2023, Vol.11, No.1

Asset Management Review 2023 KCI Impact Factor : 0.55

pISSN : 2288-6672 / eISSN : 2713-6248
https://journal.kci.go.kr/capm

Separating transitory components from recurring components of earnings is a primary task in financial statement analysis. A recent study by Rouen et al. (2021) uses a proprietary database compiled by New Constructs and evaluate whether their earnings measure after excluding transitory components from GAAP earnings has incremental explanatory power for future earnings. They show that their “core” earnings measure (as referred to in Rouen et al. (2021)) is more persistent than Compustat-defined operating income, suggesting that a core earnings measure effectively excludes transitory (i.e., less persistent) components of GAAP earnings. They also find that investors and financial analysts do not immediately incorporate the differential implications of the recurring and non-recurring components of earnings into stock prices, and that a trading strategy based on their non-core earnings components yields annual abnormal return of 8%. Given the findings of Rouen et al. (2021), it is an empirical question whether a core earnings measure distinguishes between recurring and transitory components of earnings more effectively than do other earnings measures in countries outside the U.S. In this study, we follow the procedure outlined in Rouen et al. (2021) and attempt to exclude the non-recurring components from net income of Korean listed firms. Specifically, we identify the non-recurring components of earnings from the DataGuide database and estimate a core earnings measure by excluding from net income (1) currency fluctuations, (2) discontinued operations, and (3) gains and losses labeled as “other” on the income statement. We then compare the persistence and return predictability of core earnings and various commonly used adjusted income measures such as operating income, gross margin, income before income taxes, and income from continuing operations of Korean firms listed in KOSPI and KOSDAQ from 2012 to 2020. The results suggest that operating income is most persistent, followed by core earnings, income from continuing operations, gross margin, and income before income taxes when predicting one-year ahead net income. We further report that both non-operating income (i.e., net income minus operating income) and non-core earnings (i.e., net income minus core earnings) have some information contents in that they are predictive of one-year ahead net income with smaller magnitudes compared to their counterparts. Portfolio analyses based on operating income and core earnings show that both measures are predictive of future stock returns, implying that investors in the Korean stock market act as if they fail to fully incorporate the information contained in the transitory and permanent components of current earnings into stock prices, similar to the findings of Rouen et al. (2021). Our results confirm that 1) operating income and core earnings are comparably superior to other adjusted earnings measures as key performance indicators of a firm and 2) a trading strategy based on these measures yields profitable returns in the Korean stock market. Our results should be interpreted with caution. While Rouen et al. (2021) indicate that New Constructs uses both fundamental analysis and machine learning to extract transitory components of earnings, the core earnings measure used in this study only utilizes the income statement items readily available from the DataGuide database for Korean listed firms. A procedure that closely replicates Rouen et al. (2021) (including machine learning) may yield a core earnings proxy with improved persistence and future earnings predictability by distinguishing between transitory and permanent components of earnings more effectively. Despite the differences in the measurement, our results suggest that operating income is comparable to core earnings in terms of earnings and return predictability without considering a machine-learning algorithm for estimating core earnings. Given that implementation of a machine-learning algorithm could be costly, operating income could be an effective alternative to core earnings. Future research is warranted to further establish a cost-effective measurement of permanent components of earnings.

To analyze the impact of housing finance policies (LTV, DTI, and DSR) on the housing cycle and household debt, we first estimated common factors that can represent the housing market economy. Then, we analyzed household debt determinants including common factors. After that, we analyzed both the linear and non-linear causal relationship between the housing cycle and household debt determinants. Finally, we analyzed the relations among housing policies, the housing cycle, and housing debt. The results of the empirical analysis show that each housing policy (LTV, DTI, and DSR) has a direct impact on household debt. In other words, we found that household debt increases when the LTV/DTI limit is eased or the DSR regulation is not applied. According to the analysis of the determinants of household debt, in the model including LTV/DTI, if the household debt interest rate goes up, consumer price rises, or the unemployment rate increases, the total amount of household debt would decrease. Furthermore, an improvement in the housing economy or a relaxation of the LTV/DTI regulation would result in an increase in total household debt. In the DSR model, the results showed that the total amount of household debt decreased when the consumer price index rose or the unemployment rate increased. Finally, we confirm the existence of a two-way causal relationship between the housing cycle and household debt using the linear/nonlinear model. Our study aims to confirm the effects of policy variables, namely LTV, DTI, and DSR, on household debt and to determine the two-way causal relationship between the housing cycle and household debt. However, it is important to note that our research has its limitations, and therefore, we plan to conduct a follow-up study to further explore these topics.

High Frequency Trading (hereinafter referred to as HFT) is known to play a positive role in providing liquidity to the market and improving market efficiency, while expanding volatility and spreading false liquidity by repeating correction and cancellation. Despite the tightening of regulations on HFT by overseas financial authorities, the proportion of sales by HFT continues to increase. This study defined HFT based on order speed for the domestic stock market and analyzed the investment performance and market influence of HFT. According to the analysis results, HFT continued to achieve significant results through day trading strategies and played a positive role in increasing market efficiency without negatively affecting individual stock prices and volatility. This study is differentiated in that it defined HFT based on order speed, and proved that HFT is benefiting, unlike previous studies that show that significant investment performance cannot be achieved due to transaction taxes imposed on sales in the stock market