Arbitrage Pricing Theory Research Articles

The Impact of the Last Two Presidential Periods on the Performance of the Indonesia Stock Exchanges: An Approach of Arbitrage Pricing Theory

This study aims to examine the influence of economic and non-economic macro factors on the performance of the Indonesia Stock Exchange (IDX) and the differences in their effects on the last two presidential terms using the Arbitrage Pricing Theory (APT) approach. The macro factors studied are an aggregate of 41 indicators of stock market index returns, benchmark interest rates, exchange rates, economic stability indicators, commodity prices, and corruption perception index (CPI). For the process of aggregation and identification of macro factors, this study uses Principal Component Analysis (PCA). The population studied were publicly listed companies on the IDX since October 2004 (299 companies) with a sample size of 157 companies. The observation period is 205 months: 120 months of the presidency of Susilo Bambang Yudhoyono (SBY) and 85 months of the presidency of Joko Widodo (JKW). The research design used the explanatory study method. The research model was analyzed using panel data regression. The results identified seven macro factor aggregates from 41 indicators studied: global index, macro economy, world oil prices, China index, Arabia index, inflation, and competitive resources. There was a simultaneous effect of macro factors on stock performance during the presidency of SBY and JKW. Partially, stock returns during the SBY era were positively influenced by the global index, world oil prices, the China index, the Arabian index, and inflation and negatively influenced by the macroeconomy. Meanwhile, during the JKW period, returns were positively influenced by the global, China, and Arabia indexes and inflation.

The Model of Stock Pricing Banking Sector on the Indonesia Stock Exchange during the Government of Susilo Bambang Yudhoyono and Joko Widodo

Differences in macroeconomic conditions between the SBY and JKW government periods can significantly impact stock prices within the banking sector, especially when the economy experiences fluctuations, leading to increased stock price volatility. This research aims to assess the influence of various macroeconomic factors, including the Global index, macroeconomics, world oil prices, China index, Arab index, competitive resources, and inflation, on banking sector returns from October 2004 to October 2014. The analysis utilizes the arbitrage pricing theory, difference test, and mean absolute deviation. The study divides the analysis into high-return and low-return groups for each government period. Findings indicate that macroeconomic factors exert a simultaneous influence on return groups during both SBY and Jokowi's administrations. However, for JKW high returns, these factors do not significantly impact them. Specifically, the Global Index, China Index, Arab Index, Resource Competitiveness, and Inflation show a positive correlation with both high and low returns during SBY term. Conversely, during JKW tenure, only the Global Index and China Index exhibit positive effects on high and low returns. The difference test reveals disparities in the Arab index for high and low returns of SBY, while only the Global index displays differences for JKW. The variations in high returns between SBY and Jokowi stem from discrepancies in the Global and Arab indices, while no distinctions exist in low returns between the two administrations. Additionally, the mean absolute return values for high returns during JKW era demonstrate the smallest accuracy, indicating potential limitations in the predictive power of the model during this period.

A Study on the Risks of Real Estate Investment

This paper examines the risks associated with real estate investment, leveraging Modern Portfolio Theory (MPT) introduced by Harry Markowitz and later applied to real estate by Nigel Dubben and Sarah Sayce. MPT emphasizes balancing expected returns and risk through diversification, crucial in real estate due to its low correlation with other assets and its inflation-hedging properties. Despite global economic integration increasing systemic risks, real estate remains a key asset class for risk management. The literature review highlights traditional strategies focusing on diversifying by property type and geographic location. Studies show mixed results: some suggest property type diversification yields higher returns, while others find geographic diversification more effective for risk mitigation. Chinese market studies reveal that real estate investments can provide predictable returns and act as a hedge during economic downturns. Research on Beijing and Shanghai supports the effectiveness of geographic diversification within China. Applying MPT to real estate, the paper demonstrates through case studies how investment combinations can balance risk and returns. It discusses the Efficient Frontier, CAPM, and Arbitrage Pricing Theory to illustrate the relationship between risk and return in real estate portfolios. The analysis concludes that diversification strategies, though complex, are essential for optimizing performance and mitigating risks in real estate investments.

