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This study attempts to conduct a comparative analysis between dynamic and static asset allocation to achieve the long-term target return on asset liability management (ALM). This study conducts asset allocation using the ex ante expected rate of return through the outlook of future economic indicators because past economic indicators or realized rate of returns which are used as input data for expected rate of returns in the “building block” method, most adopted by domestic pension funds, does not fully reflect the future economic situation. Vector autoregression is used to estimate and forecast long-term interest rates. Furthermore, it is applied to gross domestic product and consumer price index estimation because it is widely used in financial time series data. Based on asset allocation simulations, this study derived the following insights: first, economic indicator filtering and upper-lower bound computation is needed to reduce the expected return volatility. Second, to reach the ALM goal, more stocks should be allocated than low-yielding assets. Finally, dynamic asset allocation which has been mirroring economic changes actively has a higher annual yield and risk-adjusted return than static asset allocation.

This paper examines whether the successful bid rate of the OnBid public auction, published by Korea Asset Management Corporation, can identify and forecast the Korea business-cycle expansion and contraction regimes characterized by the OECD reference turning points. We use logistic regression and support vector machine in performing the OECD regime classification and predicting three-month-ahead regime. We find that the OnBid auction rate conveys important information for detecting the coincident and future regimes because this information might be closely related to deleveraging regarding default on debt obligations. This finding suggests that corporate managers and investors could use the auction information to gauge the regime position in their decision-making. This research has an academic significance that reveals the relationship between the auction market and the business-cycle regimes.

The purpose of this paper is to propose a Clarke-Groves Tax (CGT) type as a remedy to the criticism that the implementation of Eurobonds has raised regarding the risk of undermining fiscal discipline. In this model, a government minimizes its sovereign debt-to-GDP ratio in a given period and decides whether to join a common sovereign debt club. In doing so, it exposes itself to a positive or negative tax burden while benefiting from the liquidity premium involved in creating a secure asset. The authors found that the introduction of this tax may prevent free riding behaviours if Eurobonds were to be implemented. To illustrate this, the authors provide some numerical simulations for the Eurozone.

In the model presented, a government which optimizes a social utility function decides whether to join the common debt club.

The adoption of the proposed tax could prevent free-riding behaviours and, therefore, encourages participation by those countries with lower debt levels that would have not otherwise taken part in this common debt mechanism. Under certain circumstances, we can expect the utility of all members of this club to improve. The bias in the distribution of gains might be mitigated by regulating the tax rule determining the magnitude of payment/reward. The proportion of the liquidity premium, arising from the implementation of a sovereign safe asset, has a decisive impact on the degree of the governments’ utility enhancement.

The adoption of a CGT would require Eurobonds club members to reach an agreement on “the” theoretical model for determining the sovereign debt yield. One of the limitations of this model is considering the debt-to-GDP ratio as the sole determinant of public debt yields. Moreover, the authors assumed the relationship between the debt-to-GDP ratio and funding costs to be identical for all countries. Any progress in the implementation of the proposed transfer scheme would require a more realistic and in-depth analysis.

A new fiscal rule based on compensating countries with lower public debt levels could be a way to mitigate free-riding problems if a Eurobond mechanism is to be established.

This fiscal rule has not been proposed or analysed before in a context such as that considered by this paper.

In the early 1930s, Nicholas Kaldor could be classified as an Austrian economist. The author reconstructs the intertwined paths of Kaldor and Friedrich A. Hayek to disequilibrium economics through the theoretical deficiencies exposed by the Austrian theory of capital and its consequences on equilibrium analysis.

The author approaches the discussion using a theoretical and historical reconstruction based on published and unpublished materials.

The integration of capital theory into a business cycle theory by the Austrians and its shortcomings – e.g. criticized by Piero Sraffa and Gunnar Myrdal – called attention to the limitation of the theoretical apparatus of equilibrium analysis in dynamic contexts. This was a central element to Kaldor’s emancipation in 1934 and his subsequent conversion to John Maynard Keynes’ The General Theory of Employment, Interest, and Money (1936). In addition, it was pivotal to Hayek’s reformulation of equilibrium as a social coordination problem in “Economics and Knowledge” (1937). It also had implications for Kaldor’s mature developments, such as the construction of the post-Keynesian models of growth and distribution, the Cambridge capital controversy, and his critique of neoclassical equilibrium economics.

The close encounter between Kaldor and Hayek in the early 1930s, the developments during that decade and its mature consequences are unexplored in the secondary literature. The author attempts to construct a coherent historical narrative that integrates many intertwined elements and personas (e.g. the reception of Knut Wicksell in the English-speaking world; Piero Sraffa’s critique of Hayek; Gunnar Myrdal’s critique of Wicksell, Hayek, and Keynes; the Hayek-Knight-Kaldor debate; the Kaldor-Hayek debate, etc.) that were not connected until now by previous commentators.

This study aims to examine the influence of internal and external factors on the credit risk (represented by nonperforming financing [NPF]) of Indonesian Sharīʿah rural banks (SRBs) – a type of Islamic bank that provides Islamic financial services especially to small and medium businesses in Indonesia. Internal variables comprise capital adequacy ratio (CAR), financing to deposit ratio (FDR), return on assets (ROA), operating expense ratio (OER), financing to value (FTV) and profit and loss sharing (PLS) financing ratio. External variables comprise inflation, economic growth and interest rate.

