Robotizing bond portfolio selection on the Russian debt market on the basis of a modified strategy of riding the yield curve

The modern global debt market features historically low average interest rates, convergence of yields on bonds with different maturities, an increase of yield curve inversion emergence frequency and a large-scale trend to automate financial decision making. The researchers’ attention in these fields is mainly focused on designing models that describe the state of the debt market as whole or its individual instruments in particular, as well as on risk management methods. At the same time, the specialized literature offers very few works concerning the topic of computer algorithms for bond portfolio selection based on traditional or advanced investment strategies. The aim of the present research is to create a modification of the existing algorithm of riding the yield curve strategy application, employing, first, average bond yield over the holding period instead of traditional bond yield to maturity; second, a developed algorithm for calculating the market spread on bonds; and, third, alternative risk evaluation indicators (compensation coefficients), which allow us to measure objectively price risk, liquidity risk, transaction costs risk and a general risk. The modification and the development of the algorithm for calculating the market spread were carried out using the direct measurement of the result technique, which entails application of the strategy to the data on bond issues received through the Moscow Exchange API. The selection of financial instruments was conducted in all sectors of the Russian debt market: public bonds, sub-federal and municipal bonds, corporate bonds. The modified algorithm enabled us to get extra yield for each selected bond issue, thereby proving the high effectiveness of the technique compared to the traditional strategy. Software implementation of the algorithm can be integrated into any robotized or semi-robotized stock exchange trading application.

Corporate bond spreads and understated pension liabilities

PurposeThe question is whether debt market investors see through managers' attempts to hide their pension obligations. The authors establish a robust relation between understated pension liabilities and corporate bond yield spreads after controlling for factors that have been previously identified as having a significant impact on firms' cost of borrowing. The results support the idea that bond market investors are not being misled by the use of high pension liability discount rates by some companies to lower their reported pension obligations. For a small fraction of debt issuers, the reported pension liabilities are larger than the pension liabilities valued at the stipulated interest rate benchmarks. For these issuers with overstated pension liabilities, bond investors adjust their borrowing costs downward.Design/methodology/approachThe authors investigate the relation between corporate bond yield spreads and understated pension liabilities relative to long-term Treasury and high-grade corporate bond yields. They aim to answer two questions. First, what are the sizes of over or understated pension liabilities relative to guideline benchmarks? Second, do debt market investors see through the potential management manipulation of pension discount rates? The authors find that firms with large understated pension liabilities face higher marginal borrowing costs after taking into account issue-specific features, firm characteristics, macroeconomic conditions and other pension information such as funded status and mandatory contributions.FindingsThe average understated projected benefit obligations (PBOs) are understated by $394.3 and $335.6, equivalent to 3.5 and 3.0% of the beginning of the fiscal year market value, respectively. The average understated accumulated benefit obligations (ABOs) are understated by $359.3 and $305.3 million, equivalent to 3.1 and 2.6%, of the beginning of the fiscal year market value, respectively. Relative to AA-grade corporate bond yields, the average difference between firm pension discount rates and benchmark yields becomes much smaller; the percentage of firm pension discount rates higher than benchmark yields is also much smaller. As a result, understated pension liabilities become negligible. The authors establish a robust relation between corporate bond yield spreads and measures of understated pension liabilities after controlling for issue-specific features, firm characteristics, other pension information (funded status and mandatory contributions), macroeconomic conditions, calendar effects and industry effects.Originality/valueS&P Rating Services recognizes the issue that there is considerably more variability in discount rate assumptions among companies than in workforce demographics or the interest rate environment in which firms operate (Standard and Poor's, 2006). S&P also indicates that it would be desirable to normalize different discount rate assumptions but acknowledges that it is difficult to do so. In practice, S&P Rating Services conducts periodic surveys to see whether firms' assumed discount rates conform to the normal standard. The paper makes an initial attempt to quantify the size of understated pension liabilities and their impact on corporate bond yield spreads. This approach can be extended to study firms' costs of equity capital, the pricing of seasoned equity offerings and the pricing of merger and acquisition transaction deals, among other questions.