Effects of Selected Macroeconomic Variables on Market Capitalization of Nairobi, Kenya

This study examined the effects of selected macroeconomic variables on the market capitalization of the Nairobi Securities Exchange in Kenya during the period 2010- 2022, based on the Arbitrage Pricing Theory (APT) using quarterly data. The autoregressive distributed lag model technique was applied to test how market capitalization was affected by the macroeconomic variables. Gross domestic product, money supply, and inflation were stationary at the level while interest rate, market capitalization, and exchange rate became stationary at first difference. From the empirical results, no co-integration between market capitalization and exchange rate, money supply, interest rates, Gross domestic product, and inflation was found by use of bounds test. Money supply and inflation had a weak influence on market capitalization; interest rate, Gross Domestic Product, and first lag of the exchange rate were positive and affected market capitalization, while the exchange rate at the current level affected market capitalization negatively. It was concluded that macroeconomic variables affect market capitalization. The study recommends that the government need to put up relevant policies that increase gross domestic product. Policymakers need to consider macroeconomic variables during policy formulation on market capitalization.

Macroeconomic Factors and Stock Price Volatility at Nairobi Securities Exchange

The stock price volatility has been a problem in stock markets affecting the stock returns of firms listed at Nairobi Securities Exchange (NSE). Although some scholars have focused on the issue of high share price volatility, the issue persists and still hinders the development of the market. This study main objective is to determine the effects of macroeconomic factors on volatility of stock prices of stock market in Kenya. The specific objectives are; to establish the effects of inflation on stock prices of stock market in Kenya; to establish the effects of interest rates on volatility of stock prices of stock market in Kenya; and to establish the effects of exchange rates on stock prices of stock market in Kenya. The research was anchored on four theories and two models namely the Arbitrage pricing theory, Modern portfolio theory, information cascade theory, Fisher’s theory, Efficient market hypothesis and present value model. The research applied a causal research design, which followed the positivism philosophy of research and knowledge creation. The study focused on analyzing the effects of macroeconomic factors on volatility of stock prices of the NSE 20 share index in Nairobi Securities Exchange over a period ranging from 2009 to 2021. The design allowed collection of monthly data on the study variables and time series analysis of the hypothesized relationships between independent and dependent variables and conducted both descriptive statistical analysis and inferential statistical analysis. Descriptive statistical analysis involved generation of central tendency statistics such as mean, minimum and maximum points of the data as well as standard deviations to show the spread of the data from the mean. The inferential statistical analysis utilized in this study was Error Correction Model, guided by the results of cointegration tests. From the descriptive statistical analysis, the study found that between 2009 and 2021, interest rates demonstrated an average of approximately 9.155, inflation averaged around 150.916, foreign exchange rate maintained an average of roughly 94.219, and stock price volatility averaged 0.4%. The Error Correction Model revealed that, interest rates have no significant effect on stock price volatility of NSE 20 share index (β = -0.00034, p.value = 0.805). Similarly, inflation has no significant effect on stock price volatility of NSE 20 share index (β = -0.0000394, p.value = 0.218). However, foreign exchange has negative and significant effect on stock price volatility in Kenya (β = -0.0139, p.value = 0.017). The study concluded that interest rates and inflation have no significant effect on stock price volatility in Kenyan stock market, while foreign exchange has inverse and significant effect on stock price volatility. Thus, the study recommends that market participants, including government, investors, traders, policy makers and financial institutions, should incorporate foreign exchange rate movements into their risk management strategies. Adequate measures and policies such as market circuit breakers and enhanced risk management protocols should be in place to ensure smooth market operations and prevent any disruptions due to changing foreign exchange dynamics