The study uses the annual reports of SRBs in Indonesia as secondary data for the years 2010–2019. Auto regressive distributed lag (ARDL) is used as the analysis method to examine the short-run and long-run relationships between the variables.

The findings indicate that four variables experienced a lag in the short run, namely, NPF, inflation, CAR and PLS, with different results recorded for each of the variables. Furthermore, the long-run results show that CAR and ROA influence the NPF of SRBs positively, whereas inflation and PLS have a negative influence on NPF. The rest of the variables – notably economic growth, interest rate, FDR, FTV and OER – do not have an influence on NPF in SRBs.

The level of NPF in SRBs exceeds the provision of the Central Bank of Indonesia. The findings are expected to have implications for SRBs and the regulator to consider and to manage the factors related to NPF properly due to the important role of SRBs in small and medium businesses’ development.

This study measures the determinants of NPF using internal and external variables, including the addition of a dummy variable, notably FTV. This study also uses ARDL to analyze the financial policies involving data at the present time and lagged time.

Smart factory is the most important feature of the fourth industrial revolution in the manufacturing field. In this paper, we introduce smart factory solutions suggested by the major solution providers. Furthermore, we classify solutions by the category of three value-chain activities: product development activity, product manufacturing activity, and sales activity. In addition, the status-of-art and best practices of the smart factory solutions are introduced. To examine the current trends of smart factories, we classified the manufacturing industries into four groups based on two factors: product variety and production volumes, and suggest directions for the successful application of smart factories in each industry group. Finally, we suggest strategies to improve the actual manufacturing competitiveness of factories.

This study aims to determine whether, by means of the application of genetic algorithms (GA) through the traditional technical analysis (TA) using moving average convergence/divergence (MACD), is possible to achieve higher yields than those that would be obtained using technical analysis investment strategies following a traditional approach (TA) and the buy and hold (B&H) strategy.

The study was carried out based on the daily price records of the NASDAQ financial asset during 2013–2017. TA approach was carried out under graphical analysis applying the standard MACD. GA approach took place by chromosome encoding, fitness evaluation and genetic operators. Traditional genetic operators (i.e. crossover and mutation) were adopted as based on the chromosome customization and fitness evaluation. The chromosome encoding stage used MACD to represent the genes of each chromosome to encode the parameters of MACD in a chromosome. For each chromosome, buy and sell indexes of the strategy were considered. Fitness evaluation served to defining the evaluation strategy of the chromosomes in the population according to the fitness function using the returns gained in each chromosome.

The paper provides empirical-theoretical insights about the effectiveness of GA to overcome the investment strategies based on MACD and B&H by achieving 5 and 11% higher returns per year, respectively. GA-based approach was additionally capable of improving the return-to-risk ratio of the investment.

Limitations deal with the fact that the study was carried out on US markets conditions and data which hamper its application in some extend to markets with not as much development.

The findings suggest that not only skilled but also amateur investors may opt for investment strategies based on GA aiming at refining profitable financial signals to their advantage.

This paper looks at machine learning as an up-to-date tool with great potential for increasing effectiveness in profits when applied into TA investment approaches using MACD in well-developed stock markets.

This paper investigates the effects of information asymmetry and Credit Rating Agency's reputation on bond yield spread, which is caused by the split bond rating of CRAs. For analysis, We do multivariate analysis, using bond rating and bond yield spread data in Korea from 2004 to 2015. The empirical results are as follows.

First, we examines whether information asymmetry affects the bond yield spread. using split rating data. As a result, the information asymmetry measured by split rating variable is significant, which supports the information asymmetry hypothesis. Additionally we can find bond yield spread is decided by negative credit grade rather than positive credit grade under split rating condition.

Next, we examines the relationship between the CRA’s reputation and the bond yield spread in case of split rating. Here, samples were divided into full samples and split rating samples. Summarizing the result, both samples are suggest similar result, the bond yield spread is changed depending on the specific CRA’s grading which having conservative rating tendency.

Thus, This result suggest information asymmetry caused by split rating and CRA’s reputation measured by CRA’s rating tendency affect bond yield spread in Korea

The authors explore the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic.

The authors employ a non-linear autoregressive distributed lag (NARDL) methodology using daily data of the Indonesian economy over the period 2012–2021.

Whilst, over the full sample period, the authors find no cointegration between the exchange rate, the 10-year bond yield and stock market, for the COVID-19 period, evidence of cointegration is present. Furthermore, the results suggest that asymmetric effects are evident both in the short as well as the long run.

To the best of the authors’ knowledge, this is the first time that the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic has been explored in the case of the Indonesian economy.

This study investigates the relation between two kinds of par yield curves estimated in Korean bond market: benchmark par yield curve and company par yield curve. The former is published as a benchmark for corporate bonds with a given credit rating and the latter is utilized for valuing a specific corporate bond. Spot rate curves are extracted from the par yield curves by applying bootstrapping method. The spreads between the two spot rate curves are analyzed for 7 years (2005~2012) of corporate bond transaction data. Six results are obtained from various sub-samples classified by credit rating and maturity. 1) Most of the sample means of the spreads are above zero. 2) Negative average spreads are found mainly from the sample of BBB rated bonds. 3) Average spreads from the sample with credit greater than or equal to A tend to positively related with credit risk. 4) Absolute value of the average spreads are positively related with credit risk. 5) The average spreads are increased rapidly after the year of 2009. 6) The proportion of sub-samples having negative average spreads are decreased as the average maturity of the sample is shortened.