Dimension Reduction via Penalized GLMs for Non-Gaussian Response: Application to Stock Market Volatility

We fit U.S. stock market volatilities on macroeconomic and financial market indicators and some industry level financial ratios. Stock market volatility is non-Gaussian distributed. It can be approximated by an inverse Gaussian (IG) distribution or it can be transformed by Box–Cox transformation to a Gaussian distribution. Hence, we used a Box–Cox transformed Gaussian LASSO model and an IG GLM LASSO model as dimension reduction techniques and we attempted to identify some common indicators to help us forecast stock market volatility. Via simulation, we validated the use of four models, i.e., a univariate Box–Cox transformation Gaussian LASSO model, a three-phase iterative grid search Box–Cox transformation Gaussian LASSO model, and both canonical link and optimal link IG GLM LASSO models. The latter two models assume an approximately IG distributed response. Using these four models in an empirical study, we identified three macroeconomic indicators that could help us forecast stock market volatility. These are the credit spread between the U.S. Aaa corporate bond yield and the 10-year treasury yield, the total outstanding non-revolving consumer credit, and the total outstanding non-financial corporate bonds.

Corporate Social Responsibility and Bond Price at Issuances: U.S. Evidence

Bondholders are arm’s-length lenders with limited insider information. In this paper, we explore whether corporate social responsibility (CSR) activities could work as an information channel for bondholders to better understand the riskiness of bond-issuing firms. We find a significant negative relation between CSR scores and corporate bond yield spread, especially for firms which invest heavily in diversity and community relations, suggesting that CSR firms are less risky. The result is robust to different model specifications and endogeneity issues. In addition, the negative relation between the CSR score and bond yield spread is significant only if a firm has a strong internal governance mechanism.

Markets are overreacting to US price and rates risks

Significance The continuation of the modest manufacturing downturn follows the recent report of slower third-quarter GDP growth. Despite slower growth, bond markets are challenging an attempt by the Federal Reserve (Fed) to delink tapering from tightening by bringing forward their forecasts for rate increases: futures markets are pricing in two 25-basis-point rate hikes by end-2022. Impacts Equities are at a record high in the United States; providing ongoing support for this, real US bond yields remain in negative territory. The Brent crude oil price is near its highest since 2014; further upside will be limited but it is likely to stay high well into 2022. Germany’s ten-year bond yield, negative since April 2019, has risen by 40 basis points since end-August and will soon turn positive.

Trading Macro-Cycles of Foreign Exchange Markets Using Hybrid Models

Most existing studies on forecasting exchange rates focus on predicting next-period returns. In contrast, this study takes the novel approach of forecasting and trading the longer-term trends (macro-cycles) of exchange rates. It proposes a unique hybrid forecast model consisting of linear regression, multilayer neural network, and combination models embedded with technical trading rules and economic fundamentals to predict the macro-cycles of the selected currencies and investigate the predicative power and market timing ability of the model. The results confirm that the combination model has a significant predictive power and market timing ability, and outperforms the benchmark models in terms of returns. The finding that the government bond yield differentials and CPI differentials are the important factors in exchange rate forecasts further implies that interest rate parity and PPP have strong influence on foreign exchange market participants.

Measuring redenomination risks in the Euro area – evidence from survey data

Purpose This paper aims to introduce a new indicator to measure redenomination risks in Euro area countries. The measure is based on survey data. The influence of this indicator in determining sovereign bond yield spreads is estimated. Design/methodology/approach An autoregressive distributed lag approach is used to estimate the effects of redenomination risks on sovereign bond yields. Additional control variables are added. Findings The results for 10 European Economic and Monetary Union (EMU) countries in the period June 2012 to May 2019 show that the risk of depreciation is almost abandoned for most Euro area countries, i.e. the former crisis countries Ireland and Portugal. If anything an appreciation may occur for some countries once they leave the EMU. The only countries facing depreciation problems once leaving the monetary union are Italy and to some extent Spain. Originality/value With this new indicator, the literature on sovereign bond determination and i.e. on redenomination risks is expanded by an additional approach. Moreover, this study is one of few also looking at the period after the most severe tensions of the sovereign debt crisis in the Euro area in 2012.