The Accuracy of Balance Model in Predicting Stock Investment During The Covid-19 Pandemic on LQ 45 Index

In doing investment, an investor certainly avoids risk; thus, the investor needs a model in making predictions to forecast the return of shares. There are two models to predict this: Capital Asset Pricing Capital (CAPM) and Arbitrage Pricing Theory (APT). The purpose of this study is to find out which models are more accurate in determining investment options, especially during the Covid-19 pandemic in companies that are included in the LQ 45 Index group. The population in this study is 50 companies listed in LQ 45 from February 2020 - July 2021. The sampling technique used in this study is purposive sampling. The data used in this study will be processed through Ms.Excel and SPSS Version 21. The data analysis techniques used in this study are the Basic Assumption Test consisting of Normality Test and Homogeneity Test, Mean Absolute Deviation (MAD), and hypothesis testing consisting of independent t-test samples. The results in this study show that Model is accurate in predicting stock returns in the Covid-19 pandemic is a CAPM model this is because the value of MAD CAPM is smaller than mad APT. Furthermore, independent t-test samples showed that H0 was rejected which meant that there was a difference in accuracy between CAPM and APT in calculating the return of LQ 45 shares. The implication of this study are expected to provide references to investors and potential investors as a source of information in decision making to make investments in this pandemic period.

Relationship Between Selected Macroeconomic Variables and the Financial Performance of Investment Banks in Kenya

Currently, investment banks in Kenya are facing a lot of challenges due to persistence losses. However, the available studies are inadequate to aid investment banks in overcoming these challenges in Kenya due to mixed findings, resulting in rising uncertainty on equity investments’ performance, leading to massive losses among investment banks.  This study, therefore, sought to model the relationship between inflation, GDP, interest rates, exchange rates, and financial performance of investment banks. Arbitrage pricing theory, Modern portfolio theory as well as classical economic theory (flow-oriented model) was used. A causal research design was adopted. The study found that inflation has negative significant influence on financial performance of equity investments among investment banks in Kenya. Also, GDP has positive and significant influence on financial performance of equity investments among investment banks in Kenya. Interest rate was also found to have negative and significant influence on financial performance of equity investments among investment banks in Kenya. In addition, exchange rate has negative significant influence on financial performance of equity investments among investment banks in Kenya. The study therefore recommends any investor including financial investors to methodically analyze inflation trends and understand how it affects the company’s financial performance. Investors must also be in a position to predict the future concerning inflation changes.

Relationship Between Selected Macroeconomic Variables and the Financial Performance of Investment Banks in Kenya

Currently, investment banks in Kenya are facing a lot of challenges due to persistence losses. However, the available studies are inadequate to aid investment banks in overcoming these challenges in Kenya due to mixed findings, resulting in rising uncertainty on equity investments’ performance, leading to massive losses among investment banks.  This study, therefore, sought to model the relationship between inflation, GDP, interest rates, exchange rates, and financial performance of investment banks. Arbitrage pricing theory, Modern portfolio theory as well as classical economic theory (flow-oriented model) was used. A causal research design was adopted. The study found that inflation has negative significant influence on financial performance of equity investments among investment banks in Kenya. Also, GDP has positive and significant influence on financial performance of equity investments among investment banks in Kenya. Interest rate was also found to have negative and significant influence on financial performance of equity investments among investment banks in Kenya. In addition, exchange rate has negative significant influence on financial performance of equity investments among investment banks in Kenya. The study therefore recommends any investor including financial investors to methodically analyze inflation trends and understand how it affects the company’s financial performance. Investors must also be in a position to predict the future concerning inflation changes.

Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model

This paper tests a multi-factor asset pricing model that does not assume that the return’s beta coefficients are constants. This is done by estimating the generalized arbitrage pricing theory (GAPT) using price differences. An implication of the GAPT is that when using price differences instead of returns, the beta coefficients are constant. We employ the adaptive multi-factor (AMF) model to test the GAPT utilizing a Groupwise Interpretable Basis Selection (GIBS) algorithm to identify the relevant factors from among all traded exchange-traded funds. We compare the performance of the AMF model with the Fama–French 5-factor (FF5) model. For nearly all time periods less than six years, the beta coefficients are time-invariant for the AMF model, but not for the FF5 model. This implies that the AMF model with a rolling window (such as five years) is more consistent with realized asset returns than is the FF5 model.

Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange

This paper compares the dimension reduction or feature extraction techniques, e.g., Principal Component Analysis, Factor Analysis, Independent Component Analysis and Neural Networks Principal Component Analysis, which are used as techniques for extracting the underlying systematic risk factors driving the returns on equities of the Mexican Stock Exchange, under a statistical approach to the Arbitrage Pricing Theory. We carry out our research according to two different perspectives. First, we evaluate them from a theoretical and matrix scope, making a parallelism among their particular mixing and demixing processes, as well as the attributes of the factors extracted by each method. Secondly, we accomplish an empirical study in order to measure the level of accuracy in the reconstruction of the original variables.

Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative in the Mexican stock exchange

This paper compares the dimension reduction or feature extraction techniques, e.g., Principal Component Analysis, Factor Analysis, Independent Component Analysis and Neural Networks Principal Component Analysis, which are used as techniques for extracting the underlying systematic risk factors driving the returns on equities of the Mexican Stock Exchange, under a statistical approach to the Arbitrage Pricing Theory. We carry out our research according to two different perspectives. First, we evaluate them from a theoretical and matrix scope, making a parallelism among their particular mixing and demixing processes, as well as the attributes of the factors extracted by each method. Secondly, we accomplish an empirical study in order to measure the level of accuracy in the reconstruction of the original variables.

Methodology for calculating risk factors and assessing the sensitivity of portfolio asset returns when investing in personnel

The authors previously proposed an economic and mathematical model of investing in personnel and also the algorithm for determining of risk-free and the size of non-market risks. The goals of the article are to develop the threat rates and risk sensitive search methods during engineering of investment portfolio through arbitrage pricing theory for investment in personnel to return on investment. Questions of risk evaluation when choosing assets to invest in human capital of a company are fully provided. The covariance structure of allocation of funds of risk-free part of investment portfoliois addressed, that helps to calculate covariance between return on assets and threat rates of investment patterns that is necessary to find factor betas of portfolio. Also, the matrix of budget sharpening is offered that shows the detail investment budgets risk-free pieces split for all assets having regard to risks. The elements got standard interpretation. Then there is the balance condition that is to implement the proof of calculation. Finally, the method of how to calculate and estimate them is given.

Forecasting Euro against US dollar via combination of NARDL and Univariate techniques during COVID-19

Purpose The purpose of this study is to examine the accuracy of combined models with the individual models in terms of forecasting Euro against US dollar during COVID-19 era. During COVID, the euro shows sharp fluctuation in upward and downward trend; therefore, this study is keen to find out the best-fitted model which forecasts more accurately during the pandemic. Design/methodology/approach The descriptive design has been adopted in this research. The three univariate models, i.e. autoregressive integrated moving averages (ARIMA), Naïve, exponential smoothing (ES) model, and one multivariate model, i.e. nonlinear autoregressive distributive lags (NARDL), are selected to forecast the exchange rate of Euro against the US dollar during the COVID. The above models are combined via equal weights and var-cor methods to find out the accuracy of forecasting as Poon and Granger (2003) showed that combined models can forecast better than individual models. Findings NARDL outperforms all remaining individual models, i.e. ARIMA, Naïve and ES. By applying a combination of different models via different techniques, the combination of NARDL and Naïve models outperforms all combination of models by scoring the least mean absolute percentage error value, i.e. 1.588. The combined forecasting of NARDL and Naïve techniques under var-cor method also outperforms the forecasting accuracy of individual models other than NARDL. It means the euro exchange rate against the US dollar which is dependent upon the macroeconomic fundamentals and recent observations of the time series. Practical implications The findings could help the FOREX market, hedgers, traders, businessmen, policymakers, economists, financial managers, etc., to minimize the risk indulged in global trade. It also helps to produce more accurate results in different financial models, i.e. capital asset pricing model and arbitrage pricing theory, because their findings may not be useful if exchange rate fluctuations do not trace effectively. Originality/value The NARDL models have been applied previously in different time series and only limited to the asymmetric or symmetric relationships. This study is using it for the forecasting exchange rate which is almost abandoned in earlier literature. Furthermore, this study combined the NARDL with univariate models to produce the accuracy which itself is a novelty. Moreover, the findings help to enhance the effectiveness of different financial theories as well.