Capital Asset Pricing Model (CAPM) 2.0: Account of Business and Financial Risk

The famous Capital Asset Pricing Model (CAPM), widely used in practice, takes into account only the business risk associated with investments in a specific company [not the entire market (or industry)]. In practice, most listing companies use debt financing and operate at a non-zero leverage level. This means that the financial risk associated with the use of debt financing, along with business risk, must be taken into account. The purpose of this paper is to simultaneously account for business and financial risk. We combined the CAPM theory and the Modigliani-Miller (MM) theory, which is the perpetual limit of the BFO (Brusov-Filatova-Orekhova) theory. The article shows that R. Hamada’s attempt to take into account both business and financial risks has proved unsustainable, and the formulas he obtained, widely used in practice, are incorrect. The paper outlines the correct formulae that made it possible to generalize CAPM for the first time, taking into account both business and financial risk. The application of the new CAPM 2.0 model to a number of companies is considered and the difference between the results obtained within the framework of CAPM 2.0 and CAPM is demonstrated. CAPM is one of the main models [along with APT (arbitrage pricing theory) and WACC] within the income approach to business valuation. This significantly increases the value of the developed CAPM 2.0 approach, which can significantly improve the accuracy of the assessment.

Advancing portfolio optimization: The convergence of machine learning and traditional financial models

This paper delves into the transformative impact of machine learning (ML) on portfolio optimization, showcasing how ML algorithms can significantly enhance traditional financial models such as the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT). Through a comprehensive examination of regression analysis, classification algorithms, and reinforcement learning, we illustrate the methodologies by which ML refines the prediction of asset returns, assesses investment risks, and dynamically adjusts portfolio allocations. We discuss the integration of ML with CAPM and APT to improve the estimation of systematic risk and identify multi-factor influences on asset returns, offering a more nuanced approach to optimizing portfolios. Additionally, the paper highlights the role of big data in augmenting predictive accuracy and the application of optimization algorithms like Gradient Descent and Genetic Algorithms in achieving optimal asset allocations. By addressing challenges such as multicollinearity and overfitting, we demonstrate the potential of ML to revolutionize investment strategies, enabling more sophisticated risk management and return maximization. This study not only underscores the synergy between ML and traditional financial theories but also paves the way for future innovations in financial analytics.

Analysis of the Accuracy Level of the Balance Model in Stock Investment Prediction in the LQ45 Index

Objective –This study examines how the goal of an investor is to establish an optimal investment risk structure which maximizes profits by incurring fewer losses at a certain level of market risk. This research aims to determine the accuracy of the CAPM and APT models in predicting stock returns, as measured using Mean Absolute Deviation (MAD).Design/Methodology –The methods employed is a quantitative approach. The population for this study includes companies included in the LQ45 index during the 2020-2022 period consisting of monthly observations spanning from January to December. A purposive sampling technique was used to select 30 sample companies. The reason for using the LQ-45 index is because this index is an index in which there are 45 issuers. Apart from that, the shares included in the LQ-45 calculation are considered to reflect the movement of actively traded shares which will influence market conditions, consisting of shares with high liquidity and market capability, as well as growth prospects and fairly stable financial conditions.Results –The MAD calculation shows that the APT model is more accurate than the CAPM model. The choice of model use can be adjusted to the preferences of each investor. CAPM is a forecasting model that only uses market return factors, making it suitable for investors who want to predict stock returns easily and simply. On the other hand, APT can be used by investors who want to know in detail what macro factors influence changes in stock prices.Research Limitations/Implications – Increasing the length of time that researchers spend doing their research is recommended in order to improve the accuracy of their forecasts about future stock returns.Novelty/Originality –This instrument is highly beneficial for investors who are looking for a clear and effective technique to anticipate the returns on their stock investments. Investors who want a detailed grasp of the precise macroeconomic issues that affect swings in stock prices may find that applying the Arbitrage Pricing Theory (APT) is useful.

