitative clustering of stocks and value investing. The feasibility of this approach is tested using Finnish market data from the period 2005 to 2021. The benchmark index used in this research is the OMX Helsinki Growth Index. The strategy is based on the combination of P/E, P/CF, and P/B ratios, which serve as the basis for the k-means algorithm. The data is pre-processed by removing stocks that have not generated positive earnings and cash flow during the previous 12 months. The k-means algorithm assigns stocks to clusters, and the cluster with the lowest financial ratios is chosen as the value portfolio. The research also includes a sensitivity analysis of value portfolios, where the initial number of clusters in the clustering phase ranges from three to ten. Returns of different value portfolios are compared to each other and the benchmark index. The quality of results is evaluated using the Sharpe ratio and Jensen's alpha. According to the findings, the value portfolio constructed using nine clusters generated the highest risk-adjusted return, with an annual return of 30.27% over the 2005 to 2021 period. Furthermore, the best-performing value portfolio from 2005 to 2017 was compared to the benchmark index from 2018 to 2021. The value portfolio achieved an annual return of 26.05% during the 2018-2021 period, while the corresponding return of the benchmark index was 11.74%.

Drug use and spending under a formulary informed by cost-effectiveness.

BACKGROUND: The National Academy of Medicine has called for value-based drug formularies to address health plan prescription drug spending while maintaining access to high-value medicines. Thirty employer-sponsored plans implemented a "Value-Based Formulary-essentials" (VBF-e) program that uses cost-effectiveness evidence to inform cost-sharing and coverage exclusion. OBJECTIVE: To evaluate if the VBF-e was associated with changes in medication use and patient out-of-pocket spending and health plan spending on prescription drugs and other health care. METHODS: This was a cohort study using a difference-in-differences design from 2015 through 2019 with 1 year of follow-up after VBF-e implementation at Premera Blue Cross, the largest nonprofit health plan in the Pacific Northwest. The VBF-e exposure group was composed of all individuals aged younger than 65 years and enrolled at least 12 months prior to their employer group's VBF-e implementation date. The contemporaneous control group was composed of propensity score-matched individuals with the same inclusion criteria but their employer group that did not implement VBF-e. We prespecified the following outcomes: days of medication on hand overall and by VBF-e tier (high-value generic, brand, and specialty drugs were in tiers 1 to 3, respectively, and low-value drugs were in tier 4 or excluded from coverage); prescription drug spending; and other health care use (emergency department visits, hospital days, and outpatient visits). RESULTS: Comparing 12,111 exposed (mean age = 36.0; 49.8% female sex) participants with 24,222 control participants (mean age = 34.7; 49.6% female sex), VBF-e reduced use of low-value drugs by 0.3 days per member per month (PMPM) (95% CI = -0.5 to -0.1; 17% decrease) for tier 4 drugs and 0.4 days PMPM (95% CI = -0.5 to -0.4; 83% decrease) for excluded drugs. High-value specialty drug use increased by 0.1 days PMPM (95% CI = 0.0-0.1; 123% increase). Health plan spending decreased by $14 PMPM (95% CI = -26 to -4) and member out-of-pocket spending increased by $1 PMPM (95% CI = 1-2). Other health care use did not change significantly. CONCLUSIONS: An exclusion formulary informed by cost-effectiveness evidence reduced low-value drug use, increased high-value specialty drug use, reduced health plan spending, and increased member out-of-pocket spending without increasing acute care use. DISCLOSURES: This research was supported by a grant from the Patrick and Catherine Weldon Donaghue Medical Research Foundation's Greater Value Portfolio Program. Study Registration Number: NCT04904055.

Do commodity factors work as inflation hedges and safe havens?

This study investigates whether commodity futures factor portfolios work as hedges and safe havens against inflation shocks. We observe that momentum, basis momentum, and a combination of factor portfolios act as strong hedges against core inflation shocks, suggesting that holding the factor portfolios generates not only higher Sharpe ratios but also strong hedge effects against inflation. Moreover, the momentum, basis momentum, and value portfolios have weak safe haven properties against inflation shocks. In addition, our empirical results suggest that hedge effects for commodity future portfolios are stronger during the pre-financialization period.

Emotional Engagement and Trading Performance

Emotional involvement is known to be necessary but not sufficient for good decision making in the face of uncertainty. It has been conjectured that emotional engagement in anticipation of risky outcomes constitutes “good” emotions. We introduce a new methodology to determine whether anticipatory emotional engagement is beneficial in the context of trading in financial markets. We focus on heart rate changes because they occur at a sufficiently high frequency to discern timing relative to events in the marketplace. After conservatively adjusting for multiple hypothesis testing, we find that participants whose heart rate changes anticipate their order submissions at inflated prices earn significantly more, whereas participants whose heart rate responds to their trades earn significantly less. By investigating cointegration between skin conductance response and the dynamics of individual portfolio values, we confirm the importance of emotional involvement in determining who makes or loses money. This paper was accepted by Bruno Biais, finance. Funding: This work was supported by the Australian Research Council [Grants DP180102284 and LE130100112]. The authors also thank the Monash Business School for funds associated with the Monash Business Behavioural Laboratory facility and equipment used in this study. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4883 .