Measuring the Efficiency of State-Owned Stocks Using Capital Asset Pricing Model (CAPM)

Abstract Research aim : This study examines market efficiency using the Capital Asset Pricing Model (CAPM) method on BUMN stocks in Indonesia. Design/Methode/Approach : This model explains the relationship between risk and return in an efficient market. The analysis focuses on using stock beta calculations for 5 years. Research Finding : The research results are as follows: 1) the BUMN stock market in Indonesia is classified as efficient, namely 11 stocks where 5 companies have positive individual returns, namely ANTM, TINS, KRAS, BBNI, and PGAS. There are 6 companies that have negative returns, namely WSKT, ADHI, WIKA, PTPP, SMBR, and BBTN. 2) the analysis was carried out during the covid-19 pandemic period, the basic material sector is a collection of stocks that are classified as resilient during the pandemic showing positive and efficient returns. 3) overall, there is a high systematic risk of BUMN stocks in Indonesia, and actively responds to any changes that occur in market prices. Theoretical contribution/Originality : This contribution is expected to be a reference for investors and market participants. Practitionel/Policy implication : These results provide information for investors to choose efficient State-Owned Enterprises (BUMN) stocks in deciding to invest and still consider the nature and characteristics of each investor. Research limitation : Future research is expected to use other methods in analyzing the level of stock risk such as Arbitrage Pricing Theory (APT) or estimating future stock prices using the ARIMA / GarCH forecasting method and increasing the number of samples in the study or different market indices so that the expected results are more accurate in predicting future stock prices.

Econometric Model Using Arbitrage Pricing Theory and Quantile Regression to Estimate the Risk Factors Driving Crude Oil Returns

This work presents a novel approach to determining the risk and return of crude oil stocks by employing Arbitrage Pricing Theory and Quantile Regression. Arbitrage Pricing Theory identifies the risk factors likely to impact crude oil returns. Subsequently, Quantile Regression estimates the relationship between the selected factors and the returns across different distribution quantiles. The West Texas Intermediate (WTI) crude oil price is used in this study as a benchmark for crude oil prices. WTI’s price fluctuations can significantly impact the performance of global crude oil stocks and, subsequently, the global economy. Various statistical measures are used in this study to determine the proposed model's stability. The results show that changes in WTI returns can have varying effects depending on market conditions and levels of volatility. This study emphasizes the influence of structural discontinuities on returns. These are likely generated by changes in the global economy and the unpredictable demand for crude oil during the pandemic. The inclusion of pandemic, geopolitical, and inflation-related explanatory variables adds uniqueness to the study as it considers current global events that can affect crude oil returns. Findings show that the key factors that pose significant risks to returns are industrial production, inflation, the global price of energy, the shape of the yield curve, and global economic policy uncertainty. This implies that while making investment decisions in WTI futures, investors should pay particular attention to these elements.

Effect of Selected Macro-Economic Variables on Securities Performance at the Nairobi Securities Exchange, Kenya

The equity securities market is an integral component of any country's financial system, and its performance is intertwined with the broader macro-economic environment. Equity securities market determines the state of the economy; this is through stock and asset prices fluctuations as well as company earnings with corresponding dividend policies, and multiple macroeconomic variables. The inherent problem has been how do changes in specific macro-economic variables affect the performance of the equity securities market at the Security Exchange. This study sought to establish the influence of the selected macro-economic variables on equity securities market performance at Nairobi Securities Exchange (NSE). The specific objectives were to establish the effects of exchange rate; interest rate; inflation rate; money supply; and Gross Domestic Product on equity share prices fluctuations based on the NSE-20 from when the economy was liberalized. The study was guided by the Capital Asset Pricing Model, the Arbitrage Pricing Theory and the Efficient Market Hypothesis. An explanatory research design was used to determine the effect of selected macro-economic variables on securities exchange market performance. Annual time series secondary data for the periods 1986 to 2022 was used. Findings of diagnostic test demonstrated that there was no multicollinearity among the independent variables, residuals were homoscedastic, and there was no autocorrelation among the residuals. The results of Shapiro wilk normality test showed that the study's variables were normally distributed. The Augmented dickey fuller unit root test both showed that there was no unit root and that the variables had a short run relationship. The study findings were: the exchange rate (indicating negative and significant effect on equity security indices; the interest rate indicating negative and significant effect on equity security indices; inflation rate indicating positive and significant effect on equity security indices; money supply which indicates a negative and significant effect on equity security indices; and gross domestic product had a positive and significant effect on equity security indices. The study concluded that exchange rate, interest rate and money supply significantly affect equity security indices negatively while inflation rate and gross domestic product significantly affect equity security indices positively. The study therefore recommends the Central bank of Kenya to employ measures intervening foreign exchange markets to stabilize the currency, implement appropriate interest rate policies, and maintaining adequate foreign exchange reserves. The policy makers should prioritize price stability and monitor the growth of money supply to ensure it remains in line with the country's economic fundamentals. Policy makers should also strike a delicate balance between controlling inflation and supporting economic growth by closely monitoring economic indicators and adjusting interest rates accordingly.