PERFORMANCE EVALUATION OF STOCK PREDICTION MODELS USING EMAGRU

Stock prediction is an exciting issue and is very much needed by investors and business people to develop their assets. The main difficulties in predicting stock prices are dynamic movements, high volatility, and noises caused by company performance and external influences. The traditional method used by investors is the technical analysis based on statistics, valuation of previous stock portfolios, and news from the mass media and social media. Deep learning can predict stock price movements more accurately than traditional methods. As a solution to the issue of stock prediction, we offer the Exponential Moving Average Gated Recurrent Unit (EMAGRU) model and demonstrate its utility. The EMAGRU architecture contains two stacked GRUs arranged in parallel. The inputs and outputs are the EMA10 and EMA20, formed from the closing prices over ten years. We also combine the AntiReLU and ReLU activation functions into the model so that EMAGRU has 6 model variants. Our proposed model produced low losses and high accuracy. RMSE, MEPA, MAE, R2 and were 0.0060, 0.0064, 0.0050, and 0.9976 for EMA10, and 0.0050, 0.0058, 0.0045, and 0.9982 for EMA20, respectively.

Anchoring effect, prospect value and stock return

This paper studies the relation between prospect value and stock return under the anchoring effect. Our findings show that high (low) prospect value stocks are overpriced (underpriced) in the Chinese stock market. However, this negative relation only exists in stocks far from the 52-week high, where the long-short portfolio of prospect value earns a significant risk-adjusted return of 0.95% per month, and disappears near the 52-week high. Furthermore, we find the interaction between the 52-week high and prospect value mainly derives from the loss aversion feature. Our findings keep robust after controlling for the effect of stock fat-tail distribution.

Two-phase selection of representative contracts for valuation of large variable annuity portfolios

A computationally appealing methodology for the valuation of large variable annuities portfolios is a metamodelling framework that evaluates a small set of representative contracts, fits a predictive model based on these computed values, and then extrapolates the model to estimate the values of the remaining contracts. This paper proposes a new two-phase procedure for selecting representative contracts. The representatives from the first phase are determined using contract attributes as in existing metamodelling approaches, but those in the second phase are chosen by utilizing the information contained in the values of the representatives from the first phase. Two numerical studies confirm that our two-phase selection procedure improves upon conventional approaches from the existing literature.

EXAMINATION OF THE RELATIONSHIP BETWEEN FOREIGN INVESTORS AND BIST-100 RETURN INDEX

Purpose- The aim of this study is to analyze the relationship of foreign portfolio investments, which are gaining importance in Turkey, with the Borsa Istanbul (BIST)-100 return index. Method- The relationship between foreign investor portfolio sizes and BIST-100 return index was examined based on quarterly periods covering the years 2010 and 2021. While the data on foreign portfolio sizes and the number of foreign investors were obtained from the central registry, data on the BIST-100 return index were obtained from the official website of the Central Bank. In the study, first of all, it was tested by correlation analysis whether there was a significant correlation between the data. If there is a relationship between the data, the direction of the relationship was investigated using the Granger Causality test method developed by Granger (1969), and econometric methods such as unit root test and stationarity analysis were used to make sense of the findings. Findings- The causality direction from BIST-100 index to Foreign portfolio value was determined by reaching the one-way causality relationship that the stock returns (BIST-100 return index) are the cause of the foreign investor portfolio value. Therefore, in this research, it is concluded that the performance of the stock market in Turkey acts independently from the portfolio values of foreign investors. Conclusion- Considering the dynamics and structural features of the financial markets in Turkey, the impact of the sectoral and company-level investments of foreign investors may differ according to the general stock returns. Therefore, although these results do not definitively show that there is no effect of foreign investors, they may reveal different dimensions of the effect of foreign investors, as will be discussed in this research. Keywords: Foreign investments, Borsa Istanbul, Capital Markets, Confidence Index, Granger Causality JEL Codes: G10, G11, G15

Optimization of Daily Trading Strategy

This study proposes a novel method that integrates variational pattern decomposition (VMD), long short-term memory (LSTM) models, and particle swarm optimization (PSO) to optimize daily trading strategies for a portfolio. By analyzing historical price data for gold and bitcoin, we have successfully identified the optimal investment actions (buy, hold, or sell) for a portfolio comprising different assets. The VMD-LSTM model proposed by us mitigates the long-term dependence problem of machine learning algorithm RNN in financial time series prediction through batch normalization optimization, and realizes the accurate prediction of the future price of gold and bitcoin, whose R-squared values reach 0.99 and 0.98 respectively. We also applied the particle swarm optimization algorithm to a sequence of trading decisions generated by predicting prices, ultimately obtaining a maximum portfolio value of $20,560,265.5, which is more than 14.2 times the 10-year growth of Bitcoin and 12,310.5 times the 10-year growth of gold. By verifying the universality and local optimal properties of our model, we use the Sharpe ratio as an objective function to evaluate the effect of the portfolio. After several perturbation tests, the results show that the original scheme is superior to other perturbation schemes considering the Sharpe ratio and the final return, which verifies the validity of the investment scheme selected by us as the local optimal value. These findings make our research more coherent and provide investors with a reliable basis for making decisions to achieve long-term capital appreciation and risk control.

A patent portfolio value analysis based on intuitionistic fuzzy sets: An empirical analysis of artificial intelligence for healthcare

Evaluating patent assets from the perspective of a patent portfolio is an emerging research direction and also a concrete practical example of open innovation theory, which presents the main form of encouraging more innovation entities to participate and further innovate by disclosing technological innovation information. For a better understanding of the patent portfolio value system at the quantitative level, this paper attempts to explore the relationship between the patent portfolio and the open innovation environment of industrial clusters, thus proposing a dynamic patent value evaluation method. Therefore, through focusing on the field of artificial intelligence for healthcare, this paper implements the evaluation process based on collecting relevant patent data and building an intuitionistic fuzzy set evaluation system. The evaluation results provide a value ranking for the patent portfolio, which helps decision-makers to carry out industrial patent layout planning and implement patent licensing strategies orienting to open innovation.