Komparasi Capital Asset Pricing Model dan Arbitrage Pricing Theory dalam Memprediksi Return Saham

This research aims to determine the comparison of the Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT) in predicting stock returns of LQ45 index companies on the Indonesian Stock Exchange. Data analysis methods use Simple Regression Analysis, Multiple Regression Methods, and Difference tests. The results of data analysis show that there are differences between CAPM and APT in predicting stock returns. The emergence of varying differences in research results regarding the accuracy of the CAPM and APT models is caused by data disturbances that appear in the historical data used. The CAPM and APT models have a very important element, namely beta (????) as a measure of return on factors that are considered to influence, so this beta (????) must be BLUE (Best Linear Unbiased Estimator) so that the prediction results from the two CAPM and APT models become more accurate and reliable

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%.

Applicability Analysis of Arbitrage Pricing Model in Chinese Stock Market

This paper presents the fundamentals and benefits of the arbitrage pricing model, emphasizing the significance of utilizing arbitrage pricing theory to the growth of the stock market. Firstly, the thesis analyzes and summarizes the mature research on the arbitrage pricing model abroad. This paper, taking into account the unique circumstances of the Chinese stock market, summarizes and contemplates the empirical examination of the arbitrage pricing model's applicability in the Chinese stock market. Most research lines in the Chinese market have been found to be in agreement with the arbitrage pricing principle, yet some studies have come to the contrary result. The fundamental reason is that the analysis of the model needs to be further improved, and China's stock market needs to be developed. Proposals to foster the growth of China's stock market are finally put forth.

An Empirical Study of Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT) in Determining the Expected Return Accuracy Level

An Empirical Study of Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT) in Determining the Expected Return Accuracy Level

Comparative Analysis and Research of Investment Portfolio Management Models

Portfolio management plays a significant role in the world of finance and investing, offering benefits and contributions to both individual investors and institutions. Researchers use many mathematical models to make informed decisions about how to allocate and manage investments within a portfolio to optimize risk versus return trade-off based on the investors preference and constraints. This paper primarily employs the literature review methodology and comparative analysis methodology. Firstly, it collects, summarizes, and analyzes multiple papers on portfolio management models, including Markowiz Mean-Variance Model, Capital Market Line (CML), Arbitrage Pricing Theory (APT) and Capital Asset Pricing Model (CAPM) with the origin of the models, their key assumptions, components, and applications. Additionally, this paper also utilizes comparative analysis by comparing the similarities, differences, and respective downsides and benefits of the four main research models. Through this comparison, the paper investigates the relationships and application distinctions among the models. As a result, these models evolve and refine over time, with some building on others. CAPM extends the Markowitz Model by introducing the risk-free rate and market portfolio as benchmarks, simplifying the risk-return relationship, and introducing systematic risk. The Capital Market Line (CML), derived from CAPM, illustrates efficient portfolios made up of both market and risk-free assets and shows the risk-return trade-off. APT, a later model, can be seen as an extension of CAPM.

Dividend and Excess Return in China

Investors are always chasing excess returns. To examine the three driving factors affecting China A-share excess returns, namely systematic risk, idiosyncratic risk and market sentiment, this study divided A-shares into non-dividend and dividend-paying groups based on the Dividend Paid for Common Shares in the notes of the financial report. In addition, this study used the Capital Asset Pricing model, Single-Index model, Arbitrage Pricing theory and Fama-French three- and five-factor model to analyse the three main driving factors. The Gibbons-Ross-Shanken test was used to test the model validity, and the optimal model for each group was extracted. Our findings show that after analysing the optimal models within each group, it becomes evident that systematic risk indeed exerts an influence on both dividend-paying and non-dividend companies. Nevertheless, when considering four specific systematic risks (inflation, exchange rates, crude oil and interest rates), this study’s findings establish that these risks do not significantly impact the stock returns of any company group across all time periods. As for idiosyncratic risks, firm size and book-to-market factors emerge as substantial influencers across all firms. Additionally, market sentiment significantly affects the stock performance of small-sized dividend-paying companies.

Improving SMEs Performance Based on Strategy Management

This study aims to analyze the expected return of stocks in the pharmaceutical sub-sector based on the capital asset pricing model (CAPM), arbitrage pricing theory (APT), and the Three Factor Model (TFM) during the COVID-19 pandemic from March 2020 to December 2023, and test the accuracy of the expected return prediction using the Mean Average Deviation (MAD). Determination of the pharmaceutical sub-sector as a research sample with the consideration of producing positive return growth during the COVID-19 pandemic, while at the same time, other companies were experiencing a downturn. The results showed that the average expected return was 3.69% based on the CAPM, 3.88% based on the APT formula, and 3.66% based on TFM calculations. However, there is no significant difference in the resulting expected return. The regression analysis concludes TFM is better than other asset pricing models. Beta significantly explains the average weekly pharmaceutical stock return. The measure of the MAD test concluded the CAPM is a model that can predict the expected return more accurately by the smallest MAD value. TFM is a model with the highest MAD value. The highest level of MAD, the less accurate prediction of the expected return.

Analisis Komparasi Metode CAPM dan APT dalam Memprediksi Return Saham LQ45 2019-2021

The investment climate is developing rapidly in Indonesia, many people are interested in investing, and the capital market is a common and legal place to invest. In investing, stocks are one of the long-term financial instruments traded on the capital market. The goal of investors in stock investment is to obtain the maximum stock return with a certain risk. Financial experts have developed two approaches to predict return on investment based on risk and use certain macroeconomic variables, the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT). This study aims to determine the best model between the CAPM and APT in determining the expected return on shares of companies that are members of the IDX LQ45 for the period 2019-2021. Data is processed using Unbalance Data Panel Analysis with the Eviews application. The results of data processing show that the CAPM method is more precise or accurate than the APT method.

Uncertain mean-CVaR model for portfolio selection with transaction cost and investors’ preferences

Portfolio selection is a common practice among investors, scholars often rely on probability statistics to construct portfolios based on the Markowitz mean–variance model. However, due to the complexity of the stock market and the limitations of historical data, it may not be possible to fully predict the future market. In such situations, it is necessary to incorporate expert knowledge and judgments to obtain estimates of security return rates. This paper proposes a class of portfolio models suitable for uncertain environments. We employ the uncertainty theory and Arbitrage Pricing Theory (APT) to measure tail risk and construct a new uncertain mean-Conditional Value-at-Risk (CVaR) model. Additionally, to better match investors’ preferences, this paper takes into account transaction costs and liquidity needs. To solve this problem, we design a hybrid algorithm that incorporates these factors. Finally, we present a numerical experiment to illustrate the performance of the proposed model. In summary, our study offers a novel approach to portfolio selection that considers uncertainty theory, tail risk, and investors’ preferences. The results demonstrate that the proposed uncertain mean-CVaR model with hybrid algorithm optimization can improve portfolio performance in uncertain market conditions.